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THE ECONOMICS OF OBESITY

ADVANCES IN HEALTH ECONOMICS AND HEALTH SERVICES RESEARCH Series Editor: Michael Grossman and Bjo¨rn Lindgren Recent Volumes: Volume 15:

Health Policy Research in the States – Edited by J.C. Cantor

Volume 16:

Substance Use: Individual Behavior, Social Interaction, Markets and Politics – Edited by M. Grossman and B. Lindgren

ADVANCES IN HEALTH ECONOMICS AND HEALTH SERVICES RESEARCH VOLUME 17

THE ECONOMICS OF OBESITY EDITED BY

KRISTIAN BOLIN Lund University, Sweden

JOHN CAWLEY Cornell University, Ithaca, NY, USA

Amsterdam – Boston – Heidelberg – London – New York – Oxford Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo JAI Press is an imprint of Elsevier

JAI Press is an imprint of Elsevier The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2007 Copyright r 2007 Elsevier Ltd. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN-13: 978-0-7623-1406-5 ISBN-10: 0-7623-1406-0 ISSN: 0731-2199 (Series) For information on all JAI Press publications visit our website at books.elsevier.com Printed and bound in The Netherlands 07 08 09 10 11 10 9 8 7 6 5 4 3 2 1

CONTENTS ix

LIST OF CONTRIBUTORS

xiii

PREFACE

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ACKNOWLEDGEMENTS INTRODUCTION TO THE ECONOMICS OF OBESITY

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PART I: PREDICTING OBESITY AND RELATED BEHAVIOURS ENDOGENOUS FOOD QUALITY AND BODYWEIGHT TREND Liqun Liu, Andrew J. Rettenmaier, and Thomas R. Saving ACCESS TO FAST FOOD AND FOOD PRICES: RELATIONSHIP WITH FRUIT AND VEGETABLE CONSUMPTION AND OVERWEIGHT AMONG ADOLESCENTS Lisa M. Powell, M. Christopher Auld, Frank J. Chaloupka, Patrick M. O’Malley, and Lloyd D. Johnston

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A BEHAVIORAL MODEL OF CYCLICAL DIETING Steven M. Suranovic and Robert S. Goldfarb

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EFFECTS OF TITLE IX AND SPORTS PARTICIPATION ON GIRLS’ PHYSICAL ACTIVITY AND WEIGHT Robert Kaestner and Xin Xu

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v

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CONTENTS

OBESITY AND DIABETES: THE ROLES THAT PRICES AND POLICIES PLAY Inas Rashad

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PART II: TREATMENTS FOR OBESITY ADVANCES IN BARIATRIC SURGERY FOR OBESITY: LAPAROSCOPIC SURGERY William E. Encinosa, Didem M. Bernard, and Claudia A. Steiner ONE PILL MAKES YOU SMALLER: THE DEMAND FOR ANTI-OBESITY DRUGS John Cawley and John A. Rizzo

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PART III: LABOR MARKET IMPACTS OF OBESITY OBESITY, EMPLOYMENT AND WAGES IN EUROPE Jaume Garcia and Climent Quintana-Domeque OBESITY AND OCCUPATIONAL ATTAINMENT AMONG THE 50+ OF EUROPE. Petter Lundborg, Kristian Bolin, So¨ren Ho¨jga˚rd, and Bjo¨rn Lindgren GENDER, BODY MASS, AND SOCIOECONOMIC STATUS: NEW EVIDENCE FROM THE PSID Dalton Conley and Rebecca Glauber

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219

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PART IV: OTHER COSTS AND CONSEQUENCES OF OBESITY HEALTH INSURANCE AND THE OBESITY EXTERNALITY Jay Bhattacharya and Neeraj Sood

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Contents

OBESITY, HOSPITAL SERVICES USE AND COSTS Nana Bro Folmann, Kristine Skovgaard Bossen, Ingrid Willaing, Jan Sørensen, John Sahl Andersen, Steen Ladelund, and Torben Jørgensen HOW MUCH DOES OBESITY MATTER? RESULTS FROM THE 2001 CANADIAN COMMUNITY HEALTH SURVEY William MacMinn, James McIntosh, and Caroline Yung

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LIST OF CONTRIBUTORS John Sahl Andersen

Department of General Practice, Institute of Public Health University of Copenhagen, København K, Denmark

M. Christopher Auld

Department of Economics, University of Calgary, Calgary, Alberta, Canada

Didem M. Bernard

Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, Rockville, MD, USA

Jay Bhattacharya

CHP/PCOR, Stanford University, Stanford, CA, USA

Kristian Bolin

LUCHE (Lund University Centre for Health Economics), Lund, Sweden

Kristine Skovgaard Bossen

Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark

John Cawley

Department of Policy Analysis and Management, Cornell University, Ithaca, NY, USA

Frank J. Chaloupka

Department of Economics and Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL, USA

Dalton Conley

Department of Sociology, New York University, New York, NY, USA

William E. Encinosa

Center for Delivery, Organization, and Markets, Agency for Healthcare Research and Quality, Rockville, MD, USA

Nana Bro Folmann


Jaume Garcia

Departament d0 Economia i Empresa, Universitat Pompeu Fabra, Barcelona, Spain ix

x

LIST OF CONTRIBUTORS

Rebecca Glauber

Department of Sociology, New York University, New York, NY, USA

Robert S. Goldfarb

Department of Economics, The George Washington University, Washington, DC, USA

So¨ren Ho¨jga˚rd


Ingrid Willaing


Lloyd D. Johnston

Survey Research Center, Institute for Social Research, The University of Michigan, Ann Arbor, MI, USA

Torben Jørgensen


Robert Kaestner

Department of Economics and Institute of Government and Public Affairs, University of Illinois at Chicago, Chicago, IL, USA

Steen Ladelund


Bjo¨rn Lindgren


Liqun Liu

Private Enterprise Research Center, Texas A&M University, College Station, TX, USA

Petter Lundborg

Department of Economics, Free University, Amsterdam, The Netherlands

William MacMinn

Applied Research Analysis Directorate, Health Canada, Tunney’s Pasture, Ottawa, Ontario, Canada

James McIntosh

Economics Department, Concordia University, Montreal, Quebec, Canada

Patrick M. O’Malley

Survey Research Center, Institute for Social Research, The University of Michigan, Ann Arbor, MI, USA

List of Contributors

Lisa M. Powell

Climent QuintanaDomeque

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Department of Economics and Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL, USA Department of Economics and Industrial Relations Section, Princeton University, Princeton, NJ, USA

Inas Rashad

Department of Economics, Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA, USA

Andrew J. Rettenmaier


John A. Rizzo

Department of Economics and Department of Preventive Medicine, Stony Brook University, Stony Brook, NY, USA

Thomas R. Saving


Neeraj Sood

RAND Corporation, Santa Monica, CA, USA

Jan Sørensen

Centre for Applied Health Services Research and Technology Assessment, University of Southern Denmark Odense C, Denmark

Claudia A. Steiner

Center for Delivery, Organization, and Markets, Agency for Healthcare Research and Quality, Rockville, MD, USA

Steven M. Suranovic

Department of Economics, The George Washington University, Washington, DC, USA

Xin Xu

Department of Economics, University of Illinois at Chicago, Chicago, IL, USA

Caroline Yung

Applied Research Analysis Directorate, Health Canada, Tunney’s Pasture, Ottawa, Ontario, Canada


PREFACE

This volume is part of an annual series entitled Advances in Health Economics and Health Services Research. We express our gratitude to the series editors Bjo¨rn Lindgren and Michael Grossman for inviting us to edit this volume. In the Fall of 2005, we issued a call for submissions on the economics of obesity. From the submissions that we received, approximately 20 papers were subjected to peer review, 13 of which were selected for this volume. We want to express our gratitude to Elsevier Inc., the referees (who are listed in the acknowledgements), and the contributors; it has been a pleasure working with you. Kristian Bolin Lund, Sweden John Cawley Ithaca, USA

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ACKNOWLEDGEMENTS Each chapter was peer-reviewed. We gratefully acknowledge the efforts of the reviewers Fredrik Andersson

Lund University, Sweden

Jan Erik Askildsen

Bergen University, Norway

Susan Averett

Lafayette College, USA

Terkel Christiansen

Syddansk Universitet, Denmark

Lennart Flood

Gothenburg University, Sweden

Markus Grabka

German Institute for Economic Research (DIW), Germany

Michael Grossman

City University of New York and NBER, USA

Dorte Gyrd-Hansen

Copenhagen Business School, Denmark

Sanders Korenman

Baruch College and NBER, USA

Amnon Levy

University of Wollongong, Australia

Bjo¨rn Lindgren


Petter Lundborg


Alan Mathios

Cornell University, USA

Chad Meyerhoefer

Agency for Healthcare Research and Quality, USA

Sean Nicholson

Cornell University and NBER, USA

Jose´ Pagań

University of Texas – Pan American, USA xv

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ACKNOWLEDGEMENTS

Inas Rashad

Georgia State University and NBER, USA

Shin-Yi Chou

Lehigh University and NBER, USA

Roland Sturm

RAND Corporation, USA

INTRODUCTION TO THE ECONOMICS OF OBESITY Obesity is a major risk factor for mortality and morbidity such as stroke, coronary heart disease, diabetes, and certain types of cancer (Field, Barnoya, & Colditz, 2002; Pi-Sunyer, 2002; Manson, Skerrett, & Willett, 2002). Obesity and its related diseases are costly to society because those suffering from them consume more medical care (Thorpe, Florence, Howard, & Joski, 2004; Finkelstein, Fiebelkorn, & Wang, 2003). It is estimated that obesity-related illness is responsible for roughly 6% of health care costs in both Europe and the U.S. (WHO European Ministerial Conference on Counteracting Obesity, 2006; Wolf & Colditz, 1998). Obesity is the result of calorie surpluses: more calories consumed than expended. Diet and physical activity are individual choices influenced to some degree by genetics and environment. The economics of obesity concerns: predicting and understanding individual decisions regarding diet and allocation of time that may lead to obesity, the costs and benefits of various treatments and preventions for obesity, and the costs and consequences of obesity for individuals and society (Lakdawalla & Philipson, 2006; Cawley, 2004, 2006a). Prior to 1990, the economic literature on obesity was sparse. Two studies documented the relationship between body weight and earnings for American men (Taubman, 1975; McLean & Moon, 1980). Chernichovsky and Coate (1980) studied the relationship between diet, weight, and stature for children. More distally related research focused on predictors of healthy birth weight (Rosenzweig & Schultz, 1983) and the historic benefits of improved nutrition (Fogel, 1986). Since 1990, economic research on obesity and its related behaviours bloomed as the public and policymakers became aware of the rise in obesity in the U.S. (Flegal, Carroll, Ogden, & Johnson, 2002; Ogden, Carroll, & Curtin, 2006) and Europe (International Obesity Task Force, 2005; Joint WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases, 2002) and the salience of that trend for public health. This recent research has begun to study every aspect of the problem, positing xvii

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INTRODUCTION TO THE ECONOMICS OF OBESITY

economic models of diet, physical activity and weight, calculating the personal consequences and social costs of obesity, and evaluating prevention strategies and medical treatments for obesity; reviews of this research are available in Cawley (2006a, 2006b) and Lakdawalla and Philipson (2006). There remain many interesting and policy-relevant questions concerning obesity that can be answered using economic modelling and empirical analyses. This volume includes the most recent and significant research on the economics of obesity. The volume’s 13 papers are divided into sections with the following themes: predicting and understanding obesity-related individual behaviours, treatments for obesity, the labour market impact of obesity, and the costs of obesity.

PART I: PREDICTING OBESITY AND RELATED BEHAVIOURS Five papers in the volume use economic models to predict obesity and related behaviours. Two of the papers are theoretical. Liqun Liu, Andrew J. Rettenmaier and Thomas R. Saving analyse the importance of food quality for bodyweight. Bodyweight is treated as a variable of choice – the individual derives utility from health, food consumption and consumption of a composite good. Bodyweight is assumed to decrease health whenever it differs from its physiologically optimal level. Their model implies that much of further income growth will be used to improve food quality rather than increase caloric intake. Steven Suranovic and Robert Goldfarb use a model of bounded rationality to explain why people might cycle between a low-calorie diet and a highcalorie diet that leads to overweight. An important contribution is that they consider the life cycle pattern of weight and explain cyclical dieting with an upward drift in weight with age. The intuition of their argument is that dieting leads to temporary weight loss, which decreases the marginal disutility of weight relative to the marginal utility of food consumption, leading at some point to a resumption of a high-calorie diet. Ageing leads to a decrease in resting metabolism, contributing to an upward drift in weight. As a result, their stylized life cycle trajectory of weight oscillates around an upward trend. At the heart of economics is the relationship between prices and choices. Lisa M. Powell, M. Christopher Auld, Frank J. Chaloupka, Patrick M. O’Malley, and Lloyd D. Johnston examine the extent to which food prices and restaurant outlet density are associated with adolescent fruit and

Introduction to the Economics of Obesity

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vegetable consumption, body mass index (BMI) and the probability of overweight. Employing US data they find that that the price of a fast food meal is an important determinant of adolescent body weight and eating habits: higher fast-food prices lead to more fruit and vegetable consumption and a decrease in the prevalence of obesity. Robert Kaestner and Xin Xu study an important natural experiment in the U.S. Title IX of the Education Amendments of 1972 banned sex discrimination in all educational programmes (including sports) that receive Federal funding, which is credited with quintupling girls’ participation in high school sports. Kaestner and Xu exploit this natural experiment to estimate the impact of the legislation on girls’ overall physical activity and weight. An important trend in health economics is the increasing use of biomarkers to study health conditions and behaviours. Inas Rashad studies one important biomarker – blood glucose level – and finds that it is negatively correlated with the regional price of foods with high glycaemic indexes.

PART II: TREATMENTS FOR OBESITY One reason that obesity remains such a major public health problem is that few anti-obesity strategies or interventions produce long-term weight loss. The exception to this rule is bariatric surgery, which has been shown to result in sustained weight loss. William E. Encinosa, Didem M. Bernard, and Claudia A. Steiner note that bariatric surgery is increasingly conducted laparoscopically, and test whether outcomes differ for patients who underwent laparoscopic, as opposed to open, bariatric surgery. They find that laparoscopic bariatric surgeries had lower rates of complication both during the hospital stay and 30 days after surgery. However, such surgeries were also associated with higher probabilities of readmission to the hospital and an emergency room visit. On net, laparoscopy was associated with lower costs. Another promising treatment for obesity is anti-obesity pharmaceuticals. John Cawley and John A. Rizzo describe the market for anti-obesity drugs and study the demand for such medications. They document wide socioeconomic disparities: women are far more likely than men, and Hispanics and African-Americans are less likely than Caucasians, to use anti-obesity drugs. They also find that the vast majority of individuals who are medically qualified to use anti-obesity drugs are not using them, and that a substantial fraction of those using anti-obesity drugs do not satisfy the medical criteria

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for their use. They also find that the withdrawal of two anti-obesity drugs in 1997 had a chilling effect on the overall market for such drugs.

PART III: LABOR MARKET IMPACT OF OBESITY The volume includes three studies of the relationship between obesity and labour market outcomes. Much of the previous literature in this area is based on U.S. data for young adults. The three papers in this section provide important information regarding the labour market impact of obesity among Europeans and older U.S. adults. Jaume Garcia and Climent Quintana-Domeque study European Community Household Panel data from nine European countries. They find that the relationship between obesity and employment status or wages varies for men and women, and also varies across countries. In general, they find weak evidence that obese workers are more likely to be unemployed or tend to be more segregated in self-employment jobs than their non-obese counterparts, and find relationships between obesity and wages that are not statistically significant. A complementary paper by P. Lundborg, K. Bolin, S. Hojgard, and B. Lindgren studies data on 10 countries from the Survey of Health, Ageing and Retirement in Europe. They estimate models of instrumental variables to address the endogeneity of obesity, and discuss the merits and limitations of several possible instruments. Pooling all countries, they find that obesity is associated with a significantly lower probability of employment for both women and men. Obese European women earned 10 per cent less than their non-obese counterparts, but for men the effect was half as large and not statistically significant. Regressions estimated separately by region reveal that the effect of obesity on employment was greatest for men in southern and central Europe, while women in central Europe faced the greatest wage penalty. Dalton Conley and Rebecca Glauber estimate the relationship between obesity and wages among adults in the U.S. Panel Survey of Income Dynamics. To minimize the impact of contemporaneous labour market outcomes affecting weight, they use a measure of BMI that is lagged 15 years. Estimating models of sibling fixed effects, which eliminate the influence of shared genes and environment and use the variation in weight due to unshared genes and unshared environment, they find that obesity is associated with substantially lower wages, family income, and probability of marriage for women. Their findings demonstrate that the wage penalty for obesity


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that has been documented among younger women exists among older women as well.

PART IV: OTHER COSTS AND CONSEQUENCES OF OBESITY The last section of the book includes three papers that document the myriad costs of obesity. The first paper analyses the externality imposed on nonobese individuals by obese individuals’ greater use of health care. In the second paper the association between obesity and the utilization of hospital services is analysed, and in the third paper the relative importance of obesity for poor health outcomes is analysed. Obesity increases the risk of several serious diseases associated with greater health care expenditures. Though the financing of health care differs between countries, we know of no financing system which includes higher health insurance premiums for those who are obese. Jay Bhattacharya and Neeraj Sood point out that this means that the obese as a group impose a negative externality on other people in their insurance pool. They develop a model of weight loss and health insurance in order to estimate the size of the externality. Employing US data they find that the obesity externality imposes a welfare cost of about $150 per capita. Estimates of the fraction of health care expenditures that can be attributed to obesity vary between 2 and 6%. Increases in the prevalence of obesity as well as the introduction of new, expensive, medical treatments for obesity and obesity-related illness may raise this share further. Nana Bro Folmann, Kristine Skovgaard Bossen, Willaing Ingrid, Jan Sørensen, John Sahl Andersen, Steen Ladelund, and Torben Jørgensen examine the association between obesity and the utilization of hospital-based health care. Employing Danish Diagnose Related Groups (DRG) data they find that obese men and women utilize significantly more hospital services than normal-weight individuals. In spite of a large body of studies indicating that obesity increases the risk of various diseases, the relative importance of obesity (compared to smoking and alcohol consumption) as a cause of poor health has received little attention. William MacMinn, James McIntosh, and Caroline Yung use Canadian data to study the relative importance of obesity as a cause of morbidity and poor health. Controlling for smoking and alcohol consumption they find that obesity is negatively associated with self-perceived health and

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diabetes and heart disease. They also find that the effect of obesity on poor health declines with age.

REFERENCES Cawley, J. (2004). An economic framework for understanding physical activity and eating behaviors. American Journal of Preventive Medicine, 27(3), 1–9. Cawley, J. (2006a). Markets and childhood obesity policy. The Future of Children, 16(1), 69–88. Cawley, J. (2006b). The labor market impacts of obesity. In: Z. Acs & A. Lyles (Eds), Obesity, business, and public policy. Northampton, MA: Edward Elgar forthcoming. Chernichovsky, D., & Coate, D. (1980). The choice of diet for young children and its relation to children’s growth. Journal of Human Resources, 15(2), 255–263. Field, A. E., Barnoya, J., & Colditz, G. A. (2002). Epidemiology and health and economic consequences of obesity. In: T. A. Wadden & AJ. Stunkard (Eds), Handbook of obesity treatment. New York: Guilford Press. Finkelstein, E., Fiebelkorn, I., & Wang, G. (2003). National medical spending attributable to overweight and obesity: How much and who’s paying? Health Affairs Web Exclusive, May 14. Flegal, K. M., Carroll, M. D., Ogden, C. L., & Johnson, C. L. (2002). Prevalence and trends in obesity among U.S. adults, 1999–2000. JAMA, 288(14), 1723–1727. Fogel, R. W. (1986). Nutrition and the decline in mortality since 1700: Some preliminary findings. In: S. L. Engerman & R. E. Gallman (Eds), Long-term factors in American economic growth, income and wealth conference, Vol. 51. Chicago: UCP. International Obesity Task Force. (2005). EU platform on diet, physical activity and health. http://www.iotf.org/media/euobesity3.pdf, 15 March. Joint WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chroni Diseases. (2002). Diet, Nutrition and the Prevention of Chronic Diseases: Report of a Joint WHO/FAO Expert Consultation. World Health. Lakdawalla, D., & Philipson, T. (2006). Economics of obesity. In: A. M. Jones (Ed.), The Elgar companion to health economics. Northampton, MA: Edward Elgar. Manson, J. E., Skerrett, P. J., & Willett, W. C. (2002). Epidemiology of health risks associated with obesity. In: C. G. Fairburn & K. D. Brownell (Eds), Eating disorders and obesity: A comprehensive handbook, (2nd Edition). New York: Guilford Press. McLean, R. A., & Moon, M. (1980). Health, obesity, and earnings. American Journal of Public Health, 70(9), 1006–1009. Ogden, C. L., Carroll, M. D., Curtin, L. R., et al. (2006). Prevalence of overweight and obesity in the United States, 1999–2004. JAMA, 295, 1549–1555. Pi-Sunyer, F. X. (2002). Medical complications of obesity in adults. In: C. G. Fairburn & K. D. Brownell (Eds), Eating disorders and obesity: A comprehensive handbook, (2nd Edition). New York: Guilford Press. Rosenzweig, M. R., & Schultz, T. P. (1983). Estimating a household production function: Heterogeneity, the demand for health inputs, and their effects on birth weight. Journal of Political Economy, 91(5), 723–746. Taubman, P. J. (1975). Sources of inequality in earnings: Personal skills, random events, preferences toward risk and other occupational characteristics. New York: American Elsevier Publishing Company.


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Thorpe, K. E., Florence, C. S., Howard, D. H., & Joski, P. (2004). The impact of obesity on rising medical spending. Health Affairs, W4-480, October 20. WHO European Ministerial Conference on Counteracting Obesity. (2006). Ten things you need to know about obesity. World wide web content accessed November 2, 2006. http:// www.euro.who.int/Document/NUT/ObesityConf_10things_Eng.pdf Wolf, A., & Colditz, G. (1998). Current estimates of the economic cost of obesity in the United States. Obesity Research, 6, 97–106.

Kristian Bolin John Cawley Editors


PART I: PREDICTING OBESITY AND RELATED BEHAVIORS


ENDOGENOUS FOOD QUALITY AND BODYWEIGHT TREND Liqun Liu, Andrew J. Rettenmaier, and Thomas R. Saving ABSTRACT This paper follows previous economic studies on bodyweight by looking at bodyweight as individuals’ choices in response to changes in income and food prices. However, it goes one step further to add another relevant choice under individual control: food quality. It shows that the upward trend of bodyweight caused by economic growth and technological innovations may well be self-limiting in the sense that the bodyweight growth in the future is likely to be slower or reversed. In particular, it finds that much of further income growth will be used for improving food quality rather than increasing caloric intake. Moreover, further technological innovations that focus on lowering the price of high-quality (healthy) food relative to that of low-quality food would encourage substituting food quality for food quantity (calories).

1. INTRODUCTION Bodyweight is closely related to health. Given body height, there is a physiologically optimal bodyweight which is recommended for maximum The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 3–21 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17001-6

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longevity. When bodyweight is too low, there are suspicions of malnutrition and consumption diseases. When bodyweight is too high, in contrast there are increased risks of heart disease, diabetes, hypertension, and arthritis. Undesirable bodyweight may also have psychological and social ramifications. For example, an overweight person is more likely to have a self-image problem than an optimally weighted person, and it may be more likely for him to encounter obstacles in job and marriage markets. For all the centuries before the 20th and much of the 20th century itself, being overweight was not a major concern. However, since mid-20th century, the percentage of population being overweight and obese has increased sharply. Taking the US, for example, the percentage of adults between 20 and 74 years old that are classified as overweight (‘‘overweight’’ is a category containing ‘‘obese’’) has increased from about 45% to a stunning 65%, with 31% being obese.1 Along with this trend in bodyweight, the number of deaths linked to poor diet and physical inactivity increased 22% between 1990 and 2000 from 300,000 to 365,000.2 As a result, being overweight has become the second largest underlying cause of preventable deaths, second only to tobacco consumption. Contrary to the media and the medical profession depicting the ongoing trend of bodyweight increases as a public health crisis, the economics profession has tried to understand why utility-maximizing individuals might choose lifestyles leading to their becoming overweight and what economic factors might have caused the observed growth in bodyweight. For example, Levy (2002) and Bednarek, Jeitschko, and Pecchenino (2006) used models of rational choices of food and leisure consumption to derive equilibrium outcomes that are characterized by economically optimal bodyweight exceeding physiologically optimal bodyweight, assuming that income is sufficiently high. From this point of view, people in rich economies, though less healthy in terms of bodyweight, are certainly happier overall.3 Also using an economic approach to the issue of bodyweight, Philipson and Posner (1999) and Cutler, Glaeser, and Shapiro (2003) emphasized technological changes in explaining the world-wide positive trend in bodyweight. Some technological changes, such as washers, dryers, and industrial automation, have made home and market production less physically demanding.4 Some other technological changes, e.g., microwave ovens and dishwashers, have made it easier to prepare food and clean subsequently. From individuals’ points of view, these technological changes have generated price effects making calorie expending more expensive and the time costs of food consumption lower. Therefore, calories-in exceed calories-out, and people in general have become heavier. These technology-driven price

Endogenous Food Quality and Bodyweight Trend

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effects can also be used to explain the disparity in bodyweight between the US and Europe. Food is cheaper in the US than in Europe, which is evidenced by widely spread fast food chains and all-you-can-eat buffet meals in the US. Moreover, cars are more widely used in the US, making calorieburning exercise more expensive in the US than that in Europe.5 As a result, Americans eat more and exercise less than Europeans, and are heavier on average.6 If, as economists assert, the increasing income and the falling overall food price have caused the bodyweight growth in the past, then one may expect the overweight problem to get much worse in the future as higher incomes and lower food prices are brought about by continuing economic growth and technological innovations. However, that pessimistic view about the future would seem to be inconsistent with the casual observation that, within developed countries, individuals with higher incomes seem to be less likely to be overweight, even though on average the overweight problem is more severe in developed countries than in developing countries. This observation is partially supported by the evidence that the effect of income on calorie consumption is highly inelastic in developed countries (Strauss & Thomas, 1988). In other words, causal relationship between income and bodyweight may be different at the micro and macro levels: there is a positive correlation between bodyweight and income at the macro level (over time), but at the micro (individual) level, the relationship between bodyweight and income is not necessarily positive.7 Moreover, while some technological innovations like microwave ovens and dishwashers have lowered the overall price of food consumption, thus exacerbating the problem of excessive calorie intake, there are also other innovations that make ‘‘diet’’ food items such as non-fat milk and sugar-free soft drinks available and more affordable, encouraging a shift from traditional food to more healthy, low-calorie food, thus providing a remedy for the excessive calorie intake. This paper follows previous economic studies on bodyweight by looking at bodyweight as individuals’ choices in response to changes in income and food prices. However, it goes one step further to add another relevant choice under individual control: food quality. It shows that the upward trend of bodyweight caused by economic growth and technological innovations may well be self-limiting in the sense that the bodyweight growth in the future is likely to be slower or reversed, even though the average level of bodyweight will continue to exceed what is medically optimal. In particular, it finds that much of further income growth will be used for improving food quality rather than increasing caloric intake. It also contends that further technological innovations may well focus on lowering the price of high-quality

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(healthy) food relative to that of low-quality food, encouraging substituting food quality for food quantity (calories). In the next section, we set up a model of food consumption and bodyweight. For comparison purposes, we start with a case of exogenous (single) food quality. It is shown that equilibrium bodyweight exceeds the medically optimal one, increases in income, and decreases in the food price. In Section 3, we introduce endogenous food quality by having both a high-quality food and a low-quality food, and re-examine the effects of income and food prices on bodyweight. We conclude with a summary and some policy implications in Section 4.

2. A MODEL OF FOOD CONSUMPTION AND BODYWEIGHT: EXOGENOUS FOOD QUALITY Let an individual’s satisfaction from food consumption F and the consumption of a composite good C be captured by the Cobb–Douglas specification, F b C 1b ; where 0obo1: In addition to the consumption of food and the composite good, the individual’s utility also depends on his health status, which in turn depends on his bodyweight W. Let health status be given by H 0 lðW W 0 Þ2 ; where l40: This specification of health implies that there is a physiologically optimal bodyweight W 0 ; which corresponds to the maximum health achievable H 0 : A marginal increase in bodyweight is beneficial to health if W oW 0 but harmful if W 4W 0 : The total utility from health status and consumption of food and the composite good is specified as ½H 0 lðW W 0 Þ2 a F b C 1b

(1)

where a40: Bodyweight W is a function of food consumption F, which is denoted by W ðF Þ: Bodyweight increasing with food consumption implies W 0 40 and diminishing marginal effects implies W 00 o0: In addition, we make the following assumption on W ðF Þ; which implies food’s increasing marginal damage to health when W 4W 0 : d2 ½H 0 lðW W 0 Þ2 Assumption 1. 2l½ðW 0 Þ2 þ ðW W 0 ÞW 00 o0: dF 2 For example, within the family of W ðF Þ ¼ AF d ; where A40 and 0odo1; condition d 0:5 is sufficient for Assumption 1 to be satisfied. An individual chooses F and C to maximize (1) subject to budget constraint C þ pF ¼ y; where p is the food price and y is the individual’s


7

income. Expressing C in terms of F in the budget constraint and substituting it into (1), the first-order condition of the individual’s problem, with respect to food consumption F, can be derived as GðF ; y; pÞ 2alðW W 0 ÞW 0 F ðy pF Þ þ ½H 0 lðW W 0 Þ2 ðby pF Þ ¼ 0

ð2Þ

Suppose y is sufficiently large so that by pF 0 40; where F 0 is the food consumption leading to the physiologically optimal weight, i.e., W ðF 0 Þ ¼ W 0 : Note that condition by pF 0 40 is stronger than y pF 0 40 (i.e., the individual can afford to buy food in excess of the amount that leads to the physiologically optimal weight) because bo1: Then we can show the following based on the first-order condition (2). First, the economically optimal food consumption is larger than the physiologically optimal food consumption, or F n 4F 0 ; which implies that the economically optimal bodyweight is above its physiologically optimal level, or W ¼ W ðF Þ4W 0

(3)

Note that if W oW 0 (thus, F oF 0 ) and by pF 0 40; then (2) would not hold – a contradiction. Second, the standard comparative statics analysis yields

dF @G=@y 40 ¼ @G=@F dy dF @G=@p o0 ¼ @G=@F dp

(4)

where @G=@F o0 (the second-order condition of the utility-maximization problem) is guaranteed from Assumption 1. The proof of (4) is given in Appendix A. Summarizing the above results, we have the following proposition: Proposition 1. In the case of exogenous food quality and under the condition that income is sufficiently large, equilibrium bodyweight is above its physiologically optimal level, and increases as income grows or as the food price falls. Proposition 1 indicates that being overweight is likely to be a common condition (an equilibrium outcome) in the developed world. This may not be surprising since in our model food consumption has independent utility values beyond its effect on bodyweight. Further, Proposition 1 predicts that the higher the income or the lower the food price, the heavier the individuals

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will become. These predictions are consistent with the existing literature identifying economic growth and technological innovations as causes for the observed positive growth in bodyweight. However, even though the trend of economic growth and technological innovations will continue, there exist legitimate doubts on whether bodyweight in the developed world will continue to grow in the future as it has grown in the past. In the next section, we introduce endogenous food quality to show that the observed positive bodyweight trend may be self-limiting in the future.

3. ENDOGENOUS FOOD QUALITY AND THE BODYWEIGHT TREND 3.1. The Model Suppose that there exist two kinds of food. The food with higher quality is more expensive and generates more satisfaction – for the same amount of caloric intake – than the low-quality kind. Note that this notion of highquality food does not only cover meals in gourmet restaurants that are capital and labor intensive and literarily generate more satisfaction for the same amount of caloric intake, but also incorporates healthy food items aimed at achieving familiar flavor (the same satisfaction) with fewer calories such as ‘‘Wow’’ Potato Chips and Diet Coke. In other words, true highquality foods are both tasty and healthy. The overall quality of consumed food is determined by the portion of the high-quality food, which is denoted by q, 0 q 1: Utility from total food consumption F with quality q and other consumption C is given by a b H 0 lðW W 0 Þ2 ð1 þ qÞg F C 1b (5) where 0ogo1 because of positive but diminishing returns from quality, and bodyweight W is still determined by the total food consumption through W ðF Þ; which satisfies W 0 40; W 00 o0; and Assumption 1. An individual chooses F, q, and C to maximize (5) subject to budget constraint C þ pð1 qÞF þ bpqF ¼ y; where p is the base food price, b41 is the price factor for the high-quality food (the price factor for the low-quality food is 1), and y is the individual’s income.8 In our food quality specification, if the high-quality food is too expensive, such that b 2; then the high-quality food would be dominated by the lowquality food so that an individual would only consume the latter. To see


9

this, let us compare the degrees of satisfaction for the same food expenditure with and without high-quality food when b 2: Begin with qF units of highquality food and (1 q)F units of low-quality food, where q>0. This combination is utility-equivalent to ð1 þ qÞg F units of low-quality food, based on utility function (5). In contrast, if the money spent on the highquality portion is instead used to buy low-quality food, one could buy at least 2qF units of the latter, which, adding to the existing (1 q)F units of the low-quality food, would generate a total of (1+q)F units of low-quality food. Because ð1 þ qÞg F oð1 þ qÞF for any q>0 and F, we have that, when b 2; the issue of food quality choice would be moot: an individual would never buy any high-quality food. Therefore, we impose the following assumption to make the food quality dimension non-trivial: Assumption 2. bo2 . Expressing C in terms of F and q using the budget constraint and substituting it into (5), it can be derived that the first-order condition with respect to choice of F is GðF ; q; y; p; bÞ 2alðW W 0 ÞW 0 F ½y pð1 qÞF bpqF þ ½H 0 lðW W 0 Þ2 ½by pð1 qÞF bpqF ¼ 0

ð6Þ

and the first-order condition with respect to choice of q is QðF ; q; y; p; bÞ gby Fp½ðgb þ b 1Þ þ bð1 bÞ qFpðb 1Þðgb b þ 1Þ ¼ 0

ð7Þ

Before going into the derivation of our main results, we present the following lemma based on the first-order conditions (6) and (7), which says that, under likely conditions, the equilibrium bodyweight is above its physiologically optimal level. The lemma also gives an inequality that will be useful later. The proof of the lemma is given in Appendix B. Lemma 1. If y is sufficiently large such that by bpF 0 40; then W 4W 0 and bð1 gÞ½H 0 lðW W 0 Þ2 42alðW W 0 ÞW 0 F at W*. 3.2. Income’s Effects on Bodyweight and Food Quality From (6) and (7), the following comparative statics results with respect to income’s effects on food consumption (and hence bodyweight) and food

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quality are derived in Appendix C: dF 40 dy dq 40 dy

(8)

Comparative statics results in (8) indicate that both caloric intake (hence bodyweight) and food quality will increase with income. Moreover, as a result of the positive effect of income on food quality, the income elasticity of caloric intake is lower than the income elasticity of food expenditure. Formally, the income elasticity of food expenditure is y d½pð1 qÞF þ pbqF y dF qðb 1Þ y dq ¼ þ pð1 qÞF þ pbqF dy F dy 1 þ qðb 1Þ q dy

(9)

which is larger than the income elasticity of caloric intake y dF =F dy because dq=dy40: Summarizing the above results, we have the following proposition: Proposition 2. In the case of endogenous food quality and under the condition that income is sufficiently large, we have (i) equilibrium bodyweight is above its physiologically optimal level and increases in income; (ii) food quality increases in income; (iii) the income elasticity of caloric intake is lower than the income elasticity of food expenditure. One main implication of Proposition 2 is that, as a result of the difference in food quality between the wealthy and the poor, the caloric intake of a wealthy person may be much less than what his relatively high food expenditure might indicate. 3.3. Food Prices’ Effects on Bodyweight Similarly, comparative statics results with respect to the prices’ effects on food consumption (and hence bodyweight) are derived as follows (see Appendix E for a derivation): dF o0 dp dF 40 db

(10)


11

These results can be summarized as the following proposition: Proposition 3. In the case of endogenous food quality and under the condition that income is sufficiently large, we have (i) equilibrium bodyweight increases as the general food price falls; (ii) equilibrium bodyweight decreases as the relative price of the high-quality food falls. While technological innovations will continue to lower food prices in general, the food industry’s R&D priority seems to be shifting toward healthy food items – making them available and affordable, in response to the increased demand for healthy food. An example of such a shift in R&D priority is the increased variety of diet foods and drinks. Proposition 3 says that the effects on bodyweight of a reduction in the price of healthy food are the opposite of those of a reduction in the price of non-healthy food because the former encourages substituting quality for calories, while the latter encourages substituting calories for quality.

4. CONCLUDING DISCUSSIONS In health economics literature, the world-wide positive trend in bodyweight has often been attributed to economic growth and technological innovations. In simple choice models, equilibrium bodyweight has been shown to increase in income and decrease in the food price. This paper re-examines the income’s and the food price’s effects on bodyweight by endogenizing the choice of food quality. While the effect of income on bodyweight is still positive, a bulk of food expenditure growth due to income growth is used for improving food quality rather than increasing food quantity, perhaps explaining the very low income elasticity of caloric intake in developed countries. While the bodyweight continues to increase as the general food price declines, a fall in the relative price of healthy food induces a substitution of food quality for food quantity, reducing the total caloric intake and hence bodyweight. To the extent that economic growth means income growth for a majority of people and that demand-induced technological innovations focus more on reducing the price of healthy food than reducing the price of non-healthy food, these results suggest that the upward trend in bodyweight due to economic growth and technological innovations is likely to be a self-limiting one.9 Knowing that the overweight problem caused by economic growth and technological innovations is self-limiting is useful. Existing economic

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analyses of the bodyweight issue have concluded that government polices promoting physiologically optimal bodyweight are likely to be counterproductive, unless there exist significant negative externalities from being overweight and obese, in the sense that society as a whole would be willing to pay overweight people to lose weight. However, while there have been quite a few studies on the economic costs of obesity, estimates on its external costs are still rare and controversial.10 Nonetheless, regardless of the current size of the external costs from obesity, it probably will not become much larger given the correcting forces (substitution of food quality for quantity and innovations on healthy food items) identified in this paper. So the paper strengthens the basic message of health economists for policy-making regarding trends in bodyweight. If bodyweight is a matter of rational individual choice, then there does not exist a public health crisis that warrants government interventions. Indeed, the income growth and technological changes that have caused the positive bodyweight trend are also welfare improving. What the government can do is to protect the market from various distortions. In 2004, the House of Representatives passed the Personal Responsibility in Food Consumption Act, which would shield food vendors from lawsuits based on weight gain. That would be a government intervention in the right direction.11 Given that bodyweight is largely the result of one’s own utility-maximizing behavior, holding a third party responsible is problematic. From an economic point of view, prospects of weight gain lawsuits will force food vendors to take economically inefficient measures, increasing costs of food consumption. Such action could reduce food consumption and the incidence of obesity, but would be welfare reducing as well. As an example of policy-caused distortion, consider inkind food transfers (food stamps) and exemption of state sales taxes on raw or lightly processed food. These poverty-relief policies make sense when low-income households were characterized by underconsumption of calories, but may well have exacerbated the obesity problem among low-income individuals that has been observed more recently. With the recent emphasis on addressing obesity, these policies may need to be reconsidered. A first step might be to make poverty-relief policies less distortionary for calorie consumption choice, replacing food stamps with cash transfers and replacing the exemption of state sales taxes on food with a negative income tax. In contrast, there may be a role for educational programs regarding nutrition and dieting based on the analysis in this paper. Health economists often doubt the efficacy of these educational programs. They tend to reason that it is not that overweight individuals do not know their bodyweight would increase with food consumption, but that they simply knowingly


13

choose to eat more than what is recommended by the medical profession. However, if food quality, not just food quantity, is a choice issue, and has relevance for bodyweight, educating the public about calorie contents of various kinds of food and nutritional needs for a healthy body may well provide valuable information guiding individuals’ choices on food consumption.12 One caveat to this analysis must be noted. To the degree that individuals do not bear the full cost of health complications resulting from obesity, the self-limiting effects we identify may be mitigated to some degree. While much of the cost of obesity is borne by the individual, certain health care costs can be externalized to others through public and private health insurances. Finally, we have focused in this paper on individuals’ food choices (both quantity and quality) in the determination of bodyweight. A more comprehensive behavioral model of bodyweight would also include personal choices in some other related areas, in particular, choices of occupation and exercising activities that determine the use of calories. However, such a model would have too many endogenous variables to be analytically tractable. Nevertheless, the idea of endogenous quality can also be applied to the side of energy expenditure. Nowadays, calorie-spending exercise is moving from being labor intensive to being more and more capital intensive, reflecting increased costs of labor. The quality of exercising equipment is subject to individual choice, taking into consideration an individual’s income and the use price of the equipment. The higher the income, the more likely an individual will choose to use more efficient exercising equipment, burning more calories in the same amount of time. Further, when technological innovations make the price of exercising equipment fall, there will be more demand for the use of the equipment. Therefore, incorporating the energy expenditure side, in addition to the source side considered in this paper, would strengthen our results that the obesity problem brought about by economic growth and technological innovations is self-limiting.

NOTES 1. See National Center for Health Statistics (2004). A person is regarded as normal in weight (overweight and obese) if he has a body mass index (BMI) between 20 and 25 (above 25 and above 30). BMI is measured as weight in kilograms divided by height in meters squared. 2. These numbers are based on a recent correction by the Centers for Disease Control and Prevention (Mokdad, Marks, Stroup, & Gerberding, 2005). An earlier study by CDC, which was later found to have some computational errors, estimated

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that the number of deaths tied to physical inactivity and poor diet increased by 33% between 1990 and 2000 to 400,000. Deaths from poor diet and physical inactivity include mainly deaths from being overweight and obese, but also include deaths from malnutrition and some obesity-independent effects of physical inactivity. According to Mokdad et al. (2005), of the 65,000 increased deaths, 50,000 were due to obesity. 3. For a different economic perspective based on food addiction, see Cawley (1999), for a survey of some earlier economic studies on the overweight problem, see Philipson (2001). 4. Parallel to more sedentary home and market production, the TV, and now the Internet, have also made the leisure time less physically active. 5. However, opinions differ regarding the relative importance of these technological changes in explaining the observed bodyweight growth. For example, Philipson and Posner (1999) observed that calorie consumption has only grown modestly in developed countries, and argued that it is mainly workplace and home production becoming more sedentary that has caused the positive bodyweight trend. In contrast, Cutler et al. (2003) attributed the bodyweight growth primarily to people consuming more calories, which is in turn caused by lowering total food price. 6. We want to emphasize that this is a comparison based on average numbers. For a group of European countries (Cyprus, the Czech Republic, Finland, Germany, Greece, Malta, and Slovakia), a higher percentage of men are obese or overweight than the estimated 67% of men in the US, according to a report from the International Obesity Task Force (Associated Press, 2005). 7. At the individual level in the US, the relationship between income and body mass is less clear. Chang and Lauderdale (2005) and Zang and Wang (2004) note that lower socioeconomic status is associated with higher body mass among women and that the relationship between economic status and body mass varies by race. One possible explanation for the observation that at the individual level, income and bodyweight are sometimes negatively correlated is that education causes lower bodyweight and education is positively correlated with income. In this paper, we do not take into account the effects on individual choice of higher education. See Grossman (2000) for a human capital model of health. 8. It is easy to see why b>1 in an affluent economy. Moreover, Drewnowski and Darmon (2005) found that the price of unhealthy energy-dense foods is less than their more healthy, less energy-dense alternatives. 9. We want to point out a couple of qualifications for predicting a self-limiting bodyweight trend. First, our theoretical results are held only when the income is sufficiently large. However, a large percentage of the obese population comes from the low-income group in the US. And it is questionable whether the income of this population could become ‘‘sufficiently large’’ over time. Second, even though we have seen more and more diet food items, there is no hard evidence showing that the price of high-quality food decreases over time. For example, prices of fruit and vegetables remain relatively high. 10. See Keeler, Manning, Newhouse, Sloss, and Wasserman (1989) for an exception. They estimated that the lifetime external cost (cost to the government and to other individuals), using a 5% discount rate, is US$ 1,900 for a person with a sedentary life style. However, Bednarek et al. (2006) questioned whether all those costs are actually from externalities. Moreover, for group health insurance through an employer, a


15

situation in which there may be externalities from obesity because of uniform premiums unadjusted for individual risks associated with overweight, Bhattacharya and Bundorf (2005) found that the incremental health care costs from obesity are passed on to obese workers in the form of lower cash wages. See Cawley (2004) for a general economic framework for the discussions of externalities associated with obesity. 11. The bill failed to pass the Senate. In late 2005, the so-called ‘‘Cheeseburger Bill’’ was reintroduced and passed again in the House (HR 554), while the Senate is currently considering a companion bill. 12. Government publications, like USDA (2000), can play a very useful role here.

ACKNOWLEDGMENTS We want to thank Kristian Bolin and a referee for very helpful suggestions. All remaining errors are our own.

REFERENCES Associated Press. (2005). Supersized Europe. Brussels, Belgium: Associated Press (March 16). Bednarek, H., Jeitschko, T., & Pecchenino, R. (2006). Gluttony and sloth: Symptoms of trouble or signs of bliss? A theory of choice in the presence of behavioral adjustment costs. Contributions to Economic Analysis and Policy, 5(1), 1–46. Bhattacharya, J., & Bundorf, M. K. (2005). The incidence of the healthcare costs of obesity. Stanford University Working Paper. Cawley, J. (1999). Obesity and addiction. Ph.D. dissertation, University of Chicago, Chicago. Cawley, J. (2004). An economic framework for understanding physical activity and eating behaviors. American Journal of Preventive Medicine, 27(3S), 117–125. Chang, V. W., & Lauderdale, D. S. (2005). Income disparities in body mass index and obesity in the United States, 1971–2002. Archives of Internal Medicine, 165, 2122–2128. Cutler, D. M., Glaeser, E. L., & Shapiro, J. M. (2003). Why have Americans become more obese? NBER Working Paper 9446. Drewnowski, A., & Darmon, N. (2005). Food choices and diet costs: An economic analysis. Journal of Nutrition, 135(4), 900–904. Grossman, M. (2000). The human capital model. In: A. J. Culyer & J. P. Newhouse (Eds), Handbook of health economics (Vol. 1A, pp. 347–408). Elsevier. Keeler, E. B., Manning, W. G., Newhouse, J. R., Sloss, E. M., & Wasserman, J. (1989). The external costs of a sedentary life-style. American Journal of Public Health, 79, 975–981. Levy, A. (2002). Rational eating: Can it lead to overweightness or underweightness? Journal of Health Economics, 21, 887–899. Mokdad, A. H., Marks, J. S., Stroup, J. L., & Gerberding, J. L. (2005). Correction: Actual causes of death in the United States, 2000. Journal of the American Medical Association, 293(3), 293–294. National Center for Health Statistics. (2004). Health, United States, 2004 with chartbook on trends in the health of Americans. Hyattsville, MD: National Center for Health Statistics.

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Philipson, T. (2001). The world-wide growth in obesity: An economic research agenda. Health Economics, 10, 1–7. Philipson, T., & Posner, R. (1999). The long-run growth in obesity as a function of technological change. NBER Working Paper 7423. Strauss, J., & Thomas, D. (1988). Human resources: Empirical modeling of household and family decisions. In: H. Chenery & T. N. Srinivasan (Eds), Handbook of development economics (Vol. 3, pp. 1883–2023). Amsterdam: Elsevier Science. USDA (2000). Nutrition and your health: Dietary guidelines for Americans, 5th Edition (publisher information is unclear, but USDA 2000 can be accessed from the link below: http://www.health.gov/dietaryguidelines/dga2000/document/frontcover.htm). Zang, Q., & Wang, Y. (2004). Trends in the association between obesity and socioeconomic status in U.S. adults, 1971–2000. Obesity Research, 12, 1622–1632.

APPENDIX A. PROOF OF (4) From W 4W 0 and by pF ¼ bðy pF Þ ð1 bÞpF ; for (2) to hold, it must be the case that, at W*, b½H 0 lðW W 0 Þ2 42alðW W 0 ÞW 0 F Therefore, b H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F dF @G=@y 40 ¼ ¼ @G=@F @G=@F dy dF @G=@p F H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F ¼ ¼ o0 @G=@F dp @G=@F

APPENDIX B. PROOF OF LEMMA 1 To prove that W 4W 0 under the condition by bpF 0 40; suppose the opposite is true, namely W W 0 ; and as a result, F F 0 : Then, GðF ; q; y; p; bÞ defined in (6) is positive at F ; contrary with the fact that F is an economic optimum. So, W 4W 0 : The inequality in the lemma is obtained by using both (6) and (7) as in the following. From (6), bð1 gÞ H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F by pð1 qÞF bpqF ¼ H 0 lðW W 0 Þ2 bð1 gÞ y pð1 qÞF bpqF 2 H 0 lðW W 0 Þ bgy þ ½1 bð1 gÞ½pð1 qÞF þ bpqF ¼ y pð1 qÞF bpqF


17

which, after substituting (7), becomes ½H 0 lðW W 0 Þ2 pF ½ðgb þ b 1Þ þ bð1 bÞ qðb 1Þðgb b þ 1Þ y pð1 qÞF bpqF þ½1 bð1 gÞ½1 q þ bq H 0 lðW W 0 Þ2 pF ð2 bÞð1 bÞ ¼ 40 y pð1 qÞF bpqF

APPENDIX C. DERIVATION OF (8) Taking derivatives of (6) and (7) with respect to y while treating F and q as functions of y, we have @G dF @G dq @G þ ¼ @F dy @q dy @y @Q dF @Q dq @Q þ ¼ @F dy @q dy @y

(C.1)

where @G ¼ ½pð1 qÞ þ bpqf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g @F dð2alðW W 0 ÞW 0 F Þ ½y pð1 qÞF bpqF þ dF 0 2lðW W 0 ÞW ½by pð1 qÞF bpqF @G ¼ F ðb 1Þpf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g @q @G ¼ b½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F @y @Q ¼ ½pgb þ ðb 1Þpð1 bÞ qðb 1Þpðgb þ 1 bÞ @F @Q ¼ F ðb 1Þpðgb þ 1 bÞ @q @Q ¼ gb @y

ðC:2Þ

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Solving (C.1) for dF =dy and dq =dy; we have @G @G , @y @q dF ¼ @Q @Q D dy @y @q @G @G , @F @y dq ¼ @Q @Q D dy @F @y

(C.3)

where @G @F D ¼ @Q @F

@G @q @Q @q

which is shown in Appendix D to be positive. Substituting relevant terms in (C.2) into (C.3), we have dF ðð@G=@yÞð@Q=@qÞÞ ðð@Q=@yÞð@G=@qÞÞ ¼ D dy ¼

F ðb 1Þpð1 bÞf2alðW W 0 ÞW 0 F bð1 gÞ½H 0 lðW W 0 Þ2 g D

which is positive from Lemma 1. For dq =dy; because the second and third terms in the @G=@F expression in (C.2) are both negative (which are obtained by using Assumption 1 and Lemma 1) and @Q=@y ¼ gb40; we have dq ðð@G=@F Þð@Q=@yÞ ð@Q=@F Þð@G=@yÞÞ ¼ dy D ðð@Q=@F Þð@G=@yÞÞ ðð@G=@F Þð@Q=@yÞÞ ¼ D ð@Q=@F Þð@G=@yÞ þ ½ðb 1Þq þ pf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F gð@Q=@yÞ 4 D

ðC:4Þ


To show dq =dy40; therefore, it is sufficient to show @Q @G þ ½ðb 1Þpq þ pf½H 0 lðW W 0 Þ2 @F @y @Q 0 2alðW W 0 ÞW 0 F g @y

19

ðC:5Þ

Substituting relevant terms from (C.2), (C.5) becomes fb½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F gf½ðb 1Þpq þ pgb þ ðb 1Þpð1 bÞð1 þ qÞg þ ½ðb 1Þpq þ pf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F ggb 0 or ½ðb 1Þpq þ pgbð1 bÞ bðb 1Þpð1 bÞð1 þ qÞ H 0 lðW W 0 Þ2 þ 2alðW W 0 ÞW 0 F ðb 1Þpð1 bÞð1 þ qÞ 0

ðC:6Þ

From (6), 2alðW W 0 ÞW 0 F ¼

½H 0 lðW W 0 Þ2 ½by pð1 qÞF bpqF y pð1 qÞF bpqF

Therefore, (C.6) is equivalent to ½ðb 1Þpq þ pgbð1 bÞ bðb 1Þpð1 bÞð1 þ qÞ ½ y pð1 qÞF bpqF þ ½ by pð1 qÞF bpqF ðb 1Þpð1 bÞð1 þ qÞ 0

or gb½y pð1 qÞF bpqF ðb 1Þpð1 bÞð1 þ qÞF 0

(C.7)

However, using (7), it can be readily checked that (C.7) holds with equality. Therefore, dq =dy40:

APPENDIX D. PROOF OF D40 First, note that @G @Q 4F ðb 1Þpðgb þ 1 bÞ½ pð1 qÞ þ bpq @F @q ½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F

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so @G @F D¼ @Q @F

@G @q @G @Q @G @Q ¼ @Q @F @q @q @F @q

4F ðb 1Þpðgb þ 1 bÞ½pð1 qÞ þ bpqf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g F ðb 1Þpf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F gf½pgb þ ðb 1Þpð1 bÞ þ qðb 1Þpðgb þ 1 bÞg ¼ ð2 bÞð1 bÞðb 1Þp2 F f½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g40 in which the last inequality is from Assumption 2 and Lemma 1.

APPENDIX E. DERIVATION OF (10) Similar to the derivation of (8) in Appendix C, taking derivatives of (6) and (7) with respect to p and b, respectively, we have @G dF @G dq @G þ ¼ @F dp @q dp @p @Q dF @Q dq @Q þ ¼ @F dp @q dp @p

(E.1)

@G dF @G dq @G þ ¼ @F db @q db @b @Q dF @Q dq @Q þ ¼ @F db @q db @b

(E.2)

and

where @G=@F ; @G=@q; @Q=@F ; and @Q=@q are given in (C.2), and @G ¼ ½ð1 qÞF þ bqF f½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g @p @Q ¼ qF ðb 1Þðgb þ 1 bÞ F ½ðgb þ b 1Þ þ bð1 bÞ @p @G ¼ pqF f½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g @b @Q ¼ pqF ðgb þ 1 bÞ pF ð1 bÞ @b

(E.3)


21

Solving (E.1) for dF =dp and (E.2) for dF =db; we have @G @p dF ¼ @Q dp @p

@G @q ðð@G=@qÞð@Q=@pÞÞ ðð@G=@pÞð@Q=@qÞÞ =D ¼ @Q D @q

F 2 pð1 bÞðb 1Þðb 2Þf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g o0 D @G @G @b @q dF ðð@G=@qÞð@Q=@bÞÞ ðð@G=@bÞð@Q=@qÞÞ ¼ =D ¼ @Q @Q D db @b @q ¼

¼

F 2 p2 ð1 bÞðb 1Þf½H 0 lðW W 0 Þ2 2alðW W 0 ÞW 0 F g 40 D

ðE:4Þ


ACCESS TO FAST FOOD AND FOOD PRICES: RELATIONSHIP WITH FRUIT AND VEGETABLE CONSUMPTION AND OVERWEIGHT AMONG ADOLESCENTS Lisa M. Powell, M. Christopher Auld, Frank J. Chaloupka, Patrick M. O’Malley, and Lloyd D. Johnston ABSTRACT We examine the extent to which food prices and restaurant outlet density are associated with adolescent fruit and vegetable consumption, body mass index (BMI), and the probability of overweight. We use repeated cross-sections of individual-level data on adolescents from the Monitoring the Future Surveys from 1997 to 2003 combined with fast food and fruit and vegetable prices obtained from the American Chamber of Commerce Researchers Association and fast food and full-service restaurant outlet density measures obtained from Dun & Bradstreet. The results suggest that the price of a fast food meal is an important determinant of The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 23–48 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17002-8

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LISA M. POWELL ET AL.

adolescents’ body weight and eating habits: a 10% increase in the price of a fast food meal leads to a 3.0% increase in the probability of frequent fruit and vegetable consumption, a 0.4% decrease in BMI, and a 5.9% decrease in probability of overweight. The price of fruits and vegetables and restaurant outlet density are less important determinants, although these variables typically have the expected sign and are often statistically associated with our outcome measures. Despite these findings, changes in all observed economic and socio-demographic characteristics together only explain roughly one-quarter of the change in mean BMI and one-fifth of the change in overweight over the 1997–2003 sampling period.

1. INTRODUCTION The prevalence of overweight among American adolescents aged 12–19 has tripled over the last three decades, reaching 16.1% by 1999–2002 (Hedley et al., 2004). Parallel to this rising obesity epidemic, data based on nationwide surveys of food consumption patterns and household expenditures show a marked upward trend in total energy intake derived from away-from-home sources (Stewart, Blisard, Bhuyan, & Nayga, 2004). In 2002, nearly half of Americans’ food expenditures went toward an away-from-home food facility compared with one-quarter in 1960. The greatest growth over the 20-year period from 1982 to 2002 by outlet type in the away-from-home food market as a percent of sales was in the fast food industry. In this study, we provide econometric evidence on whether adolescent fruit and vegetable consumption and body weight respond to changes in the prices of fruit and vegetables and fast food and the availability of full-service and fast food restaurants. Previous research suggests that one explanation for the increase in overweight is higher caloric intake associated with fast food meals. Based on food consumption surveys categorizing caloric intake from food source locations by home, restaurants, fast food establishments, schools or day care, and other non-home locations, Guthrie, Lin, and Frazao (2002) reported significant increases over the last few decades in the portion of food prepared away-from-home with particularly large increases in fast food consumption. The percentage of total caloric intake from fast food sources increased between 1977 and 1978 and 1994 and 1996 by a factor of three for adults aged 18 years and over and by a factor of five for children aged 2–17 years. Nielsen, Siega-Riz, and Popkin (2002) reported a significant increase

Access to Fast Food and Food Prices

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in the consumption of particular foods such as salty snacks, french fries, cheeseburgers, pizza, and soft drinks and showed that the portion of total energy coming from restaurant and fast food places tripled among adolescents (aged 12–18) and doubled among young adults (aged 19–29) over the period from 1977 to 1996. These trends in dietary patterns mirror obesity trends for adolescents and adults. Research that has examined associations between fast food consumption and nutrient intake find that, controlling for individual and socioeconomic characteristics, for both children and adults fast food consumption is associated with higher total energy intake and density and higher intake of fat, saturated fat, carbohydrates, sugar, carbonated soft drinks and lower intake of micronutrients and fruit and vegetables (Lin, Guthrie, & Frazao, 1999; Binkley, Eales, & Jekanowski, 2000; French, Harnack, & Jeffery, 2000; French, Story, Neumark-Sztainer, Fulkerson, & Hannan, 2001b; Lin & Morrison, 2002; Paeratakul, Lovejoy, Ryan, & Bray, 2002; Bowman, Gortmaker, Ebbeling, Pereira, & Ludwig, 2004; Bowman & Vinyard, 2004). In addition to the literature tracking dietary consumption over time, a large body of research has examined the determinants of food consumption patterns. Individual, household, and regional characteristics are associated with dietary intake patterns (Lutz & Blaylock, 1993; Siega-Riz, Popkin, & Carson, 2000; Neumark-Sztainer, Wall, Perry, & Story, 2003; Xie, Gilliland, Li, & Rockett, 2003) and the demand for food away-from-home (Stewart, Blisard, Bhuyan, & Nayga, 2004). Lack of data on food prices is often a limitation of this research. Stewart et al. noted that ‘‘prices are an important determinant of demand’’ (p. 829) but due to a lack of data the authors were unable to include them in the study. In the absence of price data, several studies have examined the impact of household expenditures on food consumption patterns, but these expenditure data are likely to suffer from endogeneity problems as prices and other unobserved determinants of food expenditures are likely to be correlated with consumption patterns. Park and Capps (1997) found the demand for prepared meals to be price sensitive based on an imputed food price from expenditure data. Other research examines the determinants of total expenditure on food consumption patterns, but such studies do not allow the researcher to disentangle price and quantity (McCraken & Brandt, 1987). Studies that do include food prices show that prices and access are important determinants of food consumption. Recent research shows that own-price reductions in vending machines significantly increase children’s food purchases of low-fat snacks (French et al., 2001a; French, Jeffery, Story, Hannan, & Snyder, 1997a). Fruit and vegetable consumption in

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cafeterias is responsive to price (Jeffery, French, Raether, & Baxter, 1994; French et al., 1997b; Hannan, French, Story, & Fulkerson, 2002). Specific food demand models show that prices are important determinants of consumption of fats and oils (Yen, Kan, & Su, 2002). Economic research emphasizes changes in incentives, particularly prices, as causes of changes in body weight. Lakdawalla and Philipson (2002) argued on the basis of both theory and evidence from microdata that the obesity epidemic in the United States is a result of two changes in incentives: the relative price of consuming a calorie has fallen over time while the opportunity cost of burning a calorie has risen over time. Other econometric studies focusing on incentives as determinants of body weight show that increases in the per capita number of restaurants, lower real food prices, and higher cigarette prices may have significantly contributed to the upward trend in obesity (Chou, Grossman, & Saffer, 2004).1 Only one study to date has examined the effects of restaurant outlet density and food prices on the body mass index (BMI) of children: Sturm and Datar (2005) analyzed weight changes in children in kindergarten through the 3rd grade and found that lower fruit and vegetable prices, but not generally prices of other food items or outlet density, predicted smaller increases in body weight. Lakdawalla, Philipson, and Bhattacharya (2005) reported that lower food prices are associated with better nutritional status even in a highly developed country such as the United States. Overall, econometric analysis of individual-level data on adults and young children indicates decreases in food prices are an important cause of the obesity epidemic. This study examines the extent to which food prices and restaurant outlet density are associated with adolescent body weight and fruit and vegetable consumption. We use repeated cross-sections of individual-level data on adolescents from 1997 through 2003. We observe adolescents’ weight outcomes and fruit and vegetable consumption along with detailed socio-demographic data from the Monitoring the Future (MTF) surveys. Geographic identifiers at the zip code level are exploited to merge these data with fast food and full-service restaurant outlet density measures obtained from Dun & Bradstreet (D&B) and the price of fast food and fruits and vegetables obtained from the American Chamber of Commerce Researchers Association (ACCRA). Following economic explanations for changes in body weight and assuming food types substitute for one another, we expect consumption of fruits and vegetables to decrease with their own price and increase with the full price of fast food. Similarly, we expect consumption of fast food to be higher when its full price (including travel costs) is lower or when fruits and vegetables are more expensive. We do not


27

observe fast food consumption but estimate reduced form models of BMI and overweight. We expect that weight will be higher in times and places where the full price of fast food is lower or where the price of fruits and vegetables is higher. This is the first study to estimate the importance of such contextual factors on eating habits and weight outcomes among adolescents. The results suggest that the price of a fast food meal is an important determinant of adolescents’ body weight and eating habits. The price of fruits and vegetables and restaurant outlet density are less important determinants, although these variables typically have the expected sign and are often statistically associated with our outcome measures. Despite the evidence we present that changes in incentives are economically and statistically significant determinants of BMI, we find that in total changes in these incentives and changes in all other observed socio-demographic characteristics can only explain about one-quarter of the increase in mean BMI observed between 1997 and 2003. Further, only a small portion of the explainable variation is due to changes in food prices and restaurant densities. Similarly, most of the change in overweight is not explained by changes in observed characteristics.

2. METHODS 2.1. Data This study combines individual-level national data for 8th and 10th grade students from the MTF surveys with data on restaurant outlets obtained from business lists developed by D&B and food price data obtained from the ACCRA. The external outlet density and food price measures are matched to the individual-level data at the school zip code level for each year 1997 through 2003. 2.1.1. Monitoring the Future Survey Data The MTF study, which has annually surveyed nationally representative samples of high school seniors in the coterminous United States since 1975, is conducted by the University of Michigan’s Institute for Social Research (ISR). Since 1991 the MTF surveys have also included 30,000 8th and 10th grade students annually. Located in approximately 280 schools, these 8th and 10th grade students are selected annually for the MTF survey based on a three-stage sampling procedure (Johnston, O’Malley, Bachman, & Schulenberg, 2004). Stage 1 involves geographic area selection. Stage 2

28


involves selection of one or more schools in each area based on establishing the probability for inclusion proportionate to the size of the respective grade to be sampled. Stage 3 selects students within each selected grade. Within each school, up to 350 students per grade are selected for the study. For those schools with a smaller student body for the respective grade, all students are selected. If a school has more than 350 students then a random sample of classrooms or other random method is used to choose the final sample. This study draws on the 8th and 10th grade student samples. The data are weighted to correct for any inequalities in selection probabilities at the various stages of sampling. The summary statistics and regression analyses are weighted. Questionnaires are administered by an ISR representative in classrooms during normal class periods whenever possible. Students are informed of the importance of accurate responses and assured that their confidentiality will be protected. Neither parents nor the school are informed of individual student responses. To cover the range of topic areas in the study, 8th and 10th grade students are administered four different forms. This occurs in an ordered sequence, so as to ensure virtually identical sub-samples for each form. Approximately one-third of the questions on each form are common to all forms; these include the demographic variables used in this study. Questions that relate to food consumption, physical activity, and height and weight are all form-specific and are included on only a subset of forms. 2.1.1.1. MTF Student Samples. For the 7 years of data from 1997 through 2003 for 8th and 10th students our sample has a total of 72,854 observations on which we have information on height and weight and non-missing information on our covariates. Our sample with information on food consumption behavior totals 47,675 observations. As noted above, the reason for the differing sample sizes is that MTF consists of multiple forms that include both core and form-specific questions. The questions on food consumption behaviors and height and weight are form-specific. 2.1.1.2. Fruit and Vegetable Consumption Measure. We create a measure of frequent fruit and vegetable consumption based on the answers to the following two questions: ‘‘How often do you eat at least some green vegetables?’’ and ‘‘How often do you eat at least some fruit?’’ Student responses include the following possible categories for each question: never, seldom, sometimes, most days, nearly every day, and every day. Based on these answers we created a dichotomous indicator for frequent consumption of fruit and vegetables equal to unity if the student answered most days, nearly


29

every day, or every day for both questions and equal to zero otherwise. Table 1 shows that the majority (57%) of students frequently consume both fruits and vegetables. Table 2 shows that frequent fruit and vegetable consumption trended downward until 2002 but bounced up in 2003. 2.1.1.3. BMI and Overweight Measures. Anthropometric information is available in the MTF survey based on self-reports. Such data are likely to contain measurement error. Wang, Patterson, and Hills (2002) found underreporting of both overweight and obesity in self-reported data of older adolescents (15–19 years). Concerns stemming from measurement error are mitigated by Strauss (1999) who found that 94% of children were in the correct classification of obesity and Goodman, Hinden, and Khandelwal (2000) who found that examining self-report data among teens correctly classified 96% as obese or not obese. Using height and weight, we calculate BMI ( ¼ weight (kg)/height (m)2). Individuals’ body weight status is classified based on BMI for children and teens using the 2000 CDC Growth Chart (Kuczmarski, Kuczmarski, & Najjar, 2001); obesity is classified as BMI Z age-sex-specific 95th percentile (based on data from 1963 to 1994). Note that for children the CDC recommends using the term of ‘‘overweight.’’ We create a dichotomous indicator equal to one if the student is ‘‘overweight’’ and zero otherwise. Table 1 shows that average BMI is 21.8 and prevalence of overweight is 10.3% for the full sample of students. Table 2 shows that over the 1997–2003 period BMI trended upwards reaching 22 in 2003. Over the same period, the prevalence of overweight peaked at 11.5% in 2001 and fell somewhat to 11% by 2003. 2.1.1.4. Socio-Demographic Measures. We control for demographic measures available in the student surveys including: gender; grade; age; race/ ethnicity; highest schooling completed by father; highest level of schooling completed by mother; a rural/urban area neighborhood designation; total student real ($82–84) income (earned and unearned such as allowance; specified in $100 s in the regressions); weekly hours of work by the student and whether the mother works part-time or full-time. In our sensitivity analyses for our overweight models, we also control for participation in physical activity based on the following question: ‘‘How often do you do actively participate in sports, athletics or exercising?’’ Responses were based on a 5-point scale that included: never, a few times a year, once or twice a month, at least once a week, and almost every day. Based on these answers we created a dichotomous indicator for frequent participation in physical activity equal to unity if the student answered at least weekly or almost

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Table 1.

Summary Statistics: Outcomes, Access, Price, and Control Variables.

Body mass index (BMI) Prevalence of overweight Frequent consumption of fruit and vegetables Per capita number of full-service restaurants Per capita number of fast food restaurants Price of fast food ($82–84) Price of fruit and vegetables ($82–84) Weekly or more frequent physical activity participation Male Age 8th grade 10th grade White Black Hispanic Other race Father less than high school Father complete high school Father college or more Mother less than high school Mother complete high school Mother college or more Live with both parents Live in rural area Students’ weekly income ($82–84) Hours worked by student Mother works part-time Mother works full-time Year 1997 Year 1998 Year 1999 Year 2000 Year 2001 Year 2002 Year 2003 N

BMI/Overweight Sample

Fruit and Vegetable Consumption Sample

21.8061 (4.2932) 0.1025 –

– – 0.5687

12.8358 (9.3673)

12.8491 (9.3779)

2.4492 (2.2019)

2.4512 (2.2089)

2.7130 (0.1743) 0.7206 (0.1047) 0.7484

2.7137 (0.1744) 0.7204 (0.1046) –

0.4754 14.6570 (1.1637) 0.4852 0.5148 0.6957 0.1065 0.0971 0.1007 0.1308 0.2933 0.5759 0.1110 0.2798 0.6092 0.7997 0.2382 22.8239 (26.6573) 3.8568 (7.1437) 0.1827 0.6415 0.1471 0.1473 0.1398 0.1380 0.1392 0.1381 0.1505 72,854

0.4722 14.6606 (1.1649) 0.4828 0.5172 0.6972 0.1048 0.0970 0.1010 0.1328 0.2916 0.5756 0.1114 0.2760 0.6126 0.8004 0.2378 23.1516 (26.2636) 4.0074 (7.1902) 0.1839 0.6408 0.1474 0.1498 0.1397 0.1365 0.1392 0.1370 0.1504 47,675

Note: Standard deviations are shown in parentheses for non-dummy variables.

Trends in Outcomes, Access, and Prices.

Year

Frequent Fruit and Vegetable Consumption

BMI

Overweight Status

Per Capita Number of Full-Service Restaurants

Per Capita Number of Fast Food Restaurants

Price of Fast Food

Price of Fruit and Vegetables

1997

0.5795 (0.4937) [N ¼ 7,057] 0.5841 (0.4929) [N ¼ 7,018] 0.5683 (0.4953) [N ¼ 6,709] 0.5696 (0.4952) [N ¼ 6,445] 0.5576 (0.4967) [N ¼ 6,665] 0.5471 (0.4978) [N ¼ 6,525] 0.5719 (0.4948) [N ¼ 7,256] 0.5687 (0.4953) [N ¼ 47,675]

21.5454 (4.0497) [N ¼ 10,720] 21.6446 (4.1767) [N ¼ 10,543] 21.7325 (4.2945) [N ¼ 10,237] 21.8110 (4.2290) [N ¼ 9,979] 21.9347 (4.4579) [N ¼ 10,198] 21.9865 (4.4200) [N ¼ 10,074] 21.9979 (4.3998) [N ¼ 11,103] 21.8061 (4.2932) [N ¼ 72,854]

0.0876 (0.2827) [N ¼ 10,720] 0.0913 (0.2881) [N ¼ 10,543] 0.0984 (0.2978) [N ¼ 10,237] 0.1063 (0.3082) [N ¼ 9,979] 0.1151 (0.3192) [N ¼ 10,198] 0.1099 (0.3128) [N ¼ 10,074] 0.1102 (0.3131) [N ¼ 11,103] 0.1025 (0.3034) [N ¼ 72,854]

12.6969 (9.3399) [N ¼ 10,720] 12.3911 (7.5903) [N ¼ 10,543] 12.3945 (9.1817) [N ¼ 10,237] 11.7724 (7.3920) [N ¼ 9,979] 13.6342 (11.6241) [N ¼ 10,198] 14.2231 (11.0536) [N ¼ 10,074] 12.7800 (0.8493) [N ¼ 11,103] 12.8358 (9.3673) [N ¼ 72,854]

2.0169 (1.6154) [N ¼ 10,720] 2.2918 (1.9633) [N ¼ 10,543] 2.1182 (1.9544) [N ¼ 10,237] 2.0792 (1.6964) [N ¼ 9,979] 2.6355 (2.5242) [N ¼ 10,198] 2.9521 (2.7759) [N ¼ 10,074] 3.1363 (2.3519) [N ¼ 11,103] 2.4492 (2.2019) [N ¼ 72,854]

2.7901 (0.2167) [N ¼ 10,720] 2.7788 (0.1685) [N ¼ 10,543] 2.7699 (0.1522) [N ¼ 10,237] 2.6936 (0.1404) [N ¼ 9,979] 2.6481 (0.1514) [N ¼ 10,198] 2.6592 (0.1478) [N ¼ 10,074] 2.6477 (0.1524) [N ¼ 11,103] 2.7130 (0.1743) [N ¼ 72,854]

0.6680 (0.0696) [N ¼ 10,720] 0.7021 (0.0680) [N ¼ 10,543] 0.7053 (0.1287) [N ¼ 10,237] 0.6939 (0.0649) [N ¼ 9,979] 0.6770 (0.0796) [N ¼ 10,198] 0.8141 (0.1086) [N ¼ 10,074] 0.7835 (0.0998) [N ¼ 11,103] 0.7206 (0.1047) [N ¼ 72,854]

1998

1999

2000

2001

2002

2003

All years

31

Note: Standard deviations are shown in parentheses and numbers of observations are shown in brackets.


Table 2.

32


every day and equal to zero otherwise. The summary statistics in Table 1 show that just under half of the sample is male and that approximately 69% of the students are white, 11% are black, 10% are Hispanic, and 10% are of other (or mixed) racial/ethnic backgrounds. The average age of the sample is 14.7 and just under half of the sample is in 8th grade, whereas the second half is in 10th grade. The majority of students’ parents have at least some college education (58% of fathers and 61% of mothers). Most (80%) students live with both of their parents and just under one-quarter live in a rural area. Students work on average just under 4 hours per week. Students’ average weekly real income is about $23. Approximately 64% of students’ mothers work full-time whereas 18% of students have mothers who work part-time. The sample is evenly distributed across years with about 14% in each of the 7 years from 1997 to 2003. 2.1.2. Restaurant Outlet Density Measures Data on restaurant outlets were obtained from a business list developed by D&B.2 This list is obtained through use of D&B MarketPlace software. MarketPlace contains information on more than 14 million businesses in the United States and D&B employs a staff of more than 1,300 individuals to compile and update these records through interviews, public documents, and directories. The records are then updated quarterly to optimize their accuracy and completeness. Specifically, D&B utilizes the following sources to help update its database: yellow page directories that are matched against its database to identify new businesses; news and media sources are monitored daily to identify businesses that have merged, been acquired, closed, or claimed bankruptcy; government registries to identify business registration information; and, websites, including its own where businesses have the ability to review and update their own information. In addition to these sources, D&B has telecenters that place approximately 100 million phone calls annually to update and verify business list information. D&B has a number of quality assurance protocols in place to ensure accuracy of the data. For instance, D&B utilizes a ‘‘match grade’’ method to consolidate multiple business listings into one complete record. This method ensures that there are no duplicate entries of the same business and that data are not matched to the wrong business. D&B also assigns each business a unique numerical identifier to ensure validity of its data over time. This nine-digit number is never recycled and allows D&B to easily track changes and updates for all businesses contained in its database. MarketPlace allows sorting by multiple criteria such as zip code, company, size, location, metropolitan area, county, state, physical addresses,


33

subsidiaries, and Standard Industry Classification (SIC) codes. SIC codes allow for searching for, and selection of, specific types of businesses at varying levels of detail/specificity. Facilities may appear on the MarketPlace list by both ‘‘primary’’ and ‘‘secondary’’ SIC codes. Therefore, there is initially some level of duplication in the listings. To eliminate such duplications, we draw on the primary SIC code listing in creating the list of outlets used for this analysis. Information on restaurant outlets available in the D&B dataset was pulled by zip code for the years 1997 through 2003. The outlet density data are linked to the individual-level data by the student’s school’s zip code. While this may be a good proxy for the student’s home zip code at lower grade levels (in this case grade 8), high schools may draw their student population from beyond its own zip code. If a child lives in a different zip code to that of their school, the extent to which neighboring zip codes are similar will help to mitigate this potential source of error for differing access between time spent near and around school and time spent around their home. Information on the total number of restaurants was pulled at the four-digit SIC code level and the number of fast food restaurant outlets was pulled at the sixdigit SIC code level. Non-fast food restaurants, referred to as full-service restaurants, are defined as the number of total restaurants minus the number of fast food restaurants. Table 1 shows that on average in each zip code there are 2.4 fast food and 12.8 full-service restaurants per 10,000 people. Table 2 reveals that the per capita number of full-service restaurants remained fairly constant over the 1997–2003 period while the per capita number of fast food restaurants trended upwards, increasing by 56%. 2.1.3. Food Price Measures Food and fast food price data were obtained from the ACCRA Cost of Living Index reports. These reports contain quarterly information on prices across more than 300 US cities. The ACCRA collects 62 different prices for a range of products. Price data collection is based on establishment samples that reflect a mid-management standard of living. For consistency, national brands are stipulated where possible. Otherwise, ‘‘lowest price’’ is specified and is the average of the lowest prices found in all stores surveyed. These price data are matched to the MTF sample based on the closest city match available in the ACCRA data using school zip code geocode data. Price data are drawn from quarters one and two as these reflect the time frame of the MTF surveys. From the items provided in the ACCRA data we create two prices indices: a fruit and vegetable price index and fast food price index. All prices are deflated by the BLS Consumer Price Index (CPI) (1982–1984 ¼ 1).

34


The ACCRA also reports a cost of living index for each city which could be used to further deflate prices. Following Chou et al. (2004) we do not use this index because, given the ACCRA sample structure based on cost differentials among mid-management households, homeownership costs are likely to be over-weighted compared to an index based on an average consumer. The fruit and vegetable price index is based on the food prices available for this food category: potatoes, bananas, lettuce, sweet peas, tomatoes, peaches, and frozen corn. ACCRA reports weights for each item based on expenditure shares derived from the Bureau of Labor Statistics (BLS) Consumer Expenditure Survey. These weights are used to compute a weighted fruit and vegetable price based on the seven food items noted above. Table 1 shows that the average real price of the fruit and vegetable bundle in our sample is 72 cents. Table 2 reveals that the real fruit and vegetable price increased by 17% over the sampling period. The fast food price is based on the following three items included in the ACCRA data: a McDonald’s Quarter-Pounder with cheese, a thin crust regular cheese pizza at Pizza Hut and/or Pizza Inn, and fried chicken (thigh and drumstick) at Kentucky Friend Chicken and/or Church’s Friend Chicken. The fast food index is computed as an average of these three food prices since they have equal weights. Table 1 shows that the average real fast food price is $2.71. Table 2 shows that the real fast food price trended downwards over the 1997–2003 period, falling by roughly 5%.

2.2. Analytic Framework We follow Lakdawalla and Philipson (2002) in viewing body weight within a rational choice framework in which individuals choose food intake and physical activity to achieve ends such as health, social acceptance, and gastronomic pleasure. Observed weight is determined by the costs and benefits of gaining or losing weight at the margin, and these costs and benefits depend in part on the prices individuals face to obtain a given bundle of food products, to prepare food, to use energy through exercise, and the prices of goods and services other than food. Demand for various types of food and for physical activity obtains as a function of these parameters, and weight in turn is determined by these demands. We estimate reduced form statistical models for measures of body weight and demand equations for consumption of fruit and vegetables. Generally, weight depends on the composition of the amount and kind of food consumed, on genetics, on physical activity, and possibly other


35

environmental and behavioral factors. We focus on the total price of several forms of food intake as determinants of observed weight. Since detailed decompositions of food intake are not available in our data, we simplify by considering the availability of full service and fast food restaurants, the price of fruits and vegetables, and the price of fast food meals as key determinants of weight. Fast food meals are energy dense and the rise in obesity over time is speculated to be partially explained by decreases in price for these meals and increases in demand for food away-from-home caused in part by increases in female labor force participation (Lakdawalla & Philipson, 2002; Chou et al., 2004). Anderson, Butcher, and Levine (2003) provide evidence on the causal effect of greater maternal hours of work and increased overweight status for children. The total price of food intake includes both the prices of various types of food and the opportunity cost of the time spent acquiring the food. The time cost depends in turn on how far one must travel to obtain a given type of food. We proxy these time costs using measures of per-capita fast food and fullservice restaurant densities. The extent to which fast food alternatives are conveniently available within local communities compared to full-service restaurants that may offer more healthful food alternatives is likely to shape eating patterns. Differences in the supply of alternative choice sets for dining out, or having food delivered, or obtaining take-out food across different communities may result in systematic differences in eating patterns and weight status. Our statistical models disentangle the effects of changes in incentives, as measured by money prices and outlet densities, from other determinants of body weight and eating habits using either ordinary least squares regression or maximum likelihood probit regression. In some models, we include variables that may be simultaneously determined with the outcome (such as measures of physical activity), and our price and outlet measures may be correlated with unobserved regional characteristics which also affect weight such as peer or selection effects. We do not attempt to correct for such potential endogeneity problems but rather report sensitivity checks and bestow causal interpretations to our estimates cautiously. 2.2.1. Model of Fruit and Vegetable Consumption We report marginal effects from the maximum likelihood probit models for our binary outcome indicator of frequent fruit and vegetable consumption. Assume respondent i’s propensity to frequently consume fruit and vegetables, FVCi ; is given by FVCi ¼ b0 þ b1 ODis þ b2 PFFis þ b3 PFVis þ b4 X i þ i

(1)

36


where ODis is a vector of full-service food and fast food restaurant outlet density available to individual i in geographic area s, PFFis is the price of fast food, PFVis is the price of fruits and vegetables, Xi is a vector of individual and household characteristics, b are parameters to be estimated, and ei is a normally distributed disturbance term. Individual i is observed to frequently consume fruits and vegetables if FVCi 40: The marginal effect associated with the coefficient estimate b3 gives the own-price effect on fruit and vegetable consumption, whereas the effect associated with the estimate of b2 measure the cross-price effect between fruit and vegetable consumption and the price of fast food. We report alternative specifications, varying the control variables in X. 2.2.2. Reduced Form Models of BMI and Overweight Status We estimate reduced form models of individual BMI and OVERWEIGHT specified by: BMI=OVERWEIGHTi ¼ b0 þ b1 ODis þ b2 PFFi þ b3 PFVis þ b4 X i þ i (2) where the variables are defined as in Eq. (1) above. The BMI model is estimated by OLS and the OVERWEIGHT model is estimated as a probit model similar to the frequent fruit and vegetable consumption model. We report coefficient estimates from the BMI OLS regressions and we report marginal effects from the maximum likelihood OVERWEIGHT probit models. 2.2.3. Interpretation of Year Effect Dummies In our preferred specifications, we include a set of dummy variables indicating survey waves 1998 through 2003. Inclusion of these dummies is equivalent to non-parametrically detrending each variable in the analysis such that the estimates do not reflect common trends. For example, in our data mean BMI trends up and mean fast food prices trend down, but this common trend is removed by the time dummies and we identify the effects of interest using regional variation within years. We also report some specifications without time dummies to contrast the magnitude of the estimates on our key variables. In models with year dummies, we vary the included additional covariates to assess the ability of these covariates to explain trends in BMI and overweight. Consider the equation Y i ¼ g0 þ g1 Di þ g2 Z i þ i

(3)


37

where Yi is an outcome of interest, Di is a set of year dummies indicating the years from 1998 to 2003, and Z are covariates. If we estimate this model including only the time dummies using ordinary least squares regression (that is, set g2 ¼ 0), the estimate of the constant g0 is the mean of Yi in 1997 and each estimated parameter on the year dummies recovers changes in the mean of Yi relative to 1997. If we then include the covariates Z, the estimates of the year effects g1 measure changes in the mean of Yi over time that cannot be attributed to changes in Z.

3. RESULTS The results from the regression models of the relationship between access to fast food and food prices and adolescent frequent fruit and vegetable consumption, BMI, and overweight status are presented in Table 3. Table 3 includes results from models with the full set of control covariates with and without the year dummies. Examining first the results for fruit and vegetable consumption, we find that increased availability of full-service restaurants has a statistically significant relationship with the likelihood of adolescent frequent fruit and vegetable consumption: 10 more full-service restaurants per capita in the respondent’s region is associated with a 1.9% point increase in the probability of frequent consumption. A dollar increase in the price of fast food is statistically significantly associated with a reduction in frequent consumption of fruit and vegetables; by 7.3% points when year effects are not included and by 6.7% points when they are. A $1 increase in the price of fruit and vegetables is estimated to decrease fruit and vegetable consumption by 6.3% points (z ¼ 2.05). The estimated effect does not change when time dummies are included but loses some statistical significance (z ¼ 1.79). Turning to the BMI and overweight models, the results show that the inclusion of the year dummies substantially reduces the magnitude and statistical significance of the estimated access and price effects. Model 3, with no year effects, suggests that fast food and fruit and vegetable prices both statistically significantly impact BMI. When year effects are included, in Model 4, the magnitude of the fruit and vegetable price effect drops by more than half and loses statistical significance. The estimated effect of a one dollar change in the price of a fast food meal falls by almost half to 0.31 m/kg2, but remains statistically significant. Model 4 also suggests that BMI is higher when there are fewer full-service restaurants, more fast food restaurants, or higher fruit and vegetable prices, although none of these latter results are statistically significant. In the overweight model, the only

Effects of Access to Fast Food and Food Prices on Adolescent Frequent Fruit and Vegetable Consumption, BMI, and Overweight Status. Frequent Fruit and Vegetable Consumption Model 1 0.0019 (0.0005) 0.0028 (0.0018)

Model 3

Overweight Model 4

Model 5

Model 6

0.0019 (0.0005)

0.0048 (0.0029)

0.0039 (0.0029)

0.0002 (0.0002)

0.0002 (0.0002)

0.0029 (0.0019)

0.0187 (0.0122)

0.0084 (0.0124)

0.0005 (0.0009)

0.00003 (0.0009)

0.3066 (0.1397)

0.0398 (0.0088)

0.0730 (0.0197) 0.0633 (0.0308)

0.0669 (0.0201) 0.0632 (0.0353)

0.0037 (0.0058) 0.0183 (0.0049) 0.0046 (0.0118) 0.1445 (0.0105) 0.0984 (0.0099) 0.0079 (0.0097) 0.0438 (0.0100) 0.0777 (0.0066) 0.0419 (0.0106) 0.0796 (0.0064) 0.0670 (0.0076) 0.0118 (0.0073) 0.0002 (0.0001) 0.0004 (0.0005) 0.0132 (0.0090) 0.0411 (0.0073) – – – – – – – 47,675

0.0038 (0.0058) 0.0183 (0.0049) 0.0043 (0.0118) 0.1450 (0.0104) 0.0978 (0.0099) 0.0077 (0.0097) 0.0436 (0.0100) 0.0778 (0.0066) 0.0421 (0.0106) 0.0796 (0.0064) 0.0669 (0.0076) 0.0118 (0.0072) 0.0002 (0.0001) 0.0004 (0.0005) 0.0130 (0.0090) 0.0410 (0.0073) 0.0135 (0.0110) –0.0018 (0.0113) 0.0058 (0.0116) –0.0115 (0.0115) –0.0084 (0.0128) 0.0107 (0.0123) – 47,675

0.5757 0.6874 0.8053 0.2693 0.7503 1.1206 0.7244

(0.1321) (0.2027) (0.0410) (0.0342) (0.0810) (0.0646) (0.0888) (0.0667) (0.0706) (0.0483) (0.0817) (0.0429) (0.0499) (0.0530) (0.0010) (0.0037) (0.0636) (0.0509)

0.0482 0.4569 0.4586 0.1248 0.2013 0.2609 0.2962 0.0006 0.0084 0.1063 0.1305 – – – – – – 18.3290 (0.6246) 72,854

0.2688 (0.2392) 0.8041 (0.0409) 0.2719 (0.0341) 0.7324 (0.0804) 1.1301 (0.0639) 0.7015 (0.0864) 0.0615 (0.0669) 0.4551 (0.0705) 0.4558 (0.0483) 0.1250 (0.0816) 0.2095 (0.0430) 0.260 (0.0499) 0.2917 (0.0526) 0.0007 (0.0010) 0.0088 (0.0037) 0.1038 (0.0635) 0.1335 (0.0508) 0.0373 (0.0731) 0.1226 (0.0767) 0.1569 (0.0802) 0.2936 (0.0795) 0.2864 (0.0866) 0.3376 (0.0836) 17.7112 (0.6323) 72,854

Note: Standard errors are clustered at the school zip code level and are shown in parentheses. Represent statistical significance at 0.10. Represent statistical significance at 0.05. Represent statistical significance at 0.01.

0.0224 (0.0097) 0.0159 (0.0138) 0.0049 (0.0153) 0.0698 (0.0029) 0.0697 (0.0029) 0.0026 (0.0022) 0.0023 (0.0022) 0.0110 (0.0051) 0.0097 (0.0051) 0.0529 (0.0054) 0.0535 (0.0054) 0.0378 (0.0061) 0.0358 (0.0059) 0.0073 (0.0047) 0.0083 (0.0047) 0.0157 (0.0044) 0.0154 (0.0043) 0.0261 (0.0032) 0.0260 (0.0032) 0.0071 (0.0052) 0.0072 (0.0052) 0.0153 (0.0032) 0.0158 (0.0032) 0.0119 (0.0034) 0.0118 (0.0034) 0.0208 (0.0036) 0.0207 (0.0035) 0.00001 (0.00006) 0.00001 (0.00006) 0.000001 (0.0002) 0.00002 (0.0002) 0.0075 (0.0045) 0.0074 (0.0045) 0.0063 (0.0034) 0.0064 (0.0034) – 0.0010 (0.0053) – 0.0080 (0.0056) – 0.0135 (0.0060) – 0.0227 (0.0060) – 0.0186 (0.0068) – 0.0194 (0.0063) – – 72,854 72,854


Per capita number of fullservice restaurants Per capita number of fast food restaurants Price of fast food Price of fruit and vegetables Male Age 10th grade Black Hispanic Other race Father less than high school Father college or more Mother less than high school Mother college or more Live with both parents Live in rural area Students’ weekly income Hours worked by student Mother works part-time Mother works full-time Year 1998 Year 1999 Year 2000 Year 2001 Year 2002 Year 2003 Constant N

Model 2

BMI

38

Table 3.


39

statistically significant contextual factor is the price of fast food. Controlling for year effects, Model 6 suggests that a dollar increase in the price of a fast food meal reduces the prevalence of overweight by 2.2% points. The signs on the effects of the other price and access measures are also as expected but, as with BMI, these effects are not statistically significant. Sensitivity analyses to check the robustness of the results of our outlet density and price variables to alternative specifications are shown in Table 4.3 Results are shown that examine the sensitivity to specifications that exclude mother’s work status for both outcomes and that include student frequent participation in physical activity in the specification of the BMI model. Maternal work status is highly statistically significant but its inclusion has little effect on the estimated price and restaurant access coefficients.

Table 4.

Sensitivity Analyses of BMI and Frequent Fruit and Vegetable Consumption to Alternative Model Specifications. Frequent Fruit and Vegetable Consumption Model A

Per capita number of full-service restaurants Per capita number of fast food restaurants Price of fast food Price of fruit and vegetables Mother works part-time Mother works full-time Frequent participation in physical activity N

0.0019 (0.0005)

0.0029 (0.0019)

Model B

BMI

Model A

0.0019 (0.0005) 0.0039 (0.0029)

0.0028 (0.0019)

0.0084 (0.0124)

0.0669 (0.0201) 0.0684 (0.0201) 0.3066 (0.1397) 0.2688 (0.2392) 0.0632 (0.0353) 0.0611 (0.0354) 0.0130 (0.0090)

–

0.0410 (0.0073)

–

–

–

47,675

47,675

0.1038 (0.0635) 0.1335 (0.0508)

Model B

Model C

0.0039 (0.0030)

0.0033 (0.0029)

0.0082 (0.0124)

0.0074 (0.0124)

0.3148 (0.1402) 0.3097 (0.1396) 0.2592 (0.2407) 0.3007 (0.2384) –

0.0926 (0.0635)

–

0.1484 (0.0509)

–

–

0.5593 (0.0468)

72,854

72,854

72,854

Note: Standard errors are clustered at the zip code level and are shown in parentheses.Model A: Model 2 specification from Table 3; Model B: Model A without mothers’ work status; Model C: Model A with students’ sports participation. All of the models include socioeconomic variables: gender, age, grade, race/ethnicity, fathers’ education, mothers’ education, living arrangement, urbanization of residence, students’ weekly income, hours worked by student, and year dummies. Statistical significance at 10%. Statistical significance 5%. Statistical significance 1%.

40


Similarly, Model 5 shows that conditioning on physical activity has little effect on the price and restaurant access coefficients. Since physical activity may proxy unobserved region-varying determinants of weight outcomes, robustness to the inclusion of this variable mitigates concern over omitted variable bias. For example, social attitudes toward health may vary across regions and may be correlated with food prices and restaurant density. Since these attitudes may also be correlated with physical activity, if such unobserved factors were biasing our results we would not expect the results to be robust to the inclusion of the activity measure. Turning to the covariates other than prices and restaurant densities, the results from the frequent fruit and vegetable consumption model (Table 3, Model 2) show that older students within grade, those who are African American or Hispanic, and those students whose mother or father have less than a high school education are significantly less likely to frequently consume fruit and vegetables. The strongest associations are found for race: compared to their white counterparts, African American and Hispanic students are 14.5 and 9.8% points less likely to frequently consume fruit and vegetables. Student’s income has a very small marginally statistically significant impact on consumption. Students who live in an intact family and those with collegeeducated parents are significantly more likely to frequently consume fruit and vegetables. However, controlling for mothers’ education (and the other covariates), students with a mother who works full-time are 4% points less likely to frequently consume fruit and vegetables compared with students whose mothers do not work. The year dummy variables are not statistically significant individually, nor are they jointly statistically significant. The results for the control covariates in the BMI (Model 4) and overweight (Model 6) models are as expected. African American and Hispanic students are more likely to be overweight by 5.4 and 3.6% points, respectively, than white students, and white students have lower mean BMI. Male and 10th versus 8th grade students are have higher BMI and are more likely to be overweight. Controlling for grade, BMI increases with age but the risk of overweight does not. Students with parents who have higher education and those who live with both parents have lower BMI and are less likely to be overweight. Living in a rural area is associated with higher BMI and overweight prevalence. Controlling for maternal education, mothers’ fulltime work is significantly associated with 0.13 m/kg2 higher in BMI and marginally associated with just over half a percentage point increase in the probability of overweight. Table 5 shows the estimated year effects from several specifications. Model 1 includes no other covariates and so recovers deviations in

Year effects (omitted year 1997) Year 1998 Year 1999 Year 2000 Year 2001 Year 2002 Year 2003 Food prices and restaurant densities All other covariates

Explaining Trends in Fruit and Vegetable Consumption, BMI and Overweight with Alternative Model Specifications. Frequent Fruit and Vegetable Consumption

BMI

Overweight

Model 1

Model 2

Model 3

Model 1

Model 2

Model 3

Model 1

Model 2

Model 3

0.0046 (0.0123) 0.0113 (0.0139) 0.0100 (0.0139) 0.0220 (0.0133) 0.0325 (0.0143) 0.0076 (0.0136) –

0.0086 (0.0123) 0.0064 (0.0137) 0.0012 (0.0138) 0.0115 (0.0134) 0.0154 (0.0159) 0.0128 (0.0146) Included

0.0135 (0.0110) 0.0018 (0.0113) 0.0058 (0.0116) 0.0115 (0.0115) 0.0084 (0.0128) 0.0107 (0.0123) Included

0.0992 (0.1000) 0.1870 (0.1119) 0.2656 (0.1142) 0.3893 (0.1149) 0.4411 (0.1185) 0.4525 (0.1123) –

0.0951 (0.1011) 0.1818 (0.1130) 0.2376 (0.1177) 0.3713 (0.1191) 0.4406 (0.1393) 0.4299 (0.1277) Included

0.0373 (0.0731) 0.1226 (0.0767) 0.1569 (0.0802) 0.2936 (0.0795) 0.2864 (0.0866) 0.3376 (0.0836) Included

0.0042 (0.0059) 0.0119 (0.0063) 0.0204 (0.0070) 0.0298 (0.0066) 0.0243 (0.0069) 0.0246 (0.0066) –

0.0044 (0.0059) 0.0118 (0.0063) 0.0177 (0.0070) 0.0263 (0.0068) 0.0241 (0.0080) 0.0229 (0.0073) Included

0.0010 (0.0053) 0.0080 (0.0056) 0.0135 (0.0060) 0.0227 (0.0060) 0.0186 (0.0068) 0.0194 (0.0063) Included

–

–

Included

–

–

Included

–

–

Included


Table 5.

Note: Standard errors are clustered at the zip code level and are shown in parentheses. Significant changes in BMI relative to year 1997 at 10%. Significant changes in BMI relative to year 1997 at 5%. Significant changes in BMI relative to year 1997 at 1%.

41

42


Change in fruit & veg consumption from 1997

unadjusted means from 1997. As already shown in Table 2, mean BMI generally trends up over our sampling interval. Model 2 adds fast food and fruit and vegetable prices and the restaurant density measures as additional covariates. If changes in these variables over time explain a substantial portion of changes in obesity over time, then the estimated year effects should fall substantially toward zero, which we do not observe. In Model 3, we include all covariates (these replicate Models 2, 4, and 6 in Table 3). Fig. 1 shows that the significant drop in fruit and vegetable consumption that occurred by 2002 is substantially explained by our observed covariates with the contextual price and outlet density factors accounting for a large part of this trend. Fig. 2 shows that changes in all covariates, including prices and densities and mother’s employment, explain only roughly onequarter of the half point change in mean BMI. Similarly, Fig. 3 displays the results for overweight status over time. Overweight status trends up until

0.01

0

-0.01

-0.02 No controls Controlling for: Prices and densities All covariates

-0.03 1997 1998 1999 2000 2001 2002 2003 year

Fig 1. Changes in Frequent Fruit and Vegetable Consumption Over Time. Note: It shows changes in prevalence of frequent fruit and vegetable consumption, relative to 1997. The solid line shows unadjusted changes in the sample means. The dashed line shows changes that can be attributed to neither changes in fast food and fruit and vegetable prices nor changes in restaurant outlet densities. The dotted line shows changes that cannot be attributed to prices, densities, or any of the other covariates listed in Table 1.


43

Change in mean BMI from 1997

0.5

0.4

0.3

0.2

No controls Controlling for: Prices and densities All covariates

0.1

0 1997

1998

1999

2000 year

2001

2002

2003

Fig 2. Changes in Mean BMI Over Time. Note: It shows changes in mean BMI, relative to 1997. The solid line shows unadjusted changes in the sample means. The dashed line shows changes that can be attributed to neither changes in fast food and fruit and vegetable prices nor changes in restaurant outlet densities. The dotted line shows changes that cannot be attributed to prices, densities, or any of the other covariates listed in Table 1.

2001, then levels off or starts to fall. Contextual factors and other covariates explain little of this variation: if prices, restaurant densities, and all other characteristics were unchanged across the sampling period, the pattern of changes in overweight would not have been markedly different.

4. CONCLUSIONS We contribute to the literature examining the causes for the obesity epidemic in the United States by examining the determinants of BMI, overweight, and fruit and vegetable consumption among adolescents. We analyze repeated cross-sections from the MTF surveys, merged with data on fruit and vegetable and fast food prices and fast food and full-service restaurant outlet density measures matched at the zip code level. The results indicate that changes in prices and restaurant densities change outcomes in

44


Change in overweight from 1997

0.03

0.02

0.01 No controls Controlling for: Prices and densities All covariates

0 1997

1998

1999

2000 year

2001

2002

2003

Fig 3. Changes in Overweight Over Time. Note: It shows changes in prevalence of overweight, relative to 1997. The solid line shows unadjusted changes in the sample means. The dashed line shows changes that can be attributed to neither changes in fast food and fruit and vegetable prices nor changes in restaurant outlet densities. The dotted line shows changes that cannot be attributed to prices, densities, or any of the other covariates listed in Table 1.

the manner predicted by the standard economic model. Fruit and vegetable consumption is lower when fruit and vegetable prices are higher, or when the price of fast food is lower, or when full-service restaurants are less readily available. BMI is lower when fast food is more expensive. The price of fast food is the only contextual factor consistently associated with both fruit and vegetable consumption and with the weight outcomes and it is the only factor that is statistically significantly associated with BMI and overweight. Our preferred specifications suggest that a 10% increase in the price of a fast food meal leads to a 3.0% increase in the probability of frequent fruit and vegetable consumption, a 0.4% decrease in BMI, and a 5.9% decrease in prevalence of overweight. These results suggest that individuals at risk of overweight are more responsive to changes in price than the average individual. We also find that changes in prices and access explain very little of the one-half point increase in mean BMI between 1997 and 2003. Over this


45

period mean BMI increases and simultaneously fast food prices fall, fruit vegetable prices rise, and fast food outlet density rises. Together these changes suggest that substitution toward fast food driven primarily by fast food price changes may explain increases in weight outcomes. This explanation is correct but only accounts for perhaps 5% of the observed change in BMI and 7% of the observed change in overweight. Changes in all observed economic and socio-demographic characteristics together only explain roughly one-quarter of the change in mean BMI and one-fifth of the change in overweight. The bulk of the change in weight outcomes remains in the residual, not attributable to changes in prices, restaurant density, mother’s employment, race, age, student’s income or employment, parental education, or family structure. These results are subject to several limitations. Some of the key variables are subject to measurement error which will tend to reduce the magnitude of our estimated effects and understate the importance of, in particular, our economic explanatory variables. Information on family income is unavailable in our data; previous research has shown that income is highly correlated with body weight. To the extent that our control variables, such as parental education, do not capture variation in income our results may be subject to omitted variables bias. Also, we identify the effects of prices and densities using variation across geographic regions within years such that unobserved determinants of weight outcomes and eating habits across regions may bias our results. Despite these limitations, our results for adolescents are consistent with findings for other age ranges and suggest that food prices are statistically significant predictors of weight outcomes and eating habits. Taxing unhealthful foods, such as fast food meals, or subsidizing healthful foods are controversial policy proposals to address the obesity epidemic. Our results suggest that such policy instruments may be effective, to some extent, in reducing adolescent overweight.

NOTES 1. Also see comment by Gruber and Frakes (2005) and response by Chou, Grossman, and Saffer (2006). 2. Information on D&B’s methods was obtained from several sources that include: (1) http://www.zapdata.com; (2) ‘‘The DUNSright Quality Process: The Power behind Quality Information’’ (2005) Dun and Bradstreet; and (3) Personal communication with Todd Mertz, Relationship Leader, US DUNSright Customer Solutions, D&B, February 2, 2004.

46


3. We also undertake sensitivity analyses (not shown in this table) to assess the robustness of our results to our sample restriction on missing data. Due to a high number (13%) of missing observations on parental education, we reran our analyses including dummy indicators for missing on these variables and found that the results for all of our key contextual variables are robust to their sample exclusion. One difference does occur for the control variables; with missing information on parental education, the mother’s full-time work covariate is no longer statistically significant in the BMI and overweight regressions.

ACKNOWLEDGMENTS We gratefully acknowledge research support from the Robert Wood Johnson Foundation through ImpacTeen and Youth, Education, and Society studies. Monitoring the Future survey data were collected under a grant from the National Institute on Drug Abuse. We thank Yanjun Bao and Donka Mirtcheva at the University of Illinois at Chicago and Deborah Kloska at the University of Michigan for their excellent research assistance.

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French, S. A., Jeffery, R. W., Story, M., Hannan, P., & Snyder, P. (1997a). A pricing strategy to promote low-fat snack choices through vending machines. American Journal of Public Health, 87(5), 849–851. French, S. A., Story, M., Jeffery, R. W., Snyder, P., Eisenberg, M., Sidebottom, A., & Murray, D. (1997b). Pricing strategy to promote fruit and vegetable purchase in high school cafeterias. Journal of American Dietetic Association, 97, 1008–1010. French, S. A., Story, M., Neumark-Sztainer, D., Fulkerson, J. A., & Hannan, P. (2001b). Fast food restaurant use among adolescents: Associations with nutrient intake, food choices and behavioral and psychosocial variables. International Journal of Obesity, 25, 1823–1833. Goodman, E., Hinden, B. R., & Khandelwal, S. (2000). Accuracy of teen and parental reports of obesity and body mass index. Pediatrics, 106(1 Pt 1), 52–58. Gruber, J., & Frakes, M. (2005). Does falling smoking lead to rising obesity? NBER Working Paper No. 11483. Guthrie, J. F., Lin, B.-H., & Frazao, E. (2002). Role of food prepared away from home in the American diet, 1977–78 versus 1994–96: Changes and consequences. Journal of Nutrition Education and Behaviors, 34, 140–150. Hannan, P., French, S. A., Story, M., & Fulkerson, J. A. (2002). A pricing strategy to promote purchase of lower fat foods in a high school cafeteria: Acceptability and sensitivity analysis. American Journal of Health Promotion, 17, 1–6. Hedley, A. A., Ogden, C. L., Johnson, C. L., Carroll, M. D., Curtin, L. R., & Flegal, K. M. (2004). Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. Journal of American Medical Association, 291(23), 2847–2850. Jeffery, R. W., French, S. A., Raether, C., & Baxter, J. E. (1994). An environmental intervention to increase fruit and salad purchase in a cafeteria. Preventive Medicine, 23, 788–792. Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2004). Monitoring the future national survey results on drug use, 1975–2003 (Vol. 1). Secondary School Students, NIH Publications no. 04-5507. Bethesda, MD: National Institute on Drug Abuse. Kuczmarski, M. F., Kuczmarski, R. J., & Najjar, M. (2001). Effects of age on validity of selfreported height, weight, and body mass index: Findings from the Third National Health and Nutrition Examination Survey, 1988–1994. Journal of the American Dietetic Association, 101(1), 28–34. Lakdawalla, D., & Philipson, T. (2002). The growth of obesity and technological change: A theoretical and empirical examination. NBER Working Paper No. 8946. Lakdawalla, D., Philipson, T., & Bhattacharya, J. (2005). Food prices and nutritional status. Presented at the International Health Economic Association Meetings, July. Lin, B.-H., Guthrie, J., & Frazao, E. (1999). Away-from-home food increasingly important to quality of American diet. Agriculture Information Bulletin No. 749. Economic Research Service, U. S. Department of Agriculture, p. 12. Lin, B.-H., & Morrison, R. M. (2002). Higher fruit consumption linked with lower body mass index. Food Review, 25(3), 28–32. Lutz, S. M., & Blaylock, J. R. (1993). Household characteristics affect food choices. Food Review, 16(2), 12–18. McCraken, V. A., & Brandt, J. A. (1987). Household consumption of food-away-from-home: Total expenditure and by type of food facility. American Journal of Agricultural Economics (May), 69, 274–284.

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Neumark-Sztainer, D., Wall, M., Perry, C., & Story, M. (2003). Correlates of fruit and vegetable intake among adolescents findings from Project EAT. Preventive Medicine, 37, 198–208. Nielsen, S. J., Siega-Riz, A. M., & Popkin, B. M. (2002). Trends in food locations and sources among adolescents and young adults. Preventive Medicine, 35, 107–113. Paeratakul, S., Lovejoy, J. C., Ryan, D. H., & Bray, G. A. (2002). The relation of gender, race and socioeconomic status to obesity and obesity comorbidities in a sample of US adults. International Journal of Obesity, 26, 1205–1210. Park, J. L., & Capps, O., Jr. (1997). Demand for prepared meals by US households. American Journal of Agricultural Economics, 79, 814–824. Siega-Riz, A. M., Popkin, B. M., & Carson, T. (2000). Differences in food patterns at breakfast by sociodemographic characteristics among a nationally representative sample of adults in the United States. Preventive Medicine, 30, 415–424. Stewart, H., Blisard, N., Bhuyan, S., & Nayga, R. M., Jr. (2004). The demand for food away from home: Full-service or fast food? Agricultural Economic Report No. 829. Food and Rural Economics Division, Economic Research Service, USDA. Strauss, R. S. (1999). Comparison of measured and self-reported weight and height in a crosssectional sample of young adolescents. International Journal of Obesity, 23, 904–908. Sturm, R., & Datar, A. (2005). Body mass index in elementary school children, metropolitan area food prices, and food outlet density. Public Health, 119(12), 1059–1068. Wang, Z., Patterson, C. M., & Hills, A. P. (2002). A comparison of self-reported and measured height, weight and BMI in Australian adolescents. Australia and New Zealand Journal of Public Health, 26(5), 473–478. Xie, B., Gilliland, F. D., Li, Y.-F., & Rockett, H. R. (2003). Effects of ethnicity, family income, and education on dietary intake among adolescents. Preventive Medicine, 36, 30–40. Yen, S. T., Kan, K., & Su, S.-J. (2002). Household demand for fats and oils: Two-step estimation of a censored demand system. Applied Economics, 14, 1799–1806.

A BEHAVIORAL MODEL OF CYCLICAL DIETING Steven M. Suranovic and Robert S. Goldfarb ABSTRACT This paper presents a behavioral economics model with bounded rationality to describe an individual’s food consumption choices that lead to weight gain and dieting. Using a physiological relationship determining calories needed to maintain weight, we simulate the food consumption choices of a representative female over a 30-year period. Results show an individual will periodically choose to diet, but that diet will reduce weight only temporarily. Recurrence of weight gain leads to cyclical dieting, which reduces the trend rate of weight increase. Dieting frequency is shown to depend on decision period length, dieting costs, and habit persistence.

1. INTRODUCTION In recent years, the problem of increasing obesity in the US has sparked interest across a variety of disciplines. Fully 35% of Americans are overweight, and an additional 20% are obese (Mokdad, Bowman, Ford, Vinicor, Marks, & Koplan, 2001).1 Excess body weight has been characterized as the second-leading cause of preventable death and disease in the US; 300,000 The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 49–78 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17003-X

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STEVEN M. SURANOVIC AND ROBERT S. GOLDFARB

Americans die annually from obesity-related diseases (McGinnis & Foege, 1993; Must et al., 1999).2 The Centers for Disease Control (CDC) now refer to excess weight and obesity as an ‘‘epidemic.’’ In the economics literature there are two branches of research; empirical studies attempting to identify the underlying causes of the obesity epidemic, and behavioral models focusing on the individual decision-making process. On the empirical side, Lakdawalla and Philipson (2002) attribute 40% of overweight to lower food prices and 60% to reduced physical activity. Studies by Chou, Saffer, and Grossman (2004), and Cutler, Glaeser, and Shapiro (2003), highlight the importance of food innovations, especially fast food, the time cost of food preparation, and lower rates of smoking. On the behavioral side, modeling has focused on explaining why individuals choose something (overconsumption) that results in outcomes (overweight) that are clearly undesirable. In other words why do people choose something leading to an outcome they do not want? In addition, the choices of individuals seem to reflect ambivalence over time, displaying weight/dieting cycles, i.e., episodes of weight gain followed by dieting and weight loss repeated over and over again. There are both analytical and practical/empirical reasons for paying particular attention to dieting behavior. On the analytical side, dieting behavior presents a number of conceptual challenges for rational choice models of human behavior. In particular, ‘‘cyclical’’ or ‘‘yo-yo’’ dieting seems to some observers to be arguably inconsistent with rational behavior.3 On the practical/empirical side, dieting efforts appear to be extensive, and involve significant expenditures of effort and money. The dieting industry, including Weight Watchers, Jenny Craig, and medical facilities that oversee weight loss efforts, is a multi-billion dollar business in the US. In 2006, total revenues for Weight Watchers International alone exceeded $1 billion. In addition, there are racks of self-help diet books on the market promoting the Atkins, South Beach, and other diets. While measures of the extent and pattern of dieting are inexact and incomplete, there are some suggestive estimates worth noting. First, on the extent of dieting in the general population, Brownell and Rodin (1994, p. 1325) noted that ‘‘y 40% of women and 20% of men are dieting at any one time; 37% of men and 52% of women feel they are overweight.’’4 Even if one discounts these numbers a bit, it seems plausible that there is a great deal of attempted weight loss – dieting – going on in the US. Second, there is newly available evidence on the extent and patterns of cyclical dieting. Lahti-Koski, Mannisto, Pietinen, and Vartiainen (2005, p. 333) noted that ‘‘studies examining the prevalence of weight cycling in the

A Behavioral Model of Cyclical Dieting

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general population are scarce.’’ Their study, using a random sample from Finland, showed that among men aged 25–64, 7% were considered severe weight cyclers and 11% mild weight cyclers, whereas for women in the same age range, the respective proportions were 10 and 19%.5,6 There is also some evidence about dieting and cycling incidence in pre-adolescents and adolescents.7 Third, there are claims about the weight-regain characteristics of frequent versus infrequent dieters. In a small sample study, Pasman, Saris, and Westerterp-Plantenga (1999, p. 47) show that the ‘‘most powerful predictor’’ of weight regain ‘‘was the frequency of dieting before the study.’’ Hill (2004, p. S16) produces a list of eight studies that get the same result, namely that ‘‘baseline dieting or dietary restraint was associated with increased risk of weight gain.’’ Field et al. (2003) also showed this result for preadolescents and adolescents. In the behavioral economics literature, several attempts have been made to apply the rational addiction model to food consumption decisions to explain why individuals choose to overeat and why weight may exhibit cycles over time. Most of the addiction literature (e.g., applied to cigarettes, illegal drugs, and overconsumption of food) is based on the traditional model of rational intertemporal choice over time. It views individuals as choosing a lifetime consumption path for a product at an initial decision date. If an individual’s consumption decision about how much to smoke or eat today affects his future utility, it seems to follow that the ‘‘rational’’ way to make choices is to plan over one’s entire lifetime.8 Dockner and Feichtinger (1993) apply the Becker and Murphy (1988) rational addiction model to eating decisions. They assume that food consumption is addictive, meaning that the stock of past food consumption raises present consumption and that a rising weight stock negatively affects current utility. They demonstrate that for some parameter values, it is possible to get limit cycles. In other words, one’s consumption choices, and the consequent weight path, can exhibit cycles with gradual increases followed by gradual decreases. This result is of interest formally, because it can explain changing decisions and weight cycles in a rational choice framework. However, to obtain cycling, the individual must be away from his or her equilibrium path. But there is no straightforward intuitive interpretation – and therefore no helpful empirical interpretation – of what circumstances would generate this disequilibrium situation. Levy (2002) develops a similar intertemporal model but does not assume addiction. Instead he focuses on the health effects (increase in probability of dying sooner) of being overweight. He shows that a steady-state weight above the ‘‘most-healthy’’ weight can be optimal for the individual (a result he calls ‘‘optimal

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overweightedness’’) and also predicts explosive weight cycling under some conditions. But, as with Dockner–Feichtinger, it is hard to empirically assess the relevance of these conditions. Finally, Goldfarb, Leonard, and Suranovic (2006; hereafter GLS) develop a simple static choice model that includes both the benefits of food consumption and the negative effects that arise from weight above or below one’s ideal weight. Their framework, like Levy (2002), generates ‘‘optimal overweightedness’’ under some simple conditions. This paper also discusses alternative rationales that may motivate an individual to begin a diet.

2. A BOUNDED RATIONALITY APPROACH Our model here departs in an important way from the standard approach in behavioral economics by discarding the assumption that individuals, at t ¼ 0, choose a lifetime consumption path. In our view, this rational choice approach makes for a reasonable prescription about how to make individual choices when there are intertemporal effects, but the approach does not mimic the way a typical individual actually makes consumption decisions. As a consequence this approach has found it difficult to convincingly explain certain features of individual choice, including time inconsistency, and regret, among others.9 A viable alternative is to consider a ‘‘boundedly rational’’ choice model, following the Suranovic, Goldfarb, and Leonard (1999) model of cigarette consumption. In that analysis, excessive computational costs constrain decisions to be made only for the current consumption period. The individual does consider the future health effects of today’s smoking, but does not consider how today’s smoking will affect tomorrow’s smoking decisions or how tomorrow’s decisions will affect today’s choice.10 There are several reasons to adopt this approach. First, individuals are not super-human calculators, projecting all the implications of each decision far into the future. The mental and computational cost of solving an intertemporal decision process is very high, even for the economic modeler, let alone the typical person. Second, recent psychological research by Nobel Laureate Daniel Kahneman and others suggests that individuals are very poor at projecting the future utility from even seemingly simple choices.11 This makes it extremely dubious that individuals would be capable of a much more complex decision process. Finally, the boundedly rational


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decision process has been able to explain some choice phenomena (such as regret and cold-turkey quitting of smoking) that are more difficult and less intuitive to explain with the intertemporal approach. In this paper, we extend this boundedly rational choice process to an individual’s food consumption and dieting decisions. Nonetheless, even though decisions are made period-by-period, we simulate a time path of choices over a 30-year span. The model generates results consistent with important stylized facts about dieting behavior, including optimal overweightedness and the choice of multiple, or yo-yo diets. An important outcome of the model is a behavioral rationale for the phenomenon that more frequent dieting attempts tend to result in higher overall weight gain. Moreover, because our model includes an energy intake and expenditure formula drawn from the nutrition literature, our framework allows us to identify particular conceptual parameters that affect the patterns of weight change and dieting. Thus, it allows the study of how plausible parameter variations affect weight change and dieting patterns, thereby going beyond the previous papers by Dockner and Feichtinger (1993), Levy (2002), and GLS (2006). In the next section, we begin by describing the basic structure of the model including the physiology of weight change. We then turn to specifying specific functional forms for a hypothetical individual who we call Sallie Ann.12 Next we will solve for Sallie Ann’s optimal consumption in an initial period. Based on her choice, we update her weight and other parameters in successive periods and thus simulate Sallie Ann’s optimal consumption path over an extended period of time. Later we adjust key parameters to show the impact on Sallie Ann’s food consumption and dieting behavior.

3. THE CONCEPTUAL BEHAVIORAL CHOICE MODEL We assume food consumption has three types of effects on an individual’s utility: a standard benefit effect, a weight-utility effect, and a dieting cost effect. An individual maximizes utility each period, choosing both food and a composite good, considering these three effects of food on utility. A decision-making period will initially be assumed to be 1 month, however parameters can be adjusted to consider alternative time frames.

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The standard benefit effect is the utility gained from the consumption of food, analogous to the utility that arises from consumption of any good. We assume utility rises at a decreasing rate and there is a satiation point (i.e., too much food ingested in any 1 day makes the individual sick). The weight-utility effect connects food consumption to weight. We assume that an individual has an exogenous ideal weight. If food consumption maintains that ideal weight, then there is no weight-utility effect. However, if food consumption leads to either overweight or underweight, then there is a weight-associated disutility effect. We will assume that the weight disutility effect rises with deviations from the ideal weight. Thus, an additional kilogram of weight for a person 10 kg overweight will have greater disutility than if the person were only 5 kg overweight. These two effects combine to induce an individual to choose a food consumption level that results in a weight that is greater than ideal. The reason is that small increases in food consumption above one’s weight maintenance level do not have a large weight disutility effect. Thus, the normal marginal benefit of greater food consumption will exceed the small weight disutility effect leading to overconsumption and optimal overweightedness. This result occurs for similar reasons in GLS (2006) and Levy (2002). GLS (2006) also explore several motivations for diets. However, the basic intuition in all cases is that when weight rises, the disutility effect of food consumption on weight rises at an increasing rate. Eventually, the marginal benefit of food consumption will be less than the weight disutility effect and this inspires a reduction in food consumption, i.e., a diet. The GLS (2006) analysis, however, does not incorporate the costs or disutility associated with dieting. The third effect of food consumption is this cost, or disutility effect, of dieting. In particular, we assume sudden reductions in food consumption have strong negative psychological, and perhaps physical, effects. Typically, individuals choose food consumption levels that are relatively constant over time. We will refer to that constant level as a habitual level. If consumption is chosen below that habitual level, there is a disutility effect, which increases the greater the reduction. We will imagine there is no ‘‘cost’’ associated with excessive consumption. The presence of dieting costs means that an additional cost, above the rising disutility of overweight, must be overcome before a diet occurs. Whether a diet occurs will depend on the size of the dieting costs. If they are large, a diet may never occur. But if a diet is chosen, it may not occur until the disutility of overweight is quite high.


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In order to track the effects of food consumption decisions on weight and the subsequent effects of weight on food consumption choices, we must discuss the physiology of weight change. Incorporating the physiology into our utility model of weight change will enable us to simulate period-by-period consumption choices and track the effects of these choices on an individual’s weight path over time.

4. THE PHYSIOLOGY OF WEIGHT CHANGE The nutrition literature models weight determination and change by analyzing the balance of energy intake and energy expenditure. Weight rises (falls) if energy intake exceeds (falls short of) energy expenditure.13 Physiologists typically disaggregate energy expenditure into three components: resting or basal metabolism, food digestion, and muscular physical activity. Basal metabolic function accounts for 60–75% of energy expenditure in most individuals; another 10% is burned eating and absorbing food. Muscular activity accounts for the remaining 15–30% in a moderately active individual (McArdle, Katch, & Katch, 1996, p. 151). A continuing excess of energy intake over energy expenditure results in weight gain. A continuing view in the physiology literature is that basal metabolic energy requirements or basal metabolic rate (BMR) can be viewed as a function of age, weight, height, and sex. Long-standing empirical estimates widely used in the nutrition literature are provided by the venerable Harris– Benedict equations (Harris & Benedict, 1919).14 Energy expenditure on physical activity (and the thermic effect of food) can then be incorporated by applying a multiplier to the basal metabolism estimates.15 These long-standing empirical estimates suggest what is well documented elsewhere in the physiology/nutrition literature: that basal metabolism slows with age (see the cites in McArdle, Katch, & Katch, 1996, p. 152; see also Vaughan, Zurlo, & Ravussin, 1991). There is also evidence, if less extensive, that energy expenditure on physical activity declines later in life.16 These facts of physiology generate several important implications about patterns of weight change. First, even with constant calorie intake, age insidiously increases weight, and does so at a faster rate if exercise levels also fall as one ages. Second, these facts by themselves suggest a rationale for dieting. An aging person will, ceteris paribus, gain excess weight, and must diet (or exercise more) to avoid weight gain.

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5. SIMULATION MODEL To analyze a series of choices over time, we simulate the decisions of a hypothetical female we will call Sallie Ann. We posit plausible functional forms for each of the three utility effects and calculate Sallie Ann’s optimal consumption choice for the initial period. Once the optimum is calculated, the functions are updated with a new age, weight level, and age-specific activity (exercise) level. Based on this updated data, Sallie Ann’s optimal choice is recalculated for this second period. By repeating this process period by period we can describe Sallie Ann’s time path of food consumption choices and resulting weight path over time. We next specify specific functional forms for our three utility effects: the food benefit function, the weight loss function, and the dieting cost function.

5.1. The Food Benefit Function The main effect of food consumption is the utility or benefits it provides. Treating food as a normal good, the food benefit function has the following characteristics. Benefits rise at a decreasing rate as daily consumption increases up to a satiation level, given by the parameter M. Beyond that satiation level, benefits fall with increasing consumption. Sallie Ann’s food consumption benefits are described by the benefit function B(C): BðCÞ ¼

dk½M 2 ðC MÞ2 M2

(1)

where C represents the daily calorie consumption level, the parameter ‘‘d’’ represents the number of days over which the consumption decision is made, and ‘‘k’’ is a scale parameter that determines the peak daily utility attained at the satiation consumption level.

5.2. Weight Loss Function Consumption greater than (less than) necessary to maintain one’s weight will cause weight gain (loss). We imagine that an individual chooses an ideal weight, WI, based on health considerations, social appearance norms, or a combination of the two. The choice of WI is exogenous to the model.


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The following function describes the effects of weight on utility. Utility losses, L(C, W) due to over- or under-weight, are assumed to depend on the current level of daily consumption C, and current weight W plus several other exogenous parameters such as ideal weight WI. 2

3 d½C AF BMRðW ; A; HÞ 2 6W W I þ 7 7; 700 7 LðC; W Þ ¼ m6 4 5 WI

(2)

Here m is a scale parameter. Current consumption affects losses because under- or over-consumption will cause weight loss or gain. The right side of the numerator incorporates this effect. BMR (W, A, H) represents the basal metabolic rate and is a function of current weight (W), age (A), and height (H). As noted previously, the BMR formula for an adult female like Sallie Ann is given by the Harris–Benedict equation: BMR ðW ; A; HÞ ¼ 655 þ 9:6W þ 1:8H 4:7A

(3)

where W is Sallie Ann’s weight in kilograms, H is her height in centimeters, and A is age in years. The BMR gives the number of calories needed to maintain the current weight level of a person of given weight, height, and age assuming no additional energy is expended on activity/exercise. To obtain the full caloric intake needed for an active individual, the BMR must be multiplied by an activity factor, AF. AF is generally about 1.35 for a sedentary lifestyle and can rise to 1.9 or higher for a very active, vigorous lifestyle. Thus AF BMR (W, A, H) gives the number of calories needed to maintain a weight of W. The BMR equation indicates that the calorie intake necessary to maintain the same weight will fall as a person gets older. This reflects a shift in body composition from muscle to fat with aging.17 It is also quite common for a person to become less active as he or she becomes older, suggesting a falling activity factor AF with age. The expression ½C AF BMRðW ; A; HÞ=7; 700 in the weight loss function above represents the addition (or subtraction) from weight as a result of one’s current daily consumption choice. If C exceeds AF BMR, then one is consuming more calories than needed to maintain current weight, and weight will rise. The physiology literature indicates that 7,700 calories are equivalent to 1 kg in weight. Thus, if C exceeds AF BMR by 7,700 calories over some period, the person will gain 1 kg. This expression is multiplied by the time frame parameter (d for number of days) to represent the total weight effect over the choice period.

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If C ¼ AF BMR, then the weight loss function above reduces to m½ðW W I Þ=W I 2 ; where WI is one’s perceived ideal weight and m is a scale parameter. This simple quadratic function indicates that the farther one’s actual weight is from ideal weight, in either direction, the greater the perceived utility losses. Since current consumption can change current weight, the deviation in weight from ideal weight must include the weight change effect ½C AF BMR ðW ; A; HÞ=7; 700: Note, because of the quadratic form, an additional kilogram in weight will have a larger utility loss the farther one is from ideal weight.18 5.3. Dieting Costs People regulate their food consumption primarily by using their own body’s hunger signals, eating when hungry and stopping when full. The actual calorie intake level is rarely known with any accuracy. Nonetheless, since people must eat to prevent hunger pangs, food consumption occurs at regular intervals, normally breakfast, lunch, dinner, and perhaps snacks. The types of food consumed, the quantities consumed, and the pattern of eating typically become extremely regular in form and content. In other words, most people’s eating patterns are habitual. In contrast, dieting requires reduced calorie consumption and may force a change in types and quantities of food consumed, and perhaps even the pattern of consumption. These changes are very hard to undertake because they require breaking one’s long established habits. These difficulties in adjusting one’s food consumption habits constitute dieting, or adjustment, costs. They are not necessarily monetary expenditures, although they certainly can be if one joins a diet center or pays to participate in a weight-loss program. These dieting costs reflect the disutility that arises from attempts to curtail food consumption and to change one’s habits. The function representing dieting costs is given by AC(C) below. ( ) 1=2 if C avgco0 gd ðavgc2 C 2 Þ ACðCÞ ¼ (4) otherwise 0 where g is a scale parameter and d is the number of days in a decision period. We imagine that adjustment costs occur only for consumption levels below one’s average consumption level, avgc. In other words, eating too much 1 day is not difficult, but eating too little causes a utility loss. The period of time over which consumption is averaged represents habit persistence. For example, if avgc is averaged over 6 months, then it will take


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6 full months of lower consumption for a lower calorie intake to be viewed as the new habitual level. The function AC is chosen so that small reductions in consumption, below one’s habitual or average level avgc, result in a large decrease in utility. This implies it is very difficult to make small changes in consumption levels. However, as food consumption is reduced further, disutility continues to rise but at a decreasing rate.19

5.4. Total Utility and the Choice Problem The consumer obtains utility from consumption of food and an additional composite commodity Y, according to the following additively separable function. UT ðC; Y Þ ¼ BðCÞ LðCÞ ACðCÞ þ dY

(5)

The choice problem for the consumer is to maximize total utility UT(C, Y) with respect to C and Y, subject to the budget constraint, PC dC þ dY ¼ I

(6)

where we normalize the price of the composite good to one, PC is the price of food per calorie and d represents the number of days in the evaluation period. I represents the consumer’s income over the period. Solving the budget constraint for dY and substituting into the utility function yields, UT ðCÞ ¼ BðCÞ LðCÞ ACðCÞ þ I PC dC

(7)

Maximizing UT(C) with respect to C yields the first-order condition, B0 ðCÞ L0 ðCÞ AC 0 ðCÞ dPC ¼ 0

(8)

where B0 (C), L0 (C), and AC0 (C) correspond to the first derivatives with respect to C. More explicitly this condition can be written, 2 3 d½C AF BMRðW ; A; HÞ W W þ I 7 2dk½ðM CÞ 2dm 6 7; 700 6 7 4 5 2 2 7; 700 WI M gdC þ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ dPC ðavgc2 C 2 Þ

ð9Þ

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whenever (avgc–C)>0. If C exceeds avgc, the habitual level, we assume there are no adjustment costs, thus the third term on the left-hand side of the equation drops out in this circumstance.

6. SIMULATIONS 6.1. Base Case Suppose Sallie Ann is a 20-year old woman, 170 cm tall (50 600 ) and has an ideal weight of 60 kg (132 lbs.). Assume Sallie Ann is moderately active with an activity factor AF ¼ 1.55. If she initially weighs 60 kg, we can multiply her initial BMR (1,443 from Eq. (3)) by her activity factor to obtain her total daily energy expenditure (TDEE), measured in calories. Thus, given her energy demands, Sallie Ann at age 20 requires 2,237 daily calories to maintain her weight. We will assume Sallie Ann’s satiation consumption level (M) is 2,800 calories per day. Fig. 1 displays Sallie Ann’s benefit B(C), weight loss – L(C) and dieting, or adjustment cost functions – AC(C). The function UF(C) represents total utility from food consumption after accounting for the weight losses and the adjustment costs of dieting. Note the food benefit function rises from zero to a peak at 2,800 calories. The losses due to weight concerns increase as consumption rises or falls from the level needed to maintain weight, which for Sallie Ann is 2,237 300 250

B(C)

200 UF(C)

Utility

150 100

L(C)

50 0 -50 -100

AC(C)

-150 0

500

1000

1500

2000

2500

Daily Calories

Fig. 1.

Sallie Ann’s Age 20 Utility Functions.

3000


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calories per day.20 Finally, the dieting/adjustment costs of changing calorie intake becomes larger at a decreasing weight as calorie consumption falls below 2,237, but do not arise if consumption is greater than 2,237. This feature generates two key features of the food utility function UF. First, since adjustment costs are assumed only to arise in the downward direction, there is a kink in UF at the weight maintaining calorie level. Second, because adjustment costs rise at a decreasing rate as calories fall below 2,237, the total food utility function falls first at an increasing rate, then a decreasing rate for reductions in calories. These two features will motivate the incentive to diet.

6.2. Dynamics We will assume Sallie Ann makes a decision once per month about how much to optimally consume. At the end of each month, three things can change. First, if she does not consume precisely the number of calories needed to maintain weight, her weight will change. Second, Sallie Ann will become 1 month older. Both weight change and aging will affect her BMR. Finally, we will assume her activity factor falls at a gradual linear rate. Our assumption will make Sallie Ann more sedentary by the time she is 50 years old.21 All three changes will affect the number of calories needed to maintain Sallie Ann’s weight, and are updated monthly in Sallie Ann’s decision process. We also assume that Sallie Ann’s habits persist for 6 months. This means that she would need to reduce calorie intake for 6 months in a row to reduce her habitual consumption level to that new lower level. We will assume that Sallie Ann has a constant annual income of $25,000, approximately equal to average per capita disposable income in the US. The price of food is set at 0.003 dollars per calorie. At these values, Sallie Ann’s daily expenditures on food will be between 5 and 10% of her disposable income, which approximates average US consumption patterns. Finally, by optimizing each month and updating based on her choice, we can simulate Sallie Ann’s time path of food consumption and her weight pattern over time. In Fig. 2, we display the timepath of optimal calorie consumption choices made by Sallie Ann. Notice that Sallie Ann chooses to maintain consumption at her habitual level, 2,237 calories per day, until approximately the age of 30. At that point she decides-optimally-to reduce calorie intake to about 1,300 calories for 1 month. In other words, she chooses to diet. Just prior to her decision to diet, Sallie Ann’s benefit, weight, and adjustment cost functions are as shown in Fig. 3.

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Daily Calories

2500 2000 1500 1000 500

Base Case

0 20 22 24 26 28 30 32 34 36 38 41 43 45 47 49 Age (years)

Fig. 2.

Sallie Ann’s Consumption Choices Over Time.

300

B(C)

250 UF(C)

200 Utility

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The key difference between the functions for Sallie Ann at age 30 compared with age 20 is that the utility cost of higher weight has grown. This is reflected in the downward shift in the loss function L(C), which in turn has rotated the total utility schedule downward. To see the comparison, Fig. 4 shows the total food utility curves (UF) for Sallie Ann at age 20 and age 30. Also drawn are two lines, labeled Pr20 and Pr30, with slopes equal to the food price. The tangency between the Pr lines and the food utility functions satisfy the first-order conditions given in Eq. (9). In the top curve at age 20, Sallie Ann would clearly choose her habitual level of consumption on the kinked part of the food utility function at 2,237 calories per day. However, by age 30 the food utility curve has shifted and rotated downward until


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Sallie Ann’s optimal choice jumps from the habitual level to a level of about 1,300 calories per day. This is shown in the lower set of curves where it appears Sallie Ann would be indifferent between consuming 2,237 or 1,300. Dieting arises in this model because habitual consumption leads to weight gain and as weight rises there is increasing disutility in consumption from each unit of weight. When the disutility of rising weight becomes larger than the cost of dieting, Sallie Ann chooses to cut consumption significantly. That is, she begins a diet. Since Sallie Ann’s diet is maintained for a 30-day period, her weight will fall. With a reduced weight, the marginal losses due to weight in her utility function will also fall and the total utility function will rotate upwards. For this reason, in subsequent periods she will revert back to nearly her original habitual level.22 Immediately after the diet, Sallie Ann’s new calorie intake will remain below the level needed to maintain her new weight. Thus she will continue to lose weight after she stops dieting, albeit at a much slower rate. However, eventually a rising age, a falling activity level, and the difficulty of changing one’s habitual consumption level, will again, within several years, result in a rising weight trajectory. Sallie Ann’s weight trajectory over a 30-year period to age 50 is shown in Fig. 5.23 Notice that Sallie Ann will again choose to diet two more times before age 50. This shows the model is capable of explaining cyclical dieting. Sallie Ann’s modest success in her first diet does not eliminate the insidious pattern of weight gain since she has not sufficiently reduced her consumption to keep up with her falling caloric needs. Note also (Fig. 5) that the second diet occurs at a higher weight level than it did previously. Here is why.

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STEVEN M. SURANOVIC AND ROBERT S. GOLDFARB 80 Weight (kg)

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When weight rises back up to the level attained at age 30, the marginal effect of weight on utility will be the same as before. However, because the previous diet has reduced the habitual consumption level, the marginal benefits of consumption will be higher. (In other words, the allure of food is greater the less one consumes each day.) This implies that weight will need to rise even higher before the losses from weight gain exceed the marginal benefits from consumption. This effect implies that despite several attempts at dieting, Sallie Ann’s long-term weight trend is rising over her lifetime. Empirical support for this type of weight pattern is difficult to find. Most studies of weight rely on population averages at a point in time, or on measurements taken annually, at best. However, Black, Pack, and Hovell (1991) provide a longitudinal time-series study of the weight of two female subjects. In these cases, weight was measured daily for up to a 16-year period. Each woman experienced rising weight over time and self-initiated occasional diets to reduce weight. The weight pattern based on monthly averages for one of the subjects is shown in Fig. 6. The weight trend is similar to the simulated path in several ways. Most notably, the weight trend is inexorably upward despite repeated dieting attempts. In addition the subject chose to initiate six diets during a 16-year period, each lasting from one to several months.24

6.3. Effects of Lower Dieting Costs The timing of Sallie Ann’s diet is affected by several factors including the cost of dieting. Clearly different diet regimens have different costs associated with them. It is likely that these costs are idiosyncratic; some people may


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find diet A easier, others may face lower costs with diet B. Using the model, we can simulate the variability of dieting costs and assess how more effective diet programs would influence the dieting plans of a representative individual and that person’s lifetime weight profile. In Fig. 7, we compare Sallie Ann’s calorie choices over a 30-year period in the base case with a situation in which diet costs are reduced by half. In other words, we ask what if Sallie Ann found dieting to be easier in the sense that it caused half as much disutility. This investigates the dieting and consumption outcomes for an individual with a more effective dieting program available. During the 30-year period shown, Sallie Ann would make six diet attempts as opposed to three attempts when her dieting costs were higher. In addition, the severity of the diet, measured by the percentage reduction in calorie intake, is lower with lower dieting costs. Recall that Sallie Ann’s food utility curve (refer to either one shown in Fig. 4) is convex to the left of her habitual level (where the kink occurs). However, as calorie intake falls the curve has an inflection point whereupon it becomes concave to the origin below that point. The convex portion of the graph is deeper with larger dieting costs. Furthermore the inflection between the convex segment and the concave segment is pushed lower as dieting costs rise. Thus, as dieting costs are reduced, the inflection point increases, and that causes an increase in the calorie level chosen during a diet. Sallie Ann’s weight path with lower diet costs is compared with the base case in Fig. 8. The pattern is the same in both cases. Weight rises until a one period diet is chosen. The diet reduces weight and the habitual consumption

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level so that weight falls for several periods until it begins its incessant rise again. In the case of lower dieting costs, however, the earlier timing of the first diet and more frequent diets leads to a weight path that is everywhere below the higher dieting cost case. The trend in weight is still upwards but weight rises at a slower rate. This result demonstrates the importance of dieting costs in the timing, frequency, and severity of a diet. If weight reduction programs can be made easier to implement, this would lower weight for most individuals over their lifetime. However, marginally lower diet costs will not eliminate the incessant increase in weight caused by aging and falling activity levels. Instead, it


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will merely lower the rate at which an individual’s weight rises. Thus, more effective diet programs may not be sufficient to help people lose weight and keep it off.

6.4. Diet Decision Period A second parameter that can affect the timing of diets is the choice of diet length. In the model, the length of the diet is given exogenously. However, we can alter the length of time Sallie Ann chooses for her diets. In the base case, we assumed Sallie Ann made a monthly decision for calorie intake and then maintains her chosen consumption level for the entire month. This also means that any diet chosen by Sallie Ann must, at a minimum, be 1 month in length. One month of dieting has a significant effect upon Sallie Ann’s weight path. Her weight falls in the first month due to the diet, and then continues to fall further for several periods due to a modest reduction in her post-diet average calorie intake. Her weight path does eventually trend upward again, but it takes Sallie Ann another 9 years before she attempts another diet in the base case. In this next case, we assume that Sallie Ann makes a decision every week. Because the frequency of the dieting decision rises, we should expect that the incidence of diets should rise as well. Also, a 1-week diet will have a much smaller effect upon Sallie Ann’s weight, but should reduce weight sufficiently to cause her to drop off the diet after one period nonetheless. However, since weight will not have fallen as far as with the 1-month diet, Sallie Ann’s weight will reach the critical level much sooner thereafter. In Fig. 9, we plot Sallie Ann’s calorie consumption decisions between age 28 and 37. This shows that the incidence of diets rises substantially. In this case, Sallie Ann chooses to diet five times in about 7 years between ages 28 and 37. In the base case, she had only dieted once in that same period. Thus, we see that as the consumption decision period is shortened, the number of diet episodes rises. In Fig. 10, we show Sallie Ann’s weight path over the period. Notice that her weight continues the upward trend as in the base case. However, there are more interruptions given her more frequent dieting. Also shown in the figure is Sallie Ann’s weight path in the base case. Notice that Sallie Ann’s weight is consistently higher when she diets more frequently. By age 36, Sallie Ann’s weight given the five diet episodes previously, is about 74 kg. Her weight at age 36 in the base case, in which she had only one lengthier diet, was 71.2 kg.

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This suggests that smaller more frequent diets may result in a higher weight compared with less frequent, though lengthier diets. The model provides a clear behavioral mechanism that generates this result. In the more frequent diet case, Sallie Ann’s weight is higher because her more frequent but shorter dieting does not significantly lower her habitual consumption level. One week of dieting simply is not enough to significantly change Sallie Ann’s food consumption habit. With a 1-month diet and a 6-month period to completely change habit levels, there is sufficient reduction in habitual consumption to lower Sallie Ann’s long-term weight profile. In the base case, after a diet occurs, weight continues to fall for several periods before returning to its upward trend. This is because Sallie Ann’s habit consumption falls from 2,237 calories per day to 2,086 (6.8%) after her first diet. In the weekly diet scenario, Sallie Ann’s first diet lowers her habitual


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consumption only to 2,206 (1.4%). Thus, over the years, Sallie Ann’s shorter, more frequent diets do not lower average consumption as much as longer, less frequent diets.

6.5. Habit Persistence Another factor that can affect the incidence of diets in the model is habit persistence. Habit persistence refers to the period of time necessary for old habits to whither away. We incorporate habits into the model by assuming that the level of consumption, below which dieting costs arise, is based on a moving average over a fixed period of time in the past. In the base case, we set that period equal to 180 days, or 6 months. This means that it would take 6 straight months of changed consumption to completely eliminate all influence of past consumption on current diet costs. To examine this effect, we consider habit persistence of 90 and 360 days and compare Sallie Ann’s consumption and weight patterns with the base case of 180-day habit persistence. Since the choice period for consumption is 30 days, this means that habits persist for 3 or 12 decision periods, respectively, rather than 6 in the base case. In Fig. 11, we show Sallie Ann’s daily calorie choices with 90- and 360day habits. Relative to the base case (not shown), the change in habit persistence does not change consumption decisions in the early periods. Also, the first diet occurs with the same intensity and at approximately the same time. However, after the first diet, consumption falls to different new levels depending on habit persistence. Daily consumption falls much further with 90-day habits than it does with 360-day habits. The diet lowers the habitual consumption level to the moving average of the pre-diet consumption and the dieting consumption level. However, a 30-day diet with 90-day habits changes 1/3 of the moving average, while a 30-day diet with 360-day habits only changes 1/12 of the moving average. Hence post-diet consumption falls further with shorter habits. In the 90-day habit case, the post-diet consumption is lower than the level needed to maintain weight. Thus after the diet, Sallie Ann would continue to lose weight, albeit at a slower rate. However, in the 360-day habit case, postdiet consumption is not below the level needed to maintain weight. Thus once Sallie Ann stops the diet, her weight will begin to rise immediately again. The weight paths for different levels of habit persistence is shown in Fig. 12 and compared with the base case. The uppermost path is the case

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when habit persistence is 360 days. The next two lower weight paths are the cases with 180 and 90 days of habit persistence, respectively. Notice that as persistence falls, the number of diet episodes fall and the ability to achieve a lower weight through dieting is enhanced. In the case of the 90-day habit persistence, we see that the first diet effectively brings Sallie Ann’s weight back to her ideal at 60 kg. However, within a number of years her weight will be above the level when she began her first diet. Eventually she diets again. However, she only diets twice in the 30-year period. With 180-day habits, Sallie Ann diets three times, while with 360-day habits she diets five times in 30 years. Increasing diet frequency thus depends


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in part on habit persistence. The greater is an individual’s habit persistence the less effective a diet is in reducing average calorie intake, especially in the post-diet periods. Thus weight remains higher throughout her lifetime sparking more frequent dieting attempts.

7. SUMMARY AND CONCLUSION This paper presents an economic behavioral model to describe food consumption choices that lead to weight gain and dieting. We assume that food consumption has three possible effects on individual utility; a positive benefit from food consumption, a negative utility effect resulting from weight gain and a negative effect caused by dieting. An individual maximizes utility in each period rather than projecting the long-term impact of food on weight and future utility. In this sense, our individual has bounded rationality. Using the Harris–Benedict relationship from the physiology literature that relates age, weight, height, and activity levels to daily calorie requirements for maintaining weight, we simulate a typical female consumption and weight path over a 30-year period. The model shows that a diet will reduce weight but lasts only one period. As weight falls the disutility of weight also falls: in the next period, the allure of food (the marginal benefit in consumption), and the desire to avoid further dieting costs causes a return to a higher consumption level. However, the dieting causes habitual consumption to fall somewhat, which implies that weight will continue to fall modestly for several periods following the diet. After the diet, aging and falling activity continue to reduce average calories needed to maintain weight, so that before long weight begins to rise again. Eventually, after weight disutility rises sufficiently, the individual attempts a second diet. Thus, the model displays a cyclical dieting pattern. The second diet, and later diets too, each begin at a higher weight than the previous diet. This occurs because changes in habits reduce daily consumption, which in turn raises the marginal benefits of normal consumption. In other words, when daily consumption falls in the aftermath of a diet, food becomes more alluring. Thus, it will take greater disutility from weight gain to overcome the extra food allure. The ultimate trend in weight, therefore, is upward despite periodic diets. Nonetheless, periodic diets will reduce the rate of weight increase over a lifetime. If the decision period is reduced, so that the individual sticks to a daily calorie count for a week instead of a month, then the first diet occurs earlier and diets become more frequent. Thus, diet cycling increases. However,

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since a shorter decision period also results in shorter diets, the habitual consumption level is less affected by a diet. This implies that weight will remain at higher levels for most periods when compared with the case with a longer decision period. This result provides a behavioral explanation for the result in the nutrition literature that repeated dieting can lead to higher overall weight gain.25 Finally, we assess the impacts on dieting frequency and weight gain from reductions in dieting costs and changes in habit persistence. We show that dieting frequency rises as dieting costs are reduced and as habit persistence increases. Lower diet costs also result in earlier dieting since weight does not have to rise as much to exceed the cost of adjustment. This suggests that more effective diet programs, which make it easier to diet and also make it easier to change habits to a lower average calorie level, will help to reduce weight over a lifetime. However, the model also shows that, despite more frequent diets, weight gain is likely to continue its insidious upward trend as aging and reduced activity continue to reduce caloric needs. The model is most useful in providing an intuitive behavioral explanation for the choices individuals make concerning food consumption and dieting. Especially important is that it provides a rationale for cyclical dieting. Of course the model does not incorporate many realistic features of the dieting decision, such as uncertainty. Individuals may be unaware of the costs of dieting, especially before the first diet takes place. Also, different diets may present different costs, which may require a search process and a trial period to allow an individual to learn which diet is least costly to implement. The model can be used to address many additional questions. For example, the model could be used to assess the causes of rising obesity and overweight. In particular, one could simulate the degree to which rising incomes, falling food prices, and falling activity levels affect an individual’s lifetime weight path. Additionally, the model could be used to assess the likely impact of diet programs and exercise programs that vary in difficulty and cost for individuals with different incomes. Finally, since the BMR formula for men is different from women, the model could be used to compare the dieting experiences of men with women.

NOTES 1. Excess weight and obesity refer to the ratio of body fat to lean body mass. The body mass index (BMI) proxies for this ratio, where BMI ¼ weight (kg)/height (m)2.


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You are deemed overweight if your BMI is 25 or greater but less than 30, and you are obese with a BMI of 30 or more. 2. ‘‘An excess body weight of 30% is associated with an increase of 25% to 42% in mortality, and mortality increases with increasing body weight.’’ (Mitchell, 1997; citing Kushner, 1993). This may be why even small amounts of weight loss can lower the risk of obesity-related illnesses. (Mitchell, 1997, p. 363; citing Goldstein, 1992). ‘‘Much of the morbidity [disease] associated with obesity is due to an increase in the occurrence of hypertensiony and [type 2] diabetes mellitus, all of which contribute to an increased risk of cardiovascular disease (Sjostrom, 1992y)’’ (Mitchell, 1997, p. 359; see also Wilmore & Costill, 1999, pp. 671–672). The external costs of eating too much and exercising too little may exceed those from smoking (Manning, Keeler, Newhouse, Sloss, & Wasserman, 1991). Sturm and Lakdawalla (2004, p. 25) assert that obesity ‘‘is linked to very high rates of chronic illness – higher than living in poverty and much higher than smoking or drinking.’’ 3. For example, Dockner and Feichtinger (1993) noted that, ‘‘much of empirically-observable consumption behavior seems to contradict rational-choice theory. Here we refer to heavy eating followed by strict dietingy’’. 4. Brownell and Rodin (1994) cite several sources for this result. A related source is Williamson et al. (1992), who noted the following. Based on a large scale sample survey (the 1989 Behavioral Risk Factor Surveillance System cross-sectional survey in 39 states and DC), ‘‘approximately 25% of men and 40% of women were trying to lose weight, with the peak prevalence occurring among persons in their 40sy. Approximately 25% of both men and women who were currently trying to lose weight could be defined as long-term dieters. Thus, in the total sample, the prevalence of long-term dieting is about 6% of all men and 11% of all women. In a national 1985 Gallop Poll, 16% of women considered themselves ‘perpetual dieters’’’(p. 1255). 5. Severe weight cyclers were subjects who reported they had intentionally lost (greater than or equal to) 5 kg at least three times and regained the weight, whereas mild weight cyclers were those experiencing these losses once or twice with subsequent regain (pp. 334–335). 6. Lahti-Koski et al. (2005, p. 339), also describe two other studies based on large, but not random samples, and with varying definitions of weight cycling. In the first study, 1.4% of women were classified as severe cyclers and 18.9% as mild cyclers. (Mild cyclers in this study lost 10 pounds or more at least three times over the previous 10 years.) The second study, from Germany, classified approximately 4% of both men and women as cyclers, based on intentional weight loss and unintentional gain of more than 5 kg during the previous 2 years. 7. Field et al. (2003) reported on a ‘‘prospective study y of 8203 girls and 6769 boys who were 9 to 14 years of age in 1996, were in an ongoing cohort study, and completed at least 2 annual questionnaires between 1996 and 1999y’’. In 1996, 25.0% of the girls and 13.8% of the boys were infrequent dieters and 4.5% of the girls and 2.2% of the boys were frequent dieters. Among the girls, the percentage of frequent dieters increased over the following two years’’ (p. 900). 8. This approach is derived from an age-old question in economics about how to allocate current production between consumption and investment goods. Since this choice affects not only current utility but also future utility, the only reasonable way to make today’s decision is to plan for all future allocations today.

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9. Recent attempts to solve these issues have led to the introduction of hyperbolic discounting [see O’Donoghue & Rabin, 1999; Gruber & Koszegi, 2001] and random cue-conditioning (Bernheim & Rangel, 2002). These are ways to maintain the intertemporal choice structure. Our approach, in contrast, is to discard the choice structure in favor of one we believe is more pragmatic. 10. The term ‘‘bounded rationality’’ is used in several different ways. Gigerenzer and Selten (2001) apply this term only if behavior is described using a heuristic device, or rule-of-thumb, and does not involve optimizing behavior. In our model, we assume an individual does optimize, but is constrained due to excessive computational costs. In either case, behavioral assumptions are made that step back from the standard intertemporal choice problem. 11. Kahneman points out that ‘‘Failures of hedonic prediction are even common in the short term. The participants in a study reported by Kahneman and Snell (1992) showed little ability to anticipate how their enjoyment of a piece of music or a helping of their favorite ice cream would change over a period of eight daily episodes of consumption’’ (Kahneman, 2003a, p. 165). The more general finding Kahneman reports is that ‘‘people generally underestimate the extent of hedonic adaptation to new states.’’ In support he cites a review of the literature by Loewenstein and Schade (1999), and gives an example from a study by Gilbert and Wilson (2000), indicating that assistant professors ‘‘greatly overestimate the effects of a tenure decision on their happiness a year later.’’ (Kahneman, 2003a, p. 165). See also Kahneman (2003b) 12. This does not refer to any particular person either real or fictitious. 13. See, for example, Willett (1990, pp. 245–246), Melby, Commerford, and Hill (1998, p. 6), Forbes (1999, p. 801), and Wilmore and Costill (1999, pp. 667–668). 14. The Harris–Benedict equations indicate for men, that BMR ¼ 66+13.7 W+5 H – 6.8A. For women, BMR ¼ 655+9.6W+1.8 H – 4.7A, where BMR ¼ basal metabolic rate, H ¼ height in cm, W ¼ weight in kg, and A ¼ age in years. While numerous other equations are available, ‘‘many researchers use the Harris–Benedict method to determine BMR.’’ (Whitney, Cataldo, & Rolfes, 1998, p. 267). A literature exists that compares various BMR estimating equations, and examines how well they seem to work for more narrowly defined populations. See, for example, Cunningham (1980), Vaughan et al. (1991), Taaffe, Thompson, Butterfield, and Marcus (1995), Liu, Lu, and Chen (1995), and Wong et al. (1996). These articles typically obtain direct laboratory measures of BMR for a sample of individuals (for a discussion of the laboratory techniques used to measure BMR, see McArdle et al. (1996, pp. 139–141)). They then compare the actual BMR laboratory measures with the predicted measures of BMR from the various available predicting equations. The Harris–Benedict equations seem to have survived these explorations. 15. McArdle et al. (1996) defines the following categories: sedentary ¼ BMR 1.2 (little or no exercise, desk job); lightly active ¼ BMR 1.375 (light exercise/sports 1– 3 days/week); moderately active ¼ BMR 1.55 (moderate exercise/sports 3–5 days/ week); very active ¼ BMR 1.725 (hard exercise/sports 6–7 days/week); Extremely active ¼ BMR 1.9 (hard daily exercise/sports and physical job). Note that this specific sedentary multiplier gives a calorie usage that is 20% higher than one’s BMR. This includes the 10% thermic effect of food absorption plus some additional amount for low levels of physical activity.


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16. McArdle et al. (1996, p. 159) assert that the energy use associated with a specific exercise activity decreases from middle age on because of ‘‘the general ‘aging effect’ on aerobic capacity.’’ See also Rising et al. (1994), a study based on a sample of Pima Indians. 17. ‘‘Changes in body composition, either the decrease in fat-free mass and/or increase in body fat during adulthood, largely explain the y decrease in BMR usually observed through adulthood’’ (McCardle et al., 1996, p. 152). 18. We simplify the simulations by assuming that the BMR remains constant throughout the decision period. In reality, BMR would change each day, especially during a dieting episode when weight is falling quickly. However, since we restrict our decision period to 1 month, and later to 1 week, the error in calculation is relatively small. 19. Clearly the choice of functional forms is ad hoc. However, these forms are chosen to duplicate the nature of the relationships that we contend exist for a typical person. The parameter values, especially the scale variables, are chosen to generate plausible consumption choices. 20. This effect is difficult to see in Fig. 1 due to the small scale of the effects when Sallie Ann is at her ideal weight level. 21. The explicit function used is AF(a) ¼ 1.55 – 0.0096(a – 20) where a is Sallie Ann’s age in years. By age 50 Sallie Ann’s activity factor will fall to 1.26 reflecting a more sedentary lifestyle as discussed in footnote 15. 22. Consumption will not return quite to the original level since 1 month of dieting will lower the average consumption level and reset the habitual calorie level to a slightly lower level. How far this can fall will depend on the persistence of habits. In this simulation, we assume habits persist for 6 months. 23. Note that our simulations do account for the fact that rising weight raises the individual’s BMR and thus also raises the number of calories needed to maintain weight. However, the aging effect dominates the weight effect so calories needed to maintain weight falls with age. 24. The weight pattern for the second subject in the study also displayed periodic diets, however, the trend weight was not as persistently upward. Also, the second subject recorded weight for a shorter period of 10 years so perhaps the upward trend would have been seen over a longer span. 25. See Pasman et al. (1999), Hill (2004), and Field et al. (2003).

ACKNOWLEDGMENTS The authors wish to thank Thomas C. Leonard, Sara Markowitz and an anonymous referee for helpful discussions and comments.

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Williamson, D., et al. (1992). Weight loss attempts in adults: Goal, duration and rate of weight loss. American Journal of Public Health, 82(9), 1252–1257. Wilmore, J., & Costill, D. (1999). Physiology of sports and exercise (2nd ed.). Champaign, IL: Human Kinetics. Wong, W., Butte, N., Hergenroeder, A., Hill, R., Stuff, J., & O’Brian Smith, E. (1996). Are basal metabolic rate prediction equations appropriate for female children and adolescents? Journal of Applied Physiology, 81(6), 2407–2414.

EFFECTS OF TITLE IX AND SPORTS PARTICIPATION ON GIRLS’ PHYSICAL ACTIVITY AND WEIGHT$ Robert Kaestner and Xin Xu ABSTRACT In this study, we examined the association between girls’ participation in high school sports and the physical activity, weight, body mass, and body composition of adolescent females during the 1970s when girls’ sports participation was dramatically increasing as a result of Title IX. We found that increases in girls’ participation in high school sports, a proxy for expanded athletic opportunities for adolescent females, were associated with an increase in physical activity and an improvement in weight and body mass among girls. In contrast, adolescent boys experienced a decline in physical activity and an increase in weight and body mass during the period when girls’ athletic opportunities were expanding. Taken together, these results strongly suggest that Title IX and the

$

This research was partially supported by grants from the Robert Wood Johnson Foundation and the Chicago Center for Excellence in Health Promotion Economics of the University of Chicago and University of Illinois at Chicago.

The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 79–111 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17004-1

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increase in athletic opportunities among adolescent females it engendered had a beneficial effect on the health of adolescent girls.

1. INTRODUCTION The 1996 Surgeon General’s Report on Physical Activity and Health (US Department of Health and Human Services, 1996) is an exhaustive review of the effects of physical activity on health. It concludes that there is compelling evidence that people of all ages and of both genders benefit from physical activity, and it recommends 30 min of regular physical activity (e.g., brisk walking, social dancing, and volleyball) on most days for both children and adults. A more recent study from the Institute of Medicine (2002) goes even further, recommending an hour of physical activity per day. The Surgeon General’s study also reports the following: most Americans are not regularly physically active; 25% of American adults are not active at all; and half of American youths between the ages of 12 and 21 are not vigorously active on a regular basis. Notably, at all ages, women are less active than men. The relatively high rate of physical inactivity among Americans is an important social problem. Indeed, physical activity is one of the major public health concerns listed in the Healthy People 2010 (US Department of Health and Human Services, 2000) report, which has an objective to increase physical activity and which identifies women as a group of particular concern. To increase physical activity of children and adults, the 1996 Surgeon General’s Report recommends the expansion of school-based interventions. School-based interventions for youth are particularly promising, not only for their potential scope – almost all young people between the ages of 6 and 16 years attend school – but also for their potential impact. Nearly half of young people 12 to 21 years of age are not vigorously active; moreover, physical activity declines during adolescence. Childhood and adolescence may thus be pivotal times for preventing sedentary behavior among adults by maintaining the habit of physical activity throughout the school years. (US Department of Health and Human Services, 1996, p. 12)

A similar recommendation is made in a report by The President’s Council on Physical Fitness and Sports (1997) entitled Physical Activity & Sports in the Lives of Girls. Specific mechanisms which enhance girls’ opportunities to be physically active must be developed and supported. Recreational, school-based physical education and sport programs are ideal ways to facilitate both health-related fitness and the acquisition of fundamental motor skills for a lifetime of activity. (The President’s Council on Physical Fitness and Sports, 1997, p. xv)

Effects of Title IX and Sports Participation

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Finally, from the Centers for Disease Control (CDC), we have the following: Children and adolescents are more physically active than adults, but participation in physical activity declines in adolescence. School and community programs have the potential to help children and adolescents establish lifelong, healthy physical activity patterns. (Centers for Disease Control and Prevention, 1997, p. 2)

One of the most important justifications of these recommendations to increase youth physical activity through school-based programs is improved body composition (ratio of fat to lean mass) and reduced obesity. There is a widespread belief that school-based, physical activity programs will have a beneficial effect on child and adolescent obesity. Moreover, note that all of the recommendations imply that youth programs will have lifetime effects, which is consistent with the evidence that childhood obesity is strongly associated with adult obesity (Serdula et al., 1993; Lake, Power, & Cole, 1997; McTigue, Garrett, & Popkin, 2002; Whitaker, Pepe, Wright, Seidel, & Dietz, 1998). So the potential benefits of greater youth physical activity are large. Yet, there is little direct evidence to support the link between youth physical activity and obesity, or body composition (Cawley, Meyerhoefer, & Newhouse, 2005). Intervention studies have demonstrated that physical activity and exercise can help control weight, but observational studies of the relationship between physical activity and child obesity have reported inconsistent findings (Stefanick, 1993; Slattery, McDonald, & Bild, 1992; US Department of Health and Human Services, 1996; Kimm et al., 2005). Arguably the most important school-based intervention to increase physical activity was Title IX of the Education Amendments of 1972. This landmark legislation led to a dramatic increase in girls’ sports participation (SP) after its passage in 1972. Between the 1970–1971 and 1977–1978 academic years, the number of girls participating in high school sports increased over 600%, from 294,015 to 2,083,040 (National Federation of State High School Associations, 2001). In the same period, the rate of girls’ participation in high school sports increased from approximately 5 to 26% (see Fig. 1). Title IX has also been credited with spurring girls’ participation in community sports programs. In contrast, Title IX had almost no effect on boys. Between 1970–1971 and 1977–1978, the number of boys participating in high school sports increased approximately 20% and their rate of participation in sports hardly changed (see Fig. 1). Title IX and the unprecedented increase in girls’ sports participation that resulted provide a unique opportunity to study the effect of

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ROBERT KAESTNER AND XIN XU 0.52 0.48 0.44 0.40 0.36 0.32 0.28 0.24 0.20 0.16 0.12 0.08 0.04 0.00

Girls Boys 1971

1972

1973 1974 1975

1976 1977 1978

1979

1980

Fig. 1. Adjusted High School Sports Participation Rates by Gender, 1971–1980. Notes: The figure is based on unweighted high school sports participation rates for each state from 1971 to 1980. Due to the missing values of high school sports participation rates in some states, we adjusted for state effects. High school sports participation comes from the National Federation of State High School Associations (NFSHSA) and the population data come from the 1970 and 1980 us censuses.

school-based interventions targeted at increasing physical activity on adolescent female obesity. The primary goal of the proposed research is to obtain estimates of the effect of the dramatic increase in girls’ participation in high school sports, as a result of Title IX legislation, on the physical activity, body mass, and body composition of adolescent women. The proposed research, because it will make use of a plausibly exogenous change in physical activity, will provide perhaps the most credible evidence to date on the effect of physical activity on youth obesity. Previous researchers have not exploited this dramatic change in girls’ sports participation caused by Title IX to investigate the consequences of such participation. More importantly, the formulation of current policy and recommendations to expand school-based interventions to increase physical activity has not benefited from knowledge about the health consequences of Title IX – the largest expansion in school-based interventions in history. While Title IX has left an indelible mark on our society, and its success in creating equal opportunities for women widely celebrated, its effect on physical activity and health remains unknown. Here we begin to address this shortfall in knowledge.


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2. WHAT IS TITLE IX AND DID IT AFFECT SPORTS PARTICPATION? No person in the United States shall, on the basis of sex, be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any education program or activity receiving Federal financial assistance. (US Department of Education, 2000)

So begins Title IX of the Education Amendments of 1972 that took effect in June 1972. The language of Title IX is broad and covers all activities sponsored by educational institutions. Programs and activities which receive funds from the Department of Education must operate in a non-discriminatory manner. These programs and activities include: admissions, recruitment, financial aid, academic programs, student treatment and services, counseling and guidance, discipline, classroom assignment, grading, vocational education, recreation, physical education, athletics, housing, and employment. Title IX enforcement is the responsibility of the Office of Civil Rights of the Department of Education. Enforcement is governed by regulations issued by the Department of Health, Education and Welfare (HEW, predecessor to the Department of Education), which became effective on July 21, 1975. The regulations established a 3-year transition period to give institutions time to comply with its equal athletic opportunity requirements. That transition period expired on July 21, 1978. Compliance with Title IX involved a self-evaluation of current practices and remedial steps to eliminate the effects of any discrimination. Institutions could also take affirmative action to address limited participation by persons of a particular sex (Department of Education, 1975). While Title IX affected many programs and employment at educational institutions, arguably its greatest impact was on sports participation. Between 1970–1971 and 1977–1978, the number of girls participating in high school sports increased over 600%, and the rate of girls’ participation in high school sports increased from 5 to 26% (Fig. 1; National Federation of State High School Associations, 2002; Stevenson, 2000; Gavora, 2002). However, girls’ sports participation was increasing prior to Title IX (Gavora, 2002; Staffo, 1980; Riley, 1976) and the data on sports participation are relatively crude and subject to substantial error (see below). Moreover, as described above, Title IX was implemented gradually with federal regulations not issued until 1975, compliance not required until 1978, and little enforcement teeth until 1980 (Gavora, 2002; Women’s Sports Foundation, 2001). Nevertheless, given the large increase in girls’ sports participation that occurred subsequent to passage of Title IX and which more or less ended at the time of mandated compliance in 1978, it is difficult to argue that the law had no

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effect on girls’ sports participation, and evidence suggests that it had a substantial effect. Again, Gavora (2002), a strong critic of Title IX, concludes that Title IX clearly intensified the upward trend in girls’ sports participation. Data on girls’ sports participation prior to 1970 are sparse, but the information that does exist indicates a significant break in the trend line after passage of Title IX (Riley, 1976; Staffo, 1980). Further, there is abundant evidence that school administrators responsible for sports were cognizant of the burden of Title IX and responded accordingly (Pottker & Fishel, 1976; Riley, 1976; Staffo, 1980; American Friends Service Committee, 1977). In fact, the Department of Health, Education and Welfare worked with the American Association of School Administrators and sponsored many regional and area conferences on compliance with Title IX (Riley, 1976; American Friends Service Committee, 1977; Craig, 1977). And Gavora (2002) reports that the Department of Health, Education and Welfare was ‘‘flooded’’ with complaints from school administrators struggling to comply with Title IX regulations issued in 1975. Did Title IX affect boys’ sports participation? Clearly, recent court cases and media attention suggest that at the college level, male athletic opportunities may have been diminished by Title IX, although supporters of the law refute this claim and all of these court cases have been brought since 1988. But evidence of similar problems at the elementary and secondary levels is virtually non-existent during the 1970s and 1980s. And a review of the legal cases from the 1990s related to elementary and secondary schools that were listed on the Gender Equity in Sports website (http://bailiwick.lib.uiowa.edu/ge/) found no cases at the elementary or secondary school level in which males complained about a loss of opportunities. Finally, the aspect of Title IX that arguably had the greatest potential of adversely affecting boys was the 1979 Title IX Athletics Policy Interpretation rule issued by the Department of Education that specified that compliance with Title IX required that the proportion of girls among sports participants must be the same as the proportion of girls among the student body. This rule may provide a strong incentive to reduce boys’ sports opportunities, but importantly, it was issued in the last year of our analysis. Indeed, prior to 1980, compliance with Title IX was not vigorously enforced and the law itself explicitly forbade the use of quotas (Gavora, 2002): Nothing contained in y this section shall be interpreted to require any educational institution to grant preferential or disparate treatment to members of one sex on account of an imbalance which may exist with respect to the total number or percentage of persons of that sex participating in or receiving the benefits of any federally supported program or activity in comparison with the total number or percentage of persons of that sex in any community, state, section or other area. (US Department of Education, 2000)


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Data show that subsequent to Title IX, there was no decrease in boys’ sports participation. Between 1970–1971 and 1977–1978, the number of boys participating in high school sports increased approximately 20% and their rate of participation in sports decreased just 2 percentage points with most of this decrease occurring between 1977 and 1978, the year before compliance became mandatory and 2 years prior to the issuance of 1979 Title IX Athletics Policy Interpretation rule issued by the Department of Education (Fig. 1; National Federation of State High School Associations, 2001; Stevenson, 2000).1 Evidence in Staffo (1980), who surveyed New York State high schools in the late 1970s, is consistent with the aggregate data and supports the notion that Title IX did not adversely affect athletic opportunities for boys at the high school level. The decrease in boys’ (and girls’) sports participation observed in Fig. 1 merits further discussion. We do not believe it represents an adverse effect of Title IX on boys. First, the drop is in large part a result of implausibly large decreases in sports participation rates that is most likely a result of reporting error. For example, Arkansas, Delaware, Wisconsin, North Dakota, and Ohio reported decreases in boys’ sports participation of 25% or more. And in all but one of these states, girls’ sports participation also falls significantly, by 12 or more percent. Indeed, if we rank states by the percentage change in both boys’ and girls’ sports participations between 1977 and 1978, the correlation coefficient is quite large – 0.70, which suggests a reporting error more than an effect of Title IX. If Title IX improved opportunities for girls at the expense of boys, the rank correlation would be negative or have a small positive value, not a large value like 0.70. A regression of boys’ sports participation on girls’ sports participation yields a positive coefficient. To summarize, while it is true that athletic opportunities for women were expanding prior to Title IX, it appears that there was a significant (exogenous) increase in girls’ sports participation subsequent to passage of the law that continued until compliance with the law was mandated in 1978. Indeed, both girls’ and boys’ sports participations remain relatively flat for the next 10 years (Gavora, 2002; National Federation of State High School Associations, 2001). In addition, available information leads us to conclude that Title IX did not reduce athletic opportunities for boys during the period we study. In fact, the literature during this period is quite consistent in stating that elementary and secondary schools had been too slow to provide opportunities for girls, and that Title IX enforcement had been too weak. There is no mention of reduced opportunities for boys. And what data are available does not suggest diminished opportunities for boys.

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3. CAUSAL PATHWAYS The publicity surrounding the Surgeon General’s Report on the health benefits of physical activity and the near universal acceptance of its findings suggest that youth sports participation and physical activity would yield large health benefits for adolescents. Surprisingly, there is little evidence supporting this position. Indeed, according to Rowland (1996), ‘‘y regular exercise and sports participation should not be expected to improve the wellbeing of the general population of children and adolescents’’ (p. 154). Rowland (1996) notes that the primary mortality risks for children and adolescents are accidents, cancers, congenital abnormalities, and homicides – causes that cannot be positively affected by physical activity. Further, physical activity and sports participation are not likely to reduce the rate or severity of infectious disease. One obvious adverse effect is greater risk of injury, which may be severe and long lasting. ‘‘Children and adolescents with developing bodies are at special risk of permanent physical damage if injury occurs to the growth plates of long bones or to other bone or connective tissue structures’’ (UDHHS, 1996, p. 142). In spite of the limited scope for improvement, the Surgeon General’s Report on Physical Activity and Health (US Department of Health and Human Services, 1996) and the report of The President’s Council on Fitness and Sports (US Department of Health and Human Services, 1997) identify some positive health benefits of youth physical activity. One of the most important potential benefits is improved blood lipid profiles and lower blood pressure, two risk factors associated with coronary heart disease. Further, several studies have shown a correlation between physical activity in children and lower risks of coronary heart disease. However, most of this evidence is from observational studies, and the significant association is eliminated when controls for weight are introduced, suggesting that most of the improvement in blood lipid profiles associated with physical activity is through weight control. There is considerable clinical evidence that exercise and physical activity favorably affect body composition, weight, and fat distribution. Intervention studies have demonstrated that physical activity and exercise can help control weight (Stefanick, 1993; Slattery et al., 1992). In addition, sports participation has been shown to increase vegetable consumption in high school students (Pate, Trost, Levin, & Dowda, 2000). However, observational studies of the relationship between physical activity and child obesity have reported inconsistent findings (US Department of Health and Human Services, 1996; Kimm et al., 2005). In sum, despite the potential for harm,


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there is evidence that adolescent health, specifically weight and body composition, can be improved through greater physical activity. Title IX resulted in an expansion of school-based athletic opportunities for adolescent girls. In addition, Title IX is credited with causing an expansion of community-based athletic opportunities for girls. These increases in athletic opportunities make it more likely that girls will be physically active. In economic terms, Title IX has lowered the price of sports participation. Consequently, there should be an increase in adolescent girls’ physical activity and an improvement in their body composition (lower proportion fat). Here we assess this hypothesis directly by examining the effect of increased girls’ participation in high school sports caused by Title IX on girls’ physical activity, weight, body mass, and body composition.

4. RESEARCH DESIGN AND METHODS The statistical analyses used in this paper are based on multivariate regression models that relate girls’ physical activity, weight, body mass, and body composition to girls’ participation in high school sports. Girls’ high school sports participation is used as a proxy measure of changes in adolescent girls’ athletic opportunities caused by Title IX. We obtain estimates of the regression model in the context of a research design intended to identify the ‘‘causal’’ effect of Title IX and the increase in girls’ sports participation on adolescent health and health behaviors. Specifically, we rely on two approaches: a comparison-group approach and instrumental variables (IV). However, before describing these approaches, we discuss the assumption that Title IX is a good ‘‘natural experiment.’’ 4.1. Is Title IX a Good Natural Experiment? Earlier in the paper we provided evidence of the effect of Title IX on girls’ sports participation and concluded that Title IX caused a significant increase in girls’ sports participation. This is important because it established that part of the change in girls’ sports participation was policy driven and unrelated to changes in personal characteristics and changes in social norms. In other words, part of the increase in girls’ sports participation over the period we analyze is exogenous; Title IX provides a source of exogenous variation in athletic opportunities that can be used to help identify the causal effect of youth physical activity on adolescent female weight. Similarly, we provided evidence related to the effect of Title IX on boys’ sports

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participation and concluded that it did not affect this outcome. This finding is important because one of the empirical strategies we employ is a comparison-group approach that assumes that Title IX had no effect on boys. The descriptive information presented above supports this assumption and bolsters the credibility of the comparison group approach. 4.2. Comparison-Group Approach We obtain estimates of the effect of Title IX, as measured by changes in girls’ high school sports participation, on physical activity, weight, body mass, and body composition using multivariate regression methods. The primary problem with this approach is to distinguish the effect of girls’ sports participation from the effect of unmeasured factors that vary over time, for example, changes in women’s attitudes about body image and physical activity. One way to address this problem is to use a comparison group – similar persons who are unaffected by Title IX. We use males as a comparison group for females because the enactment of Title IX had little effect on their participation in high school sports. To illustrate this approach, we begin with a simple model of adolescent female body mass index (BMI). The sample used to estimate the model consists of female adolescents aged 12–17. The model is as follows: BMIijt ¼ ai þ bj þ dt þ g1 SPjt þ g2 Z jt þ ijt , i ¼ 12; . . . ; 17; j ¼ 1; . . . ; 51; t ¼ 1971; . . . ; 1980

ð1Þ

Eq. (1) assumes that the BMI of adolescent women in year t and state j is a function of the level of girls’ high school sports participation in year t and state j, and other factors (Z) that affect the BMI of women in year t and state j. We omit individual subscripts and individual-level variables such as race and family income to conserve on notation, but these variables will be included. Eq. (1) also controls for age (i), state (j), and year (t) effects. Since sports participation and physical activity may have cumulative effects on body mass and health, we also use the 3-year average (years t, t1, and t2) of girls’ sports participation. The problem with Eq. (1) is that information about Z–factors specific to a state and year other than girls’ sports participation – is not readily available. For example, girls’ physical activity, eating, and substance use (e.g., cigarettes) are influenced by attitudes and preferences that may vary by state and year and which are likely to influence sports participation and BMI. Similarly, community-level factors that affect BMI may vary by state and year


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and may be correlated with girls’ sports participation. Thus, estimates of Eq. (1) may be biased by the omission of some Z. In fact, we have only two Z – the proportion of females aged 20–24 enrolled in postsecondary education programs and state per capita income. One way to address the omitted variable problem is to use a comparison group. To implement this procedure, a model similar to that specified by Eq. (1) is estimated using a sample of boys: BMIijt ¼ a~ i þ b~ þ d~ t þ g~ SPjt þ g~ Z jt þ ~ijt (2) j

1

2

To obtain an estimate of the effect of sports participation on girls’ BMI, we subtract the estimate of the effect of girls’ sports participation ð~g1 Þ obtained using the sample of boys from the estimate of the effect of girls’ sports participation ðg1 Þ obtained using the sample of girls. Note that in the boys’ analysis, girls’ sports participation would still be used as the measure of sports participation. The purpose of using boys as a comparison group is to investigate whether omitted variables that affect BMI are correlated with girls’ sports participation. So it is girls’ sports participation that needs to be included in the boys’ analysis. We expect ð~g1 Þ to be zero unless girls’ sports participation is correlated with omitted variables that affect BMI. The identifying assumption of the comparison-group approach is that the effects of unmeasured, state-specific, time-varying factors that affect BMI are the same for boys and girls. One way to assess the validity of this assumption is to investigate whether measured variables that differ by state and year have the same effect on boys and girls, and whether the effects of time dummy variables are the same. Specifically, we can pool the samples of boys and girls and estimate the following: BMIijt ¼ a~ i þ ðai a~ i ÞFEM þ b~ j þ ðbj b~ j ÞFEM þ d~ t þ ðdt d~ t ÞFEM þ g~ SPjt þ ðg g~ ÞFEM SPjt 1

1

þ g~ 2 Z þ ðg2 g~ 2 ÞFEM Zjt þ ~ijt

1

ð3Þ

Using Eq. (3), we can test the following restrictions to provide evidence related to the identifying assumption of the comparison-group approach: dt ¼ d~ t (4) g2 ¼ g~ 2 Rejecting the restrictions of Eq. (4), while not a definitive rejection of the comparison-group approach, surely raises questions as to validity. In fact, we cannot reject the restrictions given in Eq. (4).2 This provides some important evidence to support the validity of the comparison-group approach.

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4.3. Instrumental Variables The second research design we use is IV. The IV procedure depends on the availability of a variable that is correlated with girls’ sports participation, but not correlated with female adolescent BMI (physical activity) after controlling for the effect of girls’ sports participation and other observed determinants. Stevenson (2000) uses an IV approach to estimate the effect of Title IX on female educational achievement and employment. The key insight of Stevenson is that the regulatory burden of Title IX differed by state, as boys’ sports participation differed significantly by state prior to Title IX (National Federation of State High School Associations, 2001). If girls’ interest in sports in a state is proportional to boys’ interest in sports, then the change in female sports participation in response to Title IX is likely to be higher in states with high boys’ participation than in states with low boys’ participation. Therefore, we can use the level of boys’ sports participation prior to implementation of Title IX as an indicator of the magnitude of the induced change brought about by Title IX.3 The IV approach is based on the following regression model, which again is estimated on individual-level data consisting of females between the ages of 12 and 17: BMIijt ¼ ai þ bj þ dt þ g1 SPjt þ g2 Z jt þ ijt (5) SPjt ¼ pi þ fj þ lt þ y1 ðYRSt SPM jð1971Þ Þ þ ujt

(6)

In Eq. (6), the variable YRSt is the number of years since Title IX was implemented and the variable SPM jð1971Þ is the boys’ sports participation rate in state j in 1971. There are two critical aspects of the IV procedure. The first is whether the instruments will be sufficiently correlated with girls’ sports participation? The answer to this is a qualified yes. The associated t-statistic (f-statistic)

with the coefficient on the instrument

YRSt SPM jð1971Þ

is 6.13 (37.6),

which is a relatively strong partial correlation according to research assessing the adequacy of instruments (Bound, Jaeger, & Baker, 1995).4 However, as we show below, IV estimates of the effect of girls’ sports participation are still too imprecise to be informative. The second criterion is whether the instrument can be excluded from Eq. (5). Essentially this is a question of whether boys’ participation in 1971 in state j is correlated with unmeasured factors that determine female adolescent body mass and which vary by year (t) and state (j), holding constant state


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and year effects. Consider cigarette smoking. Changes in social norms and advertising by tobacco companies resulted in a significant increase in cigarette smoking among girls aged 14–17 in the 1970s that may have affected weight and body composition. Are the state-specific changes in smoking during this period (e.g., 1972–1980) likely to be correlated with boys’ sports participation in 1971? Was the increase in girls’ cigarette smoking in North Dakota, Vermont, Minnesota, Oregon, and Missouri, states with high boys’ sports participation, different from the increase in girls’ cigarette consumption in North Carolina, Rhode Island, Florida, Alabama, and Utah, states with low boys’ sports participation? We do not know the answer, but we see no obvious reason to think that there is a systematic correlation between boys’ sports participation in 1971 and changes in girls’ cigarette consumption, particularly holding constant state and year effects. While we believe the exclusion restriction is reasonable (valid), the relatively imprecise IV estimates we obtain make it a moot issue. Therefore, we do not discuss it further.

5. DATA The data for the empirical analyses come primarily from the First and Second National Health and Nutritional Examination Surveys (NHANES I and II), which provide information on adolescent (age 12–17) physical activity and weight between the years 1971 and 1980, the period of most rapid growth in girls’ participation in high school sports. We limit the sample to adolescents between the ages of 12 and 17 since data on physical activity are unavailable for younger children. The age restriction results in a sample of approximately 3,300 children.5 One important advantage of the NHANES is that it includes objective measures of anthropometric indicators such as height and weight. We use several different measures of weight and body composition:

weight (conditional on height); BMI ¼ weight (kilograms)/height2 (in meters); indicators of BMIZ24,Z26, andZ28; indicators of overweight (BMIZ85 percentile NHANES I) and obesity (BMIZ95 percentile NHANES I) based on BMI; triceps skinfold in right arm (in mm); indicators of overweight (triceps skinfoldZ85 percentile NHANES I) and obesity (triceps skinfold Z95 percentile NHANES I) based on triceps skinfold; subscapula skinfold in chest (in mm); the sum of triceps and subscapula skinfolds.

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There is considerable debate over how best to measure body fat in adolescents, which is the key aspect of body composition that has been linked to greater morbidity and which is likely to be affected by greater physical activity (Goran, 1998). Here we use a variety of commonly used measures that are available in the NHANES. However, we note that none of the measures we use is ideal because none precisely measures the proportion of body fat, which is the measure that is most likely to be altered by increased exercise and physical activity. For example, increased physical activity may actually increase BMI. The only non-standard measures we use are the indicators of BMI that are greater than a specific threshold (e.g., 28). One reason we use these is because the definitions of overweight and obese that depend on an adolescent’s position in the BMI distribution (e.g., BMI Z85 percentile) are based on data from NHANES I. Since we are using observations from NHANES I, this may introduce a mechanical bias in these relative measures of overweight and obesity. Consider the following. Assume that greater athletic opportunities affected body mass of girls in the latter part (e.g., 1974) of the NHANES I study period. Thus, the definitions of overweight and obesity based on the NHANES I data will reflect the impact of Title IX. To avoid this we use the absolute cutoffs of BMI. The disadvantage of this approach is that boys and girls have different BMI distributions. So the proportion of the sample above a specified threshold will be different for boys and girls. For example, 9% of girls in our sample (NHANES I and II) have a BMI greater than 26, but only 7% of boys have a similar BMI. This may affect estimates obtained using the comparison-group approach, since these estimates are based on the absolute difference between boys and girls. A 1 percentage point increase in the proportion of boys over the BMI ¼ 26 threshold represents a 14% increase and indicates a greater degree of ‘‘obesity’’ than a similar increase in females, which represents a 11% increase. Fortunately, the differences in the distributions of BMI between girls and boys are not that large, and as we show below, the inferences drawn from estimates obtained using the comparison-group approach do not depend on small relative changes, but rather on fundamentally different associations between boys’ and girls’ BMI and girls’ sports participation. Recreational activity levels were obtained in NHANES I and II for persons aged 12 and over. The question related to recreational activity is not ideal and refers to the amount of exercise obtained from any (recreational) activity such as sports, hiking, and dancing. Specifically, in NHANES I, the question was: Do you get much exercise in things you do for recreation?


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And in NHANES II: In things you do for recreation, e.g. sports, hiking, dancing and so forth, how much exercise do you get?

For both surveys the responses were: 0 for little or no exercise, 1 for moderate exercise, and 2 for much exercise. We collapse the two lowest categories (0 and 1) into one category (0) to create a dichotomous indicator of engaging in much exercise. The change in the wording of the exercise question between the two NHANES surveys resulted in a significant decrease in the proportion of the sample that engaged in much exercise. However, the change was apparent for both boys and girls and should be adequately controlled for through the inclusion of year dummy variables. The key demographic and socioeconomic variables in the NHANES surveys are age, race/ethnicity, gender, place of birth, years of school completed, family income, number of persons in the family, and number of children. For the head of the household, there is information about gender (of head), age, and years of formal schooling completed. We create gender-specific measures of the household head’s age and schooling and we allow family income to have different effects depending on gender of household head. Data on girls’ high school sports participation come from the National Federation of State High School Associations (NFSHSA), which maintains data on participation in competitive high school sports by year, state, sport, and gender. The participation survey has been compiled every year since 1971 by the NFSHSA through cooperation with its member associations. No information is available for 1972, 1974, and 1976. For these years we use the average of the year prior and year following the missing information. There are also some obvious reporting errors in the data, as described above. While the data are crude, they serve the intended purpose, which is to proxy for the expanded athletic opportunities that occurred subsequent to Title IX. To calculate the girls’ sports participation rate in each state and year, we divide the number of girls reported to have participated in high school sports by the population of girls aged 14–17. The population data come from the 1970 and 1980 US Censuses.6 We use two different measures of girls’ sports participation: the contemporaneous participation rate and the 3-year average (years t, t1, and t2). To calculate the average girls’ sports participation in the early years of the period, when data on girls sports participation were not available, we use two methods: we assume that either girls’ sports participation prior to 1971 was the same as it was in 1971 or girls’ sports participation prior to 1971 was zero. Given that girls’ sports participation was quite low prior to 1971 (average rate

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of participation in 1971 was 4%), the two methods produce almost identical results (see appendix). Because of confidentiality and disclosure rules, the NCHS does not provide state of current residence in the public-use NHANES files. Therefore, we use state of birth instead of state of residence to merge on information about girls’ sports participation. Given the ages of the sample, 12–17, there is little difference between state of birth and state of residence.7 We also augment the NHANES data with data on state per capita income and women’s enrollment in postsecondary education in state j and year t. Specifically, we construct a measure of female participation in postsecondary education by dividing the number of females enrolled (fall) in institutions of higher education by the number of females aged 20–24. The data on postsecondary enrollment come from a survey by the National Center for Education Statistics entitled Higher Education General Information Survey, which is publicly available beginning in 1968. These data are reported by state of enrollment and year and include information on both private and public universities. Data on the population come from 1970 and 1980 Censuses. 5.1. Descriptive Analysis Table 1 provides the weighted sample proportions and means for boys and girls from NHANES I (1971–1975) and NHANES II (1976–1980). The top half of Table 1 provides descriptive statistics for the dependent variables. The figures show that boys are significantly more physically active than girls, and boys have significantly lower BMI and skinfold measurements than girls. These facts are well known (Must, Dallal, & Dietz, 1991). The figures related to physical activity illustrate the effect of the questionnaire wording change between NHANES I and II. The proportion of boys and girls that engage in much physical exercise decreased markedly (11–13 percentage points) between NHANES I and II. However, there does not appear to be a relatively smaller decrease in physical activity among girls than boys, which would be expected given the expansion of athletic opportunities (i.e., girls’ high school sports participation) between the two periods. In contrast, there was relative improvement in girls’ BMI, which remained basically unchanged between the two periods, whereas the BMI of boys increased significantly. Similarly, there was a relative improvement in the probability of being overweight. But the probability of having a BMI greater than 26 or 28 increased for girls relative to boys, and similar relative increases for girls are observed for the absolute measures of triceps and subscapula skinfolds. The bottom half of Table 1 provides the weighted sample proportions and means for the control variables used in the analysis. NHANES I and II

Descriptive Statistics of Dependent and Independent Variables, by Gender and Year.

Variables

1971–1975

1976–1980 p-valuea

Mean Boys (n ¼ 720) Girls (n ¼ 733)

p-valuea


0.80 20.28 0.13 0.07 0.04 0.14 0.05 125.79 65.63 10.31 0.14 0.07 8.85 19.16

0.62 20.96 0.17 0.09 0.05 0.18 0.06 119.73 63.25 17.15 0.15 0.05 12.52 29.67

o0.0001 0.0006 0.0107 0.0634 0.1799 0.0336 0.5418 o0.0001 o0.0001 o0.0001 0.6221 0.1291 o0.0001 o0.0001

0.69 20.62 0.13 0.06 0.03 0.16 0.04 128.26 65.74 10.62 0.13 0.05 9.76 20.37

0.48 20.99 0.15 0.10 0.06 0.15 0.06 120.46 63.41 17.77 0.17 0.06 13.76 31.53

o0.0001 0.0386 0.3138 0.0137 0.0150 0.7464 0.0617 o0.0001 o0.0001 o0.0001 0.0211 0.5628 o0.0001 o0.0001

Girls’ contemporaneous sports participation rate Female college enrollment percentage State per capita income in real term (US$ 1,000)

0.09 0.44 11.00

0.09 0.44 11.03

0.8708 0.3160 0.7014

0.23 0.53 12.27

0.22 0.53 12.25

0.3971 0.7462 0.7449

White African American Other race Age of respondents Education of respondents Child of the household headc

0.84 0.16 0.01 14.49 8.64 0.95

0.84 0.15 0.00 14.49 8.83 0.96

0.6691 0.7711 0.4241 0.9924 0.0489 0.4178

0.84 0.15 0.01 14.64 8.96 0.95

0.84 0.15 0.01 14.64 9.02 0.96

0.9111 0.6673 0.2447 0.9272 0.5073 0.3345

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Physical activityb BMI BMI Z 24 BMI Z 26 BMI Z 28 Overweight (BMI Z 85 percentile in NHANES I) Obese (BMI Z 95 percentile in NHANES I) Weight (lb) Height (in.) Triceps skinfold (TS) TS Z 85 percentile (in NHANES I) TS Z 95 percentile (in NHANES I) Subscapular skinfold (SS) Sum of TS and SS


Table 1.

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Table 1. (Continued ) Variables

1971–1975

1976–1980 p-valuea

Mean Boys (n ¼ 720) Girls (n ¼ 733) Gender of household head (male) Age of household head Education of household head Total number of person in household Total family income Residence of SMSA

0.83 44.13 11.08 5.47 18.04 0.61

0.85 44.13 10.96 5.68 18.11 0.62

p-valuea


0.2766 0.9999 0.5266 0.0563 0.6200 0.8582

0.82 43.92 11.48 5.15 19.36 0.61

0.0514 0.3775 0.0946 0.3677 0.2234 0.9469


Note: Descriptive statistics are calculated using sample weights from NHANES I and II. pr0.05. pr0.10. a p-value for t-test of means assumes equal variance. b Physical activity is dichotomous variable: 1 for much exercise and 0 for little, no, or moderate exercise. c Child of the household head is a dichotomous variable, 1 for child of the household head and 0 for other relatives.

0.78 43.59 11.73 5.07 19.23 0.61


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provide information on relatively few socioeconomic and demographic characteristics, and we have selected most of those available. The most important point to take away from these figures is that there are few differences between girls and boys, and the significant differences that are observed are relatively small and only marginally significant. Girls have more education than boys, but this difference is only significant in NHANES I, and girls come from households with a slightly higher number of members, but again, this difference is only significant in NHANES I. In NHANES II, girls are more likely to live in a female headed household than boys and in households headed by persons who are more educated. As noted, none of these differences are quantitatively large and all are only marginally significant. In general, the simple descriptive evidence in Table 1 is not consistent with the hypothesis that Title IX expanded athletic opportunities resulting in greater physical activity and improved weight. However, what is ignored in Table 1 is that there are significant differences in the effect of Title IX, as it increased athletic opportunities much more in some states than others. We address this shortcoming by examining changes in sample means and proportions between NHANES I (1971–1975) and NHANES II (1976–1980) by whether a state experienced a relatively large increase in girls’ sports participation. Specifically, we divide states into tertiles depending on the change in girls’ sports participation between 1971–1975 and 1976–1980.8 Then we examine changes in physical activity, BMI, and obesity between 1971–1975 and 1976–1980 for girls and boys. We construct simple difference-in-difference estimates using boys as a comparison group for girls. Results are presented in Table 2, which shows the results for states in the top and bottom tertiles. Estimates in Table 2 provide some support for the hypothesis that Title IX and girls’ sports participation increased physical activity and improved weight of adolescent girls. In states with relatively large increases in girls’ sports participation (top tertile) between 1971–1975 and 1976–1980, girls’ physical activity increased and boys’ physical activity decreased. The difference-in-differences estimate for states in the top tertile indicates that girls’ physical activity increased by 43 percentage points. The equivalent estimate for states in bottom tertile is 13 percentage points. Similarly, BMI and the probability of being obese decreased for girls in states that experienced a large increase in girls’ sports participation, whereas BMI and obesity remained relatively unchanged in states that experienced a relatively small increase in girls’ sports participation. Difference-in-differences estimates suggest a significant two-unit decline in BMI and a 5 percentage point decline in obesity for girls in states that had relatively large increases in girls’ sports

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Table 2. Descriptive Statistics of Dependent Variables by Change in Girls’ High School Sports Participation Rate Between 1971 and 1980. Physical Activity

Girls Boys Difference-in-difference Girls Boys Difference-in-difference BMI Girls Boys Difference-in-difference Girls Boys Difference-in-difference Obese Girls Boys Difference-in-difference Girls Boys Difference-in-difference

1971–1975

1976–1980

Difference

Top third states 0.610 0.829

0.887 0.679

0.277 (0.116) 0.150 (0.045) 0.427 (0.148)

Bottom third states 0.674 0.790

0.692 0.681

0.018 (0.104) 0.109 (0.041) 0.127 (0.131)


19.554 20.482

1.742 (0.937) 0.270 (0.364) 2.012 (1.205)


20.362 20.783

0.004 (0.996) 0.560 (0.304) 0.564 (0.953)


0.018 0.042

0.052 (0.052) 0.001 (0.957) 0.053 (0.067)


0.062 0.044

0.023 (0.036) 0.004 (0.020) 0.019 (0.051)

Note: Standard errors in parentheses. pr0.05. pr0.10.

participation. Among states in the bottom tertile, difference-in-differences estimates are much smaller and not statistically significant. We now turn to the regression estimates, which refine the analysis in Table 2.

6. REGRESSION RESULTS Our goal is to obtain estimates of the effect of girls’ sports participation on the physical activity and weight of adolescent girls. The girls’ sports participation rate is a proxy for expanded athletic opportunities caused by Title IX. To obtain estimates that can plausibly be interpreted as causal, we use


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Table 3. Difference-in-Difference Estimates of the Effect of Girls’ Sports Participation on Physical Activity and Weight (3-Year Average Sports Participation Rate, Participation Rate Prior to 1971 ¼ Participation Rate in 1971). Variables

Physical activity BMI BMIZ24 BMIZ26 BMIZ28 Overweight Obese Weight Triceps skinfold (TS) TSZ85% TSZ95% Subscapular skinfold (SS) TSS ¼ TS+SS

High School Sport Participation Rate Female

Male

DD

0.53 (0.23) 1.50 (1.65) 0.03 (0.16) 0.29 (0.14) 0.05 (0.11) 0.02 (0.17) 0.12 (0.13) 5.03 (9.53) 1.88 (3.27) 0.07 (0.17) 0.02 (0.10) 1.68 (3.54) 0.20 (6.38)

0.47 (0.19) 5.14 (1.67) 0.48 (0.15) 0.26 (0.12) 0.19 (0.09) 0.27 (0.20) 0.21 (0.12) 35.26 (10.21) 7.55 (3.35) 0.39 (0.17) 0.15 (0.10) 5.48 (3.52) 13.04 (6.70)

0.73 (0.25) 3.96 (1.48) 0.37 (0.13) 0.26 (0.11) 0.18 (0.08) 0.25 (0.14) 0.21 (0.08) 21.83 (8.18) 4.17 (2.50) 0.01 (0.13) 0.12 (0.06) 3.46 (2.62) 7.63 (4.93)

IV 0.53 0.66 0.11 0.17 0.04 0.02 0.04 2.30 1.90 0.39 0.09 3.47 5.37

(0.40) (3.14) (0.32) (0.25) (0.20) (0.31) (0.19) (17.19) (6.16) (0.30) (0.16) (5.87) (11.14)

Note: Robust standard errors, clustered at state-specific year level in parentheses. Regressions include controls for respondent demographic variables, such as indicators for gender, races, age in months, the difference between age and education level, and whether he/she is the child of the household head; family background variables, such as age, gender, years of education of household head, total family income, and number of person living in the household. Other control variables include indicators of metropolitan areas, sampling weight, proportion of female enrollment in postsecondary education programs and state per capita income, state and year fixed effects, and interaction terms of family background variables with gender of household head. Weight regression contains additional controls of height in inches. The sample size of difference-in-difference (DD) is 3,050/3,035(physical activity); among them 1,497/1,489 are females and 1,553/1,546 are males. The sample size of IV estimation is 1,497/1,489. pr0.05. pr0.10.

two approaches: a comparison-group approach in which boys are used as the counterfactual outcome and IVs. Table 3 provides these estimates. Estimates in Table 3 were obtained using a 3-year average of girls’ sports participation with years prior to 1971 set equal to 1971. Estimates using two different measures of girls’ sports participation are provided in the appendix, and results from these alternative measures are very similar to those reported. The first column of Table 3, labeled Female, presents estimates of Eq. (1) obtained using a sample of adolescent girls. These estimates basically measure the pre- and post-Title IX changes in physical activity, weight, and body

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mass using girls’ sports participation as a gauge of the policy impact of Title IX. The estimate in the first row indicates that a 20 percentage point increase in girls’ sports participation, which is roughly the increase associated with Title IX, is associated with a 10.6 percentage point (17% of NHANES I mean) increase in the probability that an adolescent female will engage in recreation that includes ‘‘much’’ physical activity. Estimates in the remaining rows of column (1) indicate that the increase in girls’ sports participation associated with Title IX is associated with a decrease in BMI, weight, and measures of overweight and obesity. However, most estimates are not statistically significant even though some are quite large in magnitude. For example, a 20 percentage point increase in girls’ sports participation is associated with a 2.4 percentage point (40% of NHANES I mean) decrease in the probability of being obese. This is a relatively large effect, but the standard error of this estimate is also relatively large. Changes in girls’ sports participation is significantly and negatively related to the probability of having a BMI greater than or equal to 26. Overall, estimates in column (1) suggest that the increase in girls’ sports participation caused by Title IX was associated with an increase in physical activity and lower weight and BMI. Estimates in column (2) of Table 2 were obtained using a sample of adolescent boys. The difference between these estimates and those for girls is striking. Among boys, increases in girls’ sports participation are associated with a statistically significant decrease in physical activity and significant increases in weight, BMI, measures of overweight and obesity (based on BMI), and skinfold. During the period when girls’ sports participation was increasing, and when girls were becoming more physically active with improved weight and body mass, boys were becoming less physically active and their weight and body mass were deteriorating. A 20 percentage point increase in girls’ sports participation, the general impact of Title IX, is associated with a 1.02 (5%) increase in BMI, and an increase in the probability: of having a BMI greater than or equal to 24 of 9.6 (74%) percentage points; of having a BMI greater than or equal to 26 of 5.2 (74%) percentage points; of having a BMI greater than or equal to 28 of 3.8 (95%) percentage points; of having a probability of being obese 4.2 (84%) percentage points. Again, while estimates are large, so are standard errors, which reflects the relatively small sample sizes and the fact that girls’ sports participation does


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not vary by individuals, but rather by state and year and the calculation of standard errors that cluster on state. The fact that girls’ sports participation is significantly related to boys’ weight and body mass merits some further discussion. These results indicate that there is an unobserved variable that is correlated with boys’ weight and body mass that is also correlated with girls’ sports participation. The underlying assumption of the research design implies that this unobserved variable has the same effect on girls. The troubling question is what this factor could be and why would it have a relatively large negative effect on girls’ and boys’ weight and body mass? Ideally, we would have observed no correlation between girls’ sports participation and boys’ weight and body mass. One potential answer is that boys were adversely affected by increases in girls’ sports participation (athletic opportunities), as suggested by critics of Title IX legislation. If true, this possibility invalidates the comparison-group research design. While we have provided some evidence that the comparison-group approach is valid, this evidence was not definitive. So we have to allow for this possibility, although we do not believe it the most likely explanation. Other explanations of this finding are less obvious, particularly because of the correlation of this unobserved factor with girls’ sports participation. For example, consider two of the commonly cited explanations of rising obesity: an increasingly sedentary life and decreasing food prices (Cutler, Glaeser, & Shapiro, 2003). Changes in these factors during the 1970s may explain the increase in weight and body mass implied by the estimates in column (2), but why would changes in these factors be correlated with increases in girls’ sports participation? While it is possible, we can bring no evidence to bear to establish its likelihood. However, we did provide evidence that the comparisongroup approach is valid, specifically the finding that unobserved trends in weight and body mass are the same for boys and girls and that the effect of increasing school enrollment on weight and body mass is the same for boys and girls. These findings imply that the effect of other unobserved factors would be the same for boys and girls, specifically those factors that are correlated with girls’ sports participation. However, the validity of this statement cannot be definitively established and is an assumption of the research design. The third column of Table 3 presents difference-in-differences estimates, which were obtained using a sample of boys and girls. Note that estimates in column (3) are not equal to the exact difference between estimates in columns (1) and (2) because for all outcomes except physical activity, we restrict the year effects and the effect of female enrollment in higher education to be the same for boys and girls. In the case of physical activity, we allowed the effect of female enrollment in higher education to differ, but restricted

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year effects to be the same for boys and girls. All of these restrictions are supported by statistical tests. The validity of these restrictions is evidence that supports the assumptions of the comparison-group approach. Estimates in column (3) suggest that Title IX and the increase in girls’ sports participation that it caused resulted in an increase in physical activity and an improvement in weight and body mass for adolescent females. A 20 percentage point increase in girls’ physical activity is associated with a 14.6 percentage point (24%) increase in the probability of engaging in ‘‘much’’ physical exercise during recreational activities. The same increase in girls’ sports participation is associated with the following effects on weight and body mass: a 0.79 unit (3.8%) decline in BMI; a 5.0 percentage point (28%) decrease in the probability of being overweight; a 4.2 percentage point (70%) decrease in the probability of being obese; a 2.4 percentage point (48%) decrease in the probability that triceps skinfold is over 95 percentile. The large confidence intervals of these estimates contain plausible results. For example, the 95% confidence interval for the difference-in-differences estimate of the effect of girls’ sports participation on obesity is 0.05–0.37; so a 20 percentage point increase in girls’ sports participation reduces obesity anywhere from 1 percentage point (17%) to 7.4 percentage points (123%). This is a large range and reflects the relative lack of precision of the estimate, but it also indicates that the confidence interval contains clearly plausible magnitudes of the estimate. Moreover, the uniformity of the estimates with respect to the various measures of weight and body mass provides strong evidence consistent with a true effect. The last column of Table 3 presents the IV estimates of the effect of girls’ sports participation.9 No IV estimate is statistically significant and the magnitudes of the standard errors are very large. In general, the IVs estimates are too imprecise to provide much information. While the first stage results suggest that the boys’ sports participation in 1971 was significantly correlated with girls’ sports participation (t-statistic of 6.1), this correlation was insufficient to produce precise estimates in the second stage given the sample size. 6.1. Sensitivity Analyses To this point in the analysis, estimates indicate that girls’ sports participation is associated with an increase in girls’ physical activity and improvement


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in weight and body mass. To assess the robustness of these findings, we conducted three additional analyses. First, we examined the association between girls’ sports participation and height. Girls’ sports participation should not be associated with height, as there is no compelling biological mechanism to link changes in physical activity to changes in height. Estimates from this analysis are presented in the top row of Table 4. Girls’ sports participation is associated with a statistically insignificant increase in height for both boys and girls. Estimates for both boys and girls are positive and the difference-in-differences estimate is virtually zero; a 20 percentage point increase in girls’ sports participation was associated with a 0.11 in. (zero) increase in girls’ height. Second, we examined the effect of girls’ high school sports participation on the weight and body mass of children aged 6–10. While Title IX may have resulted in an expansion of athletic opportunities for female children, and there may have been a spillover within families from adolescents to children, the impact of Title IX should be much smaller among female children than among female adolescents. Estimates from this analysis are also presented in Table 4. Among female children, girls’ participation in high school sports is associated with a decrease in weight, body mass, and skinfold, but none of the estimates are statistically significant and standard errors are quite large. Among male children, we find similar results. Girls’ high school sports participation is associated with a decrease in BMI, weight, and probability of being obese and estimates related to obesity and weight are statistically significant. Standard errors are also quite large. The similar trends in weight and body mass among male and female children yield differencein-differences estimates that are generally smaller than the estimates for both boys and girls and estimates that are not statistically significant. The lone exception pertains to the probability of being overweight; girls’ high school sports participation is associated with a large (70%) decrease in a female child’s probability of being overweight. However, the opposite is true for the probability of being obese – girls’ high school sports participation is associated with an increase in a female child’s probability of being obese. Finally, we examined the effect of girls’ high school sports participation on the height, weight, and body mass of adults aged 24–34. Persons in this age group should be unaffected by Title IX because it was beyond high school age throughout the period of study. Estimates pertaining to the adult sample are with one exception not statistically significant. More importantly, there is no evidence that girls’ sports participation in high school sports is associated with an increase in adult female physical activity or decrease in adult female weight and body mass. Further, difference-in-difference estimates do not indicate a

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Table 4. Sensitivity Analyses Difference-in-Difference Estimates of the Effect of Girls’ Sports Participation on Physical Activity and Weight (3-Year Average Sports Participation Rate Participation Rate Prior to 1971 ¼ Participation Rate in 1971). Variables

Adolescents (12–17) Height

3-Year Average High School Sport Participation (HSSP) Female

Male

DD

0.74 (1.05)

1.71 (1.26)

0.59 (0.76)

1.85 (1.22) 0.01 (0.18) 0.22 (0.12) 7.52 (4.30) 1.33 (2.47) 1.40 (2.05)

1.07 (1.84) 0.50 (0.28) 0.12 (0.17) 4.62 (7.25) 1.44 (3.64) 2.32 (3.17)

Children (6–10) BMI Overweight Obese Weight Triceps skinfold (TS) Subscapular skinfold (SS)

2.47 0.44 0.07 9.66 0.43 1.70

Adults (24–34) Physical activity BMI Overweight Obese Weight Triceps skinfold (TS) Subscapular skinfold (SS)

0.27 (0.14) 1.41 (2.72) 0.31 (0.22) 0.01 (0.13) 12.35 (15.70) 6.71 (4.21) 1.08 (4.81)

(1.73) (0.28) (0.15) (6.61) (3.99) (3.48)

0.36 0.08 0.10 0.22 2.26 1.52 3.86

(0.27) (2.63) (0.28) (0.19) (18.54) (5.13) (5.85)

0.12 1.08 0.22 0.26 11.75 3.95 3.98

(0.29) (4.52) (0.39) (0.26) (27.73) (6.97) (8.54)

Note: Robust standard errors, clustered at state-specific year level in parentheses. (1) Regressions of children include controls for individual demographic variables, such as indicators for gender, races, age in months, the difference between age and education level, and whether he/ she is the child of the household head; family background variables, such as age, gender, years of education of household head, total family income, and number of person living in the household. Other control variables include indicators of metropolitan areas, real state per capita income, percentage of female postsecondary education enrollment, sampling weight, state and year fixed effects, and interaction terms of family background variables with gender of household head. Weight regression contains additional controls of height in inches. The sample size of children in DD estimation is 2,353; among them 1,162 are females and 1,191 are males. (2) Regressions of adults include controls for respondent demographic variables, such as indicators for gender, race, age, living quarters, education levels, and marital status; family background variables, such as total family income and number of person living in the household. Other control variables include indicators of metropolitan areas, real state per capita income, percentage of female postsecondary education enrollment, sampling weight, state and year fixed effects, and interaction terms of family background variables with gender of household head. Weight regression contains additional controls of height (in in.). The sample size of adults in DD is 3,606/3,609 (physical activity); among them 2,255/2,252 (physical activity) are females and 1,354 are males. pr0.05. pr0.10.


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relative improvement in these outcomes vis-a`-vis adult males. In sum, estimates in Table 4 provide no evidence that the estimates in Table 3 are spurious.

7. CONCLUSIONS In this study, we examined the association between girls’ participation in high school sports and the physical activity and weight of adolescent females during the 1970s when girls’ sports participation was dramatically increasing as a result of Title IX. We argued that girls’ participation in high school sports is a good measure of athletic opportunities for adolescent girls, and that the dramatic rise in participation during the 1970s reflects an expansion of such opportunities for adolescent girls that effectively lowered the price of physical activity. As a result, we hypothesized that adolescent girls’ physical activity would increase and in turn, result in an improvement in adolescent girls’ weight and body mass. Our statistical results bear this out. An increase in girls’ participation in high school sports was associated with an increase in physical activity and an improvement in weight and body mass among adolescent girls. In contrast, boys experienced a decline in physical activity and an increase in weight and body mass during the period when girls’ athletic opportunities were expanding. Taken together, these results strongly suggest that Title IX and the rapid increase in athletic opportunities it engendered had a beneficial effect on the health of adolescent girls. It resulted in greater physical activity and improved weight and body mass. The magnitudes of the effects were non-trivial. The increase in girls’ sports participation during the 1970s was approximately 20 percentage points. This change is associated with a 24% increase in physical activity, as measured by whether an adolescent engaged in ‘‘much’’ physical activity during recreation, and a 4% decline in BMI. The increase in girls’ sports participation during the 1970s was associated with significantly larger improvements in the probability of being overweight and obese. In sum, Title IX and the expansion of school-based opportunities for physical activity among adolescent girls that it caused appear to have had a significant causal effect on adolescent girls’ physical activity and weight. These results provide evidence to support current calls for an expansion of school-based physical activities programs. Indeed, the magnitudes of the effects of expanded opportunity for physical activity among adolescent girls suggest that such programs will likely have a significant impact on obesity. These findings also illustrate the potential harm of the recent declines in school-based opportunity for exercise, for example, the percentage of high

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school students enrolled in daily physical education classes declined from 41.6% in 1991–28.4% in 2003 (Grunbaum et al., 2004). A few limitations of the study should be acknowledged. First, we used girls’ participation in high school sports to measure the impact of Title IX. Obviously, this is an indirect measure and as such may not accurately capture the impact of Title IX on adolescent girls’ athletic opportunities. It would have been preferable to have more extensive measures of athletic opportunities including participation in community sports programs and intramural sports. This may partly explain some of the large estimates of the effect of girls’ sports participation on some outcomes, as the scaling of the sports participation measure could affect the magnitude of the estimates. Moreover, data on girls’ sports participation were noisy and were measured with a fair amount of error. At a minimum, this will result in estimates biased toward zero. Similarly, information on physical activity is limited. If better measures were available, we could then directly measure physical activity and its relationship to body mass and health. Girls’ sports participation and the ‘‘natural experiment’’ of Title IX could be used to overcome (i.e., instrument) the problem that girls’ physical activity may be correlated with unmeasured factors that affect both physical activity and obesity (health). In the absence of quality measures of physical activity, we were forced to use a more reduced form approach that relates girls’ sports participation directly to the outcomes of interest. Finally, it would have been better if we had measures of weight that better reflect body composition and specifically the distribution of fat such as waist-to-hip ratio and measures of body composition determined by electrical impedance and X-ray absorptiometry. Another limitation, particularly important to the IVs approach, was that sample sizes were not sufficient to detect reliably small sized effects. The IVs procedure relies on state-level variables for identification and as a result requires relatively large samples to be effective. While NAHNES I and II provide reasonably large sample sizes, for example 3,300 adolescents, the power of the analysis was relatively low. Fortunately, many of the effects of interest were quite large and therefore we were able to reject the null hypothesis Table A3 of no effect.

NOTES 1. The trend in boys’ sports participation may have been different if Title IX had not been implemented. That is, we do not know the counterfactual – what boys’ participation would have been in the absence of Title IX.


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2. The only restriction we rejected was g2 ¼ g~ 2 when the dependent variable was physical activity. In all other cases we failed to reject the restriction in Eq. (4), although the imprecision of the estimates fails to reject some relatively large differences. 3. We also used the difference between boys’ and girls’ sports participation in 1971 as an instrument and obtained very similar results to those reported below. 4. The reported test statistics were calculated using standard errors that allowed for non-independence (clustering) of observations within state-year. 5. The NHANES I and II have information on 4,050 non-emancipated (not living alone) adolescents aged 12–17 at the time of interview. Missing data on girls’ sports participation result in a loss of 774 observations. 6. Using 1% public-use samples (PUS) of Census 1970 and 5% public-use samples (PUMS) of Census 1980, we derive the number of boys and girls aged 14–17 for each state from 1971 to 1980. The basic idea of our procedure is that, for example, the number of boys and girls aged 15–18 in Census 1980 is a good proxy for the number of boys and girls aged 14–17 in each state in 1979. This ignores migration and death at young age. To correct the potential bias which may be induced by this assumption, we employ a similar procedure to obtain another proxy of same group of people, by calculating the number of boys and girls aged 5–8 in Census 1970. The more accurate estimate of the number of boys and girls aged 14–17 in each state is then equal to the weighted average of these two proxies, where the weight depends on the time difference between the year of each proxy and the year 1970. Using same reasoning process, we can also derive the number of boys and girls aged 14–17 for each state from 1971 to 1978. The above procedure is shown by the following equation: Boys 1970 1980 ¼ ð1 W Þ ProxyCensus þ W ProxyCensus ij ij Girlsij where we get the estimates of boys and girls aged 14–17 of state i in year j (j ¼ 1971, y, 1979) and W ¼ (j 1970)/10. 7. In private correspondence, a researcher with access to the confidential data reported that 85% of adolescents aged 12–17 in NHANES II lived in the same state they were born in. 8. Tertiles were derived using state-level data (not weighted). The top tertile consists of the following states: CO, CT, ID, KS, ME, MA, MI, MO, MT, NH, OK, OR, SD, TX, WA, and WY. The bottom tertile consists of the following states: AL, CA, FL, GA, IN, KY, LA, MN, MS, NV, NY, NC, ND, SC, UT, and VA. 9. See Table A3 for a descriptive analysis of IV approach.

REFERENCES American Friends Service Committee. (1977). Almost as fairly: The first year of Title IX implementation in six southern states (pp. 2–143). U.S. Department of Health, Education and Welfare, National Institute of Education Washington DC. Bound, J., Jaeger, D. A., & Baker, R. M. (1995). On potential problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90, 443–450.

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Cawley, J., Meyerhoefer, C., & Newhouse, D. (2005). The impact of state physical education requirements on youth physical activity and overweight. NBER Working Paper no. 11411. Cambridge: NBER. Centers for Disease Control and Prevention (1997). Guidelines for school and community programs to promote lifelong physical activity among young people. Morbidity and Mortality Weekly Report, 46(RR-6), 1–36. Craig, W. L. (1977). Implementing Title IX. NASSP Bulletin, January. Cutler, D. M., Glaeser, E. L., & Shapiro, J. M. (2003). Why have Americans become more obese?. Journal of Economic Perspectives, 17(3), 93–118. Gavora, J. (2002). Tilting the playing field: Schools, sports, sex and Title IX. San Francisco, CA: Encounter Books. Goran, M. I. (1998). Measurement issues related to studies of childhood obesity: Assessment of body composition, body fat distribution, physical activity, and food intake. Pediatrics, 101, 505–518. Grunbaum, J. A., Kann, L., Kinchen, S., Ross, J., Howkins, J., Lowry, R., Harris, W. A., McManus, T., Chyen, D., & Collins, J. (2004). Youth risk behavior surveillance – United States, 2003. Morbidity and Mortality Weekly Report, 53(SS-2), 1–29. Institute of Medicine (2002). Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. Washington, DC: National Academies Press. Kimm, S. Y. S., Glynn, N. W., Obarzanek, E., Kriska, A. M., Daniels, S. R., Barton, B. A., & Liu, K. (2005). Relation between the changes in physical activity and body-mass index during adolescence: A multicentre longitudinal study. The Lancet, 366, 301–307. Lake, J. K., Power, C., & Cole, T. J. (1997). Child to adult body mass index in 1958 British birth cohort: Associations with parental obesity. Archives of Disease in Childhood, 77, 376–380. McTigue, K. M., Garrett, J. M., & Popkin, B. M. (2002). The nature history of the development of obesity in a cohort of young U.S. adults between 1981 and 1998. Annals of Internal Medicine, 136, 857–864. Must, A., Dallal, G. E., & Dietz, W. H. (1991). Reference data for obesity: 85th and 95th percentiles of body mass index and triceps skinfold thickness. American Journal of Clinical Nutrition, 53, 839–846. National Federation of State High School Associations. (2001). NFHS Handbook 2001–2002, Indianapolis, In: National Federation of State High School Associations. National Federation of State High School Associations (2002). Participation survey 2002. Indianapolis, IN: National Federation of State High School Associations. Pate, R. R., Trost, S. G., Levin, S., & Dowda, M. (2000). Sports participation and health-related behaviors among US youth. Archives of Pediatrics and Adolescent Medicine, 154, 904–911. Pottker, J., & Fishel, A. (1976). Separate and unequal: Sex discrimination in interscholastic sports. Integrated Education, 14(2), 3–7. Riley, B. (1976). The effect of Title IX of the education amendments of 1972 on the administration of girls’ competitive athletic programs in selected public high schools of Texas. East Texas School Study Council Fall. Rowland, T. W. (1996). Athleticism, physical activity and health in the early years: A question of persistence. In: The Child and Adolescent Athlete: Olympic Encyclopaedia of Sports Medicine (Vol. 6, Chapter 10). Blackwell Publishing, MA. Serdula, M. K., Ivery, D., Coates, R. J., Freedman, D. S., Williamson, D. F., & Byers, T. (1993). Do obese children become obese adults? A review of the literature. Preventive Medicine, 22(2), 167–177.


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Slattery, M. L., McDonald, A., Bild, D. E., Cann, B. J., Hilner, J. E., Jacobs, D. R., & Liu, K. (1992). Associations of body fat and its distribution with dietary intake, physical activity, alcohol, and smoking in blacks and whites. American Journal of Clinical Nutrition, 55, 943–949. Staffo, D. F. (1980). Efforts of selected New York state high school to provide equal opportunity in interscholastic athletics. Review of Sports and Leisure, 5, 93–106. Stefanick, M. L. (1993). Exercise and weight control. Exercise and Sport Sciences Review, 21, 363–396. Stevenson, B. (2000). Evidence on the effect of sports participation examining the impact of Title IX. Unpublished manuscript. Department of Economics, Harvard University, Cambridge, MA. US Department of Education. (1975). Education’s final regulations on Title IX (40FR 24128). Washington DC: US Government Printing Office. US Department of Education. (2000). Title IX and sex discrimination. http://www.ed.gov/ offices/OCR/docs/tix_dis.html, website accessed June 2, 2003. US Department of Health and Human Services (1996). Physical activity and health: A report of the surgeon general. Washington, DC: US Government Printing Office. US Department of Health and Human Services (1997). Physical activity and sports in the lives of girls: A report by The President’s Council on Physical Fitness and Sport. Washington, DC: US Government Printing Office. US Department of Health and Human Services (2000). Healthy people 2010: National health promotion and disease objectives. Washington, DC: US Government Printing Office. Whitaker, R. C., Pepe, M. S., Wright, J. A., Seidel, K. D., & Dietz, W. H. (1998). Early adiposity rebound and the risk of adult obesity. Pediatrics, 101(3), e5. Women’s Sports Foundation. (2001). Title IX legislative chronology. www.womenssportsfoundation.org/cgibin/iowa/issues/history/article.html?record=875. website accessed May 5, 2006.

APPENDIX Table A1. Difference-in-Difference Estimates of the Effect of Girls’ Sports Participation (3-Year Average Sports Participation Rate, Participation Rate Prior to 1971 ¼ 0). Variables


Physical activity BMI BMIZ24 BMIZ26 BMIZ28 Overweight Obese

Male

0.55* (0.21) 0.43* (0.17) 1.17 (1.55) 5.67* (1.49) 0.04 (0.15) 0.52* (0.13) 0.22** (0.13) 0.24* (0.11) 0.03 (0.10) 0.20* (0.08) 0.01 (0.15) 0.36* (0.18) 0.04 (0.11) 0.21** (0.11)

DD

IV

0.70* (0.23) 0.53 (0.36) 3.59* (1.32) 0.34 (2.89) 0.35* (0.12) 0.16 (0.29) 0.21* (0.10) 0.11 (0.23) 0.14** (0.07) 0.07 (0.18) 0.25* (0.12) 0.07 (0.29) 0.17* (0.07) 0.00 (0.17)

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Table A1. Variables


Weight Triceps skinfold (TS) TSZ85% TSZ95% Subscapular skinfold (SS) TSS ¼ TS+SS

(Continued ).

Male

3.33 (8.94) 2.29 (3.06)

DD

IV

38.30* (9.21) 19.82* (7.32) 0.22 (15.82) 8.13* (2.97) 3.65 (2.22) 2.52 (5.67)

0.09 (0.16) 0.04 (0.09) 0.82 (3.17)

0.42* (0.16) 0.15 (0.09) 6.44 (3.18)

1.47 (5.84)

14.57* (6.00)

0.02 (0.12) 0.36 (0.28) 0.10** (0.06) 0.04 (0.15) 3.07 (2.34) 3.89 (5.39) 6.73 (4.39)

6.41 (10.27)

Note: See notes to Table A3.

Table A2. Difference-in-Difference Estimates of the Effect of Girls’ Sports Participation on Physical Activity and Weight Contemporaneous Sports Participation Rate. Variables


Physical activity BMI BMIZ24 BMIZ26 BMIZ28 Overweight Obese Weight Triceps skinfold (TS) TSZ85% TSZ95% Subscapular skinfold (SS) TSS ¼ TS+SS

Male

DD

0.61* (0.19) 0.22 (0.19) 0.64* (0.25) 2.31 (1.53) 3.71* (1.65) 3.67* (1.45) 0.11 (0.15) 0.30** (0.17) 0.35* (0.12) 0.26** (0.14) 0.19** (0.11) 0.20** (0.11) 0.02 (0.10) 0.13 (0.09) 0.10 (0.08) 0.19 (0.17) 0.15 (0.19) 0.27* (0.11) 0.03 (0.10) 0.17 (0.11) 0.11 (0.09) 10.17 (9.04) 22.63* (10.35) 18.84* (8.12) 1.48 (3.40) 5.09 (3.34) 2.62 (2.48) 0.17 (0.17) 0.03 (0.10) 4.00 (3.34)

0.26 (0.18) 0.05 (0.09) 3.31 (3.39)

0.02 (0.12) 0.06 (0.06) 2.76 (2.48)

5.48 (6.49)

8.40 (6.57)

5.38 (4.79)

Note: See notes to Table A3.

IV 0.63 1.07 0.10 0.17 0.03 0.01 0.03 7.55 1.37

(0.39) (3.35) (0.36) (0.26) (0.21) (0.35) (0.19) (18.68) (6.70)

0.30 (0.33) 0.00 (0.17) 1.21 (6.36) 2.57 (12.26)


Table A3.

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Weighted Descriptive Statistics of Dependent Variables by Boys’ Sports Participation Rate in 1971.

Physical Activity

Girls Boys Difference-in-difference Girls Boys Difference-in-difference BMI Girls Boys Difference-in-difference Girls Boys Difference-in-difference Obese Girls Boys Difference-in-difference Girls Boys Difference-in-difference

1971–1975

1976–1980

Difference


0.518 0.646

0.143 (0.061) 0.119 (0.056) 0.024 (0.083)


0.463 0.693

0.200 (0.048) 0.099 (0.043) 0.101 (0.064)


20.650 20.467

0.657 (0.477) 0.106 (0.405) 0.763 (0.626)


21.006 20.682

0.550 (0.366) 0.129 (309) 0.421 (0.479)


0.046 0.007

0.031 (0.031) 0.031 (0.020) 0.000 (0.037)


0.063 0.051

0.025 (0.018) 0.007 (0.019) 0.018 (0.026)

Note: Standard errors in parentheses. *pr0.05. *pr0.10.


OBESITY AND DIABETES: THE ROLES THAT PRICES AND POLICIES PLAY Inas Rashad ABSTRACT The entrance of economics into the literature on obesity and diabetes has been instrumental in showing how people respond to incentives when maximizing their health. In this paper some of the roles that prices and policies have played in the surging obesity and diabetes rates across the world are addressed. The paper focuses on the possible role that prices of foods with high glycemic indexes play in determining blood sugar levels, and addresses the recent concern with high fructose corn syrup and genetically modified goods across the world. The possible links and implications suggest that future research in the area is urgently needed.

1. INTRODUCTION The entrance of economics into the health literature and, most recently, into the obesity epidemic, has been instrumental in showing how people respond to incentives when maximizing their health. Advances in technology have allowed for changing monetary and time costs at both the producer and The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 113–128 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17005-3

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consumer levels. Government policies, such as the crackdown on smoking and agricultural subsidies, and in some cases government inaction, have had unintended consequences in themselves. In this paper I address some of the roles that prices and policies have played in the surging obesity and diabetes rates across the world. An obese person is one having a body mass index (BMI) of 30 kg/m2 or greater, as defined by the National Center for Health Statistics. This definition is what the Centers for Disease Control use to track obesity over time. Although the BMI can overestimate obesity in athletes with a muscular build and underestimate it in older people who have lost muscular mass, it is the most convenient measure available. According to the National Health and Nutrition Examination Surveys, which have nationally representative data on weight and height, the percentage of obese adults in the US has more than doubled since the early 1970s, comprising a third of the adult population. There has been concern about overweight children and adolescents, who are much more likely to grow into obese adults than are their leaner counterparts. The number of overweight children and adolescents has almost tripled since the early 1970s, going from 5 to about 15%. Infants are now larger, with one of many factors attributed to less breastfeeding (Dewey, Heinig, Nommsen, Peerson, & Lonnerdal, 1993). Tables 1 and 2

Table 1. Survey NHES I NHANES NHANES NHANES NHANES NHANES a

I II III 99 01

Trends in Body Mass Index and the Percentage Obese, Persons 18 Years of Age and Oldera. Period

Body Mass Indexb

Percentage Obesec

1959–1962 1971–1975 1976–1980 1988–1994 1999–2000 2001–2002

24.91 25.14 25.16 26.40 27.85 27.92

12.73 13.85 13.95 21.62 29.57 29.65

The surveys are as follows: National Health Examination Survey I (NHES I), National Health and Nutrition Examination Survey I (NHANES I), National Health and Nutrition Examination Survey II (NHANES II), National Health and Nutrition Examination Survey III (NHANES III), National Health and Nutrition Examination Survey 1999–2000 (NHANES 99), and National Health and Nutrition Examination Survey 2001–2002 (NHANES 01). Survey weights are employed in all computations. b Weight in kilograms divided by height in meters squared. Values are based on physical examinations. c Percentage with a BMI greater than or equal to 30 kg/m2.

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Table 2. Trends in Body Mass Index and the Percentage Overweight, Persons 2–17 Years of Agea. Survey NHES II–III NHANES I NHANES II NHANES III NHANES 99 NHANES 01

Period

Body Mass Indexb

Percentage Overweightc

1963–1970 1971–1975 1976–1980 1988–1994 1999–2000 2001–2002

18.53d 18.16 18.36 18.86 19.26 19.69

4.41 4.81 5.01 10.35 14.07 15.48

a

The surveys are as follows: National Health Examination Survey I (NHES I), National Health and Nutrition Examination Survey I (NHANES I), National Health and Nutrition Examination Survey II (NHANES II), National Health and Nutrition Examination Survey III (NHANES III), National Health and Nutrition Examination Survey 1999–2000 (NHANES 99), and National Health and Nutrition Examination Survey 2001–2002 (NHANES 01). b Weight in kilograms divided by height in meters squared. Values are based on physical examinations. c Percentage with a BMI equal to or greater than the 95th percentile based on Centers for Disease Control and Prevention growth charts. d Data pertain to 6–17 year olds.

show trends in the BMI and percentage obese in the US for adults and children, respectively. Obesity has been associated with numerous medical complications, including Type II diabetes (Must et al., 1999; Mokdad et al., 2003; National Institute of Diabetes and Digestive and Kidney Diseases, 1996; Oddy et al., 2004). Life expectancy, currently at an historical high of 77.7 years in the US, might actually be depressed, with latest predictions being that it could even decline by 2050 (Olshansky et al., 2005). Several studies have shown that obese persons, particularly females, earn lower wages (Baum & Ford, 2004; Hamermesh & Biddle, 1994; Cawley, 2004b; Morris, 2006). Being obese is a strong predictor of contracting diabetes mellitus, although it is by no means a prerequisite. Many people of normal weights have been diagnosed with diabetes.1 According to the American Diabetes Association, diabetes is the fifth leading cause of death in America, and about 6.3% of the population has diabetes.2 It has affected much of the world, as can be seen in Fig. 1, which shows diabetes prevalence estimates across the world. According to one estimate, the global prevalence of diabetes is expected to rise from 2.8% in 2000 to 4.4% in 2030, likely an underestimate given that this assumes obesity rates remain constant (Wild, Roglic, Green, Sicree, & King, 2004).

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Fig. 1. Prevalence Estimates of Diabetes Mellitus Around the World, 2003. No Data are Available in this Year for Greenland and Burma. Source: International Diabetes Federation and the British Heart Foundation.

Over 90% of diabetes cases are those of Type II diabetes, largely affected by lifestyle. The annual cost of diabetes in medical expenditures and lost productivity was estimated to be $132 billion as of 2002, according to the National Institutes of Health, and has been shown to affect the economies of local communities where diabetes prevalence is high (Brown, Estrada, Hazarika, & Bastida, 2005). Many diabetes patients who are hospitalized do not receive the attention necessary for those diagnosed with diabetes, which might increase the probability of rehospitalization (Robbins & Webb, 2006). Persons with diabetes are less likely to work and are more likely to have health-related work limitations compared to persons without diabetes (Tunceli et al., 2005). For these reasons, it is important to turn our attention to health problems such as obesity and diabetes, as these figures will likely skyrocket if left unchecked.


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2. ECONOMIC CAUSES OF OBESITY Economics provides a useful tool in explaining how people behave and respond to incentives by maximizing their utility (pleasure or happiness) subject to their time and budget constraints.3 It might also provide a reason for why well-intentioned health advice often goes unnoticed (Reeves & Rafferty, 2005). Even though some foods contain hormones and can thus influence genes, the increase in obesity probably does not reflect a change in the genetic makeup of the US, since the gene pool did not change significantly between 1990 and 1994 (Koplan & Dietz, 1999). This leads us to try to seek explanations based on economic factors contributing to the epidemic. Almost all economic studies on obesity have centered around how technological advancements and changes in the built environment have drastically altered our behavior, and therefore had unintended consequences for our health.4 Emphasis is now being placed on social–environmental factors in addressing obesity prevention rather than individual characteristics (Zhang & Wang, 2004). Lakdawalla and Philipson (2002) have found that reductions in the strenuousness of work and declines in the real price of grocery food items (largely due to technological advances in agriculture) have been key factors in causing the rise in obesity. While in essence our bodies are used to saving food in case of famine, we now have an excess of food, as agribusiness provides 500 more calories a day than it did 30 years ago (Pollan, 2003). However, the abundance of food is not equally distributed across the world. Perhaps ironically, in a report released by the Food and Agriculture Organization of the United Nations in December of 2004, the estimated number of people going hungry around the world has gone up, in spite of the fact that the world as a whole is becoming wealthier and producing more food than necessary (Becker, 2004). Reduced time costs associated with meal preparation (as with the use of microwave ovens), also due to technological advances, have been looked at as chief contributors to the epidemic by Cutler, Glaeser, and Shapiro (2003), while my colleagues and I have focused on the increased prevalence of fastfood and full-service restaurants and their reduced prices (Chou, Grossman, & Saffer, 2004; Rashad, Chou, & Grossman, forthcoming). In addition, we have looked at the unintended effect of the campaign on smoking (through numerous cigarette excise taxes and clean indoor air laws intended to discourage cigarette smoking), causing people to stop smoking and gain weight. Anderson, Butcher, and Levine (2003) show that children of working mothers tend to be more obese (at least for those mothers who work more hours). Ewing, Schmid, Killingsworth, Zlot, and Raudenbush (2003)

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use a measure of urban sprawl created by Smart Growth America that takes into account residential density, land use mix, degree of centering, and street accessibility, and measures how conducive a city is to exercise. They find a positive association between the degree of urban sprawl and obesity in a metropolitan statistical area. While most nations across the world seem to be becoming more obese, the US ranks among the highest. Europeans tend to drive fewer miles, largely because they substitute public transportation, walking, or biking for driving (Squires, 2002). ‘‘[S]ome of the cultures that set their culinary course by the lights of pleasure and habit rather than nutritional science are actually healthier than we are,’’ pointed out Pollan (2004).5

3. DIABETES AS AN OUTCOME My aim in this section is to focus on diabetes mellitus, largely affected by obesity, and look at the effect that prices have on blood glucose levels. A potential problem with including people who have been diagnosed with diabetes is that they may alter their behavior after being diagnosed.6 Excluding those who have been diagnosed with diabetes, I use levels of glycosylated hemoglobin, or HbA1c, in the blood, as my outcome variable. Values of HbA1c below 7% of total hemoglobin are associated with persons without diabetes or those keeping their diabetes in check (Saudek, Kalyani, & Derr, 2005; Pendleton et al., 2005), yet my sample also includes persons with values above this.7 A further advantage of using levels of HbA1c in the blood is that it is not affected by daily fluctuations of glucose in the blood, as it is an indicator of blood sugar level in the past three months.8 I use price averages of two groups of goods, one that I predict will raise levels of glycosylated hemoglobin, and one that I believe will lower them. I divide the two groups using the glycemic index (GI). Evidence has been put forth suggesting that foods with high GIs have an adverse effect on the probability of being diagnosed with diabetes.9 A GI value greater than 70 is considered high, while a value below 55 is considered low.10 Using values put together by researchers at the University of Sydney,11 values of 46 and 51 were assigned to orange juice and bananas, respectively, which make up my low GI foods. GI values for ice cream were in the 62–80 range, while bread was assigned a value of 70. Bread and ice cream therefore make up my high GI group. (Note that these two foods also commonly contain high fructose corn syrup, while those in the low GI group do not.) In addition to prices, I control for gender, age, race/ethnicity, education, health insurance


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status, whether or not a parent was ever diagnosed with diabetes, BMI, and phase of the survey. I therefore estimate the following: HbA1c ¼ fðPrhighGI; PrlowGI; XÞ, where HbA1c is glycosylated hemoglobin, PrhighGI is the average price of foods with a high GI, PrlowGI is the average price of foods with a low GI, and X is a vector of demographic variables. 3.1. Data The Third National Health and Nutrition Examination Survey (NHANES III) is a national sample of the US and was conducted by the National Center for Health Statistics between 1988 and 1994. Phase 1 took place between 1988 and 1991, while phase 2 took place between 1991 and 1994. NHANES III provides results of blood tests through its laboratory component and objective measures of weight and height, based on physical examinations, in addition to demographic data. Using average prices of bananas, orange juice, bread, and ice cream from the Bureau of Labor Statistics, which I adjust for inflation, I merge PrhighGI and PrlowGI with the NHANES III data by phase and region.12 3.2. Results Results are reported in Table 3. Regressions run were weighted by sampling weights and clustered by region. We see that the coefficients on the prices have the expected signs, although they are not significant at the 10% level. Higher prices of foods with a high GI, in general are not good if one would like to control his or her blood sugar, and are associated with lower blood sugar levels. Similarly, higher prices of foods with a low GI are associated with higher blood sugar levels. In the sample, being an older, nonwhite female, with a high BMI and parent who has diabetes, would substantially raise the chances of contracting diabetes.13

4. OUR FOOD SUPPLY 4.1. A Closer Look at Saccharides High fructose corn syrup, or HFCS, is arguably sweeter and is absorbed differently from table sugar, having an effect on obesity and diabetes

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Table 3.

Dependent Variable: Glycosylated Hemoglobin, All Ages, NHANES IIIa,.

Dependent Variable: HbA1c Independent Variable PrhighGI PrlowGI High school Some college College Black Hispanic Other race Male Age Age squared Health insurance Parent with diabetes BMI Phase 2 Constant R2 Observations

0.5600 (1.94) 0.6810 (1.63) 0.1012 (11.44) 0.1369 (5.07) 0.1995 (6.61) 0.2450 (17.10) 0.1275 (8.11) 0.1242 (9.34) 0.1055 (8.61) 0.0120 (8.77) 0.000001 (0.08) 0.0303 (1.46) 0.0905 (12.25) 0.00001 (1.56) 0.1682 (7.54) 4.8732 (202.21) 0.184 20,691

Note: Employs NHANES sampling weights and clusters by region. a T-ratios are reported in parentheses. Significant at 10% level. Significant at 5% level. Significant at 1% level.

(Bray, Nielsen, & Popkin, 2004), although some believe this to be incorrect.14 Suggestions that HFCS has played a role in obesity have aroused the attention of manufacturers such as Archer Daniels Midland, CPC International, and Staley Manufacturing, who worry that negative attention and lawsuits might hurt the lucrative industry. While I will not touch on this debate here, it is clear that the introduction of HFCS in the 1970s spurred an array of opportunities for foodmakers and has increased the per capita consumption of sugars in general. Bray et al. (2004) shed light on the rapid increase in consumption of HFCS in the US between 1970 and 1990 (consumption increased>1,000%) and how it mirrors the rapid increase in obesity, although they do not establish causality. They use data from the US Department of Agriculture food consumption tables from 1967 to 2000. These trends can be seen in Fig. 2, which shows sucrose and HFCS per


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80

Supply in Thousands of Short Tons

70

60

50

40

30

20

10

HFCS Supply per cap Sugar Supply per cap

0 1965

1970

1975

1980

1985

1990

1995

2000

2005

Year

Fig. 2.

Sugar and High Fructose Corn Syrup Per Capita in US, 1967–2003. Source: Economic Research Service, USDA.

capita supply between 1967 and 2003. As shown, HFCS was practically nonexistent in the US prior to 1967. Fig. 3 shows HFCS utilization in the US from 1992 to 2004. Unfortunately data on HFCS is only available on the national level, although it would be possible to look at foods in the US that HFCS turns up in. An additive that is cheaper and easier to use than sugar, HFCS ‘‘is the leading sweetener in soda and fruit juice, and turns up in everything from pizza and yogurt to breakfast cereal, baby food and beer’’ (Cohen, 2004). Few data are available on foods containing HFCS in countries other than the United States (Vuilleumier, 1993). 4.2. The Controversy Over Genetically Modified Foods Very little research can be done on genetically modified (GM) goods because they are virtually impossible to trace. While it is very difficult to know which

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Thousands of Short Tons

8000 7000 6000 5000 4000 3000 2000 1000 0 1990

Fig. 3.

1992

1994

1996

1998 Year

2000

2002

2004

2006

High Fructose Corn Syrup Disappearance Less Exports in US, 1992–2004. Source: Economic Research Service, USDA.

foods contain GM ingredients, it is estimated that 75% of processed foods in the US contain them, and that 40% of the corn grown in the country is of a GM variety (Are You Eating Genetically Modified Food? 2005). Biotech companies such as Monsanto and DuPont have prospered and struggle to sell their corn and soybean to other countries, many of which have placed restrictions on GM foods. In USDA’s Agricultural Economic Report 687, it states: ‘‘The development of agricultural biotechnology offers the opportunity to increase crop production, lower farming costs, improve food quality and safety, and enhance environmental quality.’’ The US has thus filed a complaint at the World Trade Organization against the EU for its trade practices. The EU discourages the use of GM crops, the long-term effects of which are largely unknown. Arguments against GM foods include unknown long-term effects on the environment, how it can breed resistance to antibiotics, and how it will not solve world hunger (GM Crops ‘Won’t Solve Hunger’, 2003; Specter, 2000). Having been developed in the early 1980s, it is too early to detect long-term effects. Yet it has allowed for the production of more corn at a faster rate, which has been consumed by people and by animals ultimately eaten by people.15


Table 4.

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Dependent Variable: Diabetes Prevalence, 174 Countriesa,.

Dependent Variable: Diabetes Independent Variable 0.0092 (2.01) 0.0011 (9.03) 0.0297 (5.97) 0.324 174

Restriction Sugar and sweetener supply Constant R2 Observations a

T-ratios are reported in parentheses.

Significant at 10% level. Significant at 5% level Significant at 1% level.

Using data from the Center for Food Safety and data from the International Diabetes Federation,16 I found that those countries with no restrictions on GM foods have an average diabetes prevalence of 6.5%, versus an average of 5.8% for those countries that have some kind of restriction. Restrictions include one of the following: a protocol on biosafety, labeling requirements, a ban on commercialization, or a ban on imports. Of course, it is impossible to draw any kind of conclusion from this. Although these are aggregate data, I use a sample of 174 countries to regress diabetes prevalence on a dummy indicating some type of ban on GM goods, and domestic supply of sugar and sweeteners, obtained from the Food and Agriculture Organization of the United Nations.17 Results are shown in Table 4. The negative coefficient on the restriction variable is debatably the expected one. The coefficient on sugar and sweetener supply is positive: As domestic supply per capita of sugar and sweeteners goes up by 1 kg, diabetes prevalence is predicted to go up by 0.11%, holding any restrictions on GM goods constant. Since there are many missing variables, sugar and sweeteners supply could also be viewed as a proxy for a greater domestic food supply in general.

5. DISCUSSION Obesity and diabetes are diseases largely affected by lifestyle, and tend to more severely afflict affluent nations. Monetary and time costs play a powerful role, verifying that people respond to incentives. The prices of food and physical inactivity have gone down. Since the early 1970s, if not before,

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there have been subsidies from the government that have encouraged corn production as well as trade policies protecting American farmers (Critser, 2003; Cook, 2004; Fields, 2004), which might have had negative consequences in terms of health. I find that prices of foods with high glycemic indexes, said to have detrimental effects on one’s health if consumed in excess, could influence blood glucose levels. I also find that domestic supply of sugar and sweeteners around the world could possibly influence diabetes prevalence. Genetically modified foods, which we cannot say have an effect on health currently, have nevertheless accelerated the mass production of a few kinds of crops, which are further subsidized by the government. A surplus of food has spawned demand, much of it through marketing. Government intervention can be justified on the grounds of imperfect information, negative externalities, or irrational decision-making.18 Consumers are given ample information although could receive mixed messages from advertisements. And while we believe we are now aware of the consequences of our behaviors, the full outcome of the changes in our built environment in many aspects has only recently become apparent. Negative externalities can exist through publicly financed health care, lost productivity at work, and early retirement. These in turn have effects on the economies of many communities. One argument has been to charge higher health insurance premiums to those who are more obese. ‘‘Many states require that health insurers charge the same premiums for any member of a group health plan, regardless of risk. This means the costs of the donuts-and-pizza couch potato’s unhealthy decisions are imposed on the gym rat who diets carefully and watches his cholesterol’’ (Cannon & Balko, 2004). Yet charging the obese higher health premiums could be questionable if obesity has a large genetic component, or if a person has a high BMI yet is physically active19. If intervention can be justified, we might want to increase the prices of additive sugars such as HFCS for producers, raising the cost of including it as a key ingredient in many foods, or place restrictions similar to those in many European countries on GM goods. Intervention can also be in the form of removing an existing policy that has had negative consequences. It has been argued that there is little benefit in having agricultural subsidies in the US and in Europe, as they do not aid those they were originally intended to, the small farmers; often only hurt poorer nations’ economies with the excess supply that they generate; and contribute to poor nutrition by supporting the efficient production of only a few crops – such as corn, wheat, and soybeans – while ignoring other fruits and vegetables (Cook, 2004; Fields, 2004). Little has been done, however, to


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remove these subsidies. If they also have a negative effect on health, there is further rationale for their removal, a case for not having government intervention where it is already in place. There is no simple solution or preventive measure to obesity and diabetes. Government intervention might not have the intended effects, as people shift their behaviors in response to government involvement as their incentives change. Further research should be done on the food supply and care should be taken in tracing ingredients. In addition, the long-term effects that agricultural policies might have on health need to be explored.

NOTES 1. In my weighted NHANES III sample, to be described in Section 3, about 45% of adults who have diabetes are also obese. 2. Source: http://www.cdc.gov 3. For a thorough discussion on the seminal role of economics in this area as well as its limitations, see Cawley (2004a), in addition to the other papers dealing with the economics of obesity in the 2004 American Journal of Preventive Medicine supplement, volume 27(3S). 4. For a nice discussion on how advances in technology have lowered the price of food while raising the price of physical activity, see Variyam (2005). 5. A book recently released that has been touted (and criticized) is one entitled ‘‘Why French Women Don’t Get Fat’’ by a French CEO (Guiliano, 2004). 6. However, it was recently estimated that two-thirds of those with diabetes in the US are not controlling their blood sugar (Neergaard, 2005). 7. According to the Centers for Disease Control and Prevention, an estimated one third of those with diabetes do not know that they have it. 8. It should be noted that HbA1c levels are not used in diagnosing diabetes, and that a value above 7 does not necessarily mean that a person has diabetes. 9. For example, see Brand-Miller, Liu, Petocz, and Baxter (2005); Trout, Hallfrisch, and Behall (2004); Wolever, Brand-Miller, Foster-Powell, and Colagiuri (1999). 10. Values are compared with sucrose, with a GI of 100. 11. See http://www.glycemicindex.com 12. This is the lowest geographic level publicly available. The most recent publicly-available NHANES data do not contain any geographic identifiers. In merging with data from the BLS, I use years 1988–1990 for phase 1 and 1992–1994 for phase 2. 13. While conducting my research on obesity using NHANES III with geographic identifiers, I found that the price of food away from home had a significant, negative effect on the level of HbA1c in the blood, indicating the roles prices and BMI have on diabetes. The price of food away from home was measured on the state level and obtained from the American Chamber of Commerce Researchers Association (ACCRA). I controlled for whether the respondent had been diagnosed with diabetes, health insurance status, race/ethnicity, and age. Results are available upon request.

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14. See, for example, http://www.hfcsfacts.com, a website created by the Corn Refiners Association. 15. Feeding cows corn readies them for slaughter sooner and might have been a factor contributing to the emergence of bovine spongiform encephalopathy (BSE), or ‘‘mad cow disease’’ (Cook, 2004). 16. See http://www.idf.org 17. See http://faostat.fao.org 18. See Rashad and Grossman (2004) and Rashad (2005) for a discussion on government intervention in the obesity epidemic. 19. Physical activity can reduce the risk of health decline even if an individual cannot achieve his or her ideal weight.

REFERENCES Anderson, P., Butcher, K., & Levine, P. B. (2003). Maternal employment and overweight children. Journal of Health Economics, 22, 477–504. Are You Eating Genetically Modified Food? (2005). CNN.com, March 24. Available at: http:// www.cnn.com/2005/HEALTH/diet.fitness/03/24/genetic.foods.ap/index.html Baum, C. L., & Ford, W. F. (2004). The wage effects of obesity: A longitudinal study. Health Economics, 13, 885–899. Becker, E. (2004). Number of hungry rising, U.N. Says. New York Times, December 8. Available at: http://query.nytimes.com/gst/fullpage.html?sec=health&res=9D02E7DB1231 F93BA35751C1A9629C8B63. Brand-Miller, J. C., Liu, V., Petocz, P., & Baxter, R. C. (2005). The glycemic index of foods influences postprandial insulin-like growth factor-binding protein responses in lean young subjects. American Journal of Clinical Nutrition, 82, 350–354. Bray, G. A., Nielsen, S. J., & Popkin, B. M. (2004). Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. American Journal of Clinical Nutrition, 79, 537–543. Brown, H. S., Estrada, J. K., Hazarika, G., & Bastida, E. (2005). Diabetes and the labor market: The community-wide economic cost in the Lower Rio Grande Valley. Diabetes Care, 28, 2945–2947. Cannon, M., & Balko, R. (2004). Trimming fat two ways. Washington Times, October 21. Available at: http://www.washtimes.com/commentary/20041020-092925-6680r.htm. Cawley, J. (2004a). An Economic framework for understanding physical activity and eating behaviors. American Journal of Preventive Medicine, 27(3S), 117–125. Cawley, J. (2004b). The impact of obesity on wages. Journal of Human Resources, 39, 451–474. Chou, S., Grossman, M., & Saffer, H. (2004). An economic analysis of adult obesity: Results from the Behavioral Risk Factor Surveillance System. Journal of Health Economics, 23, 565–587. Cohen, M.F. (2004). What’s Worse Than Sugar? AARP Bulletin, April 2004. Available at: http://www.aarp.org/bulletin/yourhealth/Articles/a2004-04-22-sugar.html. Cook, C. D. (2004). Diet for a dead planet. New York: The New Press. Critser, G. (2003). Fat land: How Americans became the fattest people in the world. New York: Hougton Mifflin Company.


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Cutler, D. M., Glaeser, E. L., & Shapiro, J. M. (2003). Why have Americans become more obese? Journal of Economic Perspectives, 17, 93–118. Dewey, K. G., Heinig, M. J., Nommsen, L. A., Peerson, J. M., & Lonnerdal, B. (1993). Breastfed infants are leaner than formula-fed infants at 1 year of age: The DARLING Study. American Journal of Clinical Nutrition, 57, 140–145. Ewing, R., Schmid, T., Killingsworth, R., Zlot, A., & Raudenbush, S. (2003). Relationship between urban sprawl and physical activity, obesity, and morbidity. American Journal of Health Promotion, 18, 47–57. Fields, S. (2004). The fat of the land: Do agricultural subsidies foster poor health? Environmental Health Perspectives, 112, A820–A823. GM Crops ‘Won’t Solve Hunger’ (2003). CNN.com, May 28. Available at: http:// www.cnn.com/2003/TECH/science/05/28/gmcrops/index.html Guiliano, M. (2004). French women don’t get fat: The secret of eating for pleasure. New York: Knopf, Inc. Hamermesh, D. S., & Biddle, J. E. (1994). Beauty and the labor market. American Economic Review, 84, 1174–1194. Koplan, J. P., & Dietz, W. H. (1999). Caloric imbalance and public health policy. JAMA, 282, 1579–1581. Lakdawalla, D., & Philipson, T. (2002). The Growth of Obesity and Technological Change: A Theoretical and Empirical Examination. NBER Working Paper No. 8946. Mokdad, A. H., Ford, E. S., Bowman, B. A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J. S. (2003). Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA, 289, 76–79. Morris, S. (2006). Body mass index and occupational attainment. Journal of Health Economics, 25, 347–364. Must, A., Spadano, J., Coakley, E. H., Field, A. E., Colditz, G., & Dietz, W. H. (1999). The disease burden associated with overweight and obesity. JAMA, 282, 1523–1529. National Institute of Diabetes and Digestive and Kidney Diseases. (1996). Statistics related to overweight and obesity. Washington DC: US Government Printing Office. Neergaard, L. (2005). Most diabetics not controlling their blood sugar. Atlanta Journal-Constitution, May 18. Available at: http://www.ajc.com/news/content/health/0505/19diabetes.html Oddy, W. H., Sherriff, J. L., de Klerk, N. H., Kendall, G. E., Slyjj, P. D., Beilin, L. J., Blake, K. B., Landau, L. I., & Stanley, F. J. (2004). The relation of breastfeeding and body mass index to asthma and atopy in children: A prospective cohort study to age 6 years. American Journal of Public Health, 94, 1531–1537. Olshansky, S. J., Passaro, D. J., Hershow, R. C., Layden, J., Carnes, B. A., Brody, J., Hayflick, L., Butler, R. N., Allison, D. B., & Ludwig, D. S. (2005). A potential decline in life expectancy in the United States in the 21st century. New England Journal of Medicine, 352, 1138–1145. Pendleton, N., Clague, J. E., Cobain, M., Thethi, K., Jones, M., Horan, M. A., McInnes, L., & Rabbitt, P. M. (2005). Relationship between self-reported prevalence of diabetes mellitus using the Cornell Medical Index (CMI) and prevalence determined by glycosylated hemoglobin (HbA(1c)) in an elderly community-dwelling population. Archives of Gerontology and Geriatrics, 41, 289–296. Pollan, M. (2003). You want fries with that? New York Times, January 12. Available at: http:// query.nytimes.com/gst/fullpage.html?res=9E05E1D9103FF931A25752C0A9659C8B63

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Pollan, M. (2004). Our national eating disorder. New York Times, October 17. Rashad, I. (2005). Whose fault is it we’re getting fat? Obesity in the United States. Public Policy Research, 12, 30–36. Rashad, I., Chou, S., & Grossman, M. (2006). The super size of america: An economic estimation of body mass index and obesity in adults. Eastern Economic Journal, 32, 133–148. Rashad, I., & Grossman, M. (2004). The economics of obesity. Public Interest, 156, 104–112. Reeves, M. J., & Rafferty, A. P. (2005). Healthy lifestyle characteristics among adults in the United States, 2000. Archives of Internal Medicine, 165, 854–857. Robbins, J. M., & Webb, D. A. (2006). Diagnosing diabetes and preventing rehospitalizations: The Urban Diabetes Study. Medical Care, 44, 292–296. Saudek, C. D., Kalyani, R. R., & Derr, R. L. (2005). Assessment of glycemia in diabetes mellitus: Hemoglobin A1c. Journal of The Association of Physicians of India, 53, 299–305. Specter, M. (2000). The Pharmageddon Riddle. New Yorker, April 10. 76, 58–71. Squires, G. D. (2002). Urban sprawl and the uneven development of metropolitan America. In: G. D. Squires (Ed.), Urban sprawl: Causes, consequences, and policy responses. Washington, DC: Urban Institute Press. Trout, D. L., Hallfrisch, J., & Behall, K. M. (2004). Atypically high insulin responses to some foods relate to sugars and satiety. International Journal of Food Sciences and Nutrition, 55, 577–588. Tunceli, K., Bradley, C. J., Nerenz, D., Williams, L. K., Pladevall, M., & Lafata, J. E. (2005). The impact of diabetes on employment and work productivity. Diabetes Care, 28, 2662–2667. Variyam, J. N. (2005). The price is right. Amber Waves, 3, 20–27. Vuilleumier, S. (1993). Worldwide production of high-fructose syrup and crystalline fructose. American Journal of Clinical Nutrition, 58(Suppl), 733S–736S. Wild, S., Roglic, G., Green, A., Sicree, R., & King, H. (2004). Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care, 27, 1047–1053. Wolever, T. M. S., Brand-Miller, J. C., Foster-Powell, K., & Colagiuri, S. (1999). The glucose revolution: The authoritative guide to the glycemic index – the groundbreaking medical discovery. New York, NY: Marlowe & Company. Zhang, Q., & Wang, Y. (2004). Trends in the association between obesity and socioeconomic status in U.S. adults: 1971 to 2000. Obesity Research, 12, 1622–1632.

PART II: TREATMENTS FOR OBESITY


ADVANCES IN BARIATRIC SURGERY FOR OBESITY: LAPAROSCOPIC SURGERY$ William E. Encinosa, Didem M. Bernard, and Claudia A. Steiner ABSTRACT Context. The most advanced and fastest growing form of bariatric surgery is laparoscopic gastric bypass. Very little is known about populationbased 180-day laparoscopic bypass costs, complication rates, readmission rates, and post-operative care. Objective. To examine the 6-month costs and outcomes of laparoscopic vs. open bariatric bypass surgery using a national population-based sample. Design. We use the 1998–2003 Nationwide Inpatient Sample to examine national trends in the rate of laparoscopic bypass. To examine postoperative outcomes, we examine insurance claims for 2,384 bariatric bypass surgeries, at 308 hospitals, among a population of 5.6 million nonelderly people covered by large employers across 49 states in 2001 and

$

Funded by the Agency for Healthcare Research and Quality. The views herein do not necessarily reflect the views or policies of AHRQ, nor the U.S. Department of Health and Human Services.


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2002. Multivariate logit regression analysis is performed to risk-adjust outcomes. Main Outcome Measures. 180-day outcomes: 12 complications specific to bariatric surgery and 44 general post-operative conditions, readmission rates, ER rates, and expenditures following bariatric surgery. Results. Between 1998 and 2003, the national percentage of bariatric bypass surgeries that were laparoscopic grew from 1.5 to 17.1%. There was no significant difference in in-hospital mortality between laparoscopy and open surgery. With the 2001–2002 claims data, we find that of the patients having bypass surgery, men had 48% lower odds of having laparoscopy and that high bariatric volume hospitals were close to four times more likely to use laparoscopy. Laparoscopic bypass, compared with open bypass, had 34% lower odds of a complication during the initial surgical stay, 27% lower odds of a 30-day complication, but no statistically significant difference in 180-day complications. Laparoscopy had 49% higher odds of having the general 44 post-operative conditions, with 45% higher odds of a readmission and 54% higher odds of an ER visit. However, overall, laparoscopy resulted in a 23% lower number of hospital days and 9% lower 180-day expenditures. Conclusion. The laparoscopic cost-savings during the less invasive initial surgery stay outweigh the increase in post-discharge utilization. Further cost-savings will only emerge from laparoscopy only if its late post-operative complications are reduced. More cost-savings will also emerge as more physicians switch to the use of laparoscopy for bypass surgery.

1. INTRODUCTION A recent study by Thorpe, Florence, Howard, and Joski (2004) showed that the obesity epidemic is responsible for over a quarter of the spiraling growth in U.S. health care costs over the past 15 years. With this recent epidemic of obesity, bariatric surgery is fast emerging as the leading method of weight loss among the morbidly obese. Weight-loss surgeries have been popularized as well-known media personalities have successfully undergone them (Steinbrook, 2004). In fact, the number of bariatric surgeries grew by 740% between 1998 and 2003. Hospital costs for bariatric surgery grew ninefold and were $1.7 billion in 2003 (Encinosa, Bernard, & Steiner, 2005). An estimated 11.5 million adults were candidates for the surgery in 2002 (Encinosa, Bernard, Steiner, & Chen, 2005).

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Current research shows that there are potential long-term health benefits to bariatric surgery. A recent meta-analysis of the literature found that the percentage of excess weight loss was 47.5% under gastric banding, 68.2% under gastroplasty, 61.6% under gastric bypass, and 70.1% under bilopancreatic diversion or duodenal switch bypass. Moreover, diabetes was completely resolved in 76.8% of the patients. Hyperlipidemia was improved in 70% of the patients, while hypertension was resolved in 61.7% of the patients (Buchwald et al., 2004). A recent study found that gastric bypass patients had an 89% reduced relative risk of death (Christou et al., 2004). Another recent study found that 3% of gastric bypass patients younger than 40 died within 13.6 years, compared with 13.8% of obese patients that did not have bariatric surgery (Flum & Dellinger, 2004). However, despite these benefits, bariatric treatments have come under intense scrutiny by insurers. For example, Blue Cross and Blue Shield of Florida and Nebraska are discontinuing coverage for bariatric surgery due to the high demand and high costs (Stein, 2004). The state of Arkansas, where the rate of morbid obesity is 25% compared with 5% for the country, recently eliminated bariatric coverage from its self-funded employee benefit plan. Similarly, the nation’s largest employer, the Wal-Mart Corporation, has recently excluded coverage for bariatric surgery (Alt, 2003). Overall, only about a half of all large employers cover bariatric surgery, according to a 2003 William Mercer National Survey of Employer-Sponsored Health Plans (Haberkorn, 2004). As a result, bariatric surgery is the single most frequently appealed medical-surgical procedure in the U.S. (Hall, 2003). Insurers have also expressed concerns over the high rate of complications under bariatric surgery. One recent study followed patients for 1 year and demonstrated that the death rate following bariatric surgery among Medicare patients increased from 2.2% after 30 days to 4.6% after 1 year (Flum et al., 2005). Other recent studies have shown high rates of readmission from 7 to 11% and high post-discharge complication rates between 20 and 40% (Encinosa, Bernard, Chen, & Steiner, 2006; Santry, Gilen, & Lauderdale, 2005; Zingmond, McGory, & Ko, 2005). Recent advances in bariatric surgery have attempted to address these two concerns of insurers and patients – high costs and high complication rates. The main advancement has been the introduction of laparoscopy to bariatric bypass surgery in 1994. Laparoscopic gastric bypass is performed through five small abdominal incisions, with the abdomen insufflated with carbon dioxide gas to create a space to work within via a small video camera. In contrast, open gastric bypass is performed through a large

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incision in the abdominal wall. By reducing the size of the surgical incision and the trauma associated with the operative exposure, laparoscopy may reduce the rate of infection and complication and allow the patient to recover quicker, saving costs in the long run. While there have been a few random controlled trials on the outcomes of laparoscopic bypass surgery, there has not yet been a population-based study of national trends in the growth of laparoscopic bariatric surgery, nor any population-based study of the post-discharge outcomes and costs of bariatric laparoscopy (Higa, Ho, & Boone, 2001; Nguyen, Goldman, & Rosenquist, 2001; Podnos, Jimenez, Wilson, Stevens, & Nguyen, 2003). In this paper, we address this information gap. We use insurance claims data from 45 large employers nationwide with over 5.6 million covered lives to examine the 180day complication rates, readmission rates, and post-operative care for laparoscopic bariatric surgeries among the privately insured in 2001 and 2002.

2. DATA AND METHODOLOGY Our first data source is the Nationwide Inpatient Sample (NIS) of the Healthcare Cost and Utilization Project for 1998 and 2003.1 The NIS is a national inpatient care database containing data from about 1,000 hospitals sampled to approximate a 20% stratified sample of U.S. community hospitals. Using the NIS we will estimate trends in the use of laparoscopic bypass surgery and the in-hospital mortality rate for laparoscopy. Our primary source of data is the 2001 and 2002 MarketScan Commercial Claims and Encounter Database. This database was created by the Medstat Group, Inc., and contains claims data for inpatient care, outpatient care, and prescription drugs for about 5.6 million enrollees under the age of 65 in employer-sponsored benefit plans for 45 large employers around the nation in all 50 states. Overall, there were 170.8 million Americans under the age of 65 enrolled in employer-sponsored benefit plans in 2002.2 Thus, the MarketScan data represent 3% of people with employer-sponsored health insurance coverage in the U.S. We identified gastric bypass bariatric surgeries by the following 2001– 2002 Current Procedural Terminology (CPT-4) codes: 43846 (Roux-en-Y gastric bypass) and 43847 (other types of gastric bypass and malabsorptive procedures, such as long limb bypass and bilopancreatic diversion). Bypass surgeries were performed laparoscopically as indicated by CPT-4 codes 43651–43659 (stomach laparoscopy) or 44200–44209 (intestinal laparoscopy). Index bariatric surgeries included in the analysis are (1) bariatric


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surgeries that took place from February 1, 2001 to July 1, 2002 and (2) where the patient did not have a previous bariatric surgery during the 30 days prior to admission. We excluded bariatric surgeries with any primary or secondary diagnoses codes for stomach or intestinal cancer (International Classification of Disease, Clinical Modification, ICD-9-CM): 1500 – 1599 or in situ 2301 –2309 . This resulted in a final sample of 2,384 index bariatric bypass surgeries taking place at 308 hospitals nationwide. We present two categories of safety measures related to bariatric surgeries: (1) complications: 12 post-operative complications specifically related to bariatric surgery (e.g., anastomosis leaks), and (2) post-operative conditions: 44 post-operative conditions and symptoms that can occur more generally following any type of major surgery. The 12 post-operative complications are identified using ICD-9 diagnosis and CPT-4 procedure codes, reported on claims for the index hospital stay or during any 180-day post-operative care including inpatient hospital visits, outpatient hospital visits, and office visits. The 12 complications are listed in Table 3. A list of the complication coding algorithms can be obtained from the authors. These complications were based on a meta-analysis of complications in the bariatrics literature, conducted by the Southern California-RAND Evidenced-Based Practice Center funded by AHRQ (Shekelle et al., 2004; Maggard et al., 2005). A broader set of post-operative conditions and symptoms are also examined to capture better post-operative health care utilization potentially related to bariatric surgery. While many of the 12 complications described above are diagnosed during inpatient stays, a final specific clinical diagnosis may not be diagnosed during outpatient visits. For example, a symptom of ‘‘abdominal pain’’ may be the final diagnosis reached and treated during an ER visit or office visit. The post-operative conditions examined are based on the 2005 AHRQ Clinical Classification Software (CCS) which groups all ICD-9 diagnosis codes into 260 mutually exclusive diagnosis categories. We selected 44 of the 260 CCS groups that are related to post-operative problems.3 These 44 conditions are listed in the appendix. We examine the following utilization measures within 180 days following discharge after the index surgery. Readmissions refer to any overnight stay at an inpatient hospital beginning within 180 days after the index discharge. Outpatient hospital visits include post-operative 1-day (same-day) visit to an inpatient hospital, outpatient hospital, or an ambulatory surgery center. ER visits are emergency room visits. Office visits include office visits or home health care visits. Post-discharge visits with post-operative conditions include all readmissions, outpatient hospital visits, ER visits, and office visits with any of the 44 post-operative conditions diagnosed. Finally, health care

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expenditures include all payments made to physicians and facilities for all services, except outpatient prescriptions, during the 6 months following surgery. Length of stay included all overnight hospital days summed over the index stay and any 180-day readmissions. Using a logit multivariate regression analysis, the complication rates across the settings (inpatient, ER, office, etc.) were risk-adjusted for the patient, surgery, and plan characteristics of Table 1: laparoscopy, hourly wage worker indicator (as proxy for low income), number of comorbidities, sex, age, insurance plan type, region, and year. As a secondary analysis, we also used a Cox proportional hazard regression model with the covariates of Table 1 to estimate the risk-adjusted hazard rate for the onset of the patient’s first complication over the 6 months. In OLS regressions, the log of health care expenditures and the log of length of stay were risk-adjusted for the covariates of Table 1. Robust standard errors are estimated using the Huber/White sandwich estimator. Table 1.

Descriptive Statistics of Index Gastric Bypassesa.

Laparoscopic bypass Hourly wage worker Number of comorbidities Zero comorbidities (other than obesity) One comorbidity Two or more comorbidities Female Male Age 18–39 Age 40–49 Age 50–64 Capitated HMO Point-of-service HMO PPO Fee-for-service County doctors per 100,000, low (o138) County doctors per 100,000, medium (138–266) County doctors per 100,000, high (>266) Northeast West South Central Year 2002

66.2 27.3 6.5 85.3 14.7 37.9 33.4 28.7 26.6 13.6 35.9 23.8 34.0 32.7 33.3 11.4 16.0 29.4 43.2 54.0

N

2,384

a

Data source: 2001 and 2002 MarketScan employer data.

9.8% 29.8


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The 30 comorbidities (not including obesity) considered in the risk adjustment include sleep apnea, congestive heart failure, arrhythmias, valvular disease, pulmonary circulation disease, peripheral vascular disease, hypertension, paralysis, other neurological disorders, chronic pulmonary disease, diabetes, diabetes with chronic complications, hypothyroidism, renal failure, liver disease, peptic ulcer disease with bleeding, lymphoma, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis/collagen, coagulopathy, aids, weight loss, fluid and electrolyte disorders, chronic blood loss anemia, deficiency anemias, alcohol abuse, drug abuse, psychoses, and depression. One limitation is that we do not observe body mass index. However, it is unlike that we have patients with extremely high outlier body mass indices since most extremely obese patients are disabled and covered by Medicare and Medicaid, not by private insurance.

3. RESULTS There are two forms of bariatric surgery. The first type (gastric banding and gastroplasty without bypass) simply reduces the size of the stomach, either through stapling the stomach (gastroplasty) and/or by placing a tight band around the stomach. The second type (gastric bypass) includes a reduction in the size of the stomach and a bypassing of part of the intestines to reduce the absorption of food. Using the NIS, we find that between 1998 and 2003, the national percentage of bariatric surgeries that involved gastric bypass increased from 79.5 to 91.9%. The increase in the use of gastric bypass also involved an increase in the use of laparoscopy. Between 1998 and 2003, the national percentage of gastric bypass surgeries that were laparoscopic increased from 1.5 to 17.1%, from 166 surgeries to 17,788 surgeries. The inhospital death rate under laparoscopy dropped from 3.3 to 0.1%, while the death rate for open surgery dropped from 0.9 to 0.2%. However, the difference in 2003 in-hospital mortality between laparoscopy and open surgery was not statistically significant. Table 1 presents the descriptive statistics for the 2001–2002 MarketScan employer claims data. Laparoscopic bypass accounted for 9.8% of gastric bypass surgeries. From 2001 to 2002, the rate of laparoscopy increased from 7.7 to 13.8%. Before examining outcomes of laparoscopy, there may be concern that the gastric bypass patients selecting laparoscopy may be healthier than the open bypass patients, biasing the estimates on outcomes. In the first column of Table 2, we conducted a Heckman test for any unobserved laparoscopic selection bias on 180-day complications. The

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Table 2.

The Probability of Laparoscopic Surgery Among Bypass Patients.

Variables Medium bariatric hospital volume High bariatric hospital volume Hourly wage worker One comorbidity Two or more comorbidities Male Age 40–49 Age 50–64 Point-of-service HMO PPO Fee-for-service Medium level of doctors per capita High level of doctors per capita Year 2002 Inverse mills ratio N

Heckman Test

Logit Odds Ratio

1.872 (0.739)

3.780 (1.358)

0.208 (0.085) 0.120 (0.082) 0.016 (0.160)

0.665 (0.114) 1.246 (0.196) 1.020 (0.310)

1.702 (0.487) 0.953 (0.289) 1.008 (0.440)

0.315 (0.116) 0.162 (0.087) 0.095 (0.092) 0.054 (0.113)

0.541 (0.127) 0.726 (0.123) 0.824 (0.149) 1.115 (0.244)

0.812 1.171 1.673 1.278

0.066 (0.089) 0.613 (0.127) 0.279 (0.093)

1.175 (0.201) 0.292 (0.081) 1.714 (0.314)

0.518 (0.187) 0.222 (0.127) 1.145 (0.411)

0.218 (0.095)

1.541 (0.291)

1.383 (0.471)

0.240 (0.075) 0.145 (0.257)

1.608 (0.233)

1.772 (0.489)

2,384

593

2,384

Logit Odds Ratio

(0.319) (0.378) (0.548) (0.419)

Note: The Heckman test is for a laparoscopic selection bias in the probability of a 180-day complication. Omitted groups are low volume, no comorbidities, age 18–39, capitated HMO, and low level of doctors per capita. Significantly different from the reference group at the 10% level. Significantly different from the reference group at the 5% level. Significantly different from the reference group at the 1% level.

exclusion restriction in the Heckman test requires having at least one variable in the selection equation that is not included in the outcome equation. For the exclusion restriction, we used the number of physicians per 100,000 county residences, since the chance of having laparoscopy increases with the number of doctors per capita, but the actual laparoscopic outcome is not associated with the county-level supply of doctors. The Heckman selection bias as measured by the inverse Mills ratio is statistically insignificant (p ¼ 0.57). Thus, there does not appear to be a selection bias for the selection of laparoscopy among bariatric patients.


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In fact, the number of comorbidities and a near-elderly age did not have any influence on the selection of laparoscopy in the second column of Table 2, where we present odds ratios for characteristics that might predict the choice of laparoscopy over open bypass. However, men have 46% lower odds of having laparoscopy, and fee-for-service patients have 71% lower odds than capitated HMO patients of having laparoscopy. Both of these results may be due to the fact that laparoscopy is contraindicated for extremely high body mass indices, which may be more common among men and obese patients that select into FFS. Also, note that patients in counties with high levels of doctors per capita have 54% higher odds of having laparoscopy than in counties with low supplies of doctors, and patients in 2002 have 61% greater odds of having laparoscopy than patients in 2001. In the last column of Table 2, only 593 observations in our sample had non-missing hospital bariatric volume. We find that high volume hospitals were 3.8 times more likely than low volume hospitals to conduct laparoscopic surgery (low volume hospitals were defined as having less than 132 bypass cases per year, and high volume hospitals as having more than 279 bypass cases per year). Overall, the 180-day death rate was very low (0.2%), with all five deaths occurring under open bypass. In Table 3, laparoscopic patients had a 38% lower complication rate during the initial surgical stay than did open bypass patients (14.0 vs. 22.7%). However, the complication rate increase by 139% for laparoscopic patients over the 6 months following surgery (from 14 to 33.5%), while the rate increased by 77% for open bypass patients, from 22.7 to 40.2%. These increases are to be expected, since some bariatric complications like strictures and hernias take time to develop. After 180 days, the complication rate for laparoscopic bypass was 16.7% lower than for open bypass. Complications ranged from quite serious conditions, such as respiratory failure, to not life-threatening but clinically important conditions, such as dumping, vomiting, and diarrhea. The five most common complications for open surgery were dumping syndrome (19.6%), complications of the anastomosis (12.4%), abdominal hernias (7.6%), infections (5.8%), and pneumonia (4%). For laparoscopy, the five most common complications were dumping syndrome (19.5%), complications of the anastomosis (11.4%), pneumonia (5%), infections (3.4%), and marginal ulcer (2.5%) tied with deep vein thrombosis/pulmonary embolism (2.5%). There were only three of the 12 complications for which laparoscopy had statistically significant lower rates: abdominal hernias, wound dehiscence, and respiratory failure. Laparoscopy had statistically significant higher rates of marginal ulcers. Table 4 presents complications risk-adjusted for the covariates of Table 1 (excluding physician supply). Laparoscopy had 34.4% lower odds of having

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Table 3. 180-Day Bariatic Gastric Bypass Outcomes. Open Bypass

Laparoscopic Bypass

Complication rate during index surgery 30-day complication rate 180-day complication rate

22.7% 33.3 40.2

14.0% 23.7 33.5

Specific complications Anastomosis complications Marginal ulcer Abdominal hernia Dumping, vomiting, diarrhea, etc. Hemorrhage Wound dehiscence Infection DVT/PE Respiratory failure Pneumonia PO AMI PO stroke

12.4 0.8 7.6 19.6 1.7 1.9 5.8 2.3 3.3 4.0 0.3 0.0

11.4 2.5 2.1 19.5 1.7 0.4 3.4 2.5 0.4 5.1 0.0 0.0

Overall PO condition rate Readmission with PO condition ER visit with PO condition Outpatient hospital visit with PO condition Office visit with PO condition

76.0 10.1 5.2 44.9 56.6

83.5 14.8 9.3 57.6 61.4

180-day inpatient hospital days 180-days payments Post-discharge expenditures

6.0 $33,731 $6,193

4.3 $27,386 $5,055

2,286

236

N

Significantly different from open bypass at the 10% level. Significantly different from open bypass at the 5% level. Significantly different from open bypass at the 1% level.

a complication during the initial surgical stay compared with open bypass. The predicted risk-adjusted complication rates are 22.4% for open bypass and 16.1% for laparoscopy (compared with the unadjusted rates of 22.7 and 14% in Table 3). The odds of a 30-day complication are 27.2% lower under laparoscopy. For 180-day complications, the difference between laparoscopy and open bypass is not statistically significant (the risk-adjusted complication rates are 40.0% for open surgery and 36.8% for laparoscopy). Similarly, not shown, a Cox proportional hazard regression model showed that at the end of 180 days, laparoscopy does not have a different hazard rate than open surgery for the onset of the patient’s first complication.


Table 4. Variables

Laparoscopy Hourly wage worker One comorbidity Two or more comorbidities Male Age 40–49 Age 50–64 Point-of-service PPO Fee-for-service West South Central Year 2002 N

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Risk-Adjusted Odds Ratios of Laparoscopic Bypass Outcomes. Initial Surgery Complication

30-Day Complication

180-Day Complication

180-Day PostOperative Conditions

0.657 (0.132) 1.203 (0.131) 0.880 (0.104) 1.036 (0.215)

0.728 (0.120) 1.224 (0.119) 0.906 (0.093) 1.039 (0.190)

0.870 (0.130) 1.300 (0.122) 0.955 (0.094) 1.300 (0.230)

1.493 (0.289) 0.895 (0.097) 1.057 (0.120) 2.154 (0.537)

1.110 (0.155) 1.284 (0.159) 1.569 (0.201) 1.310 (0.234) 1.432 (0.203) 1.725 (0.272) 1.145 (0.258) 1.389 (0.281) 1.421 (0.282) 1.082 (0.111)

1.127 (0.139) 1.314 (0.142) 1.585 (0.178) 1.268 (0.194) 1.327 (0.159) 1.338 (0.185) 0.915 (0.172) 1.200 (0.200) 1.363 (0.225) 1.098 (0.101)

1.041 (0.124) 1.306 (0.134) 1.421 (0.153) 1.196 (0.172) 1.186 (0.134) 1.245 (0.164) 1.004 (0.175) 1.183 (0.184) 1.386 (0.215) 1.069 (0.093)

0.711 (0.095) 1.250 (0.148) 1.217 (0.151) 1.448 (0.252) 1.209 (0.157) 0.985 (0.151) 0.515 (0.112) 0.604 (0.121) 0.549 (0.110) 1.053 (0.107)

2,384

2,384

2,384

2,384

Note: Complications are the 12 listed in Table 3. Post-operative conditions are the 44 listed in the appendix. Significantly different from the reference group at the 10% level. Significantly different from the reference group at the 5% level. Significantly different from the reference group at the 1% level.

However, in the last column of Table 4, laparoscopy has 49.3% higher odds of having one of the 44 post-operative conditions during a post-operative visit than open surgery. In Table 3, 83.5% of the laparoscopic patients had a postdischarge visit (readmission, ER, outpatient, or office visit) with one of the 44 post-operative conditions, compared with 76% for open surgery patients. Laparoscopy had 44.6% higher odds of a readmission with a post-operative condition (p ¼ 0.08), 54% higher odds of an ER visit (p ¼ 0.03), and 95.8% higher odds (po0.01) of an outpatient hospital visit with a post-operative condition compared with open surgery. The difference in rates of visits to the office with post-operative conditions was not statistically significant. While laparoscopy has a higher number of post-operative visits, the 180day expenditures spent on post-discharge care is less under laparoscopy than under open surgery: $5,055 vs. $6,193 (Table 3). However, this difference is not statistically significant once we risk-adjust it in the last column of

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Table 5. The Estimated Costs and Utilization of Laparoscopic Bypass Surgery. Variables

Laparoscopy Hourly wage worker One comorbidity Two or more comorbidities Male Age 40–49 Age 50–64 Point-of-service PPO Fee-for-service West South Central Year 2002 N

Log (Days)

Log (Total Costs)

Log (Post-Discharge Costs)

0.232 (0.047) 0.056 (0.030) 0.045 (0.031) 0.238 (0.068) 0.044 (0.043) 0.111 (0.032) 0.159 (0.036) 0.046 (0.045) 0.019 (0.039) 0.005 (0.043) 0.079 (0.055) 0.000 (0.053) 0.119 (0.050) 0.057 (0.028)

0.085 (0.046) 0.017 (0.031) 0.025 (0.031) 0.078 (0.055) 0.006 (0.039) 0.049 (0.033) 0.069 (0.036) 0.170 (0.046) 0.128 (0.038) 0.108 (0.043) 0.257 (0.053) 0.241 (0.045) 0.436 (0.045) 0.069 (0.028)

0.056 (0.102) 0.037 (0.064) 0.035 (0.066) 0.158 (0.114) 0.096 (0.085) 0.128 (0.069) 0.245 (0.075) 0.066 (0.097) 0.081 (0.075) 0.096 (0.092) 0.068 (0.107) 0.046 (0.098) 0.180 (0.097) 0.015 (0.059)

2,384

2,104

2,135

Significantly different from the reference group at the 10% level. Significantly different from the reference group at the 5% level. Significantly different from the reference group at the 1% level.

Table 5. The main reason for this is that while open surgery patients have a lower rate of readmission (10.1 vs. 14.8%), they spend more days in the hospital during the readmission than do readmitted laparoscopy patients (13.2 vs. 8.5 days). However, including the initial surgery and all postdischarge care, laparoscopy was 8.5% less expensive than open surgery (column 2 of Table 5). This was primarily due to the overall reduction in hospital days over the whole time period. Laparoscopy required 23.2% fewer days in the hospital than did open surgery (column 1 of Table 5).

4. DISCUSSION AND FINAL REMARKS Recently, the largest growth in the type of bariatric surgery for obesity has been for laparoscopic gastric bypass. This introduction of laparoscopy was intended to make bariatric surgery less invasive and less costly for the 11.5 million Americans clinically eligible for bariatric surgery. In 2002, 83% of bariatric surgeries were covered by private insurance (Encinosa et al., 2005).


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In this paper we provide the first population-based analysis of late surgery outcomes, safety and costs for laparoscopic bypass by examining a 3% sample of the privately insured across 49 states in 2001–2002. We found that laparoscopy was about 9% less expensive than open bypass surgery for all medical care over the 6 months. Most of this cost-reduction occurred during the first surgical stay, since laparoscopy is less invasive and the patient can leave the hospital earlier. However, costs over the 6 months after the surgery were about the same between laparoscopy and open surgery. While the average laparoscopic patient had more readmissions and ER visits with post-operative conditions, the average open bypass patient had a longer readmission stay. A recent meta-analysis of the literature on laparoscopic bypass studies between 1994 and 2002 found that laparoscopy reduced complications such as infection, hernias, and mortality (Podnos et al., 2003). We did find a similar reduction in hernias. However, we found no difference in mortality and infection rates. But, we did find that laparoscopy had a reduced rate of wound dehiscence and respiratory failure. The meta-analysis also found that laparoscopy had higher rates of bowel obstruction and stenosis (our anastomosis complications), and hemorrhage. However, we found no difference in these two complications. We did find a higher rate of marginal ulcers under laparoscopy, a complication not studied by the meta-analysis. There are several potential explanations for these differences in our results and the meta-analysis. First, our study looks at more recent data. Second, our study is a population-based study, whereas almost all of the studies reviewed by the meta-analysis were case studies of a small number of patients from selected surgery centers or selected physicians. Small case studies may be biased as they are typically studies of experienced physicians with low complication rates. Population-based studies include all patients from all doctors in the population. As a result, population-based studies will generally have higher complication rates than case studies. For example, one very recent population-based study of all 4,685 bariatric surgeries in Pennsylvania in 2001 found a respiratory complication rate of 7.7% (Courcoulas, Schuchert, Gatti, & Luketich, 2003). This is similar to our combined 30-day rate for respiratory failure and pneumonia, 6.4% (our rate is lower since our sample does not include elderly patients and Medicaid patients). Another recent population-based study of all bariatric surgeries in California in 2000 found a respiratory complication rate of 5.5% (Liu et al., 2003). In comparison, the EPC reports a respiratory complication rate of only 2.4% among 26 case studies. The average number of patients in these case studies was 205 (Shekelle et al., 2004). Thus, it appears that case studies are not

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representative of the general population undergoing bariatric surgery (Livingston, 2004).4 Another potential explanation for the difference in complication rates that we find is that unlike previous studies, we include not only inpatient complications, but also complications diagnosed at any outpatient site, including doctors’ offices. Complications diagnosed during outpatient hospitals visits alone (not including office visits) account for about 7 percentage points of the 30-day complication rate of 33%. Moreover, most previous studies do not cover complications over a 6-month period. We find that the complication rate increases by 7–10 percentage points between 30 and 180 days. Thus, there are a significant number of late complications following bariatric surgery. As a result, any additional cost-savings from laparoscopy will come only by a reduction in post-operative conditions, readmissions, and ER visits. The three primary post-operative condition diagnoses for the laparoscopic readmissions were ‘‘complications of the surgical procedure’’ (33%), ‘‘intestinal obstructions,’’ and ‘‘nausea and vomiting,’’ accounting for 46% of their readmissions. For open surgery, the top three diagnoses for readmissions were ‘‘complications of the surgical procedure’’ (27%), ‘‘other disorders of the stomach and duodenum,’’ and ‘‘fluid and electrolyte disorders,’’ accounting for 38% of the readmissions. Thus, the nature of the post-operative conditions are slightly different between open bypass and laparoscopy. There are few limitations of our study. First, we could not track death outside of the hospital. Thus, our low death rate of 0.1–0.2% was only for deaths observed in the hospital. A second limitation of our study is that we did not know the patient’s body mass index. Patients with a higher body mass index may be at a greater risk of complications and may be less likely to receive laparoscopic bypass. Third, in our risk-adjusted regressions, we could not control for the surgeon and hospital bariatric volume. Hospitals with more experience may have fewer complications. However, in a subset of 593 surgeries in which we had bariatric volume, we did find that the risk of complication declined with volume after adjusting for age, sex, and number of comorbidities. Another limitation of this paper is that while the MarketScan data is national, the data is not nationally representative since it over-represents the South and under-represents the West. For example, although 36% of the U.S. population resides in the South, 51% of the MarketScan population resides in the South. One other limitation is that we could not examine nutritional complications in detail. We found that the 180-day complication rate for nutritional abnormalities (such as malabsorption, malnutrition, protein and vitamin deficiencies, etc.) was 35%. However, we cannot determine if these were complications or just routine


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diagnoses used as justification to start the patient on multivitamins or to refer the patient to a nutritionist. Future research should examine the nature of these nutritional abnormalities. A final limitation of this study is that in 2001–2002, a separate CPT-4 procedure code for laparoscopic bariatric surgery did not exist. As noted earlier, we have used intestinal and stomach laparoscopy CPT-4 codes to identify laparoscopy, and, thus, we may have undercounted laparoscopic bariatric surgery. However, these preliminary results suggest that future research should examine post-operative care after laparoscopic bariatric surgery in greater detail. As patients, payers, and their physicians increasingly consider gastric bypass surgery for treatment of morbid obesity, this study provides representative information regarding complications and clinical risks following surgery for the privately insured, relatively young population. Clinical studies have demonstrated the long-term risks and complications of obesity, as well as the reduction in chronic conditions and mortality following successful bariatric surgery. Our study demonstrated a low inpatient mortality rate following both laparoscopic and open bariatric bypass surgery, and a 9% reduction in costs under laparoscopy due to a 23% lower number of hospital days. However, the risk of a complication and readmission was significant for both open surgery and laparoscopy, which provides incentive for intervention and improvement.

NOTES 1. Healthcare Cost and Utilization Project (HCUP), HCUP NIS Database Documentation. 2005. Agency for Healthcare Research and Quality, Rockville, MD. See www.hcup-us.ahrq.gov/databases.jsp (24 February 2005). We thank the 37 state organizations that participated in the NIS. 2. Authors’ calculation based on the Household Component of the Medical Expenditure Panel Survey from 2002. 3. The 2005 AHRQ Clinical Classification Software. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed August 26, 2005. 4. A recent population-based study by E. H. Livingston (2004), finds an in-hospital complication rate of 10%. However, Livingston only examines a subset of complications with ICD-9 codes of 996 to 999 .

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Thorpe, K., Florence, C., Howard, D., & Joski, P. (2004). The impact of obesity on rising medical spending. Health Affairs, W4, 480–486. Zingmond, D., McGory, M., & Ko, C. (2005). Hospitalization before and after gastric bypass surgery. JAMA, 294, 1918–1924.

APPENDIX Diagnosis groups used to identify post-operative conditionsa. Septicemia (except in labor) Bacterial infection; unspecified site Nutritional deficiencies Fluid and electrolyte disorders Deficiency and other anemia Acute post-hemorrhagic anemia Non-specific chest pain Cardiac arrest and ventricular fibrillation Acute cerebrovascular disease Transient cerebral ischemia Phlebitis, thrombophlebitis and thromboembolism Varicose veins of lower extremity Pneumonia (except that caused by tuberculosis or sexually transmitted disease) Other upper respiratory infections Aspiration pneumonitis, food/vomitus Pleurisy, pneumothorax, pulmonary collapse Respiratory failure, insufficiency, arrest (adult) Other lower respiratory disease Other upper respiratory disease Intestinal infection Gastroduodenal ulcer (except hemorrhage) Gastritis and duodenitis Other disorders of stomach and duodenum Abdominal hernia Intestinal obstruction without hernia Peritonitis and intestinal abscess Biliary tract disease Pancreatic disorders (not diabetes) Gastrointestinal hemorrhage

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Non-infectious gastroenteritis Other gastrointestinal disorders Acute and unspecified renal failure Urinary tract infections Calculus of urinary tract Complication of device; implant or graft Complications of surgical procedures or medical care Fever of unknown origin Gangrene Shock Nausea and vomiting Abdominal pain Malaise and fatigue E codes: Adverse effects of medical care E codes: Adverse effects of medical drugs a

These groupings of ICD-9 diagnosis codes were constructed with the 2005 U.S. Department of Health and Human Services’. AHRQ Clinical Classification Software. The software and its documentation can be found at http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. These 44 groups are a subset of 260 groups that exists.

ONE PILL MAKES YOU SMALLER: THE DEMAND FOR ANTI-OBESITY DRUGS$ John Cawley and John A. Rizzo ABSTRACT The doubling of obesity in the U.S. over the last 25 years has led policymakers and physicians to encourage weight loss, but few methods of weight loss are effective. One promising avenue is pharmacotherapy. However, little is known about the use of anti-obesity drugs. This paper describes the market for anti-obesity drugs and studies the utilization of anti-obesity drugs using data from the Medical Expenditure Panel Survey for 1996–2002, a period that is interesting because it covers the introduction of three, and the withdrawal of two, anti-obesity drugs from the market. Our results point to wide sociodemographic disparities in anti-obesity drug use. Women are almost 200% more likely than men to use antiobesity drugs. Hispanics and African-Americans are only 39% as likely as Whites to use them. Those with prescription drug coverage are 46% more likely to use anti-obesity drugs.

$

This research was supported with an unrestricted educational grant from the Merck Company Foundation, the philanthropic arm of Merck & Co., Inc.


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JOHN CAWLEY AND JOHN A. RIZZO

We also find that the vast majority of subjects who are approved to take these drugs are not taking them, and a significant number who are not approved to take the drugs are taking them. We find strong evidence that the well-publicized 1997 withdrawal of fenfluramine and dexfenfluramine had a chilling effect on the overall market for anti-obesity drugs. We find little difference in observed characteristics between those who took the withdrawn drugs and those who took the other anti-obesity drugs in the market.

1. INTRODUCTION In the last 25 years in the U.S., the age-adjusted prevalence of obesity has more than doubled, from 15 to 30.4% (Hedley et al., 2004; Flegal, Carroll, Ogden, & Johnson, 2002). The costs of this trend are substantial. By contributing to diabetes, gallbladder disease, cardiovascular disease, cancer, and musculoskeletal disease, obesity was responsible for US$ 92.6 billion (in 2002 dollars), or 9.1% of all U.S. medical expenditures in 1998 (Finkelstein, Fiebelkorn, & Wang, 2003). The increase in health care costs associated with obesity is greater than that associated with smoking, heavy drinking, or aging 20 years (Sturm, 2002). The rise in health care spending on obese Americans accounted for 27% of the growth in overall health care spending between 1987 and 2001 (Thorpe et al., 2004). Obesity also imposes negative externalities on society; half of the medical expenditures on obesity are borne by Medicare and Medicaid (Finkelstein et al., 2003). In response to the rise in obesity, the U.S. government issued a Surgeon General’s report encouraging more active lifestyles and healthier diets (U.S. D.H.H.S., 2001) and setting the goal of cutting the prevalence of obesity in half by 2010 (U.S. Department of Health & Human Services, 2000). However, conventional methods of reducing body weight by exercise, diet, and behavior modification are generally ineffective; weight lost in the short term is usually regained (Arbeeny, 2004; Yanovski & Yanovski, 2002; Manson & Faich, 1996). Bariatric surgery has shown effectiveness for long-term weight loss, but it is typically reserved for the morbidly obese (defined as body mass index or BMI Z 40) who have repeatedly failed at other methods of weight loss; moreover, it carries significant risks to the patient and is costly, making it unattractive or unavailable to many patients (Buchwald et al., 2004). Pharmacologic treatments for obesity offer the hope of greater success than conventional treatments for a wider group of patients than is feasible for bariatric surgery.

One Pill Makes You Smaller: The Demand for Anti-Obesity Drugs

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This paper describes the market for anti-obesity drugs and estimates the correlates of use and expenditures on these drugs. There is currently little evidence on who gets anti-obesity drug therapy and how such access varies by individual characteristics. Knowing the correlates of utilization is important for two reasons. First, to the extent that anti-obesity drugs are efficacious, inequities in their use could exacerbate inequities in health outcomes in the U.S. Second, most anti-obesity drugs involve the potential for serious adverse, even fatal, side effects (Bray, 2002), so it is important to understand which patients are exposed to this risk.

2. THE MARKET FOR ANTI-OBESITY DRUGS Table 1 lists the drugs that have been approved by the FDA to treat obesity. The FDA approved these medications for use in obese patients with a BMI of at least 30 (i.e., the clinically obese) or for patients with a BMI between 27 and 30 if they also have at least one obesity-related comorbidity such as Type II diabetes or hypertension (Expert Panel on the Identification, Evaluation, Table 1. Drug

FDA-Approved Anti-Obesity Drugs.

Year FDA Approved

Current Status

Short-term use Desoxyephedrine

1947

Phenmetrazine

1956

Phentermine Diethylpropion Phendimetrazine Benzphetamine Mazindol Fenfluramine

1959 1959 1959 1960 1973 1973

A methamphetamine (highly addictive) no longer prescribed for weight loss An amphetamine (highly addictive) no longer prescribed for weight loss Still in use Still in use Still in use Still in use Still in use Pulled from market in September 1997 at request of FDA because drug damaged heart valves

Long-term use Dexfenfluramine

1996

Sibutramine Orlistat

1997 1999

Source: U.S. Food and Drug Administration (2004).

Pulled from market in September 1997 at request of FDA because drug damaged heart valves Still in use Still in use

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and Treatment of Overweight and Obesity in Adults, 1998). The drugs approved for short-term use (defined as 12 weeks in one 12-month period) are: phentermine (approved in 1959), diethylpropion (1959), phendimetrazine (1959), benzphetamine (1960), and mazindol (1973). Two drugs have been approved for long-term use: sibutramine (approved in 1997) and orlistat (1999). The brand names under which each of these drugs is sold are listed in the appendix. With the exception of orlistat, all of these drugs suppress appetite or increase satiety by modifying central nervous system neurotransmission (Padwal, Li, & Lau, 2003). Orlistat, in contrast, inhibits the absorption of dietary fat in the intestines (ibid.). The effectiveness of these drugs is typically described as modest (Buchwald et al., 2004; Arbeeny, 2004; Gura, 2003). Randomized clinical trials (RCTs) of a year or more in duration exist only for orlistat and sibutramine. A metaanalysis of these RCTs calculated that average weight loss was 2.7 kg (2.9%) higher among obese patients taking orlistat than among those taking placebo, and 4.3 kg (4.6%) higher among obese patients taking sibutramine than among those taking the placebo (Padwal et al., 2003). While the amount of weight lost under these drugs may be modest, even weight loss of 5–10% among obese patients is associated with improvements in health (Arbeeny, 2004; Fujioka, 2002), so anti-obesity drugs offer the potential for health improvements and perhaps lower health care costs. Because anti-obesity drugs tamper with the biochemistry of metabolism and/or affect neurotransmitters and receptors that control other body processes, the drugs frequently have adverse, even fatal, side effects (Gura, 2003). Fenfluramine and dexfenfluramine offer the most infamous example of adverse side effects; both were pulled from the market in September 1997 because each damaged heart valves when taken in conjunction with phentermine, a combination nicknamed fen-phen (Jick, 2000). Between 1.2 and 4.7 million Americans consumed fenfluramine or dexfenfluramine from 1995 to 1997 (U.S. Department of Health and Human Services, 1997). Even the most recent drugs have adverse side effects. Orlistat prevents the absorption of not only fat but also water, causing cramping and severe diarrhea (Gura, 2003). Sibutramine has been linked to higher blood pressure and was pulled from the Italian market after two patients died (ibid.). Sales of anti-obesity prescription medications totaled US$ 191.6 million in 2003; the best seller was sibutramine (Meridia), which held 39.4% of the market. In second place were various preparations of phentermine, which together represented 26.5% of the market (Mirasol, 2004). With the growing prevalence of obesity, the market for anti-obesity drugs is expected to rise to US$ 1.3 billion by the year 2010 (Farrigan & Pang, 2002). Even after the


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harmful effects of fen-phen were well publicized, an NIH report on obesity stated that ‘‘there is great interest in weight loss drugs among consumers’’ (National Heart, Lung, and Blood Institute, 1998, p. 86). Anti-obesity drugs are seen within the pharmaceutical industry as the ‘‘holy grail’’ because of the large numbers of potential customers and because the drugs must be continued to maintain weight loss (Mirasol, 2004). Breakthroughs in understanding the endocrinology of hunger and satiation have led to promising new drugs (Vastag, 2003) though there is some skepticism about their efficacy (Gura, 2003). A recent article listed 35 anti-obesity drugs that were in preclinical and clinical development as of August 2004 (Arbeeny, 2004). The review of these drugs may be expedited. Recently the FDA, seeking to encourage pharmacologic treatment for obesity, initiated its first review in nearly a decade of how it assesses new obesity medications (Stein, 2004). An FDA expert panel recently recommended approving new antiobesity drugs based on studies with just 1 year of follow-up instead of the 2 years currently required (ibid.).1 Given the history of adverse side effects in this therapeutic class, there is a tradeoff between expediting efficacious drugs to market and testing more thoroughly for side effects.

3. DEMAND FOR ANTI-OBESITY DRUGS: CONCEPTUAL ISSUES We seek to estimate consumer demand for anti-obesity drugs. As Farley (1986) has noted, the demand for medical care is a two-part process. In the context of anti-obesity drugs, the consumer initiates the decision to obtain this treatment. In part two, the physician and patient jointly determine how much treatment to provide. The first decision is a decision to seek care, while the second concerns the nature and extent of such care. The impacts of the individual determinants of demand may differ between these two decisions in magnitude and even in direction. Unfortunately, our data do not include any information about the prescribing physician, or interactions between physician and patient in the decision-making process. Thus, we set aside explicit consideration of the agency relationship between consumer and physician and study consumer use of drugs as an outcome; thus, our results reflect consumer behavior under the average agency relationship. Nevertheless, to help capture this sequential decision-making process as best as we can given the data, we estimate a two-part model. We select this approach rather than a Tobit model because we are interested in observing

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how factors such as gender and insurance coverage may affect the probability of any use and, separately, how the same factors affect conditional use. While there is debate about the relative merits of two-part models and Tobit models, both remain accepted methods in health econometrics (Jones, 2000). In part one, we estimate the probability that a consumer obtains antiobesity treatment: ProbðGETRXÞ ¼ a þ bX þ

(1)

where GETRX ¼ 1 when subject gets treatment with an anti-obesity drug and 0 otherwise; X is a vector of demand determinants; a and b are the coefficients to be estimated; and e is an error term. In the second stage, we estimate the determinants of demand conditional on use: ln RxSCRIPS ¼ a0 þ b0 X þ 0

(2)

where ln RxSCRIPS is the natural logarithm of number of anti-obesity scrips filled; a0 and b0 are the coefficients to be estimated; X is a vector of demand determinants; and e0 is an error term. We also estimate models similar to (2), but with expenditures on antiobesity drugs replacing numbers of scrips as the dependent variable. There is little research on the correlates of anti-obesity drug use. Blanck, Khan, and Serdula (2004) studied 1998 data from the Behavioral Risk Factor Surveillance System (BRFSS) and found that women were four times more likely than men to use anti-obesity drugs. They also found that use was significantly more likely among White than that among African-American females, and that those with household incomes over US$ 75,000 were roughly twice as likely to use the drugs as those with lower incomes. A limitation of their data is that the BRFSS inquired about use of anti-obesity drugs in the past 2 years and the survey was conducted in the immediate aftermath of the 1997 withdrawal from the market of Pondimin and Redux. As a result, the reported 2-year utilization of anti-obesity drugs reflects both years of peak utilization prior to the withdrawal of the drugs, and years of low utilization after the withdrawals (Cawley & Rizzo, 2005). Stafford and Radley (2003) report national trends in the use of anti-obesity drugs using IMS Health data for 1991–2002 but do not report the characteristics of those who took the drugs. They document a dramatic rise in use between 1994 and 1997, driven by large increases in prescriptions of fenfluramine and dexfenfluramine. After those two drugs were pulled from the market in


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September 1997, overall anti-obesity drug use fell dramatically but began to rebound with the introduction of orlistat and sibutramine. They estimate that 2.8 million obese Americans used anti-obesity prescription drugs in 2002.2 This paper estimates models of the use of, and expenditures on, antiobesity drugs using longitudinal data from the Medical Expenditure Panel Survey (MEPS). We investigate whether race, ethnicity, gender, age, insurance status, education, and income are related to the use of anti-obesity drugs. Relative to Blanck et al. (2004), we study drug utilization during a specific calendar year (they study use at any point in the past 2 years) and we study 7 years of data spanning 1996–2002 rather than the 2-year period 1996–1998. The later years are particularly interesting as they include the introductions of two anti-obesity drugs to the market, and witnessed continued increases in the prevalence of obesity. We also investigate two additional outcomes (the number of scrips filled and the expenditures on anti-obesity drugs) and control for a richer set of covariates, including whether the respondent satisfied the FDA clinical guidelines for using antiobesity drugs and whether they had health insurance in general and prescription drug coverage in particular. There exist two pharmacologic substitutes for FDA-approved drugs to treat obesity: physicians may prescribe on an ‘‘off-label’’ basis drugs approved by the FDA for conditions other than obesity, or consumers may use over-the-counter (OTC) weight-loss medications (which are not regulated by the FDA). Two drugs that are frequently prescribed off-label for obesity are bupropion (sold under the brand name Wellbutrin), which was approved for depression and smoking cessation, and topiramate (Topamax), which was approved for treatment of epilepsy; however, their effectiveness for weight loss has not been established by long-term studies (Fujioka, 2002). OTC weight-loss products most frequently include ephedrine, which stimulates thermogenesis or higher energy expenditure (ibid.). Prior to 2000, another popular ingredient in OTC weight-loss products was phenylpropanolamine, but the FDA has requested that manufacturers voluntarily remove those products because it is linked to strokes in women (U.S. Food and Drug Administration, 2000). It is estimated that 7.0% of the U.S. population (10.9% of women and 2.9% of men) used non-prescription weight-loss products between 1996 and 1998 (Blanck, Khan, & Serdula, 2001). A limitation of our data is that we cannot determine whether respondents used a prescription medication to treat obesity in an off-label use or whether the respondent was taking OTC anti-obesity products; we limit our attention to the demand for prescription drugs approved by the FDA to treat obesity.

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4. HYPOTHESES REGARDING USE OF ANTIOBESITY DRUGS In this paper, we estimate logit models for use of an anti-obesity drug, and log-transformed OLS models of the number of scrips of anti-obesity drugs that a person receives in a year. Our hypotheses are listed below. As we will describe, there are sometimes opposing factors, forcing us to hypothesize which of the conflicting forces is stronger. H1. Use of anti-obesity drugs is lower among African-Americans and Hispanics. There are conflicting reasons to believe that use should be higher or lower among these groups. On the one hand, one might think that use would be greater because obesity tends to be a particularly significant problem among female members of disadvantaged minority groups (Fontaine & Bartlett, 2000; Sobal & Stunkard, 1989). In 1999–2002, 49.0% of African-American women and 38.4% of Mexican-American women were obese compared with 30.1% of non-Hispanic White American women (Hedley et al., 2004). To the extent that BMI does not fully capture differences in fatness, or to the extent that our corrections for reporting error in weight are imperfect, minority status may be correlated with unobserved obesity and therefore demand for anti-obesity drugs. On the other hand, all else equal, minorities may have a lower demand for anti-obesity drugs; African-American and Hispanic women report larger ideal body sizes than Caucasian women, and African-American men are more likely than Caucasian males to report a willingness to date heavy women (Williamson & O’Neil, 1998; Powell & Kahn, 1995), suggesting that heavy women in these groups face a lower social cost to being overweight and obese. In addition, the wage penalty for obesity is lower for AfricanAmericans and Hispanics than for White females (Cawley, 2004; Averett & Korenman, 1999). Moreover, disadvantaged minorities may have less information about the availability of anti-obesity drugs and less access to providers who could furnish such information. On net, we hypothesize that use of anti-obesity drugs is less among African-Americans and Hispanics than among Whites. H2. Use of anti-obesity drugs is greater among women. Research in sociology and psychology has consistently found that obesity is associated with lower self-esteem in women than men (Williamson &


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O’Neil, 1998) and with greater social stigma for women than men (Sobal, 2004). Research in economics has documented that the wage penalty for obesity is greater for women than that for men (Cawley, 2004; Averett & Korenman, 1996). Moreover, heavier women tend to marry men with lower incomes (Conley & Glauber, 2005). As a result, the cost of obesity is higher for women than men and therefore we predict that women are more likely to use anti-obesity drugs. H3. Use of anti-obesity drugs decreases at advanced ages. We hypothesize that a major reason for using anti-obesity drugs is to be attractive to members of the opposite sex. As people age out of traditional childbearing years, the social cost of being unattractive may fall. While the health consequences of obesity worsen with age, we suspect that this will lead people to begin to consume drugs that treat the comorbidity (e.g., Type II diabetes and high cholesterol) rather than obesity itself. H4. Use of anti-obesity drugs is greater among those with health insurance. Health insurance coverage for physician office visits lowers the cost of visiting a doctor to get a scrip, and coverage of anti-obesity drugs lowers the cost of filling the scrip. Therefore, we expect the average out-of-pocket cost associated with purchasing anti-obesity drugs to be lower, and therefore consumption to be higher, among those who are insured and those who enjoy prescription drug coverage. While the economic costs of obesity are substantial, many health insurers do not cover anti-obesity drugs because the ability of these drugs to reduce health care costs elsewhere in the system is uncertain. Managed care organizations may refuse to cover anti-obesity drugs because they suspect that, with high turnover of enrollees, they will pay the costs but that the benefits will be enjoyed by another managed care organization. The less-than-universal coverage of anti-obesity drugs by health insurers (even those with a prescription drug benefit) works against us finding statistically significant and positive coefficients on insurance status and prescription drug coverage. H5. Use of anti-obesity drugs rises with education. Education is strongly correlated with good health, as it leads people to choose a better mix of health inputs and to use those inputs more effectively in producing their health (Grossman & Kaestner, 1997). While better-educated people may be more likely to avoid obesity, we hypothesize that educated people are more likely to be aware of anti-obesity drugs as an option and therefore that overall use will rise with education.

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H6. Use of anti-obesity drugs rises with income. We hypothesize that anti-obesity drugs are a normal good; all else equal, as income rises, people will tend to buy more of them.

5. DATA: MEDICAL EXPENDITURE PANEL SURVEY This paper uses 1996–2002 data from the MEPS, which is collected by the Agency for Healthcare Research and Quality (AHRQ). The MEPS database is drawn from the National Health Interview Survey (NHIS) sample, and each year of the MEPS data may be linked to information from the previous year’s NHIS survey. The MEPS has an overlapping panel design in which two calendar years of information are collected from each household through six interviews over 2.5 years. We pool both calendar-year observations on each adult (age 18 and older) and pool years 1996–2002. Our final sample is 102,077. The MEPS database consists of a number of files. We link the Full-Year Consolidated File to the Prescribed Medicines File for each year. The Full-Year Consolidated File is at the person-year level and includes information on health care utilization and expenditures, demographic and socioeconomic characteristics, and health insurance status. The Prescribed Medicines File is an event-level file that includes information on specific drug use, the amounts paid for those drugs by patient and insurers, and the length of time that the drug was taken. We convert this event-level data into person-year data and link it to the consolidated MEPS files, which include patient-year level information on the other variables included in this analysis. We use the Multum Lexicon File to identify anti-obesity drugs. Specifically, we classify as anti-obesity drugs: (1) any member of the anorexiant (appetite suppressant) therapeutic class and (2) orlistat, which is not an anorexiant but inhibits the absorption of fat in the intestines. We study consumer utilization of drugs by calendar year; for example, our dependent variable for utilization equals 1 if the MEPS respondent reported using an anti-obesity drug at any time during the calendar year in question. We study adults aged 18 and over because no anti-obesity drug was approved for use by adolescents during 1996–2002. The numbers of people in the MEPS database who had a scrip for at least one anti-obesity drug by year are listed in Table 2. The percentage of MEPS adults using anti-obesity drugs rose from 0.83 in 1996 to 0.96 in 1997 but fell thereafter in the wake of

Use of Anti-Obesity Drugs in MEPS Sample, 1996–2002 (Number of Adults and Percentage of Adults).

Anti-Obesity Drug [Generic Name (Brand Name)]

Adults with Scrips for Anti-Obesity Drug, by Year 1996

Sibutramine (Meridia) Orlistat (Xenical) Fenfluramine (Pondimin) or dexfenfluramine (Redux) All others (various) Any anti-obesity drug

93 (0.59) 98 (0.62) 132 (0.83)

1997

122 (0.52) 170 (0.72) 227 (0.96)

1998

1999

2000

2001

2002

15 (0.09)

15 (0.09) 17 (0.10)

14 (0.08) 22 (0.13)

24 (0.10) 23 (0.10)

16 (0.06) 25 (0.09)

64 (0.39) 73 (0.45)

48 (0.28) 75 (0.44)

37 (0.21) 68 (0.39)

56 (0.24) 98 (0.42)

51 (0.19) 87 (0.32)

Note: (1) Each cell of the table contains the number of adults with a scrip for that drug in that year, and below it in parentheses the percent of adults with a scrip for that drug in that year. (2) Dexfenfluramine (Redux) was introduced in 1996. (3) Sibutramine (Meridia) was introduced in 1998. (4) Orlistat (Xenical) was introduced in 1999. (5) Fenfluramine (Pondimin) and Dexfenfluramine (Redux) were pulled from the market in September 1997. (6) All others include phentermine, diethylpropion, phendimetrazine, benzphetamine, and mazindol.


Table 2.

159

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the drug withdrawals in September 1997 such that in no year during 1998– 2002 is the percentage higher than 0.45, less than half its level in 1997. We study the following outcomes: (1) an indicator that equals 1 if the respondent in that year had a scrip for an anti-obesity drug; (2) number of scrips filled for anti-obesity drugs conditional on use;3 and (3) spending on anti-obesity drugs in that year (both total and out-of-pocket) conditional on use. We control for the following variables in each regression: indicator variables for gender, African-American, Hispanic, other race/ethnicity, married, whether the respondent has health insurance, whether the respondent’s health insurance includes prescription drug coverage, age categories, urban residence, year, Census Region categories, income categories, and education categories. There exist several measures of, or proxies for, the out-of-pocket price of anti-obesity drugs, each with its advantages and drawbacks. MEPS respondents list the amount they paid out of pocket for each drug, but the prices faced by those who did not buy drugs are not observed. We have purchased from Medi-Span the prices of anti-obesity drugs during the period covered by MEPS, but these are national average wholesale prices and they are collinear with the year fixed effects so their inclusion would prevent us from examining the time effects associated with drug introductions and withdrawals. Fig. 1 depicts the nominal average wholesale prices of antiobesity drugs during the period covered by our data. There is little movement in prices; for the most part, price hikes seem to occur sporadically to adjust for inflation. With so little variation in prices, we are unable to estimate the impact of price changes on the use of these drugs and we exclude prices from the set of regressors. To control for out-of-pocket price while avoiding problems of multicollinearity, we use two proxies. The first is an indicator variable for whether the respondent lacked health insurance; uninsurance raises the cost of a physician visit to get a scrip. The second price proxy is an indicator for whether the respondent’s health insurance includes prescription drug coverage, which would lower the cost of filling a prescription. These indicators for health insurance coverage are also, strictly speaking, endogenous; one might worry that those who sought to consume large quantities of anti-obesity drugs would most aggressively seek out health insurance and prescription drug coverage. However, this seems unlikely to be an important factor in the decision to seek insurance coverage. Generic anti-obesity drugs are available at prices that are about equal to typical copayments for branded drugs in this class. In some of our models, we also control for whether the respondent meets the clinical guidelines for using anti-obesity drugs. The FDA approved antiobesity drugs for use in patients with a BMI of at least 30 (i.e., the clinically


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Fig. 1. Average Wholesale Prices of Selected Anti-Obesity Drugs, 1995–2003. Note: (1) Nominal prices are shown (i.e., prices are not corrected for inflation) in order to better illustrate the timing of nominal price hikes. (2) Data: Medi-Span. (3) The generic names of the listed drugs are shown in parentheses: Adipex (Phentermine), Bontril (Phendimetrazine), Didrex (Benzphetamine), Meridia (Sibutramine), Pondimin (Fenfluramine), Redux (Dexfenfluramine), Tenuate (Diethylpropion), and Xenical (Orlistat). (4) Pondimin (Fenfluramine) and Redux (Dexfenfluramine) were withdrawn from the market in September 1997. (5) The following drugs were introduced to the market during this time: Redux (Dexfenfluramine) in 1996, Meridia (Sibutramine) in 1997, and Xenical (Orlistat) in 1999. (6) Prices may not be available in all years because the specific combination of medicinal strength and number of pills to which the prices correspond was not always available.

obese) or for patients with a BMI between 27 and 30 if they also have at least one obesity-related comorbidity (Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults, 1998).4 The source of BMI data in MEPS varies by year; for 2002 and 2001, the MEPS contains BMI based on self-reports of weight and height (but not the self-reports of weight and height themselves). In 2000, the MEPS contains

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self-reported weight and height. For 1996–1999, the MEPS itself does not contain either BMI or weight and height, so we link the MEPS to the NHIS and use self-reports of weight and height from the NHIS. Previous research has documented error in self-reports of weight which can cause substantial misclassification of individuals by clinical weight category such as obesity (Nieto-Garcia, Bush, & Keyl, 1990). We correct for this reporting error, which has the potential to bias regression coefficients, using the method of Lee and Sepanski (1995) and Bound, Brown, and Mathiowetz (2002).5 For those with a reporting-error-corrected BMI of less than 30, we reclassify them as obese if they have ICD9 code 278 (obesity). We drop women who are pregnant because the standard BMI cutoffs for overweight and obese are inapplicable for that group. The indicator for meeting the clinical guidelines for using anti-obesity drugs is set equal to 1 if the respondent has a reporting-error-corrected BMI of 30 or higher, an ICD9 code for obesity, or a corrected BMI of 27 or higher and also has at least one obesity-related comorbidity; in particular, we use ICD9 codes in the MEPS to determine whether respondents have at least one of the following conditions: hypertension, cardiovascular disease, hyperlipidemia, or diabetes.6 There are valid reasons to both include and exclude from the set of regressors the measure of whether the respondent met FDA criteria for using anti-obesity drugs. On the one hand, it is desirable to control for whether respondents met the medical criteria for using anti-obesity drugs because the prevalence of obesity was rising during the period covered by our data and we do not want that trend to cause omitted variables bias in the coefficients on year indicator variables. On the other hand, obesity status is partly determined by the use of obesity drugs, although the effectiveness of these drugs is typically described as modest and insufficient to reverse obesity.7 One potential solution is to find valid instruments for meeting the medical criteria for anti-obesity drug use and estimate a model of instrumental variables, but we have not yet found such instruments. As an alternative, we estimate models both with and without an indicator for whether the respondent meets the medical criteria for the use of anti-obesity drugs. Since using these drugs may reduce obesity, the effect of endogeneity in this context would be to decrease the estimated impact of satisfying the medical criteria on use of the drugs, resulting in conservative estimates of the effect of this factor on the use of anti-obesity drugs. The greatest number of observations is lost due to missing values in the variables that measure prescription drug coverage and whether the respondent meets the medical criteria for using anti-obesity drugs. The


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prescription drug coverage variable is missing for 12,880 (9.2%) of all observations, and the medical criteria variable is missing for 26,517 (18.9%) of all observations due to missing data on BMI. Overall, 26% of MEPS observations are dropped because one or both of these variables are missing. We assume that these data are missing at random. Table 3 lists summary statistics for our final sample of 102,077.

6. RESULTS Empirical results for utilization are presented in Table 4. Columns 1 and 2 present odds ratios and z-statistics from logit regressions in which the dependent variable is an indicator for whether the person had a scrip for any anti-obesity drug in that year. Columns 3 and 4 present coefficients and t-statistics from OLS regressions in which the dependent variable is the natural log of the number of scrips for anti-obesity drugs filled in that year, conditional on filling any. Empirical results for expenditures are presented in Table 5. Columns 1 and 2 present coefficients and t-statistics from OLS regressions in which the dependent variable is the natural logarithm of the total expenditures on anti-obesity drugs, while columns 3 and 4 present results for OLS regressions in which the dependent variable is the natural logarithm of out-ofpocket expenditures on anti-obesity drugs. For each dependent variable, we estimate two versions of the same basic model: the first excludes, and the second includes, an indicator for whether the respondent meets the clinical standards for being prescribed an antiobesity drug. To account for the complex survey design of the MEPS, the STATA svylogit command is used to estimate the probability of utilization. The regressions conditional on use (number of scrips filled and expenditures) involve too few observations to use STATA svy commands and so the data are unweighted in those regressions, although standard errors are cluster-corrected to account for correlation in the error term among observations for the same person over time. We next present and discuss the evidence relating to each of our hypotheses. H1. Use of anti-obesity drugs is lower among African-Americans and Hispanics. Without controlling for whether respondents meet the medical criteria, African-Americans are only 51% as likely as Whites to use anti-obesity drugs (column 1 of Table 4). Obesity is particularly common among

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Table 3.

Summary Statistics, MEPS Sample 1996–2002 (N ¼ 102,077). Mean

S.D.

Minimum

Maximum

Currently using an anti-obesity drug Number of scrips filled for anti-obesity drugs (N ¼ 589) Natural log of total expenditures on anti-obesity drugs (N ¼ 589) Natural log of out-of-pocket expenditures on anti-obesity drugs (N ¼ 576) Female Hispanic African-American Other race Married Age 30–49 Age 50–64 Age >65 Main respondent Urban Midwest South

0.0058 3.86 4.41 3.97 0.52 0.18 0.13 0.17 0.64 0.43 0.21 0.16 0.64 0.78 0.22 0.37

0.08 4.03 1.22 1.33 0.50 0.39 0.34 0.38 0.48 0.50 0.41 0.36 0.48 0.41 0.41 0.48

0 1 0.96 0.01 0 0 0 0 0 0 0 0 0 0 0 0

1 40 7.5 7.02 1 1 1 1 1 1 1 1 1 1 1 1


Variable

0.24 0.34 0.22 0.13 0.09 0.24 0.22 0.28 0.13 0.66 0.13 0.10 0.07 0.16 0.21 0.24 0.31 0.26

0.43 0.47 0.41 0.34 0.28 0.43 0.42 0.45 0.34 0.47 0.34 0.30 0.25 0.36 0.41 0.43 0.46 0.44

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1


West High school graduate Some college College graduate Graduate school Family income 25–45k Family income 45–70k Family income >70k Uninsured Has prescription drug coverage Year: 1997 Year: 1998 Year: 1999 Year: 2000 Year: 2001 Year: 2002 Meets medical criteria for use of anti-obesity drugs Obese

165

166


Table 4. Utilization of Anti-Obesity Drugs (Columns 1 and 2: Logit Odds Ratios and z-Statistics; Columns 3 and 4: OLS Coefficients and t-Statistics). Variable

Female Hispanic African-American Other race Married Age 30–49 Age 50–64 Age >65 Respondent is the primary interviewee Urban Midwest South West High school graduate Some college College graduate Graduate school Family income 25–45k Family income 45–70k Family income o70k

Any Use

ln (No. of Scrips)

(1)

(2)

(3)

(4)

2.919 (8.84) 0.794 (1.26) 0.510 (3.35) 0.684 (1.92) 0.922 (0.70) 1.216 (1.14) 0.902 (0.54) 0.468 (2.42) 1.582 (3.53) 0.845 (1.03) 0.999 (0.01) 1.863 (3.62) 1.233 (1.12) 1.768 (3.14) 1.797 (2.97) 1.654 (1.97) 1.880 (2.47) 1.041 (0.24) 1.111 (0.60) 1.081 (0.42)

3.027 (8.93) 0.686 (2.09) 0.388 (4.69) 0.744 (1.37) 0.925 (0.68) 0.919 (0.49) 0.579 (2.75) 0.321 (3.64) 1.457 (2.82) 0.925 (0.49) 0.919 (0.43) 1.765 (3.32) 1.268 (1.30) 1.837 (3.31) 1.972 (3.45) 2.044 (2.78) 2.409 (3.47) 1.071 (0.42) 1.167 (0.88) 1.215 (1.05)

0.121 (1.23) 0.147 (1.14) 0.248 (1.91) 0.038 (0.18) 0.110 (1.29) 0.022 (0.21) 0.051 (0.40) 0.251 (1.41) 0.135 (1.31) 0.062 (0.70) 0.238 (1.84) 0.263 (2.29) 0.063 (0.49) 0.099 (0.76) 0.055 (0.40) 0.092 (0.59) 0.332 (1.91) 0.084 (0.71) 0.090 (0.76) 0.111 (0.90)

0.119 (1.21) 0.156 (1.20) 0.256 (1.96) 0.045 (0.21) 0.106 (1.24) 0.024 (0.23) 0.057 (0.45) 0.254 (1.42) 0.131 (1.26) 0.055 (0.61) 0.237 (1.84) 0.253 (2.20) 0.055 (0.43) 0.095 (0.73) 0.060 (0.44) 0.095 (0.61) 0.333 (1.91) 0.085 (0.72) 0.090 (0.76) 0.112 (0.91)


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Table 4. (Continued ) Variable

Uninsured Has prescription drug coverage Year: 1997 Year: 1998 Year: 1999 Year: 2000 Year: 2001 Year: 2002

Any Use (1)

(2)

(3)

0.700 (1.23) 1.490 (2.27) 1.185 (1.34) 0.527 (3.43) 0.509 (2.87) 0.407 (4.95) 0.468 (4.90) 0.412 (5.01)

0.718 (1.13) 1.462 (2.16) 1.168 (1.22) 0.495 (3.74) 0.464 (3.22) 0.342 (5.73) 0.387 (6.06) 0.317 (6.02) 6.047 (13.03)

0.044 (0.23) 0.041 (0.37) 0.256 (2.44) 0.380 (2.54) 0.337 (1.88) 0.424 (3.05) 0.343 (2.77) 0.328 (1.53)

102,077

102,077

589

Meets medical criteria for using anti-obesity drugs Constant Observations

ln (No. of Scrips) (4)

0.052 (0.28) 0.050 (0.44) 0.253 (2.41) 0.383 (2.56) 0.337 (1.88) 0.430 (3.09) 0.346 (2.80) 0.333 (1.55) 0.074 (0.94) 1.521 1.467 (6.38) (5.99) 589

Notes: Absolute value of t-statistics in parentheses. The following symbols indicate statistical significance. Significant at 10%. Significant at 5%. Significant at 1%.

African-American women (Flegal et al., 2002), so when we control for whether the respondent meets the medical criteria for using anti-obesity drugs, African-Americans are only 38.8% as likely as Whites to use them (column 2 of Table 4). We find a greater disparity between African-Americans and Whites than do Blanck et al. (2004); they find that African-Americans are roughly two-thirds as likely to use anti-obesity drugs as White females. We also find that, conditional on using anti-obesity drugs, AfricanAmericans tend to fill fewer scrips than Whites. Specifically, among those who use anti-obesity drugs, African-Americans fill roughly one-quarter fewer scrips than Whites (columns 3 and 4 of Table 4). Overall, the results strongly support the hypothesis that use of anti-obesity drugs is lower

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Table 5. Expenditures on Anti-Obesity Drugs (Columns 1 and 2: Percent Change in Total Expenditures and t-Statistics; Columns 3 and 4: Percent Change in Out-of-Pocket Expenditures and t-Statistics). Variable

Female Hispanic African-American Other race

Age 30–49 Age 50–64 Age >65 Respondent is the primary interviewee Urban Midwest South

(1)

(2)

0.054 (0.40) 0.285 (1.58) 0.235 (1.31) 0.296 (1.01) 0.081 (0.68) 0.076 (0.52) 0.116 (0.67) 0.103 (0.41) 0.287 (2.00) 0.119 (0.96) 0.146 (0.81) 0.470 (2.95)

0.050 (0.37) 0.308 (1.71) 0.254 (1.41) 0.278 (0.95) 0.072 (0.61) 0.072 (0.49) 0.101 (0.58) 0.110 (0.44) 0.278 (1.94) 0.098 (0.80) 0.146 (0.82) 0.446 (2.80)

OLS, ln(Out-of-Pocket Expenditures) (3) 0.297 (1.84) 0.308 (1.45) 0.311 (1.46) 0.284 (0.83) 0.213 (1.51) 0.042 (0.24) 0.217 (1.05) 0.304 (1.03) 0.185 (1.09) 0.194 (1.33) 0.275 (1.30) 0.184 (0.98)

(4) 0.290 (1.81) 0.344 (1.62) 0.342 (1.61) 0.256 (0.75) 0.198 (1.42) 0.035 (0.20) 0.192 (0.93) 0.315 (1.07) 0.170 (1.00) 0.162 (1.11) 0.276 (1.31) 0.148 (0.78)


Married

OLS, ln(Total Expenditures)

High school graduate Some college College graduate Graduate school Family income 25–45k Family income 45–70k Family income o70k Uninsured Has prescription drug coverage Year: 1997 Year: 1998 Year: 1999 Year: 2000 Year: 2001 Year: 2002

0.310 (1.74) 0.059 (0.33) 0.293 (1.54) 0.284 (1.32) 0.623 (2.59) 0.032 (0.20) 0.016 (0.10) 0.008 (0.04) 0.122 (0.47) 0.214 (1.39) 0.190 (1.38) 0.196 (0.97) 0.227 (0.94) 0.273 (1.45) 0.379 (2.26) 0.776 (2.65)

0.122 (0.58) 0.135 (0.63) 0.319 (1.42) 0.284 (1.11) 0.775 (2.72) 0.051 (0.27) 0.059 (0.31) 0.139 (0.70) 0.252 (0.83) 0.449 (2.48) 0.568 (3.53) 0.595 (2.51) 0.096 (0.34) 0.031 (0.14) 0.282 (1.43) 0.435 (1.26)

0.093 (0.44) 0.147 (0.69) 0.334 (1.49) 0.293 (1.15) 0.778 (2.74) 0.041 (0.21) 0.061 (0.32) 0.133 (0.67) 0.278 (0.92) 0.481 (2.66) 0.553 (3.45) 0.602 (2.55) 0.091 (0.32) 0.059 (0.27) 0.269 (1.37) 0.411 (1.20)

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0.329 (1.85) 0.050 (0.28) 0.282 (1.48) 0.278 (1.29) 0.621 (2.58) 0.038 (0.23) 0.014 (0.09) 0.011 (0.06) 0.104 (0.41) 0.193 (1.26) 0.200 (1.45) 0.192 (0.95) 0.229 (0.94) 0.290 (1.54) 0.387 (2.30) 0.790 (2.69)


West

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Table 5. (Continued ) Variable

OLS, ln(Total Expenditures) (1)

(2)

4.747 (14.43) 589

0.190 (1.73) 4.614 (13.68) 589

Meets medical criteria for using anti-obesity drugs Constant Observations

(3)

(4)

4.147 (10.67) 589

0.298 (2.30) 3.942 (9.92) 589


Note: Absolute value of t-statistics in parentheses. The following symbols indicate statistical significance. Significant at 10%. Significant at 5%. Significant at 1%.

OLS, ln(Out-of-Pocket Expenditures)


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among African-Americans. Lower utilization could be due to the cultural preferences for higher body weight documented in the sociological studies cited earlier, but given the health impact of obesity, this gap merits further attention. Hispanics are also less likely than Whites to use anti-obesity drugs. When the indicator variable for meeting the medical criteria for use is included in the regressors (column 2 of Table 4), we find that Hispanics are 68.6% as likely as Whites to use anti-obesity drugs. This is inconsistent with the finding of Blanck et al. (2004), who report that use was 62% more likely among Hispanic than that among non-Hispanic White men.8 Conditional on using anti-obesity drugs, the difference in the number of scrips for antiobesity drugs that are filled by Hispanics relative to Whites is not statistically significant (columns 3 and 4 of Table 4). There are few significant differences across race and ethnicity in spending on anti-obesity drugs, conditional on using them. The only significant difference is that Hispanics tend to spend 30.8% less overall on the drugs than non-Hispanics when one controls for whether the respondent is medically qualified to use the drugs (column 2 of Table 5). H2. Use of anti-obesity drugs is greater among women. Column 1 of Table 4 indicates that women are 192% more likely than men to use anti-obesity drugs. This strongly supports our hypothesis. (Blanck et al. (2004) found that women were unconditionally 300% more likely than men to use anti-obesity drugs.) However, conditional on using any anti-obesity drug, women fill no more scrips (columns 3 and 4 of Table 4) than men. Conditional on using anti-obesity drugs, we find that women spend on average 29% more out-of-pocket on such drugs, with no difference in total spending which includes both out-of-pocket and what is paid by an insurer (columns 3 and 4 of Table 5). H3. Use of anti-obesity drugs decreases at advanced ages. The results in Table 4 confirm that use of anti-obesity drugs falls significantly at age 65 and above; in particular, use is less than half as likely for those over age 65 as it is for those between the ages of 18 and 29 (column 1 of Table 4). After one controls for meeting the medical criteria for using anti-obesity drugs (column 2 of Table 4), the difference is even greater; those over 65 are only one-third as likely to use anti-obesity drugs. In addition, use is significantly lower among those 50–64; they are only 57.9% as likely to use anti-obesity drugs as those 18–29. These results stand in contrast to

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Blanck et al. (2004), who find no statistically significant difference in use across age groups. Conditional on use, the number of scrips filled is relatively constant over age (columns 3 and 4 of Table 4). Also conditional on using the drugs, there are no statistically significant differences across age in either out-of-pocket or total spending on the drugs (Table 5). H4. Use of anti-obesity drugs is greater among those with health insurance. In logit regressions for use (columns 1 and 2 of Table 4), the coefficient on the indicator variable for lacking health insurance is not statistically significant, but the coefficient on the indicator for prescription drug coverage is significant at the 5% level. Those with drug coverage are 49% more likely to use anti-obesity drugs than those who lack it; if we control for whether the respondent is medically qualified, that figure falls to 46% more likely. These results are consistent with our hypothesis. However, columns 3 and 4 of Table 4 indicate that, conditional on using any anti-obesity drug, those who lack health insurance or prescription drug coverage do not fill fewer scrips. Conditional on using anti-obesity drugs, we find no difference in spending (either out-of-pocket or total) between the insured and uninsured, but we find that those with prescription drug coverage tend to spend 45–48% less out of pocket on such drugs than those who lack prescription drug coverage, with no difference in total spending on such drugs (Table 5). H5. Use of anti-obesity drugs rises with education. Table 4 indicates that the odds of using any anti-obesity drug are greater for those who have graduated high school than for those who have dropped out. Controlling for whether the respondent meets the medical criteria for using anti-obesity drugs (column 2 in Table 4), high school graduates are 83.7% more likely to use anti-obesity drugs than high school dropouts. In comparison, those with some college are 97.2% more likely, college graduates are 104% more likely, and those with graduate school are 141% more likely to use anti-obesity drugs than high school dropouts. Note that these differences in utilization across education control for income and insurance status. In contrast, Blanck et al. (2004) found no difference in utilization with education for men. For women, the only statistically significant difference was that those with some college were 62% more likely to use the drugs than high school dropouts. However, female college graduates were not more likely to use the drugs than female high school dropouts.


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The number of scrips filled conditional on use does not generally vary with education, with the exception that those who attended graduate school filled 33% more scrips than high school dropouts. Overall, the results support the hypothesis that use of anti-obesity drugs rises with education; however, the pattern is not linear - the major difference seems to be that high school dropouts are less likely than all others to use such drugs. Conditional on using anti-obesity drugs, the only variation in spending with education is that those with a graduate school education spend 78% more out-of-pocket on such drugs and 62% more overall on such drugs (Table 5) relative to high school dropouts. H6. Use of anti-obesity drugs rises with income. There is no detectable pattern of use with income. In Table 4, the coefficients of indicators for income category are generally insignificant and there is no clear pattern in their point estimates. Conditional on use, the number of scrips (Table 4) and expenditures on anti-obesity drugs (Table 5) are also uncorrelated with income. We find no support for our income hypothesis. In contrast, Blanck et al. (2004) found a consistent increase in the odds of utilization by income for women. However, they did not control for insurance status and prescription drug coverage, which are likely held by those with the highest incomes.

7. OTHER FINDINGS As mentioned earlier, the indicator for whether the respondent meets the medical criteria for receiving anti-obesity prescription drugs must be considered endogenous. For this reason, we first estimate models without that variable among the regressors, but we also re-estimate models including it as a regressor. Table 4, column 2, indicates that those who meet the medical criteria for using anti-obesity drugs are 504% more likely to take antiobesity drugs as those who do not meet the medical criteria. Conditional on use, however, those who meet the medical criteria fill no more scrips than those who do not meet the medical criteria (column 4 of Table 4). Blanck et al. (2004) are unable to control for whether the respondent meets the medical criteria for using anti-obesity drugs, but they proxy for qualification using whether the respondent’s self-reported weight is in the overweight or obese category. They find that obese women are 127% more likely than healthy-weight women to use anti-obesity drugs, and that obese men are 524% more likely than healthy-weight men to use them.

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While we find that the sign on the indicator for meeting the medical criteria for using anti-obesity drugs is in the expected direction, a logical question is: why is anyone who does not meet the medical criteria taking anti-obesity drugs? In our sample, 34% of all those taking anti-obesity drugs do not meet the medical criteria; in other words, for roughly every two individuals taking an anti-obesity drug who satisfy the medical criteria, another individual who does not meet the medical criteria is also taking it. One possible explanation is that when these people were prescribed the drugs, they did meet the medical criteria, but the drugs were so effective that the respondents no longer meet the criteria, but they continue to take the drugs to maintain weight loss. This scenario is unlikely for the vast majority of patients, however, given the modest reductions in body weight associated with these drugs. Moreover, Blanck et al. (2004) find that roughly one-third of those using prescription anti-obesity medication report that they were not obese prior to taking the medication. Alternatively, it may be that some of these are false negatives attributable to the fact that we do not account for those with sleep apnea when we classify individuals as satisfying the medical criteria for use. Another possibility is that people who are not obese but value weight loss are able to convince their physicians to prescribe the drug. Consistent with this, Blanck et al. (2004) find that 10% of users of prescription anti-obesity drugs in 1998 report that their source of these drugs is a family member, friend, or other non-physician. In our sample, only 1.3% of those who do meet the medical criteria are taking anti-obesity drugs (compared with 0.29% of those who do not meet the medical criteria). An intriguing feature of the market for anti-obesity drugs is that the vast majority of those who meet the medical criteria for their use are not taking them, while a substantial percentage of those taking them do not meet the medical criteria for their use. Who are the people taking anti-obesity drugs for which they are not medically qualified? We attempted to answer this question by testing for unconditional differences in observed respondent characteristics by whether they satisfy the medical criteria for using the drugs. There are no statistically significant unconditional differences in gender, race or ethnicity, age, marital status, education, or income. The one statistically significant difference is that prescription drug coverage is enjoyed by 84.3% of users who fail to meet the medical criteria and by 76.8% of users who do meet the criteria; this suggests there may be moral hazard associated with prescription drug coverage – those with such coverage are more likely to take drugs they do not obviously need.


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We also find in our regression results that, conditional on using antiobesity drugs, those who meet the medical criteria for taking them spend 29.8% more out-of-pocket, and 19% more overall, on such drugs than those who are not qualified (Table 5). We also find interesting patterns in the coefficients on the indicator variables for year. In studying trends in utilization, conditional on observables, we control for whether the respondent was medically qualified to use antiobesity drugs because the prevalence of obesity was rising over this period (Hedley et al., 2004; Flegal et al., 2002) and failure to control for respondent obesity status would bias upwards the trend in use over time. In considering the trends we are about to report, keep in mind that the key events in the market are the withdrawal of Pondimin and Redux in late 1997 and the introduction of Meridia in 1998 and Xenical in 1999; these events will be captured in the year effects. We find that, conditional on observables, use of anti-obesity drugs was significantly lower from 1998 to 2002 than it had been in 1996 (column 2 of Table 4). Relative to 1996, use was only 49.5% as likely in 1998, 46.4% as likely in 1999, 34.2% as likely in 2000, 38.7% as likely in 2001, and 31.7% as likely in 2002. It is striking that 5 years after the withdrawal of Pondimin and Redux, use of anti-obesity drugs remains less than one-third as likely as before the withdrawal. Moreover, the number of scrips filled conditional on using an anti-obesity drug fell by one-third after 1996 and remained significantly lower until 2002 (when the point estimate is similar but is not statistically significant). The well-publicized withdrawal of Pondimin and Redux from the market in late 1997 appears to have had a chilling effect on the market, one that not even the introduction of Meridia in 1998 and Xenical in 1999 offset. Other research finds that in many cases the withdrawal of a prescription drug has negative spillovers to other drugs that remain available in the same therapeutic class (Cawley & Rizzo, 2005). We also find interesting trends in expenditures on anti-obesity drugs over time. Relative to 1996, which was prior to the withdrawal of Pondimin and Redux, the amount spent out of out-of-pocket was 55.3% lower in 1997 (the year the drugs were withdrawn) and 60.2% lower in 1998, but not significantly lower thereafter. There was no change in overall expenditures on anti-obesity drugs, however, which suggests that the withdrawals led those paying the most out-of-pocket to quit the class of drugs. In 2001 the total expenditure on anti-obesity drugs was 38% higher than that in 1996, and in 2002 it was 79% higher (Table 5); however, there was no increase in out-of-pocket spending. Given that prices of these drugs were relatively constant over the period examined (see Fig. 1), the rise in total

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expenditures is likely due to a shift away from generic drugs and toward newly introduced drugs that still enjoyed patent protection and therefore had higher prices (specifically: sibutramine, introduced in 1997, and orlistat, introduced in 1999). We examined whether respondents who used Pondimin or Redux in 1996 or 1997 before their withdrawal differed from respondents who used other anti-obesity drugs in those years, but found virtually no statistically significant differences in unconditional means. The only exception was that those who took Pondimin or Redux were 3.3 years older, on average, than respondents who took other anti-obesity drugs in those years. We also tested whether respondents who use one of the two on-patent anti-obesity medications, Meridia and Xenical, since their introductions in 1997 and 1999, differ from those who use generic anti-obesity drugs. This is interesting both because these drugs are the only two approved by the FDA for long-term use and because they tend to have a higher price than offpatent drugs (see Fig. 1). However, we find no statistically significant differences in unconditional mean characteristics between users of patented versus off-patent (which are also the long-term versus short-term) drugs.

8. DISCUSSION Despite the importance of pharmacotherapy as a method of treating obesity, there exists little previous research on the correlates of the use of antiobesity drugs. We offer six hypotheses and test them using nationally representative data for 1996–2002 from the MEPS. There are several strong findings. Use of anti-obesity drugs is only onethird as likely among African-Americans as that among Whites. Use is roughly twice as likely among women as that among men. We also find that the probability of using anti-obesity drugs falls significantly after age 65. Prescription drug coverage is strongly correlated with utilization; those with such coverage are 49% more likely to use anti-obesity drugs than those who lack such coverage. Use is also correlated with education; those who drop out of high school are significantly less likely to use them. Unexpectedly, we find no correlation of income with use; this may be due to the fact that we control for health insurance coverage and prescription drug coverage. We also find that a substantial fraction (31.8%) of those who are taking anti-obesity drugs do not meet the medical criteria for their use; given the measured effectiveness of anti-obesity drugs, this is unlikely to be due to the beneficial effects of being on anti-obesity drugs. Moreover, we find that


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almost 99% of those who satisfy the medical criteria for receiving antiobesity drugs are not taking them. Intriguingly, the vast majority of those who are approved to take these drugs are not taking them, but a significant number who are not approved to take these drugs are taking them. We find that users of anti-obesity drugs who do not meet the medical criteria are slightly more likely to have prescription drug coverage, but there are no significant differences in education, income, race, ethnicity, or gender. Further research on the use of anti-obesity drugs by the non-obese is warranted. The well-publicized 1997 withdrawal from the market of the anti-obesity drugs fenfluramine and dexfenfluramine for damaging heart valves appears to have exerted a chilling effect on the market. All else equal, people were less than one-third as likely to use an anti-obesity drug in 2002 as they were in 1996. The finding that African-Americans are considerably less likely to use anti-obesity drugs is striking and merits further exploration. It would be useful to know if the lower utilization is due to a lack of access to physicians, a lack of information from physicians, or disinterest because of cultural or philosophical differences about obesity. Lower utilization is potentially troubling because the prevalence of obesity is particularly high among minority women in the U.S., and these drugs represent one modestly effective way to decrease body weight. At the same time, it should be acknowledged that anti-obesity drugs have historically involved adverse side effects, so it is not unambiguous that taking anti-obesity drugs always results in a net improvement in health. There is a deep pipeline for anti-obesity drugs, so the wide disparities in their current use may foreshadow even greater disparities in the future as additional anti-obesity drugs are approved by the FDA and enter the market. If these drugs prove to be safe and more effective, the health implications of the racial disparities in utilization we have documented will become even more serious. We also study determinants of out-of-pocket and total expenditures on anti-obesity drugs and find that, predictably, those with drug coverage pay less out-of-pocket but do not have higher total expenditures, which suggests there may be little moral hazard in anti-obesity drug use associated with insurance. Females spend 29–30% more out of pocket. We also find that out-of-pocket payments fell after the withdrawal of two anti-obesity drugs in 1997, which, combined with no change in total expenditures on these drugs during that time, suggests that those who paid the most out of pocket were the most likely to quit the class of drugs. More recently we document a significant rise in total spending on anti-obesity drugs, which cannot be

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explained by increases in price but may be due to a shift away from lower cost generics and toward higher priced patented drugs. We also find little difference in observed characteristics between those who took the drugs later withdrawn from the market in 1997 as opposed to other drugs available at that time and those who currently take patentprotected drugs approved for long-term use as opposed to off-patent drugs approved only for short-term use. A limitation of our research is that we are unable to track utilization of alternative weight-loss methods such as dieting and increased physical activity. Our findings that certain groups (e.g., men and African-Americans) are less likely to use anti-obesity drugs may not mean that those groups are less likely to pursue some weight-loss method overall; it could be that they are simply using OTC weight-loss medications or non-pharmacologic methods. Our data from the MEPS are limited in that they do not include data on diet or physical activity, so we are not able to resolve this question with the current dataset and this remains a question for future research.

NOTES 1. The FDA has also expanded the market for anti-obesity drugs. In December 2003 it approved the use of orlistat (Xenical) for adolescents; this was the first time that a weight-loss drug was approved for minors (O’Connor & Grady, 2003). 2. The BRFSS records less use of anti-obesity prescription drugs; while Stafford and Radley (2003) record annual drug mentions of 10.6 million in 1996, 9.4 million in 1997, and 3.7 million in 1998 in the IMS Health data, Khan, Serdula, Bowman, and Williamson (2001) calculate using BRFSS data that only 4.6 million U.S. adults used prescription drugs for weight loss during 1996–1998. 3. Each time a patient receives an initial set of pills or a refill from the pharmacy, it counts as a scrip. Using NDC codes, it is possible to determine the number of pills that a patient received in a given prescription fill, but we do not know how many pills the patient has been instructed to take each day. Because we are unable to determine the patient’s treatment regimen, we focus on the number of scrips as an outcome. 4. The risk factors and diseases that justify pharmacotherapy for those with BMI between 27 and 30 are: hypertension, dyslipidemia, coronary heart disease, Type II diabetes, and sleep apnea (Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults, 1998). 5. To correct for reporting error in BMI, we use the National Health and Nutrition Examination Survey III (NHANES III) as validation data. NHANES III is ideal for this purpose because it contains both self-reports and measures of actual height and weight. By regressing BMI calculated using actual values of weight and height on BMI calculated using self-reported values of weight and height in NHANES III, ‘‘transporting’’ the coefficients to the MEPS, and multiplying them by the self-reported values, we generate measures of BMI corrected for reporting error.


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The NHANES III data confirm that substantial misclassification would occur in the absence of the correction; slightly more than 24% of those who are truly obese report weights and heights that imply a BMI that is less than obese. (In contrast, only 2.3% of the non-obese report weights and heights that imply a BMI that is obese.) 6. Sleep apnea is another comorbidity that justifies the use of anti-obesity drugs for those with a BMI between 27 and 30, but in the MEPS, sleep apnea is coded within a large category of conditions. Given the choice between including a wide variety of conditions and risking many false positives, and excluding sleep apnea and risking false negatives, we elected the latter. 7. See Arbeeny (2004), Gura (2003), and Farrigan and Pang (2002). 8. We experimented with including interactions of the African-American and Hispanic indicators with gender, but these interactions were not statistically significant.

ACKNOWLEDGMENTS We apologize to Lewis Carroll and Grace Slick for the title. We thank Rebecca Friedkin for excellent programming assistance and we thank Kristian Bolin and two anonymous referees for their helpful comments and suggestions.

REFERENCES Arbeeny, C. M. (2004). Addressing the unmet medical need for safe and effective weight loss therapies. Obesity Research, 12(8), 1191–1196. Averett, S., & Korenman, S. (1996). The economic reality of the beauty myth. Journal of Human Resources, 31(2), 304–330. Averett, S., & Korenman, S. (1999). Black–White differences in social and economic consequences of obesity. International Journal of Obesity, 23, 166–173. Blanck, H. M., Khan, L. K., & Serdula, M. K. (2001). Use of nonprescription weight loss products: Results from a multistate survey. JAMA, 286(8), 930–935. Blanck, H. M., Khan, L. K., & Serdula, M. K. (2004). Diet and physical activity behavior among users of prescription weight loss medications. International Journal of Behavioral Nutrition and Physical Activity, 1, 17. Bound, J., Brown, C., & Mathiowetz, N. (2002). Measurement error in survey data. In: J. Heckman, & E. Leamer (Eds), Handbook of econometrics (Vol. 5). New York: SpringerVerlag. Bray, George A. (2002). Pharmacological Treatment of Obesity. In: Bray, George A., Clude Bouchard, & W. P. T. James (Eds), Handbook of Obesity (New York: Marcel Dekker, Inc.). Buchwald, H., Avidor, Y., Braunwald, E., Jensen, M. D., Pories, W., Fahrbach, K., & Schoelles, K. (2004). Bariatric surgery: A systematic review and meta-analysis. JAMA, 292, 1724–1737. Cawley, J. (2004). The impact of obesity on wages. Journal of Human Resources, 39(2), 451–474.

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Cawley, J., & Rizzo, J. A. (2005). The competitive effects of prescription drug withdrawals. NBER Working Paper no. 11223. Conley, D., & Glauber, R. (2005). Gender, body mass, and economic status. NBER Working Paper no. 11343. Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. (1998). Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Archives of Internal Medicine, 158, 1855–1867. Farley, P. (1986). Theories of the price and quantity of physician services: A synthesis and critique. Journal of Health Economics, 5, 315–333. Farrigan, C., & Pang, K. (2002). Obesity market overview. Nature Reviews Drug Discovery, 1(April), 257–258. Finkelstein, E., Fiebelkorn, I., & Wang, G. (2003). National Medical Spending Attributable to Overweight and Obesity: How Much and Who’s Paying? Health Affairs Web Exclusive, May 14. Flegal, K. M., Carroll, M. D., Ogden, C. L., & Johnson, C. L. (2002). Prevalence and trends in obesity among U.S. adults, 1999–2000. JAMA, 288(14), 1723–1727. Fontaine, K., & Bartlett, S. (2000). Access and use of medical care among obese persons. Obesity Research, 8, 403–406. Fujioka, K. (2002). Management of obesity as a chronic disease: Nonpharmacologic, pharmacologic, and surgical options. Obesity Research, 10(Suppl. 2), 116S–123S. Grossman, M., & Kaestner, R. (1997). Effects of education on health. In: J. R. Behrman & N. Stacey (Eds), The social benefits of education. Ann Arbor, MI: University of Michigan Press. Gura, T. (2003). Obesity drug pipeline not so fat. Science, 299, 849–852. Hedley, A. A., Ogden, C. L., Johnson, C. L., Carroll, M. D., Curtin, L. R., & Flegal, K. M. (2004). Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA, 291(23), 2847–2850. Jick, H. (2000). Heart valve disorders and appetite-suppressant drugs. JAMA, 283(13), 1738– 1740. Jones, A. M. (2000). Health econometrics. In: A. J. Culyer & J. P. Newhouse (Eds), Handbook of health economics (chap. 6). New York: Elsevier. Khan, L. K., Serdula, M. K., Bowman, B. A., & Williamson, D. E. (2001). Use of prescription weight loss pills among U.S. adults 1996–1998. Annals of Internal Medicine, 134, 282–286. Lee, L.-F., & Sepanski, J. H. (1995). Estimation of linear and nonlinear errors-in-variables models using validation data. Journal of the American Statistical Association, 90(429), 130–140. Manson, J. E., & Faich, G. A. (1996). Pharmacotherapy for obesity – Do the benefits outweigh the risks? New England Journal of Medicine, 335(9), 659–660. Mirasol, F. (2004). Biotechs pursue anti-obesity drugs, the holy grail of pharmaceuticals. Chemical Market Reporter, 265(9), 8. National Heart, Lung, and Blood Institute. (1998). Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The evidence report. Bethesda, MD: National Institutes of Health, N.I.H. Publication no. 98-4083. Nieto-Garcia, F. J., Bush, T. L., & Keyl, P. M. (1990). Body mass definitions of obesity: Sensitivity and specificity using self-reported weight and height. Epidemiology, 1(2), 146–152.


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Padwal, R., Li, S. K., & Lau, D. C. W. (2003). Long-term pharmacotherapy for overweight and obesity: A systematic review and meta-analysis of randomized control trials. International Journal of Obesity, 27, 1437–1446. Powell, A. D., & Kahn, A. S. (1995). Racial differences in women’s desires to be thin. International Journal of Eating Disorders, 17, 191–195. Sobal, J. (2004). Sociological analysis of the stigmatisation of obesity. In: J. Germov & L. Williams (Eds), A sociology of food and nutrition: The social appetite, (2nd ed). Melbourne: John Wiley. Sobal, J., & Stunkard, A. J. (1989). Socioeconomic status and obesity: A review of the literature. Psychological Bulletin, 105, 260–275. Stafford, R. S., & Radley, D. C. (2003). National trends in antiobesity medication use. Archives of Internal Medicine, 163, 1046–1050. Stein, R. (2004). Seeking a slim victory, drugmakers press FDA: Pipeline is bulging with diet pills. Washington Post, p. A01, September 17. Sturm., Roland. (2002). The Effects of Obesity, Smoking and Drinking on Medical Problems and Costs. Health Affairs, 21(2), 245–253. Thorpe, Kenneth E., Curtis, S. Florence, David H. Howard, & Peter Joski. (2004). The Impact of Obesity on Rising Medical Spending. Health Affairs Web Exclusive, W4–481. O’Connor, Anahad & Denise Grady. (2003). FDA Moves to Let Drug Treat Obese Teenagers. New York Times, December 16. U.S. Department of Health and Human Services. (1997). Cardiac valvulopathy associated with exposure to fenfluramine or dexfenfluramine. Morbidity and Mortality Weekly Report, 46(45), 1061–1066. U.S. Department of Health and Human Services. (2000). Healthy people 2010: Understanding and improving health (2nd ed.). Washington, DC: U.S. Government Printing Office. U.S. Department of Health and Human Services. (2001). The Surgeon General’s Call to Action to Prevent and Decrease Overweight and Obesity. (Washington, DC: U.S. Government Printing Office). U.S. Food and Drug Administration. (2000). Food and drug administration public health advisory; subject: Safety of phenylpropanolamine. Press release, November 6. U.S. Food and Drug Administration. (2004). FDA approved obesity drugs. World Wide Web content, http://www.fda.gov/ohrms/dockets/ac/04/briefing/2004-4068B1_05_ApprovedDrugs.htm Vastag, B. (2003). Experimental drugs take aim at obesity. JAMA, 289(14), 1763–1764. Williamson, D. A., & O’Neil, P. M. (1998). Behavioral and psychological correlates of obesity. In: G. A. Bray, C. Bouchard & W. P. T. James (Eds), Handbook of obesity. New York: Marcel Dekker, Inc. Yanovski, S. Z., & Yanovski, J. A. (2002). Obesity. New England Journal of Medicine, 346(8), 591–602.

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APPENDIX: BRAND NAMES OF FDA-APPROVED ANTI-OBESITY DRUGS 1. Orlistat a. Xenical 2. Sibutramine a. Meridia b. Reductil 3. Benzphetamine or Amfepramone Benzfetamine a. Didrex 4. Diethylpropion hydrochloride a. Tenuate b. Tenuate Dospan c. Tepanil Ten-Tab 5. Mazindol a. Mazanor b. Sanorex 6. Phendimetrazine a. Adipost b. Bontril PDM c. Bontril slow-release d. Melfiat e. Obezine f. Phendiet g. Phendiet-105 h. Plegine i. Prelu-2 j. PT 105 7. Phentermine hydrochloride a. Adipex b. Adipex-P c. Banobese


d. e. f. g. h. i. j. k. l. m. n. o. p. q. r. s. t. u. v. w.

Dapex Duromine Fastin Ionamin Obenix or Obe-nix Obephen Obermine Obestin Oby-Cap Oby-Trim Panshape Phentercot Phentride Pro-Fast Pro-Fast HS Pro-Fast SA Pro-Fast SR Teramine T-Diet Zantryl

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PART III: LABOR MARKET IMPACTS OF OBESITY


OBESITY, EMPLOYMENT AND WAGES IN EUROPE Jaume Garcia and Climent Quintana-Domeque ABSTRACT This paper examines the associations between obesity, employment status and wages for several European countries. Our results provide weak evidence that obese workers are more likely to be unemployed or tend to be more segregated in self-employment jobs than their non-obese counterparts. We also find difficult to detect statistically significant relationships between obesity and wages. As previously reported in the literature, the associations between obesity, unemployment and wages seem to be different for men and women. Moreover, heterogeneity is also found across countries. Such heterogeneity can be somewhat explained by some labor market institutions, such as collective bargaining coverage and employer-provided health insurance.

1. INTRODUCTION It is a well-known fact that obesity is one of the most important public health concerns: obesity is a risk factor for numerous health problems and many chronic diseases (WHO, 2002), and its prevalence has increased by 10–40% in most European countries over the last decade (WHO, 2003).1 Moreover, The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 187–217 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17008-9

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obesity affects not only adults but also teenagers and children, especially, in Southern Europe (IOTF, 2002, 2003). For all these reasons, it is important to assess both the determinants and the consequences of obesity (Philipson, 2001). The effects of obesity on labor market outcomes for the United States have been assessed in a large number of studies.2 One of their most robust findings is that obese women tend to earn less than their non-obese counterparts and that there are differences by ethnicity and/or race (Cawley, 2007). However, the available empirical evidence for Europe is more limited. On one hand, there are some studies for particular European countries: UK (Sargent & Blanchflower, 1994; Morris, 2005, 2006), Finland (Sarlio-Lahteenkorva & Lahelma, 1999), Germany (Cawley, Grabka, & Lillard, 2005) and Denmark (Greve, 2005). In the work by Sargent and Blanchflower (1994), hourly earnings of women at age 23 are found to be lower conditioned on being obese at age 16, but no such a relation is found for men. More recently, Morris (2005, 2006) shows that body mass index (BMI) has a positive and significant effect on mean hourly occupational earning in males and a negative and significant effect in females, although the association for males is not robust across different specifications. However, after using the mean BMI (and/or the prevalence of obesity) across individuals living in the same health authority area as an instrument for individual BMI, he finds no statistically significant effect, either for men or for women. In Finland, obese females are found to have lower income levels than non-obese ones, but that is not the case for males (Sarlio-Lahteenkorva & Lahelma, 1999). The empirical evidence for Germany shows that obesity is negatively associated with wages, both for men and for women (Cawley et al., 2005). Moreover, using genetics as a natural experiment, the authors cannot reject the hypothesis of no causal impact of weight on wages. Finally, preliminary evidence for Denmark shows a negative effect of obesity and overweight on employment for women, while for men overweight seems to have a positive effect on employment (Greve, 2005).3 On the other hand, there are some studies for Europe as a whole, using the European Community Household Panel (ECHP), the dataset used in the present article. d’Hombres & Brunello (2005) analyze the effect of BMI on wages in Europe. Pooling all the countries together, they find that the association between BMI and wages is negative for women, and positive for men. Using BMI from biological family members as an instrument for individual BMI, they report a negative effect of BMI on wages for both men and women. However, as these authors recognize, assuming a common relationship between obesity and wages across different European countries

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is too restrictive, given the different characteristics in labor markets across such countries. They distinguish between two groups of countries: ‘‘olivebelt’’ (Greece, Italy, Portugal and Spain) and ‘‘beer-belt’’ (Austria, Belgium, Denmark, Finland and Ireland). According to their instrumental variable (IV) estimates, the sign of the relationship between BMI and wages depends on the group of countries: BMI has a positive effect on wages for both men and women in the ‘‘beer-belt’’ countries, while this effect is negative in the ‘‘olive-belt’’ countries. They argue that one plausible explanation for such a finding is the interaction between BMI and weather: the ‘‘olive-belt’’ countries have warmer weather, while those belonging to the ‘‘beer-belt’’ have colder weather. From a human capital point of view, investment in body size can be seen as a way of enhancing productivity in colder places. This argument might be reasonable in a rural-farm economy, but we do not consider it is appropriate in the case of developed European countries. Moreover, the fact that different BMI–wage relationships are found for each of these two groups is a strong reason to allow each relationship to be different across countries. Sousa (2005) applies the propensity score technique (matching estimator) to assess the causal effect of BMI on labor market outcomes in Europe using the ECHP. Pooling all the countries together, she finds that the average treatment effect for those having a BMI above 25 decreases labor force participation for women, but it increases male labor force participation. Moreover, when she allows for different average treatment effects in Northern and Southern countries, her qualitative results are the same. However, the average treatment effect is not estimated for each country separately. Finally, there is a recent study by Lundborg, Bolin, Ho¨jga˚rd, & Lindgren (2007) using the Survey of Health, Ageing and Retirement in Europe (SHARE), where the authors analyze the effect of obesity on employment, hours worked and hourly wages in 10 European countries for people aged 50 and above. Pooling all the countries, they find that obesity is negatively associated with being employed for both men and women and with female hourly wages. Moreover, their results by country-group (Nordic, Central and Southern) suggest that the effects of obesity on labor market outcomes differ across Europe. Unfortunately, none of these studies provide a fully comparable countryby-country European analysis, which seems necessary according to the empirical evidence in d’Hombres & Brunello (2005) and Lundborg et al. (2007). Thus, the main purpose of the present work is to provide empirical estimates of the associations between body size variables and labor market outcomes in a large number of European countries, without restricting all associations

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to be equal across countries or groups of countries. As far as we know, none of the existent studies has analyzed the correlations between body size and the labor market status: employee, self-employment and unemployed. Additionally, we go one step further providing some evidence on the potential role played by labor market institutions and cultural factors in explaining these associations. It is important to mention that we do not aim to provide a causal analysis, but we are interested in offering just a description, as accurate as possible, of the relationship between body size and labor market outcomes. The paper is organized as follows. Section 2 describes the issues of correlation and causality when interested in the relation between body size and labor market outcomes. In Section 3, we present the dataset, the variables used and our main results. Section 4 includes a first exploration of the role played by different cultural and labor market institutions on the association between obesity and labor market outcomes. Finally, Section 5 concludes with suggested possible avenues for further research.

2. BODY SIZE AND LABOR MARKET OUTCOMES: CORRELATION AND CAUSALITY As mentioned in the previous section, the purpose of this paper is not to identify the causal effect of body size on labor market outcomes, since this is a challenging task without a credible quasi-experiment or experimental data. Rather we aim to provide a descriptive analysis. As emphasized by Cawley (2007), the associations between body size and labor market outcomes can reflect three possible relations: the effect of obesity on labor market outcomes (discrimination and/or productivity), the effect of labor market outcomes on obesity (see Morland, Wing, Roux, & Poole, 2002), and the effect of a third factor on both obesity and labor market outcomes (e.g., individual time preference). To disentangle causality from correlation in the relationship between body size and labor market outcomes, several empirical strategies have been used: lagged measures of the BMI (see, e.g., Conley & Glauber, 2005, 2007), fixedeffects strategies (e.g., individual differences like in Averett & Korenman, 1996, or using monozygotic twins, see Behrman & Rosenzweig, 2001), IVs (e.g., genetic variation, like in Cawley, 2000, 2004, or in d’Hombres & Brunello, 2005; obesity medication taken by parents or their mortality status, in Greve, 2005; average BMI (and prevalence of obesity) across individuals living in the same health authority area, in Morris, 2005, 2006; the presence


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of other obese persons in the household, being an oldest child, and having sisters only, in Lundborg et al. 2007), and propensity score (Sousa, 2005). However, all these identification strategies are somewhat disappointing, since the assumptions they rely on are very strong. First, in the lagged specification strategy, the independence of the lagged BMI variable on the residual term is required, which is very unlikely to be true, because the error term is likely to capture some omitted variable related to both past BMI and the contemporaneous labor market outcome of interest (e.g., through self-esteem). Second, fixed-effects strategies require the regressors to be strictly exogenous and that all the omitted relevant and unobserved individual characteristics remain constant over time. BMI’s strict exogeneity is defined as BMI being uncorrelated with the error term for all leads and lags, which is highly implausible when using individual fixed-effects, since the lagged BMI measure might be correlated with the contemporaneous error term (see the explanation above in the lagged strategy case). Furthermore, the individual fixed-effects strategy involves a particular implicit trade-off between precision and consistency. The shorter the time period, the lower the probability that unobserved individual differences arise over time, which is favorable to the assumption of fixed differences across individuals. However, the shorter the period of time, the higher the imprecision of the estimated effect. In the limit, there is no change at all, and such estimation is not possible. On the contrary, the longer the time period, the higher the precision of our estimates, but then it is also unlikely that an individual fixed-effect is capturing all the relevant omitted variables. Hence, our estimates are likely to be inconsistent. Moreover, there is no a priori argument why we should prefer imprecision to inconsistency (Deaton, 1997), as it can be easily seen from the previous extreme case. Even in the hypothetical case that these assumptions were satisfied, reverse causality could not be discarded, which is likely to be an issue in this context (Morland et al., 2002). Third, when using IVs techniques, the usual relevance and exogeneity conditions are required (Wooldridge, 2001). In Cawley (2000), the weight of a child is used as an instrument for the weight of the child’s mother. At first glance one may think this constitutes a valid instrument, a source of exogenous variation in weight due to genetics. However, if for instance the genetic component of the child associated with weight is also related to other factors regarding employment and wage of the mother, this kind of instrument does not satisfy the exogeneity condition. Recently, Cawley (2004) has used sibling weight adjusted for sex and age to instrument individual weight, justifying again the exogeneity of such instrument on the grounds of genetic

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variation. The problem is that, as recognized by Cawley (2004, 2007), there exists the possibility that a substantial part of the genes responsible for obesity are also responsible for other factors that affect labor market outcomes, such as willingness to delay gratification (time discount rate) or other kind of unobserved characteristic. Since the current knowledge on which particular genes are responsible for obesity and other factors related to wages and employment is scarce, we doubt the validity of these instruments.4 It might be the case that for this reason, Sousa (2005) decides to use a propensity score approach. Using the propensity score, she creates similar groups of people based on observable characteristics, and it provides consistent estimates under the assumption that those groups, which are constructed to be similar in observable characteristics, are also similar in their unobservable characteristics. However, this method requires a large number of observations to be able to construct enough groups of individuals based on their observable characteristics, and the required sample size will be higher, the higher the number of covariates used in the construction of such groups. Indeed, Sousa (2005) faces a problem of small sample size because of the relative low number of observations in the ECHP compared with other micro databases. This forces her to pool all the countries together (or at most to estimate the effect of body size for only two groups of countries: a subsample of Northern and a subsample of Southern countries), restricting the estimated average treatment effect on the treated (those with a BMI above 25) to be the same in each country (or in each group of countries). The problem of estimating an average treatment effect on the treated (ATT) for Europe as a whole (or only for Southern and Northern European countries) is twofold. First, if the ATT differs across countries, the estimated ATT cannot be informative for a particular country. Second, even if we are interested in an average ATT for Europe as a whole, we should properly weigh each country’s specific ATT. In this paper we focus on two main labor market outcomes: employment status in the labor market and hourly wage.5 First, we are concerned with the relationship between body size and labor market status, because it is likely to provide direct and understandable signs on the existence of discrimination in the labor market. For this reason, instead of estimating the probability of being employed versus being unemployed, we propose a multinomial logit with the following outcomes: employee, self-employed and unemployed. There are two justifications for such an approach. First, this specification is logical from a timing point of view: these outcomes are observable conditioned on participating in the labor force, a condition clearly violated by a specification of the employed-versus-unemployed/inactive type. Second, and


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more important, this econometric model sheds more light on the existence of discrimination. Under physical discrimination in the hiring process, we should expect to find not only that unemployed people are more likely to be obese than those who are employed, but also that self-employed tend to be more obese than employed. Secondly, we are interested in the relationship between body size and wages. If, after controlling for observable characteristics determining wages, we find wage differentials between obese and nonobese workers, this will be a sign of the potential existence of discrimination in the labor market, although obviously not conclusive, in the sense that we are not dealing with BMI’s endogeneity.

3. EMPIRICAL ANALYSIS 3.1. Dataset The data used in this paper come from the ECHP, Eurostat, a survey based on a standardized questionnaire that involves annual interviewing of a representative panel of households and individuals in Member States of the European Union during the period 1994–2001. The ECHP covers a wide range of topics on living conditions and its standardized methodology and procedures yield comparable information across countries. We only use the ECHP waves since 1998 (fifth wave), because this was the first time in which anthropometric data were collected. Moreover, in our analysis we only include countries with a full ECHP data format and those in which anthropometric data were collected: Austria, Belgium, Denmark, Finland (starting in 1999), Greece, Ireland, Italy, Portugal and Spain.6 We focus on two main labor market outcomes: the employment status in the labor market and the hourly wage. The employment status is defined as a categorical variable with possible states reported by the individual: (1) working more than 15 hours per week for an employer in a paid employment, paid apprenticeship, or under other related schemes; (2) working more than 15 hours in self-employment or in a family enterprise (unpaid work); (3) unemployed. Individuals working less than 15 hours represented a 0.5% of the sample and were dropped from the analysis. The wage variable is defined as the natural logarithm of the hourly wage. The hourly wage is constructed by dividing the variable ‘‘net monthly wage and salary earnings’’ by the number of monthly hours (including paid overtime) worked in the main job, which are computed approximately as four times the number of weekly hours (including paid overtime) worked in the main job.

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We estimate different models for each country, allowing for a flexible econometric specification, in the sense that none of the coefficients are restricted to be the same across countries. Moreover, the models are estimated for men and women separately. To estimate the associations between labor market status and body size, a multinomial logit model is specified. The associations between wages and the alternative body size measures are estimated through standard ordinary least squares. Three different measures of body size are used in this paper: (1) BMI (weight in kilograms divided by height in meters squared); (2) weight in kilograms (controlling for height in centimeters); and (3) an indicator for being obese (controlling for the rest of BMI categories).7 All these variables are constructed using self-reported height and weight measures. An important point is that we are going to focus on the specification containing BMI categories, and we will devote our attention to the obesity indicator. This decision is based on two main advantages of such an approach. First, given that a non-monotonic relationship between body size and labor market outcomes cannot be discarded on a priori grounds (see the empirical evidence in Saporta & Halpern, 2002), the BMI dummies approach offers a flexible and straightforward procedure for addressing this issue. Second, since the main purpose of the paper is to estimate the association between obesity and labor market outcomes, this body size variable is the one we are more interested in. Apart from the body size variable, both the participation and labor market status equations include the following covariates: age, age squared, two dummies indicating the highest completed education level, household income once individual earnings are discounted (which is adjusted to equivalent units using the OECD conversion scale and standardized at the country level), country dummies, year dummies, and the interactions between country and year dummies. To take account of the relationship between children, female labor supply, and pregnancy-related weight gains, the employment equation includes one dummy for being married, the number of children below 14 in the household, and the number of children between 14 and 15 in the household. Nevertheless, these controls have been added to the male equations as well for comparability purposes. We estimate standard wage equations which include: age, age squared, two dummies indicating the highest completed education level, experience (defined as the corresponding ECHP wave minus the age at which the individual had her first job), experience squared, and tenure (number of years in the current job), country effects, year effects and interactions between year and country effects.


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Finally, we decided to control for interview effects, adding an indicator whether the year of the interview differs from the one regarding the information being asked, quarter of interview dummies, and an indicator whether the mode of interviewing was face-to-face. All these interview controls are assumed to have common effects across countries. The sample is restricted to people between 25 and 54, the demographic group with the highest employment rate. The appendix contains a table with the basic descriptive statistics for each country. ECHP personal weights are used in all estimations described in this paper. The standard error for each reported coefficient is robust to heteroskedasticity and calculated with clustering by individual to account for correlations in the error terms of each individual over time. 3.2. Estimated Associations In this subsection we present the empirical results. In Tables 1A and 1B we report the estimated associations between employment status and obesity obtained from the estimation of the multinomial logit model. For the three body size measures, the reported associations are presented in terms of relative risk ratios (RRRs) between the probability of working as a selfemployed (Table 1A) and the probability of being unemployed (Table 1B) both with respect to the probability of working as an employee. As we can see in Table 1A, in seven out of nine countries, the RRRs for women are higher than one, which means that obese women tend to be more self-employed rather than working as employees. However, only in three out of nine cases are the RRRs statistically significant, ranging from approximately 1.6 in both countries Greece and Italy to 2.1 in Ireland. Roughly speaking, obese Greek and Italian women have a 50% higher probability of working as self-employed workers rather than as employees, while in Ireland, obese women tend to be two times more concentrated in self-employment rather than working as employees. For men, similar results arise, finding statistically significant RRRs higher than 1 in Greece (1.6), Ireland (1.8) and Spain (1.4). Some caution must be taken when interpreting these results. On one hand, we find six out of eighteen coefficients (counting both men and women) to be statistically significant, which means 1/3 of the estimated coefficients appear to be statistically significant. Furthermore, taking into account that we are looking at the effects of obesity on labor market status across several countries, the probability of rejecting non-significance of a particular coefficient when in fact this coefficient is not significant is very high. This

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Table 1A.

Labor Market Status Equations.

Multinomial Logit: Relative Risk Ratios for Body Size Measures Dependent Variable: [Prob. of Self-Employed/Prob. of Employee] Female

Austria Belgium Denmark Finland Greece Ireland Italy Portugal Spain Pseudo-R2 N

Male

(1) BMI

(2) Weighta

(3) Obeseb

(1) BMI

(2) Weighta

(3) Obeseb

1.038 (0.026) 0.983 (0.031) 1.021 (0.051) 1.018 (0.021) 1.045 (0.016) 1.054 (0.032) 1.023 (0.017) 0.981 (0.023) 1.029 (0.019)

1.014 (0.009) 0.996 (0.011) 1.007 (0.018) 1.007 (0.008) 1.017 (0.006) 1.025 (0.012) 1.010 (0.007) 0.994 (0.009) 1.013 (0.007)

1.005 (0.362) 0.876 (.407) 1.428 (0.856) 1.012 (0.343) 1.589 (0.362) 2.076 (0.906) 1.587 (0.414) 0.979 (0.289) 1.291 (0.306)

1.027 (0.027) 0.978 (0.024) 0.979 (0.032) 1.006 (0.021) 1.034 (0.014) 1.069 (0.024) 1.019 (0.012) 1.045 (0.025) 1.029 (0.013)

1.009 (0.008) 0.995 (0.008) 0.997 (0.009) 1.001 (0.006) 1.011 (0.005) 1.023 (0.007) 1.008 (0.004) 1.017 (0.009) 1.010 (0.005)

1.249 (0.360) 0.817 (0.250) 0.608 (0.252) 0.912 (0.226) 1.629 (0.251) 1.784 (0.445) 1.188 (0.178) 1.584 (0.450) 1.383 (0.225)

0.12 48,743

0.12 48,743

0.12 48,743

0.09 66,884

0.09 66,884

0.09 66,884

Note: All models include age, age squared, two educational dummies, standardized rest of household income in equivalent units, a dummy of married, the number of children under 14 in the household, the number of children between 14 and 15 in the household, country dummies, annual dummies, interaction between country and annual dummies, quarter of interview dummies, an indicator if the year of the interview differs from the panel wave and an indicator for face-to-face interview. Standard errors robust to heteroskedasticity (clustered at the individual level) are in parentheses. Observations have been weighted using the ECHP personal weights. a Height is also included. b Underweight and overweight categories are also included. Significant at the 10% level; Significant at the 5% level; Significant at the 1% level.

means that even by chance we may find statistically significant associations. To address this issue, p-values are adjusted using Bonferroni’s method for multiple testing.8 On the other hand, the relationship between body size and labor market status, although it may capture the effect of discrimination


Table 1B.

197

Labor Market Status Equations.

Multinomial Logit: Relative Risk Ratios for Body Size Measures Dependent Variable: [Prob. of Unemployed/Prob. of Employee] Female

Austria Belgium Denmark Finland Greece Ireland Italy Portugal Spain Pseudo-R2 N

Male

(1) BMI

(2) Weighta

(3) Obeseb

(1) BMI

(2) Weighta

(3) Obeseb

1.068 (0.038) 1.029 (0.022) 0.993 (0.029) 1.031 (0.022) 1.041 (0.024) 0.992 (0.052) 1.055 (0.017) 0.995 (0.035) 1.034 (0.016) 0.12 48,743

1.021 (0.013) 1.010 (0.008) 0.997 (0.010) 1.012 (0.008) 1.015 (0.009) 0.997 (0.020) 1.021 (0.006) 1.000 (0.014) 1.013 (0.006) 0.12 48,743

1.416 (0.650) 2.150 (0.601) 0.928 (0.331) 1.283 (0.354) 1.336 (0.390) 1.497 (0.779) 1.711 (0.508) 1.242 (0.503) 1.914 (0.355) 0.12 48,743

0.992 (0.040) 1.077 (0.033) 1.008 (0.056) 0.945 (0.031) 0.966 (0.033) 0.921 (0.032) 0.981 (0.021) 0.920 (0.043) 0.957 (0.018) 0.09 66,884

0.998 (0.013) 1.025 (0.010) 1.001 (0.016) 0.983 (0.010) 0.981 (0.0100) 0.971 (0.012) 0.994 (0.008) 0.974 (0.016) 0.985 (0.006) 0.09 66,884

1.159 (0.407) 3.051 (0.947) 2.011 (1.185) 0.503 (0.195) 0.959 (0.269) 0.609 (0.211) 0.956 (0.272) 0.582 (0.256) 0.922 (0.192) 0.09 66,884

Note: All models include age, age squared, two educational dummies, standardized rest of household income in equivalent units, a dummy of married, the number of children under 14 in the household, the number of children between 14 and 15 in the household, country dummies, annual dummies, interaction between country and annual dummies, quarter of interview dummies, an indicator if the year of the interview differs from the panel wave and an indicator for face-to-face interview. Standard errors robust to heteroskedasticity (clustered at the individual level) are in parentheses. Observations have been weighted using the ECHP personal weights. a Height is also included. b Underweight and overweight categories are also included. Significant at the 10% level; Significant at the 5% level; Significant at the 1% level.

against obese people in the hiring process, more importantly it is likely to reflect reverse causality from labor market status (employee, self-employed, or unemployed) to obesity, or some type of selection or sorting in employment versus self-employment. If we are willing to assume that an important channel through which reverse causality and selection occur is through

198


health problems, a crude way to address these concerns is using a sample readjustment. The idea is to exclude people who declare being hampered by any kind of physical or mental disability affecting their daily life activities and trimming some observations falling outside the [15, 45] BMI interval (88 and 55 for women and men, respectively). After performing this crude sample readjustment, we find that obesity is statistically significantly associated with a higher relative probability of being self-employed with respect to being an employee in four and two out of nine cases for women and men respectively. However, once we compute the Bonferroni’s adjusted p-values for the new sample, we find no statistically significant associations for women, and only one significant association is found for men: in Greece, obese men are found to be 70% more likely to be self-employed rather than being employees with respect to non-obese. Overall, there is weak evidence that obese workers tend to be more segregated in self-employment than the non-obese ones. According to our results, there is no evidence on discrimination against obese in terms of being relatively more likely to be self-employed workers rather than employees, neither for men nor for women. When analyzing the ratio between the probability of being unemployed with respect to the probability of working as an employee, Table 1B seems to suggest very different results for women and men: obesity seems to be associated with a higher relative probability of being unemployed in all countries but Denmark for the former, while for the later, in six out of nine countries, we find the opposite result: obesity is associated with a lower relative probability of being unemployed. However, statistical significant associations are found in only four countries: Belgium, 2.2 for women, 3.1 for men; Finland, 0.5 for men; Italy, 1.7 for women; and Spain, 1.9 for women. Applying the same logic as in the analysis of Table 1A, once we exclude hampered people and potential outliers, statistically significant associations remain in Belgium (2.2 for women, 3.3 for men) and in Spain (1.7 for women). Moreover, the relationship in Belgium is robust to adjusted p-values for both men and women: obese women in Belgium are more than twice likely to be unemployed rather than working as employees, and for men this ratio is even higher, more than three times. As in the previous analysis, if anything, there is weak evidence that obese workers are more likely to be unemployed rather than their non-obese counterparts. Finally, Table 2 presents the correlations between log of hourly wages and body size measures. For women, all such correlations are negative in all countries, except for the case of Ireland where the correlation between the log of hourly wage and weight is zero. Something similar happens for men


Table 2.

199

Wage Equations.

Ordinary Least Squares: Marginal Effects for Body Size Measures Dependent Variable: log(Hourly Wage) Female

Austria Belgium Denmark Finland Greece Ireland Italy Portugal Spain N

Male

(1)

(2)

(3)

(1)

(2)

(3)

BMI

Weighta

Obeseb

BMI

Weighta

Obeseb

0.006 (0.003) 0.002 (0.003) 0.008 (0.003) 0.008 (0.004) 0.004 (0.004) 0.001 (0.003) 0.004 (0.003) 0.006 (0.002) 0.006 (0.004) 17,971

0.002 (0.001) 0.001 (0.001) 0.003 (0.001) 0.002 (0.001) 0.002 (0.001) 0.000 (0.001) 0.001 (0.001) 0.002 (0.001) 0.002 (0.002) 17,971

0.023 (0.042) 0.037 (0.043) 0.092 (0.029) 0.099 (0.043) 0.084 (0.055) 0.025 (0.047) 0.067 (0.050) 0.074 (0.044) 0.054 (0.059) 17,971

0.003 (0.003) 0.005 (0.003) 0.002 (0.003) 0.001 (0.003) 0.002 (0.003) 0.007 (0.004) 0.000 (0.002) 0.007 (0.005) 0.001 (0.003) 29,429

0.001 (0.001) 0.002 (0.001) 0.001 (0.001) 0.000 (0.001) 0.001 (0.001) 0.003 (0.002) 0.001 (0.001) 0.003 (0.002) 0.001 (0.001) 29,429

0.047 (0.032) 0.084 (0.033) 0.011 (0.031) 0.010 (0.033) 0.008 (0.037) 0.069 (0.062) 0.020 (0.028) 0.042 (0.049) 0.022 (0.032) 29,429

Note: All models include age, age squared, two educational dummies, experience, experience squared, tenure, country dummies, annual dummies, interaction between country and annual dummies, quarter of interview dummies, an indicator if the year of the interview differs from the panel wave and an indicator for face-to-face interview. Standard errors robust to heteroskedasticity (clustered at the individual level) are in parentheses. Observations have been weighted using the ECHP personal weights. a Height is also included. b Underweight and overweight categories are also included. Significant at the 10% level; Significant at the 5% level; Significant at the 1% level.

with the two continuous body size measures (BMI and weight), but the correlation goes in the opposite direction: all such correlations are positive. Nevertheless, once we focus on obesity, the results for men are mixed. However, emphasis must be put on statistically significant associations. In

200


the case of obesity, these are found in Denmark, Finland and Portugal. On one hand, obese Danish female workers tend to earn a 9% lower hourly wage than their non-obese counterparts. In Finland and Portugal the female obesity wage gaps are 10 and 7%. On the other hand, we only find a significant relationship in the case of men: obese Belgian employees tend to earn a higher hourly wage (8%) than their non-obese counterparts. It is worth noting that we detected that 16 observations (those falling outside of the [15, 45] BMI interval) from the initial sample of employees were responsible for the statistical negative association for women in Portugal. Given the potential sensibility of our estimates to outliers, we decided to trim the data outside the [15, 45] BMI interval, as we did previously in the labor market status equations. Moreover, we check the robustness of such associations estimating the following augmented specifications: (1) adding occupational dummies and firm size, (2) adding occupational dummies, firm size, and self-reported health status dummies, and (3) adding occupational dummies, firm size, self-reported health status dummies and excluding hampered people.9 In two countries, we find robust statistically significant correlations for all four specifications. In the specification (3) obese Danish women employees are found to earn a 7% lower wage than their non-obese counterparts. In Belgium, obese men tend to earn higher wages than nonobese ones. Nevertheless, none of these associations are statistically significant once the p-values are adjusted for multiple testing. In summary, our results indicate that it is difficult to detect statistically significant associations between wages and obesity, both for men and for women. Comparing our results to those in d’Hombres & Brunello (2005), and bearing in mind that both their sample and estimation strategy differ from ours (they include people between 18 and 65 and they use IV), we do not find that BMI and wages are positively associated in ‘‘beer-belt’’ countries and negatively in ‘‘olive-belt’’ ones. Nevertheless, they do not report the OLS estimations when allowing for differences between olive-belt and beer-belt countries.10 To finish this section, we would like to mention some shortcomings regarding our empirical analysis. First of all, the body size measures used in this study are self-reported, which means that they are potentially measured with error. In fact, there is evidence showing that this measurement error is not random, and the direction of the bias, and its extent, vary systematically with age and sex (Thomas & Frankenberg, 2000). However, their results also show there is little variation in the bias with age from ages 25 to 54, which might possibly also be true for European data. Moreover, since we


201

are estimating equations for men and women separately, the sex bias variation is not a concern. Second, special attention should be devoted to the estimated wage–body size correlations from the augmented specifications. When controlling for occupational dummies and firm size, we should realize that this is only a crude control for taking into account unobserved individual differences associated with jobs and wages. So it is necessary to keep in mind that we are incurring into a bias due to sample selection, provided that there exists non-random sorting into different occupations and/or small versus large firms. For example, it might be the case that obese workers were more likely to be hired by small firms and/or in specific occupations, and hence, once we control for these endogenous variables, we do not find an effect of body size on wage. Similar problems arise when controlling for health status. Third, it should be noted that there are potential selection issues in both labor market status and wage equations. However, since standard selectivity corrections techniques depend on specific functional form assumptions and the exogeneity of the variables of the selection equation, we think this approach would add noise to our descriptive analysis.

4. THE ROLE OF CULTURAL FACTORS AND LABOR MARKET INSTITUTIONS Cawley (2007) formulates an interesting research question on the relationship between obesity and wages: Is there a universal pattern across countries or does it vary with culture and labor market institutions? The empirical evidence in d’Hombres & Brunello (2005) and Lundborg et al. (2007), discussed before, suggests that culture and labor market institutions may be relevant for understanding the associations between obesity and wages. In this section, we take a first look at the role played by different cultural and labor market factors on the associations between obesity and labor market outcomes. Nevertheless, we should be aware of the exploratory character of such analysis, since we have only nine data points, and hence any possible explanation based on such evidence has, at most, a tentative character. On the labor market institutions side, existing empirical evidence shows that unions reduce wage inequality and that this compression effect is strongest in countries where union membership and bargaining coverage are high, and bargaining is centralized and/or coordinated (Blau & Kahn, 1999). There is also some evidence that the degree of collective bargaining coverage (CBC) is positively associated with the relative wage of youths, older workers

202


and women (OECD, 2004). This may also be the case for obese workers. For this reason, we focus on the CBC rate (the number of employees covered by a collective agreement over the total number of employees).11 Our working hypothesis is that the CBC rate which seems to have a positive effect on the relative wages of youths, older and women, tends to reduce also the obesity wage ‘‘penalty’’. However, if firms are constrained in their abilities to adjust wages due to CBC, then an undesired effect might emerge through an increase of the obesity ‘‘penalties’’ in the hiring process. Hence, we might expect to find two effects from higher CBC: (1) a lower obesity wage ‘‘penalty’’ and/or (2) a lower probability of being hired (or more exactly, a higher relative probability of being unemployed with respect to being an employee) for obese workers. Another labor market institution which might be relevant for the association between obesity and labor market outcomes is the percentage of employees receiving health care or medical insurance paid or subsidized by the employer.12 Bhattacharya & Kate Bundorf (2005) find evidence that obese workers earn lower wages than non-obese workers because the cost to employers of providing health insurance for obese workers is higher. However, there might be also a negative effect in the hiring process of obese workers. If employers are limited in their ability to pay lower wages to obese workers, because of the existence of collective agreements, then we might expect to find that firms tend to hire less obese workers. Thus, once again, we might expect to find two effects from higher employer-provided health care (or medical insurance) rates: (1) a higher obesity wage ‘‘penalty’’; and/ or (2) a lower probability of being hired (or more exactly, a higher relative probability of being unemployed with respect to being an employee) for obese workers. On the cultural factors side, we would like to present some evidence on the relationship between stronger cultural norms for thin body types and discrimination against obese people, in terms of both, hiring and payment in the labor market. Measuring cultural factors in a quantitative fashion is always a challenging task. However, we propose the use of two proxies for cultural norms regarding the acceptability of obesity: prevalence of obesity and the degree of social interactions. Under the assumption that cultural norms for thin body types are inversely related to the obesity prevalence in a society, its prevalence can be thought of as being a crude indicator for the social degree of acceptance of obesity in that country. Our working hypothesis is that in societies with high obesity rates, we should expect to find low labor market penalties associated to obesity.


203

We define the degree of social interactions as how often individuals meet friends or relatives not living with them (Costa-Font & Gil, 2004).13 However, we use this at the country level, as a crude measure for the intensity of a country’s social life. Hence, it seems intuitive to think of countries where individuals meet friends more frequently as those countries where social interactions are also more important. If we are willing to assume that body size concerns are more important in countries with more social interactions, and the ability to interact with people is valued in the labor market, we should expect to find higher labor market penalties for obese people in countries with higher levels of social interactions. Fig. 1 contains eight graphs illustrating the relationship between labor market institutions and obesity labor market outcomes for females and males separately. Obesity labor market outcomes are the estimated coefficients from column (3) in Table 1B and Table 2.14 Two interesting results arise from the graphs presented in Fig. 1. First, according to Graph 1.1, for women there is a positive association, albeit weak, between CBC and the probability of being unemployed with respect to being an employee, but Graph 1.2 shows no clear relationship for men. Moreover, once we look at the associations between CBC and the obesity wage gaps for women and men in Graphs 1.3 and 1.4 respectively, we find a strong positive association for women and no clear relationship for men. For women, these results tend to be consistent with our previous hypothesis: the higher the CBC, the lower the ability by the firm to penalize obese female workers in terms of wages, so the firm applies this penalization through the hiring process. For men, no clear conclusion can be drawn. Second, Graphs 1.5 and 1.6 illustrate respectively two opposite strong signed associations – once we do not consider the two influential observations, Ireland in the case of women, and Belgium in the case of men – between employer-provided health care and the probability of being unemployed with respect to being an employee. In terms of wages (Graphs 1.7 and 1.8), no clear relationship emerges either for women or men. Once again, for women, these results seem to be to some extent consistent with our previous hypothesis: the higher the employer-provided health care, the higher the penalization through the hiring process, since firms tend to incorporate in their hiring decisions the higher expected health care costs associated to obese workers. For men, the opposite striking result is drawn in Graph 1.6: the higher the employer-provided health care, the lower the penalization through the hiring process. Unfortunately, we do not have a satisfactory explanation for such a result.

(Pr. Unemployed / Pr. Employee) for Obese

2

Spain Italy

1.5

0

20

40 60 Collective Bargaining Coverage (%)

Denmark

1.5

80

20


Graph 1.3. Female Austria Belgium

-4 Spain -6

Belgium

6

0 Denmark

Finland

-10 40 60 Collective Bargaining Coverage (%)

80

Denmark Italy Spain

-2 0

100

20



Graph 1.5. Female

Spain

20

Denmark

2 1.5

Greece

Finland

Austria Greece Italy Spain Ireland Portugal

1

Denmark 0

0.5

40 60 Employer-provided Health Care to Female (%)

80

100

0

20

Obesity Wage Gap (%)

Belgium

Belgium

4

Italy Portugal

-8

2 Greece

0

Greece

Denmark Italy

Denmark -2

Finland

-10 0

20

100

Ireland Austria Portugal

6

Spain

-6

80

Graph 1.8. Male 8

Austria Ireland

-4

Finland

40 60 Employer-provided Health Careto Male (%)

Graph 1.7. Female -2

100

2.5

Belgium

1

Finland

Belgium

3

1.5

80

Graph 1.6. Male

2.5

Italy Ireland Austria Portugal

Austria

Portugal

2

Portugal -8

2

100

8

4

Italy

20

80

Graph 1.4. Male

-2

0

Finland

Portugal 0

100

Austria

Italy Spain

1 0.5

40 60 Employer-provided Health Care to Female (%)

Fig. 1.

80

100

0

20

Finland Spain

40 60 Employer-provided Health Care to Male (%)

Labor Market Institutions and Obesity Labor Market Outcomes.

80

100



Austria Finland

Portugal

Belgium

2.5 2

Denmark

1

Graph 1.2. Male

3 Belgium

204

Graph 1.1. Female

2.5


205

Overall, the reported evidence suggests that labor market institutions can have unintended negative and positive effects for obese female workers: negative in terms of finding a job, positive in terms of increasing their relative wages. Fig. 2 presents eight graphs illustrating the relationship between cultural factors and obesity labor market outcomes for men and women separately. As we explained before, two indicators for cultural factors have been defined: prevalence of obesity and social interactions. The results regarding the prevalence of obesity and obesity labor market outcomes are shown in Graphs 2.1–2.4. The prevalence of obesity is negatively associated with the relative probability of being unemployed for both men (excluding the influential observation of Belgium) and women, which is in favor of our hypothesis about the social acceptability of obesity: the higher the prevalence of obesity, the higher the social acceptability of it, and hence the lower the penalties for obese people in the society, and in particular in the labor market. Nevertheless, Graph 2.3 shows that a higher prevalence of obesity is associated with a higher wage penalty for obese female workers. This does not need to be inconsistent with our previous hypothesis, if one is willing to assume the existence of negative spillovers: the higher the prevalence of obesity among workers, the lower the productivity of a firm. For obese male workers, the higher the prevalence of obesity, the higher their wage premium. However, we do not want to push this interpretation too much. Finally, Graphs from 2.5 to 2.8 present the associations between labor market outcomes and a crude indicator of social interactions (frequency of meetings with friends and relatives, see note 13). On one hand, looking at Graphs 2.5 and 2.7, it seems to be the case that no clear relation is observed for women. On the other hand, we can realize that, for obese males, the probability of being unemployed with respect to being an employee is negatively related with the degree of social interactions (Graph 2.6). However, it is difficult to assess which kind of relation exists between the obesity wage premium and the degree of social interactions. If one assumes that Ireland can be considered an outlier, then we get a negative relationship, which may be at odds with the association regarding employment status. If, on the contrary, it is not assumed that Ireland is an outlier, then a non-linear relationship cannot be discarded. In general, some evidence is found on the role of culture on obesity labor market outcomes: prevalence of obesity seems to be related to the social acceptance of obesity, which may be translated to labor market outcomes for obese people. Nevertheless, any conclusion taken from


Graph 2.2. Male Belgium

3 2.5

Belgium 2

Spain Austria

Ireland Greece

1 6

1.5 Portugal Finland

12

6

8


Austria

-4 -6

Ireland Belgium

8

Spain

4

Belgium Ireland Austria Portugal

2 Greece

0 Denmark

Finland

-10 6

8 Female Obesity Prevalence (%)

10

6

12


Finland

Denmark

Italy

-2

Spain 8

10

12

14

Male Obesity Prevalence (%)

Graph 2.5. Female

Graph 2.6. Male Belgium

3

2.5 Belgium

2.5

2

Spain

Denmark

2

Italy 1.5

Austria Portugal

1

Finland

Ireland

Greece

1.5 Austria

1

4 4.2 4.4 Frequency of Meetings with Friends and Relatives

4.6

Finland 4 4.2 4.4 Frequency of Meetings with Friends and Relatives

3.8

Austria

Ireland Belgium

-4 -6

Italy

Portugal

3.8

4

Belgium

8

Ireland Austria

4

Portugal

2 Greece

0

Greece

Denmark Finland

-10

4.6

6 Spain

-8

Spain Ireland

Graph 2.8. Male


Greece

Italy Portugal

.5

Denmark 3.8


14

6

Portugal Greece

4

12

Graph 2.4. Male

Italy

-8

Spain Finland

10 Male Obesity Prevalence (%)


Austria ugaland PIrelort

Greece

Italy

.5

Denmark 10

8 Female Obesity Prevalence (%)

1

Finland Denmark

-2 4.2

4.4

Frequency of Meetings with Friends and Relatives

Fig. 2.

4.6

3.8

4

Italy 4.2

4.4

Frequency of Meetings with Friends andRelatives

Cultural Factors and Obesity Labor Market Outcomes.

Spain 4.6


4

Denmark

2

Italy 1.5

206

Graph 2.1. Female 2.5


207

this whole analysis is limited by two main factors. First, although the qualitative results can be useful to think more carefully on the role of cultural factors and labor market institutions, we are only exploiting seven to nine pieces of available information to capture country specific factors, and hence the statistical rigor of such an approach is null. Second, the scatter plots are drawn with many non-statistically significant coefficients.

5. DISCUSSION This article has examined the associations between obesity, employment status (employment, self-employment and unemployment) and wages for several European countries. Moreover, it has analyzed somewhat the role of culture and labor market institutions on such associations. From this empirical analysis, four main results should be emphasized. First, there is weak evidence that obese workers are more likely to be unemployed or tend to be more segregated in self-employment jobs than their non-obese counterparts. Second, our reported estimates also indicate it is extremely difficult to detect statistically significant associations between obesity and wages. Third, the associations tend to be different for men and women, particularly those regarding unemployment and wages. Fourth, these same associations seem to be heterogeneous across countries and can be somewhat explained by the role of some labor market institutions, such as collective bargaining coverage and employer-provided health insurance. Our paper complements previous studies analyzing the relationship between obesity and labor market outcomes (wages in d’Hombres & Brunello, 2005; employment and labor force participation in Sousa, 2005) using the ECHP, but in which the effect of obesity on such outcomes is assumed to be the same across countries, only allowing differences between Northern and Southern European countries. It also complements the recent work by Lundborg et al. (2007) on the effect of obesity on occupational attainment for people aged 50 and above in Europe. Moreover, our study illustrates that, without restricting the relationship between obesity and labor market outcomes to be equal across countries or groups of countries, the statistically significance of such associations is low. There are fruitful avenues for further research. First, studying the effect of obesity on wages across countries within different occupations (e.g., white versus blue collar workers) might show more statistically significant

208


associations. Second, exploring the effect of obesity on wages across countries in different parts of the wage distribution through Quantile regression might provide some new information, since wage ‘‘penalties’’ might be different in different parts of the wage distribution because the position of a worker in the wage distribution reflects characteristics of her job. We think that such extensions can shed light on the relationship between obesity and wages across European countries.

NOTES 1. According to the WHO, an obese individual is defined as having a body mass index (BMI) of 30 or above. BMI is equal to weight in kilograms divided by height in meters squared. 2. See, for example, Register & Williams (1980), Hamermesh & Biddle (1994), Averett & Korenman (1996), Pagań & Da´vila (1997), Cawley (2000, 2004), Behrman & Rosenzweig (2001), Saporta & Halpern (2002), Baum & Ford (2004), and Conley & Glauber (2005, 2007). 3. These effects are estimated using whether or not the individual’s parents have ever taken medication related to obesity (or obesity related diseases) and their mortality status as instruments for individual BMI. 4. We also doubt the validity of the instruments for BMI that have been suggested recently. Morris (2005, 2006) uses mean BMI (and/or prevalence of obesity) across individuals living in the same health authority area. His identification strategy is flawed because of the potential existence of non-random sorting in health authorities where individuals live (depending on unobservable factors related to obesity and occupational attainment). Greve (2005) uses whether or not the individual’s parents have ever taken medication related to obesity (or obesity related diseases) and their mortality status. It is difficult to believe that such instruments are not related to unobservable factors affecting both BMI and labor market outcomes, such as children depression in the case of the later, or parental investments in the former. Lundborg et al. (2007) recognize some of the problems of the instruments they use in their paper. 5. We do not present associations between obesity and labor force participation because of the strong reverse causality. These results are available from the authors upon request. 6. For more detailed information on the ECHP, visit EuroPanel Users Network at http://epunet.essex.ac.uk 7. The standard BMI classification is the following: below 18.5 is underweight, between 18.5 and 25 is healthy (normal), between 25 and 30 is overweight, and 30 and above is obese. 8. The idea behind the Bonferroni’s adjustment is to minimize the probability of making a Type-I error. Although it is a conservative procedure, it is use is justified on two key grounds: (1) we have no a priori well-defined hypothesis on how these


209

associations should differ across countries; and (2) we are searching for associations without pre-established hypotheses. 9. There are nine occupational categories: Legislators, senior officials and managers; Professionals; Technicians and associate professionals; Clerks; Service workers and shop and market sales workers; Skilled agricultural and fishery workers; Craft and related trade workers; Plant and machine operators and assemblers; and Elementary occupations. Firm size is a variable taking value 0 if there is none regular paid employees in the local unit in the current job, 2.5 if 1–4, 12 if 5–19, 34.5 if 20–49, 74.5 if 50–99, 299.5 if 100–499 and 500 if 500 or more. Self-reported health status categories are very good, good, fair, bad and very bad. 10. We also estimated several models for both groups of countries separately and pooled, and we did not find their reported associations. 11. The collective bargaining coverage data come from Employment Outlook OECD (1997, Chapter 3). The collective bargaining coverage rates used in the empirical analysis refer to 1994, except in Finland (1995), Italy (1993) and Portugal (1993). 12. We compute the rate of health insurance provided by employers to female and male workers in each country using the information in the ECHP, where individuals are asked about whether medical insurance or health care is provided by the employer (free or subsidized). 13. This variable is constructed using the question ‘‘How often do you meet friends or relatives not living with you, whether here at home or elsewhere?’’ and recodifying the answers to (5) On most days, (4) Once or twice a week, (3) Once or twice a month, (2) Less often than once a month and (1) Never. 14. We do not report the associations for the estimated coefficients from Table 1A because any association between self-employment in different European countries and labor market institutions is very likely to be contaminated by differences in the regulatory system for starting own businesses across countries. However, these results are available from the authors upon request.

ACKNOWLEDGMENTS This work is motivated by authors’ previous research on obesity and labor market outcomes in Europe, ‘‘Obesidad y Mercado Laboral en la Unioń Europea: una primera aproximacioń’’, included in the ISOS Project, June 2003, European Union Contract Number: VS/2001/0497. We thank John Cawley, Angus Deaton, Marco Gonza´lez-Navarro, Maia Guëll, Alan Krueger, Elaine Liu, Giovanni Mastrobuoni, Jordi Mondria, Cecilia Rouse, Ty Wilde, participants at the Princeton Labor Lunch and one anonymous referee for useful suggestions. Garcia acknowledges financial support from BEC2002-04294 and SEJ2005-08783-C04-01. Quintana-Domeque acknowledges financial support from Banco de Espanã, Fundacioń Rafael del Pino

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and the Princeton University Graduate School. Any remaining error is entirely due to our own responsibility.

REFERENCES Averett, S., & Korenman, S. (1996). The economic reality of the beauty myth. Journal of Human Resources, 31, 304–330. Baum, II, C. Ford, W. (2004). The wage effects of obesity: A longitudinal study. Health Economics, 13, 885–899 Behrman, J., & Rosenzweig, M. (2001). The returns to increasing body weight. PIER, Working Paper 01-052. Bhattacharya, J., & Kate Bundorf, M. (2005). The incidence of the healthcare costs of obesity. NBER, Working Paper 11303. Blau, F., & Kahn, L. (1999). Institutions and laws in the labor market. In: O. Ashenfelter, & D. Card (Eds), Handbook of labor economics (Vol. 3, pp. 1399–1461). Amsterdam: NorthHolland. Cawley, J. (2000). Body weight and women’s labor market outcomes. NBER, Working Paper 7841. Cawley, J. (2004). The impact of obesity on wages. Journal of Human Resources, 39, 451–474. Cawley, J. (2007). The labor market impact of obesity. In: Z. Acs, & A. Lyles (Eds), Obesity, business, and public policy, in press Northampton: Edward Elgar. Cawley, J., Grabka, M., & Lillard, D. (2005). A comparison of the relationship between obesity and earnings in the U.S. and Germany. Journal of Applied Social Science Studies (Schmollers Jahrbuch), 125, 119–129. Conley, D., & Glauber, R. (2005). Gender, body mass and economic status. NBER, Working Paper 11343. Conley, D., & Glauber, R. (2007). Gender, body mass, and socioeconomic status. In: K. Bolin, & J. Cawley (Eds), Advances in health economics and health services research (Vol. 17, pp. 255–278), The Economics of Obesity. Amsterdam: Elsevier. Costa-Font, J., & Gil, J. (2004). Social interactions and the contemporaneous determinants of individuals’ weight. Applied Economics, 36, 2253–2263. Deaton, A. (1997). The analysis of household surveys: A microeconometric approach to development policy. Baltimore: The John Hopkins University Press for the World Bank. Greve, J. (2005). Obesity and labor market outcomes: New Danish evidence. Unpublished manuscript, Cornell University and Aahus School of Business, Denmark. Hamermesh, D., & Biddle, J. (1994). Beauty and the labor market. American Economic Review, 84, 1174–1194. d’Hombres, B., & Brunello, G. (2005). Does obesity hurt your wages more in Dublin than in Madrid? Evidence from the ECHP. IZA, Discussion Paper No. 1704. IOTF. (2002). Obesity in Europe. The case for action, International Obesity Task Force and European Association for the Study of Obesity. IOTF. (2003). Obesity in Europe 2. Waiting for a green light for health? Europe at the crossroads for diet and disease, International Obesity Task Force and European Association for the Study of Obesity. Lundborg, P., Bolin, K., Ho¨jga˚rd, S., & Lindgren, B. (2007). Obesity and occupational attainment among the 50+ of Europe. In: K. Bolin, & J. Cawley (Eds), Advances in


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health economics and health services research (Vol. 17 pp. 221–254), The Economics of Obesity. Amsterdam: Elsevier. Morland, K., Wing, S., Roux, A., & Poole, C. (2002). Neighborhood characteristics associated with the location of food stores and food service places. American Journal of Preventive Medicine, 22, 23–29. Morris, S. (2005). Body mass index and occupational attainment. Tanaka Business School Discussion Papers: TBS/DP05/41. Morris, S. (2006). Body mass index and occupational attainment. Journal of Health Economics, 25, 347–364. OECD. (1997). OECD Employment Outlook 1997. OECD. (2004). OECD Employment Outlook 2004. Pagań, J., & Da´vila, A. (1997). Obesity, occupational attainment, and earnings. Social Science Quarterly, 78, 756–770. Philipson, T. (2001). The world-wide growth in obesity: An economic research agenda. Health Economics, 10, 1–7. Register, C., & Williams, D. (1980). Wage effects of obesity among young workers. Social Science Quarterly, 71, 130–141. Saporta, I., & Halpern, J. (2002). Being different can hurt: Effects of deviation from physical norms on lawyers’ salaries. Industrial Relations, 41, 442–466. Sargent, J., & Blanchflower, D. (1994). Obesity and stature in adolescence and earnings in young adulthood. Analysis of a British Birth Cohort. Archives of Pediatrics and Adolescent Medicine, 148, 681–687. Sarlio-Lahteenkorva, S., & Lahelma, E. (1999). The association of body mass index with social and economic disadvantage in women and men. International Journal of Epidemiology, 28, 445–449. Sousa, S. (2005). Does size matter? A propensity score approach to the effect of BMI on labour market outcomes. Unpublished manuscript, European University Institute and University of Minho. Thomas, D., & Frankenberg, E. (2000). The measurement and interpretation of health in social surveys. RAND, Working Paper DRU-2550-NIA. WHO. (2002). The world health report 2002 – Reducing risks, promoting healthy life. WHO. (2003). Diet, nutrition, and the prevention of chronic diseases. WHO Technical Report 916. Wooldridge, J. (2001). Econometric analysis of cross section and panel data. Cambridge: The MIT Press.

APPENDIX Variable

Female N

Descriptive statistics: Austria Age 5,949 Third level education 5,949 (ISCED 5–7)

Male

Mean

SD

N

Mean

SD

39.46 0.09

8.35 0.29

5,859 5,859

39.04 0.08

8.39 0.27

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APPENDIX (Continued ) Variable

Second stage of secondary level education (ISCED 3) Less than second stage of secondary education (ISCED 0–2) Body mass index Prevalence of obesity (BMI Z30) Participation in the labor force Employee (working for an employer more than 15 h/week) Self-employed (working more than 15 h/week) Unemployed Log (hourly wage)

Female

Male

N

Mean

SD

N

Mean

SD

5,949

0.66

0.47

5,859

0.81

0.39

5,949

0.25

0.43

5,859

0.11

0.32

5,949 5,949

23.93 0.08

4.08 0.28

5,859 5,859

25.75 0.11

3.42 0.31

5,949

0.69

0.46

5,859

0.92

0.27

4,263

0.79

0.41

5,601

0.81

0.40

4,263

0.16

0.37

5,601

0.15

0.36

4,263 1,978

0.04 4.50

0.21 0.38

5,601 3,093

0.04 4.72

0.19 0.35

39.59 0.43

8.06 0.49

4,539 4,539

40.12 0.41

8.04 0.49

0.33

0.47

4,539

0.34

0.47

0.24

0.43

4,539

0.25

0.43

23.69 0.09

4.36 0.28

4,539 4,539

25.45 0.11

3.92 0.32

0.71

0.45

4,539

0.91

0.28

0.79

0.41

4,344

0.81

0.39

Descriptive statistics: Belgium Age 5,056 Third level education 5,056 (ISCED 5–7) Second stage of 5,056 secondary level education (ISCED 3) Less than second stage of 5,056 secondary education (ISCED 0–2) Body mass index 5,056 Prevalence of obesity 5,056 (BMI Z30) Participation in the labor 5,056 force 4,033 Employee (working for an employer more than 15 h/week)


213


Self-employed (working more than 15 h/week) Unemployed Log (hourly wage)

Female N

Mean

SD

N

Mean

SD

4,033

0.09

0.29

4,344

0.14

0.34

4,033 903

0.12 5.72

0.33 0.29

4,344 1,211

0.05 5.81

0.22 0.32

39.17 0.34

8.54 0.47

4,495 4,495

39.41 0.31

8.46 0.46

0.52

0.50

4,495

0.55

0.50

0.14

0.35

4,495

0.14

0.35

23.99 0.09

4.09 0.28

4,495 4,495

25.34 0.09

3.48 0.29

0.82

0.38

4,495

0.90

0.29

0.89

0.31

4,197

0.88

0.32

0.04

0.18

4,197

0.09

0.28

0.07 4.39

0.26 0.27

4,197 2,556

0.03 4.48

0.18 0.27

4,711 4,711

40.62 0.44

8.56 0.50

4,666 4,666

40.47 0.32

8.57 0.47

4,711

0.41

0.49

4,666

0.49

0.50

4,711

0.16

0.36

4,666

0.19

0.39

Descriptive statistics: Denmark Age 4,460 Third level education 4,460 (ISCED 5–7) Second stage of 4,460 secondary level education (ISCED 3) Less than second stage of 4,460 secondary education (ISCED 0–2) Body mass index 4,460 Prevalence of obesity 4,460 (BMIZ30) Participation in the labor 4,460 force 3,936 Employee (working for an employer more than 15 hours/week) Self-employed (working 3,936 more than 15 h/week) Unemployed 3,936 Log (hourly wage) 1,402 Descriptive statistics: Finland Age Third level education (ISCED 5–7) Second stage of secondary level education (ISCED 3) Less than second stage of secondary education (ISCED 0–2)

Male

214



Female

Male

N

Mean

SD

N

Mean

SD

4,711 4,711

24.74 0.12

4.40 0.32

4,666 4,666

25.98 0.12

3.67 0.32

4,711

0.81

0.39

4,666

0.90

0.30

4,118

0.81

0.39

4,443

0.74

0.44

4,118

0.11

0.32

4,443

0.21

0.41

4,118 1,487

0.07 3.79

0.26 0.30

4,443 2,238

0.05 3.93

0.22 0.33

9,249 9,249

39.33 0.18

8.69 0.38

8,929 8,929

39.29 0.21

8.68 0.41

9,249

0.35

0.48

8,929

0.37

0.48

9,249

0.47

0.50

8,929

0.42

0.49

9,249 9,249

24.50 0.08

4.06 0.27

8,929 8,929

26.19 0.10

3.39 0.29

9,249

0.52

0.50

8,929

0.91

0.29

5,359

0.56

0.50

8,535

0.54

0.50

5,359

0.33

0.47

8,535

0.41

0.49

5,359 1,595

0.11 7.06

0.32 0.37

8,535 2,692

0.05 7.25

0.22 0.40

Descriptive statistics: Ireland Age 5,055

39.40

8.65

4,841

39.50

8.64

Body mass index Prevalence of obesity (BMI Z30) Participation in the labor force Employee (working for an employer more than 15 hours/week) Self-employed (working more than 15 h/week) Unemployed Log (hourly wage) Descriptive statistics: Greece Age Third level education (ISCED 5–7) Second stage of secondary level education (ISCED 3) Less than second stage of secondary education (ISCED 0–2) Body mass index Prevalence of obesity (BMI Z 30) Participation in the labor force Employee (working for an employer more than 15 h/week) Self-employed (working more than 15 h/week) Unemployed Log (hourly wage)


215


Third level education (ISCED 5–7) Second stage of secondary level education (ISCED 3) Less than second stage of secondary education (ISCED 0–2) Body mass index Prevalence of obesity (BMI Z30) Participation in the labor force Employee (working for an employer more than 15 h/week) Self-employed (working more than 15 h/week) Unemployed Log (hourly wage) Descriptive statistics: Italy Age Third level education (ISCED 5–7) Second stage of secondary level education (ISCED 3) Less than second stage of secondary education (ISCED 0–2) Body mass index Prevalence of obesity (BMI Z30) Participation in the labor force

Female

Male

N

Mean

SD

N

Mean

SD

5,055

0.20

0.40

4,841

0.20

0.40

5,055

0.41

0.49

4,841

0.38

0.48

5,055

0.39

0.49

4,841

0.42

0.49

5,055 5,055

24.39 0.09

4.14 0.29

4,841 4,841

25.98 0.10

3.42 0.30

5,055

0.57

0.50

4,841

0.88

0.33

2,982

0.89

0.31

4,552

0.70

0.46

2,982

0.07

0.25

4,552

0.24

0.42

2,982 1,586

0.04 1.75

0.20 0.38

4,552 2,056

0.07 1.95

0.25 0.44

15,937 15,937

38.61 0.12

8.72 0.32

15,708 15,708

38.43 0.11

8.66 0.32

15,937

0.43

0.50

15,708

0.42

0.49

15,937

0.45

0.50

15,708

0.47

0.50

15,937 15,937

23.13 0.05

3.69 0.22

15,708 15,708

25.27 0.07

3.33 0.26

15,937

0.51

0.50

15,708

0.85

0.36

216



Employee (working for an employer more than 15 h/week) Self-employed (working more than 15 h/week) Unemployed Log (hourly wage)

Female

Male

N

Mean

SD

N

Mean

SD

9,371

0.70

0.46

14,517

0.65

0.48

9,371

0.16

0.37

14,517

0.26

0.44

9,371 2,295

0.14 2.36

0.35 0.33

14,517 4,201

0.09 2.51

0.28 0.33

39.24 0.13

8.85 0.34

10,293 10,293

38.19 0.08

8.80 0.27

0.13

0.34

10,293

0.13

0.34

0.74

0.44

10,293

0.78

0.41

24.78 0.11

4.24 0.31

10,293 10,293

25.78 0.09

3.34 0.29

0.69

0.46

10,293

0.90

0.29

0.76

0.43

9,603

0.74

0.44

0.18

0.38

9,603

0.23

0.42

0.07 6.2

0.25 0.48

9,603 5,381

0.03 6.43

0.18 0.43

38.20 0.28

8.66 0.45

11,890 11,890

38.08 0.27

8.52 0.44

0.19

0.39

11,890

0.21

0.41

Descriptive statistics: Portugal Age 10,515 Third level education 10,515 (ISCED 5–7) 10,515 Second stage of secondary level education (ISCED 3) Less than second stage of 10,515 secondary education (ISCED 0–2) Body mass index 10,515 Prevalence of obesity 10,515 (BMI Z30) Participation in the labor 10,515 force 7,743 Employee (working for an employer more than 15 h/week) Self-employed (working 7,743 more than 15 h/week) Unemployed 7,743 Log (hourly wage) 3,630 Descriptive statistics: Spain Age 12,110 Third level education 12,110 (ISCED 5–7) 12,110 Second stage of secondary level education (ISCED 3)


217


Less than second stage of secondary education (ISCED 0–2) Body mass index Prevalence of obesity (BMI Z30) Participation in the labor force Employee (working for an employer more than 15 h/week) Self-employed (working more than 15 h/week) Unemployed Log (hourly wage)

Female

Male

N

Mean

SD

N

Mean

SD

12,110

0.53

0.50

11,890

0.51

0.50

12,110 12,110

23.96 0.09

4.13 0.28

11,890 11,890

26.11 0.13

3.71 0.34

12,110

0.50

0.50

11,890

0.86

0.35

6,938

0.71

0.46

11,092

0.72

0.45

6,938

0.13

0.34

11,092

0.20

0.40

6,938 3,095

0.16 6.70

0.37 0.45

11,092 6,001

0.09 6.89

0.28 0.45


OBESITY AND OCCUPATIONAL ATTAINMENT AMONG THE 50+ OF EUROPE Petter Lundborg, Kristian Bolin, So¨ren Ho¨jga˚rd, and Bjo¨rn Lindgren ABSTRACT This paper brings a European perspective to the mainly U.S.-based literature on the relationship between obesity and labour-market outcomes. Using micro-data on workers aged 50 and over from the newly developed SHARE database, the effects of obesity on employment, hours worked, and wages across 10 European countries were analysed. Pooling all countries, the results showed that being obese was associated with a significantly lower probability of being employed for both women and men. Moreover, the results showed that obese European women earned 10% less than their non-obese counterparts. For men, however, the effect was smaller in size and insignificant. Taking health status into account, obese women still earned 9% less. No significant effect of obesity on hours worked was obtained, however. Regressions by country-group revealed that the effects of obesity differed across Europe. For instance, the effect of obesity on employment was greatest for men in southern and central Europe, while women in central Europe faced the greatest wage penalty. The results in this study suggest that the ongoing rise in the prevalence of obesity in Europe may have a non-negligible effect on the European labour market. The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 219–251 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17009-0

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PETTER LUNDBORG ET AL.

1. INTRODUCTION Obesity, defined as a body mass index (BMI) Z 30,1 is associated with a number of adverse health outcomes.2 Obesity also appears to be associated with adverse labour-market outcomes. Controlling for a large number of individual characteristics, previous studies indicate that for women, and for white women in particular, obesity is associated with lower wage rate (Register & Williams, 1990; Sargent & Blanchflower, 1994; Averett & Korenman, 1996, 1999; Pagan & Davila, 1997; Cawley, 2004; Harper, 2000; Baum & Ford, 2004; Cawley, Grabka, & Lillard, 2005; Cawley & Danziger, 2005; d’Hombres & Brunello, 2005; Conley & Glauber, 2006) and lower probability of being employed (Pagan & Davila, 1997; Haskins & Ransford, 1999; A`sgeirsdo´ttir, 2004; Cawley & Danziger, 2005).3 For men, Register and Williams (1990), Averett and Korenman (1996), Harper (2000) and Baum and Ford (2004) found a negative association between obesity and wage rate for white and Hispanic, but a positive association for black men.4 A`sgeirsdo´ttir (2004) and Garcia and Quintana-Domeque (2006) did not find any statistically significant results for the association between obesity and employment probability for men. Despite the fact that only obesity (and not overweight or weight-increases of normal weight people) has been shown to have adverse health effects (SBU, 2002), there are some studies indicating a negative association between the value of BMI and wage rate and/or employment probability (Sargent & Blanchflower, 1994; Cawley, 2000, 2004; A`sgeirsdo´ttir, 2004; Baum & Ford, 2004; d’Hombres & Brunello, 2005). These results indicate that there may be other factors than the health effects of obesity that account for the observed correlation between obesity and labour-market performance. In the literature, a number of mechanisms have been suggested to be responsible for the observed correlation between labour-market outcomes and obesity. These can be broadly divided into those that work through the productivity of the individual – and, hence, support the view that obesity causes adverse labour-market outcomes – and those that account for other channels of influence: first, obesity may affect the individual’s productivity – the adverse effect on health is assumed to impair productivity5 – and/or trigger discrimination – employers, co-workers or customers may have preferences for non-obese employees.6 Second, the correlation may be due to reversed causality and/or that obesity and labour-market outcomes are caused by some third unobserved variable (for instance, time preferences).7 Most previous studies on the effects of obesity on labour-market outcomes originate from the United States. There are some recent European

Obesity and Occupational Attainment Among the 50+ of Europe

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studies, however, using the European Household Panel (d’Hombres & Brunello, 2005; Garcia & Quintana-Domeque, 2006). Moreover, there are studies from the United Kingdom (Sargent & Blanchflower, 1994; Harper, 2000; Morris, 2006), Germany (Cawley et al., 2005) and work in progress from Iceland (A`sgeirsdo´ttir, 2004). Most previous U.S. studies have concerned a relatively young population, since the studies have been mainly based on data from the National Longitudinal Survey of Youth. Only Hamermesh and Biddle (1994), Cawley et al. (2005), and Conley and Glauber (2006) contained both men and women above the age of 40. In contrast to the United States, the European labour market comprises several language areas and several different labour-market legislations. In addition, there may be differences in the general living conditions between the U.S. and Europe that are relevant for the way in which employers regard obesity. Thus, in a European context, the question of to what extent the effects of obesity on labour-market outcomes differ between different institutional settings is germane, and, moreover, in principle possible to analyse. Accordingly, this paper explores a new European cross-national micro-database in order to study this issue. In Europe, the prevalence of obesity has risen dramatically during the past decades (IOTF & EASO, 2002; OECD, 2005, p. 87). It has been estimated that at least 135 million EU citizens are affected, and about 70 million in the countries applying to join the EU. Moreover, in the southern parts of Europe, a third of all children are classified as overweight. The direct health care costs of obesity have been estimated to 2–5% of total health care costs in western countries, such as Australia, France, Netherlands, Sweden, and U.S.A. (Levy, Levy, Le Pen, & Basdevant, 1995). Our study used data from the Survey of Health, Ageing and Retirement in Europe (SHARE), which includes information from 11 European countries on men and women of age 50 and older and their spouses. Thus, our contributions will be (1) to analyse whether or not the effect of obesity on employment, wages and hours worked varies between the different institutional settings of Europe, (2) to analyse whether or not obesity affects employment, wages and hours worked for those older than 50 years of age, (3) to analyse whether or not the effects found for the U.S. labour market also apply for the European labour market and (4) to analyse to what extent the effects of obesity on employment, wages and hours worked runs through reduced health status. The paper proceeds as follows. First, the data will be presented. Second, the empirical methods used in the paper are described. Third, the results are

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presented. The paper is concluded with a discussion of the results and some suggestions for further research.

2. DATA The Survey of Health, Ageing and Retirement in Europe is a multidisciplinary and cross-national micro-database containing approximately 22,000 Europeans over the age of 50 and from 11 countries. The first wave of data was collected in 2004. Our analyses included only people who were employed, leaving us with a sample of between 4,189 and 4,330 individuals, depending on the outcome studied, distributed across 10 countries. The database contains representative samples from the non-institutionalised population in respective participating country. The countries represent Northern Europe (Denmark and Sweden), Central Europe (Austria, France, Germany, Switzerland, Belgium and the Netherlands) and the Southern Europe (Spain, Italy and Greece). In this paper, data from Belgium was not included, since it was not yet collected. The database comprises information on self-reported height and weight, which was used to construct BMI, which, in turn, was used to construct our indicator of obesity. Moreover, the database contains health-related variables, for instance, self-reported health, physical functioning, cognitive functioning, psychological health, well-being, life satisfaction and health-behaviour, for instance, the utilisation of healthcare facilities; labour-market variables, for instance, wages, hours worked, current work activity, job characteristics, opportunities to work past retirement age; economic variables, for instance, sources and composition of current income, wealth and consumption. Other variables include education, housing, and social support variables, for instance, assistance within families, transfers of income and assets, and social networks. SHARE follows the design of the U.S. Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA). A description of methodological issues can be found in Bo¨rsch-Supan and Ju¨rges (2005). 2.1. Dependent Variables Three different labour-market outcomes were addressed in this paper: employment, hourly wage rate and hours worked (past month). These variables were constructed, using information given by the respondents themselves. In order to obtain hours worked, the respondent was first asked to state the


223

number of contracted hours each month in his or her job, excluding meal breaks and any paid or unpaid overtime. Next, the respondent was asked about the actual number of hours worked, regardless of the number of contracted hours, but including any paid or unpaid overtime. In the sample, the mean number of actual hours worked was somewhat larger than the mean number of contracted hours; 35 versus 33 h. To assess the respondent’s hourly wage rate, the respondent was first asked about the frequency of payment. Second, he or she was asked about how much the last payment was, before deductions for tax, national insurance or pension and health contributions, union dues and so on. Together with the information on contracted hours, the hourly wage rate was then calculated.8 2.2. Obesity A measure of BMI was constructed using self-reported information on height and weight. Obesity was defined as having a BMI of 30 or above. The average person in the sample had a BMI of 26, and 14% were classified as obese. 2.3. Background Variables Essential background information such as age, gender, years of education, marital status, number of children and country of birth were included as explanatory variables. The typical person was a married, 55-year-old person with two children. He or she had 12 years of education and was born in the country where he or she resided. Smokers constituted 28% of the sample and the typical person consumed alcohol once or twice a week. Two additional variables, potentially affecting the wage rate, were included. These were an indicator of part-time work and a variable indicating whether the person worked in the public or in the private sector. In the sample, 18% worked part-time and 28% in the public sector. Four health indicators were used. The first variable was self-reported health, measured on a 5-point scale where 1 indicated perfect health and 5 bad health. The second and third variables indicated the number of chronic conditions and symptoms, respectively, that the respondent reported. Finally, a variable indicating limitations to mobility was created, taking on the value one, if the respondent reported any such limitations. Separate dummies for each country were included in order to account for unobserved factors at the country-level affecting the wage rate and hours worked. A description of all variables used is given in Table 1.

224

Table 1.


Descriptives. All Variables. Mean and Standard Deviation. Mean

Dependent variables Employed Log of contracted hoursa Log of hourly wage ratea Independent variables Obese Age Age squared Female Years of education Public sectora Part-timea Country of birth Married Smoker Former smoker Frequent alcohol consumption Self-reported bad health Number of chronic conditions Symptoms Mobility Austria Spain Netherlands Germany Italy France Denmark Greece Switzerland Sweden Instruments Household obese Oldest child Only sisters a

1 if employed Log(1+contracted hours worked) Log(1+hourly wage rate)

SD

0.46 2.27 3.48

0.50 0.61 0.44

1 if obese (BMI Z 30) Age in years Age2 1 if female Total number of years of education 1 if working in the public sector 1 if working part-time (20 h or less per week) 1 if born in country where the interview took place 1 if married 1 if smoker 1 if former smoker 1 if drinking once a week or more

0.18 56.26 3191.19 0.57 11.01 0.28 0.16

0.39 5.12 562.91 0.50 4.16 0.45 0.37

0.91

0.29

0.77 0.27 0.26 0.56

0.42 0.44 0.44 0.50

1 if self-reported bad or very bad health Number of chronic conditions out of 14 listed Number of symptoms out of 11 listed Number of limitations with mobility out of 10 described

0.16 1.14

0.36 1.23

1.21 0.93

1.40 1.66

0.10 0.07 0.15 0.14 0.13 0.08 0.09 0.09 0.04 0.10

0.30 0.26 0.36 0.35 0.33 0.27 0.29 0.29 0.21 0.30

0.11

0.31

0.29 0.19

0.45 0.40

1 if any other member of the household is obese 1 if the respondent was the oldest child 1 if the respondent have only sisters

Conditional on being employed.


225

3. METHOD The first outcome studied was whether or not the respondent was employed. Since the indicator of employment was a binary variable, we used the standard probit model. The employment equation was formulated as: E i ¼ f ðOi ; HS i ; X i ; C i ; i Þ,

(1)

where Ei takes on the value one if the person i is employed. Oi is an indicator of obesity, HSi indicates health status, Xi socio-economic and demographic characteristics, Ci country and ei an unobserved error term. Following the convention in the literature on obesity/labour-market outcomes, the wage equation was formulated as: ln W i ¼ f ðOi ; HS i ; X i ; J i ; C i ; Zi Þ,

(2)

where lnWi denotes the log of the hourly wage rate of person I, Ji job characteristics and Zi an unobserved error term. The corresponding equation for hours worked is as follows: ln HW i ¼ f ðOi ; HS i ; ln W i ; X i ; J i ; C i ; ui Þ,

(3)

where lnHWi denotes the log of hours worked. The independent variables are the same as in Eq. (2), with the exceptions that the log of the hourly wage rate now was included, and the indicator of part-time work excluded. Eqs. (2) and (3) were estimated using OLS regression. Since obesity is a potentially endogenous variable in the employment, wage- and hours-worked regressions, due to reverse causality and/or because both obesity and wages/hours worked are related to some third factor, instrumental-variables (IVs) methods were considered. In the case of the wage- and hours-worked equations, the conventional 2SLS-estimator used in most previous studies (e.g. Behrman & Rosenzweig, 2001; Cawley, 2000, 2004; A`sgeirsdo´ttir, 2004) may, however, produce inconsistent standard errors, since the use of individual-level cross-section data means that heteroscedasticity is likely to be a problem. If so, tests of overidentifying restrictions and endogeneity may be invalid. In order to better deal with heteroscedasticity of unknown form, we used the IV-GMM estimator, described by Baum, Schaffer, and Stillman (2003), which allows for efficient estimation under unknown heteroscedasticity. Since GMM comes at the cost of possibly poor finite sample performance, we tested for heteroscedasticity using the Pagan and Hall (1983) test, designed for testing for the presence of heteroscedasticity in the context of IV estimation.9

226


In the employment equation, where the dependent variable was dichotomous, we used the Amemiya generalized least squares (AGLS) estimator, which estimates a probit model with an endogenous explanatory variable. The computations were performed using the IVPROB programme in STATA, which provides asymptotically efficient standard errors. In the first-stage regression, the endogenous explanatory variable was treated as linear functions of the instruments and the exogenous variables. In the second stage, the prediction from the first stage was included as an explanatory variable in the main equation. For a summary of how AGLS works, see Maddala (1983, pp. 247–252). Good instruments should be (1) correlated with the potentially endogenous right-hand-side variable and (2) orthogonal to the error process. We tested the former requirement by conducting an F-test of the joint significance of the instruments, as suggested by Bound, Jaeger, and Baker (1995). It has been suggested, as a rule of thumb, that an F-statistic below 10 is a cause of concern, since it signals a problem of weak instruments (Staiger & Stock, 1997). The second requirement, the validity of the overidentifying restrictions, was tested in the wage- and hours-worked equations via the J statistic by Hansen (1982). This is a commonly used test for testing the suitability of the instruments within the context of GMM. In the employment equation, the validity of the exclusion restrictions was tested by including all but one of the instruments in the structural equation that control for endogeneity and testing their joint significance with a Wald test (Bollen et al., 1995). The test result does not depend on which instrument is left out. With valid exclusion restrictions, these should not be significant predictors of employment after controlling for obesity. Once instruments that fulfilled our requirements were found, we tested the hypothesis that obesity could be treated as an exogenous variable in the regressions. In the wage- and hours-worked equations, we used a version of the test allowing for robust estimation, the C-statistic (Baum et al., 2003). In the probit regression, we computed a test of exogeneity for a probit model proposed by Smith and Blundell (1986). Finding decent instruments, in order to deal with the potential endogeneity of obesity, is a challenging task.10 We struggled a great deal with various candidates before settling for the following. The two first instruments related to the birth order and the sibling sex composition of the respondent. The birth order of the respondent, and especially being the oldest child, has been found to be associated with a greater risk of developing obesity as an adult (Stettler et al., 2000; Celi et al., 2003). The effect has been attributed to various factors connected to the gestational period


227

and postnatal period. Birth order has also been found to predict educational outcome as an adult (Conley & Glauber, 2005). However, since we control for education, birth order should at least not be correlated with the unobserved factors also determining occupational attainment for that reason. Moreover, to the extent that birth order affects later health outcomes, we also control for the latter in the regressions.11 Experimenting with various variables reflecting the number of siblings and their sex composition, one variable was in particular found to predict obesity among women. This was an indicator variable of whether or not having only sisters. The variable was used as a proxy for growing up with sisters only.12 We believe that the effect of the variable may reflect the choices of food and activities in the household when growing up, and thereby affect the risk of developing obesity later in life.13 Finally, we used a variable indicating the presence of any other obese household member.14 Unfortunately, we could not construct a variable indicating only the presence of an obese child or parent. Such an indicator would have been preferred, since parents share about half of their genes with each child and about half of the variation in weight has been found to be genetic (Commuzie & Allison, 1998). In SHARE, however, since eligible participants were all household members 50+ and their partners, children below 50 were not included. Since very few had children 50+ living in the household, an indicator variable of child obesity was not meaningful. Moreover, the fraction having an obese parent living in the household was too small to make the creation of an indicator variable of parent obesity meaningful. Our instrument variable will, therefore, indicate the presence of an obese spouse. Spousal correlation in eating behaviours and BMI has been found in a number of studies (Tambs et al., 1991; Provencher et al., 2005). Moreover, there is evidence of assortative mating by bodyweight (Silventoinen, Kaprio, Lahelma, Viken, & Rose, 2003). Our identifying assumption is thus that the presence of an obese spouse is correlated with the obesity status of the respondent, but not with the unobserved factors affecting occupational attainment. We admit that this assumption is stronger than would have been the case, if we were able to use the presence of an obese child/parent as instrument. For example, bias may result if the weight of the spouse is still correlated with some unobserved trait or background characteristic of the respondent that may also affect his/her occupational attainment. For instance, living in a low socio-economic neighbourhood may lead to both worse employment prospects and an increased risk of developing obesity. Spousal obesity may, thus, be correlated with unobserved neighbourhood characteristics of the respondent. More directly, as suggested by an

228


anonymous referee, the instrument may reflect reverse causality if poor people are more likely to be obese, and obese people tend to marry each other. The results should, therefore, be interpreted with some caution.

4. RESULTS 4.1. Descriptive Statistics In Table 2, the prevalence of obesity, the mean employment rate, median and mean wages and mean hours worked by country are shown. As revealed in the table, the prevalence of obesity varies to a substantial extent across Europe. A north–south gradient can be detected, where the northern countries Denmark and Sweden have a lower prevalence of obesity than most countries in central and southern Europe, Switzerland being the exception. In turn, the countries in southern Europe have higher obesity rates than several of the countries in central Europe. The employment rate is greatest in the northern countries Sweden and Denmark, and lowest in Austria, Spain and Italy. Median wages are lower in southern than in central and northern Europe. In the Table, a pattern can be detected where the countries with the lowest median wages, e.g. Spain, Italy, Greece and Austria, also have the highest prevalence of obesity. For hours worked, no similar pattern can be immediately detected. Hours worked are, for instance, roughly similar in northern and southern Europe, whereas the prevalence of obesity differs quite dramatically. Table 2.

Southern group Spain Italy Greece Central group France Netherlands Germany Austria Switzerland Nordic group Denmark Sweden n

Descriptives. Main Variables. Mean and Median by Country (SD). Obesity (Mean)

Employed (Mean)

Wage Rate (Median)

Wage Rate (Mean)

0.24 (0.43) 0.19 (0.39) 0.21 (0.40)

0.34 (0.47) 0.28 (0.45) 0.47 (0.50)

7.01 10.18 9.11

11.92 (26.66) 13.52 (10.29) 13.01 (16.30)

40 36 40

36.16 (10.41) 33.35 (10.36) 35.62 (10.73)

0.19 0.16 0.16 0.23 0.14

0.46 0.45 0.44 0.29 0.61

(0.50) (0.50) (0.50) (0.45) (0.49)

12.44 16.75 15.19 10.97 22.40

16.93 23.09 19.48 19.67 32.28

(23.95) (32.64) (28.76) (66.34) (54.60)

35 33.6 37.5 38.5 40

33.41 29.64 32.37 35.28 35.78

0.62 (0.49) 0.72 (0.45) 10,149

21.22 14.12 3,960

23.19 (14.59) 15.82 (66.35) 3,960

37 40 4,040

34.72 (9.71) 36.70 (8.24) 4,040

(0.39) (0.37) (0.37) (0.42) (0.35)

0.15 (0.36) 0.15 (0.36) 10,149

Hours Worked (Median)

Hours Worked (Mean)

(10.22) (10.70) (11.78) (13.27) (15.28)


229

4.2. Employment Table 3 shows the results from the regressions on the employment probability. Excluding the health status variables, the marginal effect of being obese was negative (0.07) and significant for the full sample. The effect differed by gender, though. For women, the effect was 0.05, whereas the corresponding effect for men was twice as high, 0.10. Including the health-status variables, however, substantially reduced the magnitude of the effect of obesity on employment probabilities, as shown in columns 5–7. For women the effect was reduced from 0.05 to 0.01, while for the corresponding reduction was from 0.10 to 0.04. Moreover, the effects were no longer statistically significant. Consequently, it seems that the effect of obesity on employment mainly went through its effect on the health of the individual. Several of the health variables showed significant effects on the probability of employment. Self-reported bad health, for instance, was associated with a large reduction in the employment probability for both men and women. The effect was to lower the employment probability by 0.21 for men and 0.13 for women. For men, other results were that age, the number of children, education, being married, being born in the resident country and being a frequent alcohol consumer showed significant and positive effects on the probability of employment, while the opposite effect was obtained for age squared, being a smoker, number of chronic conditions, reduced mobility, bad self-reported health and the number of symptoms. For women, age, education and being a frequent alcohol consumer showed significant and positive effects on the employment probability, whereas age squared, the number of children, being married, the number of chronic conditions, reduced mobility and self-reported bad health showed the opposite effects. Next, we treated obesity as an endogenous variable. A summary of the results are shown in Table 4. For men, two instruments indicating whether or not the respondent was the oldest child and whether there was any other obese person in the household was used. These were found to predict obesity well in the first-stage regression. An F-test of joint insignificance of the instruments yielded an F-statistics of 21.88, and the hypothesis was rejected at the 1% level. The F-statistic was well above the suggested rule of thumb (Staiger & Stock, 1997). Moreover, the instruments passed the test of overidentifying restrictions, suggesting that they could be validly excluded from the main equation. The effect of treating obesity as endogenous in the regression for men was to increase the magnitude of the marginal effect to 0.296. The effect was, however, far from being significant. Moreover, Smith–Blundell

Effect of Obesity on Probability of Employment for Full Sample and for Women and Men Separately. Probit Regression with Robust Standard Errors. Health Status Excluded Full Sample

Health Status Included Men

Full Sample

Women

Men

(0.019) (0.020) (0.000) (0.006)

0.101 (0.023) 0.287 (0.042) 0.003 (0.000) 0.012 (0.007)

0.021 (0.016) 0.257 (0.022) 0.003 (0.000) 0.003 (0.005) 0.206 (0.012) 0.026 (0.002) 0.044 (0.014) 0.017 (0.015) 0.070 (0.012) 0.063 (0.022) 0.004 (0.015) 0.027 (0.006) 0.038 (0.005) 0.170 (0.018) 0.003 (0.006) 0.320 (0.018) 0.376 (0.015) 0.298 (0.020) 0.210 (0.027) 0.409 (0.012) 0.281 (0.020) 0.350 (0.016) 0.352 (0.017) 0.229 (0.022) 9,973

0.007 (0.020) 0.193 (0.021) 0.002 (0.000) 0.019 (0.006)

0.039 (0.025) 0.309 (0.045) 0.003 (0.000) 0.018 (0.007)

0.029 (0.002) 0.024 (0.018) 0.010 (0.019) 0.068 (0.015) 0.019 (0.026) 0.055 (0.018) 0.033 (0.008) 0.030 (0.006) 0.130 (0.022) 0.008 (0.007) 0.341 (0.013) 0.349 (0.014) 0.299 (0.017) 0.238 (0.023) 0.354 (0.012) 0.210 (0.023) 0.328 (0.015) 0.325 (0.016) 0.196 (0.023) 5,660

0.018 (0.003) 0.081 (0.023) 0.032 (0.022) 0.051 (0.021) 0.133 (0.036) 0.077 (0.024) 0.018 (0.009) 0.054 (0.008) 0.214 (0.029) 0.022 (0.010) 0.126 (0.046) 0.359 (0.035) 0.201 (0.047) 0.091 (0.057) 0.441 (0.029) 0.374 (0.034) 0.313 (0.035) 0.310 (0.037) 0.234 (0.042) 4,313

0.075 (0.015) 0.240 (0.021) 0.003 (0.000) 0.007 (0.004) 0.214 (0.012) 0.029 (0.002) 0.067 (0.014) 0.003 (0.014) 0.094 (0.012) 0.072 (0.021) 0.011 (0.014)

0.053 0.183 0.002 0.022

0.032 (0.002) 0.042 (0.017) 0.021 (0.019) 0.086 (0.015) 0.037 (0.025) 0.042 (0.018)

0.024 (0.002) 0.113 (0.022) 0.004 (0.021) 0.090 (0.020) 0.121 (0.036) 0.091 (0.023)

0.309 (0.019) 0.378 (0.015) 0.307 (0.020) 0.192 (0.028) 0.410 (0.013) 0.277 (0.020) 0.349 (0.016) 0.365 (0.016) 0.245 (0.021) 9,983

0.341 (0.014) 0.355 (0.014) 0.304 (0.017) 0.226 (0.025) 0.354 (0.013) 0.205 (0.024) 0.330 (0.016) 0.331 (0.016) 0.208 (0.023) 5,662

0.112 (0.044) 0.352 (0.034) 0.231 (0.044) 0.082 (0.057) 0.454 (0.027) 0.372 (0.033) 0.315 (0.034) 0.356 (0.034) 0.261 (0.040) 4,321

Note: Robust standard errors in parentheses. Significant at 5%. Significant at 1%.


Obese Age Age squared Number of children Female Years of education Smoker Former smoker Frequency of alcohol consumption Country of birth Married Number of chronic conditions Mobility Self-reported health Symptoms Greece Italy Spain Switzerland Austria France Netherlands Germany Denmark Observations

Women

230

Table 3.


231

Table 4. Effects of Obesity on Employment. Obesity Treated as an Endogenous Variable. Instrumental Variables Probit Regression. Marginal Effects. Women Obese F-test of first-stage instruments (p-value of null of jointly ¼ 0) p-value of null hypothesis of valid exclusion restrictions Smith–Blundell exogeneity test

0.43 (0.14)* 42.68 (po0.01) – 5.31 (p ¼ 0.02)

Men 0.296 (0.274) 21.88 (po0.01) P ¼ 0.276 2.355 (p ¼ 0.125)

exogeneity test could not reject the null hypothesis that obesity was exogenous, hence supporting the specification presented in Table 3. For women, the variable indicating whether or not the respondent was the oldest child did not predict obesity in the first-stage regression. It was, thus, dropped, leaving the variable indicating the presence of any other obese person in the household as the sole instrument in the first-stage regression. The predictive power of the latter variable was good, however, and the F-test yielded an F-statistic of 42.68, which was significant at the 1% level. Since only one instrument was used, our exclusion restriction could not be tested. Treating obesity as endogenous for women dramatically increased the magnitude of the effect to 0.43. Moreover, the effect was significant at the 6% level (p ¼ 0.056). The Smith–Blundell test rejected the hypothesis of obesity being exogenous for women (p ¼ 0.02). The result shows that treating obesity as exogenous may understate the true effect for women, if anything. The result should be interpreted with caution, though, since we were not able to formally test the exclusion restriction and since the strict exogeneity of the instrument could be discussed. In Table 5, the impact of obesity on the employment probability is presented by country-group. In order to preserve space, only the coefficients of the obesity variable are shown. For the Nordic countries (Denmark and Sweden), the association between obesity and the employment probability for the full sample was significant at only the 10% level. The effect of being obese was to lower the employment probability by 0.054. The effect was negative for both men and women but was not significant when men and women were analysed separately. Including the health-status variables, the effect of obesity became positive. The effect was insignificant for both the full sample, and in the regression by gender, though.

232


Table 5. Effect of Obesity on the Probability of Employment. Regressions by Country-Group. OLS with Robust Standard Errors. Health Status Excluded Full Sample

Women


Full Sample

Nordic countries 0.049 (0.046) 0.060 (0.048) 0.022 (0.033) Obese 0.054 (0.033) Observations 1,915 1,061 854 1,915 Central European countries Obese 0.084 (0.020)0.068 (0.025)0.106 (0.033) 0.025 (0.022) Observations 5,141 2,887 2,254 5,135 South European countries 0.018 (0.025)0.108 (0.041) 0.035 (0.024) Obese 0.055 (0.024) Observations 2,927 1,714 1,213 2,923

Women

Men

0.027 (0.047) 1,061

0.012 (0.048) 854

0.021 (0.028) 2,885

0.027 (0.036) 2,250

0.008 (0.025) 1,714

0.076 (0.042) 1,209

Note: Robust standard errors in parentheses. Significant at 10%. Significant at 5%. Significant at 1%.

For the central European countries (Germany, France, Netherlands, Austria and Switzerland), being obese showed a statistically significant effect, lowering the employment probability by 0.084. The effect was somewhat stronger among men (0.106) than among women (0.068). In both cases, the effect was significant at the 1% level. Adding the health-status variables substantially reduced the magnitude of the effect. The effect was no longer significant, neither for the full sample, nor for the analyses by gender. For the southern European countries (Spain, Italy and Greece), the effect of being obese was statistically significant at the 5% level, when excluding the health-status variables. The effect was to lower the employment probability by 0.055. The effect, however, was almost ten times greater among men than among women. In the former case, the effect was significant at the 1% level and lowered the employment probability by 0.108. Among women, the effect (0.018) was not significant. Adding health status to the regressions lowered the effect of being obese and rendered it insignificant for the full sample. Among men, the effect was significant at the 10% level and somewhat reduced in magnitude (0.076). 4.3. Hours Worked The results regarding the impact of obesity on hours worked are shown in Table 6. When excluding the health status variables, the effect of obesity on contracted hours for the full sample was positive but not significant. The

Effect of Obesity on Hours Worked for the Full Sample and for Women and Men Separately. OLS Regression with Robust Standard Errors. Health Status Excluded Full Sample

Women

Men

Full Sample

Women

Men

0.010 (0.019) 0.081 (0.020) 0.084 (0.023) 0.001 (0.000) 0.011 (0.005)

0.028 (0.030) 0.036 (0.027) 0.055 (0.025) 0.001 (0.000) 0.015 (0.008)

0.036 (0.024) 0.135 (0.028) 0.218 (0.051) 0.002 (0.000) 0.006 (0.007)

0.009 (0.020) 0.084 (0.020) 0.089 (0.024) 0.001 (0.000) 0.009 (0.005)

0.048 (0.030) 0.039 (0.027) 0.059 (0.025) 0.001 (0.000) 0.012 (0.008)

0.017 (0.024) 0.141 (0.028) 0.222 (0.051) 0.002 (0.000) 0.005 (0.006)

0.273 (0.014) 0.014 (0.002)

0.000 (0.000) 0.018 (0.004)

0.000 (0.000) 0.007 (0.003)

0.262 (0.014) 0.012 (0.002)

0.000 (0.000) 0.016 (0.004)

0.035 (0.015) 0.001 (0.016) 0.004 (0.016) 0.025 (0.015)

0.034 0.002 0.009 0.040

0.027 (0.024) 0.045 (0.015) 0.154 (0.030) 0.183 (0.030) 0.115 (0.039) 0.112 (0.037) 0.147 (0.029) 0.141 (0.027) 0.322 (0.023) 0.262 (0.026) 0.124 (0.023)

0.044 (0.033) 0.107 (0.022) 0.148 (0.052) 0.176 (0.046) 0.115 (0.062) 0.252 (0.054) 0.244 (0.046) 0.147 (0.043) 0.493 (0.035) 0.349 (0.039) 0.154 (0.030)

(0.021) (0.024) (0.024) (0.022)

0.014 0.015 0.015 0.011

(0.020) (0.020) (0.020) (0.018)

0.016 (0.035) 0.030 (0.021) 0.148 (0.035) 0.181 (0.040) 0.110 (0.045) 0.067 (0.047) 0.039 (0.034) 0.126 (0.029) 0.138 (0.025) 0.132 (0.033) 0.076 (0.034)

0.035 (0.015) 0.006 (0.016) 0.009 (0.016) 0.020 (0.015) 0.029 (0.024) 0.046 (0.015) 0.145 (0.030) 0.171 (0.030) 0.108 (0.039) 0.111 (0.037) 0.136 (0.030) 0.133 (0.027) 0.314 (0.022) 0.253 (0.026) 0.117 (0.023)

0.033 0.008 0.011 0.034

(0.021) (0.024) (0.024) (0.022)

0.049 (0.033) 0.109 (0.021) 0.135 (0.052) 0.166 (0.046) 0.100 (0.063) 0.252 (0.054) 0.235 (0.047) 0.141 (0.043) 0.481 (0.035) 0.343 (0.039) 0.147 (0.029)

0.000 (0.000) 0.007 (0.003) 0.014 0.009 0.007 0.008

(0.020) (0.020) (0.020) (0.018)

0.018 (0.036) 0.028 (0.021) 0.144 (0.035) 0.168 (0.039) 0.111 (0.046) 0.066 (0.046) 0.027 (0.033) 0.116 (0.029) 0.132 (0.025) 0.120 (0.032) 0.068 (0.034)

233

Obese Hourly wage rate Age Age squared Number of children Female Years of education Public sector Smoker Current smoker Frequent alcohol consumption Country of birth Married Greece Italy Spain Switzerland Austria France Netherlands Germany Denmark

Health Status Included


Table 6.

234

Table 6. (Continued ) Health Status Excluded Full Sample Number of chronic conditions Mobility Self-reported health Symptoms Constant Observations R-squared

2.036 (0.609) 3,948 0.15

Women

2.531 (0.626) 2,035 0.15


1.652 (1.356) 1,913 0.09

Full Sample

Women

Men

0.004 (0.007)

0.002 (0.012)

0.004 (0.009)

0.024 (0.009) 0.073 (0.033)

0.029 (0.012) 0.050 (0.047)

0.019 (0.013) 0.090 (0.045)

0.001 (0.008) 1.900 (0.617) 3,944 0.16

0.000 (0.011) 2.423 (0.634) 2,034 0.15

0.006 (0.010) 1.734 (1.354) 1,910 0.10




235

effect was qualitatively different for men and women, however. For men, the effect was negative, whereas the opposite was true for women. In neither case was the effect significant. When self-reported health, chronic conditions, symptoms and mobility were added, the effect of obesity was reduced for men, from 0.36 to 0.017, and amplified for women from 0.028 to 0.048. The effects were, again, statistically insignificant. In sum, the inclusion of the health-status variables did not affect the effect of obesity on hours worked to any important extent. Several of the health-status variables showed significant effects on hours worked. Self-reported bad health showed a negative and statistically significant effect on hours worked for men, but not for women. For men, the effect was to decrease the number of hours worked by 9%. Reduced mobility was associated with 3% reduction in the hours worked for women, but showed no effect on the hours worked by men. For men, other results were that age and education showed a positive and significant association with hours worked, while the opposite was true for the hourly wage rate and age squared. For women, age and education showed a positive effect on hours worked, whereas age squared and being married showed a negative effect. In Table 7, we show the results when treating obesity as endogenous. For men, we used the same instruments as in the employment regression, i.e. whether the respondent is the oldest child and whether there is any other obese member of the household. These instruments predict reasonably well in the first-stage regression; the test of joint significance yielded an F-statistic of 9.21, which was significant at the 1% level. Moreover, the hypothesis that the exclusion restrictions were valid could not be rejected, as suggested by the Hansen J statistic of 0.014 (p ¼ 0.91). Treating obesity as endogenous Table 7.

Effects of Obesity on Hours Worked. Obesity Treated as an Endogenous Variable. IV-GMM Regression. Women

Obese F-test of first-stage instruments (p-value of null of jointly ¼ 0) Overidentification test, Hansen J statistic (p-value of null of valid exclusion restrictions) Exogeneity test, C-statistic, (p-value of null of exogeneity)

Men

0.050 (0.029) 6.65 (po0.01)

0.186 (0.238) 9.21 (p>0.01)

1.42 (p ¼ 0.23)

0.014 (p ¼ 0.91)

0.63 (p ¼ 0.43)

0.77 (p ¼ 0.38)

236


among men, the effect became positive, but was still insignificant. The hypothesis of obesity being exogenous could not be rejected (p ¼ 0.38). For women, the indicator of whether or not being the oldest child was, again, insignificant in predicting obesity in the first-stage regression. The variable indicating the presence of any other obese member of the household, however, was highly significant. After experimenting with a number of different instruments, we also added the variable indicating whether or not the respondent only had sisters alive. The two instruments were significant, both individually and jointly, yielding an F-statistic of 6.65. Moreover, the hypothesis that the instruments could be validly excluded from the main equation could not be rejected (p ¼ 0.23). The effect of obesity, once treating it as endogenous, became negative (0.236). It was, however, insignificant. Furthermore, the hypothesis that obesity was exogenous could not be rejected (p ¼ 0.43). Table 8 presents the results from the regressions on hours worked by country-group. For the Nordic countries (Denmark and Sweden), the effect of being obese was negative and twice as high among men (0.010) than among women (0.005). The effect, however, was not significant in any of the regressions. It could be noted that the effect of being obese was negative, when excluding the health-status variables, but became positive for the full sample and for women, when including them. For the central European countries (Germany, France, Netherlands, Austria and Switzerland), the effect of being obese was positive but insignificant for the full sample. This result disguised some interesting differences Table 8.

Effects of Obesity on the Hours Worked. Regressions by Country-Group. OLS. Health Status Excluded Full Sample

Nordic countries Obese 0.014 (0.033) Observations 1,240 Central European countries Obese 0.020 (0.029) Observations 2,023 South European countries Obese 0.027 (0.038) Observations 685


Women

Men

0.005 (0.044) 708

0.010 (0.041) 532

0.022 (0.034) 1,240

0.051 (0.048) 708

0.011 (0.042) 532

0.101 (0.044) 1,031

0.062 (0.037) 992

0.038 (0.029) 2,020

0.107 (0.044) 1,030

0.025 (0.037) 990

0.047 (0.073) 296

0.008 (0.038) 389

0.021 (0.038) 684

0.037 (0.071) 296

0.006 (0.039) 388


Full Sample

Women

Men


237

by gender, though. For women, the effect was positive (0.101) and significant at the 5% level. This result suggests that being obese was associated with 10% more hours worked. Among men, however, the effect was negative (0.062) at the 10% level. Controlling for health status rendered this negative effect insignificant. Among women, including health status did not change the effect of obesity to any great extent; it was still positive, similar in magnitude, and significant at the 5% level. In the southern European countries (Spain, Italy and Greece), the effect of being obese was negative, and substantially greater in magnitude among women (0.047) than among men (0.008). The effect was not statistically significant for neither women, nor men. This result did not change when including the health variables.

4.4. Hourly Wage Rate Table 9 shows the results from the wage equations. When excluding the health-status variables, the effect of obesity for the full sample was negative and statistically significant. Obese people had a 7.4% lower wage rate that non-obese people. The effect differed by gender, though. While the effect for women was significant and large, 0.10, the corresponding effect among men was roughly half in size (0.05) and insignificant. When health-status variables were added, the effect of obesity for the full sample was still negative and statistically significant, but slightly reduced in magnitude. Being obese was now associated with having a 5.8% lower wage rate. Interestingly, the inclusion of the health variables did not change the effect of being obese among women to any great extent. The effect was still significant and only slightly reduced to 0.09. For men, the effect was more than halved, from 0.05 to 0.02, but was still insignificant. The result obtained for women suggests that only a slight part of the effect of obesity on wages worked via lower productivity due to worse health status, and that the major part of the effect must be explained by other factors, such as employer discrimination. Few of the health indicators showed any significant effect of the wage rate. Reduced mobility was associated with a significantly lower wage rate among men, but the effect among women was insignificant. Other findings were that among men, age, education, working part-time, being married and being a frequent drinker were associated with a significantly higher wage rate, whereas the opposite effect was obtained for age squared, and working in the public sector. For women, age and education were significantly and

238

Table 9.

Effect of Obesity on Hourly Wage Rate for the Full Sample and for Women and Men Separately. OLS Regression with Robust Standard Errors. Health Status Excluded Full Sample

Women

Men

0.090 (0.036) 0.051 (0.025) 0.000 (0.000) 0.015 (0.010) 0.000 (0.000) 0.052 (0.004) 0.001 (0.033) 0.007 (0.027) 0.007 (0.026) 0.020 (0.029) 0.043 (0.024)

0.020 (0.032) 0.144 (0.063) 0.001 (0.001) 0.014 (0.009) 0.000 (0.000) 0.049 (0.004) 0.230 (0.073) 0.057 (0.028) 0.056 (0.032) 0.009 (0.029) 0.105 (0.029)

0.055 (0.033) 0.035 (0.038) 0.092 (0.061) 0.053 (0.033) 0.031 (0.038) 0.041 (0.019) 0.023 (0.025) 0.068 (0.030) 0.039 (0.019) 0.022 (0.025) 0.442 (0.041) 0.360 (0.063) 0.509 (0.053) 0.435 (0.041) 0.347 (0.064) 0.260 (0.039) 0.229 (0.055) 0.300 (0.055) 0.251 (0.039) 0.222 (0.055) 0.423 (0.050) 0.416 (0.084) 0.434 (0.061) 0.416 (0.050) 0.406 (0.083)

0.101 (0.061) 0.064 (0.030) 0.508 (0.053) 0.286 (0.055) 0.441 (0.060)

0.074 (0.024) 0.056 (0.023) 0.001 (0.000) 0.002 (0.007) 0.200 (0.018) 0.053 (0.002) 0.045 (0.031) 0.023 (0.019) 0.032 (0.021) 0.015 (0.020) 0.074 (0.018)

Men

0.099 (0.035) 0.051 (0.032) 0.049 (0.025) 0.138 (0.063) 0.000 (0.000) 0.001 (0.001) 0.016 (0.010) 0.013 (0.009)

Full Sample

0.058 (0.025) 0.059 (0.023) 0.001 (0.000) 0.001 (0.007) 0.195 (0.018) 0.053 (0.003) 0.051 (0.004) 0.052 (0.003) 0.052 (0.031) 0.004 (0.033) 0.212 (0.073) 0.005 (0.027) 0.056 (0.028) 0.022 (0.020) 0.009 (0.026) 0.065 (0.032) 0.027 (0.021) 0.020 (0.029) 0.004 (0.029) 0.018 (0.021) 0.046 (0.023) 0.110 (0.029) 0.070 (0.018)


Obese Age Age squared Number of children Female Years of education Part-time Public Smoker Former smoker Frequency of alcohol consumption Country of birth Married Greece Italy Spain

Women


0.386 (0.043) 0.349 (0.059) 0.445 (0.062) 0.385 (0.043) 0.355 (0.060) 0.235 (0.043) 0.247 (0.061) 0.221 (0.060) 0.227 (0.043) 0.235 (0.062) 0.004 (0.033) 0.050 (0.048) 0.048 (0.046) 0.010 (0.033) 0.055 (0.048) 0.086 (0.026) 0.110 (0.038) 0.078 (0.036) 0.088 (0.026) 0.113 (0.039) 0.177 (0.031) 0.193 (0.038) 0.141 (0.049) 0.171 (0.031) 0.186 (0.038) 0.184 (0.025) 0.199 (0.035) 0.166 (0.037) 0.190 (0.025) 0.204 (0.035) 0.007 (0.009) 0.004 (0.013)

0.682 (0.613) 3,948 0.30

0.739 (0.646) 2,035 0.27


1.677 (1.716) 1,913 0.30

0.018 (0.009) 0.051 (0.034) 0.003 (0.009) 0.592 (0.619) 3,944 0.30

0.016 (0.011) 0.079 (0.047) 0.015 (0.011) 0.699 (0.649) 2,034 0.27

0.437 (0.062) 0.211 (0.061) 0.043 (0.045) 0.075 (0.037) 0.130 (0.049) 0.170 (0.037) 0.016 (0.012) 0.032 (0.016) 0.012 (0.048) 0.023 (0.013) 1.821 (1.737) 1,910 0.31


Switzerland Austria France Netherlands Germany Denmark Number of chronic conditions Mobility Self-reported health Symptoms Constant Observations R-squared

239

240


positively associated with the wage rate, while age squared showed a negative association. Table 10 shows the results from the regressions treating obesity as endogenous. We struggled hard to find suitable instruments to be used in the wage equations. For women, we settled for the variable indicating whether or not the respondent had only sisters. While significant at the 5% level in the first-stage regression, an F-statistic of 4.21 was obtained, suggesting a potential problem of weak instruments. The effect of the obesity variable (0.12) was quite similar to the effect obtained when treating obesity as exogenous, but the precision of the estimate was very low (p ¼ 0.87). Moreover, we were unable to reject the hypothesis that obesity was exogenous.15 For men, few variables were found to be suitable as instruments. Again, a single instrument was used; the variable indicating whether or not the respondent was the oldest child. Its predictive power was weak, however, and the variable was only significant at the 7% level in the first-stage regression. Consequently, the precision of the estimated obesity effect (0.86, SE ¼ 0.97) was very low and should be interpreted with great caution. The hypothesis of obesity being exogenous could not be rejected by the Durbin–Wu–Hausman test (p ¼ 0.32). In Table 11, we present regressions by country-group. For the Nordic countries (Denmark and Sweden), the effect of being obese was to lower the wage by 4.7%. The effect was, however, only significant at the 12% level. For women, the effect was larger (0.063) than the corresponding effect among men (0.024). In neither case was the effect significant, though. Adding the health-status measures reduced the magnitude of the effect for both men and women, but the effect was still insignificant in both cases. In the group of central European countries (Germany, France, Netherlands, Austria and Switzerland), being obese showed a negative and statistically significant effect on the wage rate. The estimated coefficient suggested Table 10.

Effects of Obesity on Wages. Obesity Treated as an Endogenous Variable. 2SLS Regression.

Obese F-test F-test of first-stage instruments (p-value of null of jointly ¼ 0) Exogeneity test, Durbin–Wu–Hausman test, (p-value of null of exogeneity)

Women

Men

0.12 (0.73) 4.21 (po0.05) –

0.86 (0.97) 3.15 (p ¼ 0.07) –

0.003 (p ¼ 0.96)

0.99 (p ¼ 0.32)


241

Table 11. Effects of Obesity on the Wage Rate. Regressions by Country-Group. OLS with Robust Standard Errors. Health Status Excluded Full Sample

Women


Nordic countries Obese 0.047 (0.030) 0.063 (0.044) 0.024 (0.040) Observations 1,240 708 532 Central European countries Obese 0.113 (0.038)0.153 (0.055) 0.062 (0.049) Observations 2,023 1,031 992 South European countries Obese 0.049 (0.054) 0.016 (0.081) 0.036 (0.067) Observations 685 296 389

Full Sample

0.029 (0.032) 1,240

Women

Men

0.046 (0.046) 0.001 (0.043) 708 532

0.096 (0.039) 0.146 (0.056) 0.025 (0.051) 2,020 1,030 990

0.038 (0.056) 684

0.008 (0.082) 0.015 (0.070) 296 388


that being obese was associated with a 11.3% lower wage rate. The effect was more than twice as large among women (0.153) than among men (0.062). Moreover, the effect was only significant among women, where it was significant at the 1% level. Adding the health-status variables only slightly affected the effect of being obese. For the full sample, the effect was reduced to 0.096 , while it among women was reduced to 0.146. In the former case, the effect was significant at the 5% level, whereas it was significant at the 1% level in the latter case. For men, the effect of being obese on the wage rate was still insignificant. For the southern European countries (Spain, Italy and Greece), the effect of being obese was to reduce wages by 4.9%. The effect, which was more than twice as high among men as among women, was not significant, though, for either men or women. Adding health measures substantially reduced the magnitude of the effects, but it remained insignificant.

5. DISCUSSION This paper brings a European perspective to the mainly U.S.-based literature on the relationship between obesity and labour-market outcomes.

242


Using data from the newly developed SHARE database, we were able to address whether the effects of obesity on labour-market outcomes differed across Europe. In contrast to the U.S., the European labour market comprises several language areas and several different labour-market legislations. In addition, there are differences in the general living conditions between the U.S. and Europe that are relevant for the way in which employers (and employees) regard obesity. Our study is, to the best of our knowledge, the first to use the SHARE-database to study the relationship between obesity and labour-market performance. In addition, we were able to contribute to the literature by investigating if there were similar effects of obesity on labour-market outcomes for older individuals, as for younger individuals, who have been the primary focus for prior studies. Finally, using an extensive set of health measures we were able to address to what extent the effects of obesity on employment, wages and hours worked ran through reduced health status. First, pooling all 10 countries, the results showed that being obese was associated with a significantly lower probability of being employed for both men and women. When controlling for health status, however, the effects became insignificant. Second, the results suggested that being obese was associated with a wage penalty of approximately 7%. The effect differed by gender, though. For women, the wage penalty was 10% and significant, whereas among men it was 5% and insignificant. Taking the health status of the individual into account, obese women still earned 9% less, whereas the effect among men, although still insignificant, was reduced to 2%. In other words, after the potential negative productivity aspects of being obese were taken into account, a significant wage penalty persisted for women. One explanation for this may be employer discrimination. The data, however, did not allow us to test this hypothesis. Third, we analysed the effect of obesity on hours worked. The results showed no significant effects of obesity on hours worked, neither for men nor for women. Regressions were also performed on three different country groups, according to a north–south gradient. The results showed that the effect of obesity on labour-market outcomes varied to a substantial extent across Europe. Starting with the probability of employment; in the Nordic group the effect was negative and significant when analysing men and women together. When conducting separate analyses by gender, however, the effect was still negative, but not significant. Including health status rendered the effect of obesity on the employment probability smaller in magnitude and insignificant for the full sample. In the central European group, the effect of obesity on employment was negative and significant for both men and


243

women. The effect was greater than the corresponding effect obtained in the Nordic group. Including health status greatly reduced the magnitude of the effect for both men and women and rendered it statistically insignificant. In the southern group, the effect of obesity on employed was, interestingly, only significant for men. The effect on employment on obesity among men was large, 0.11, and was still significant when including health status, although it was somewhat reduced in magnitude. In sum, the greatest effect on employment was obtained for men in southern and central Europe, where being obese reduced the employment probability by almost 11 percentage points. Regarding wages, the effect of being obese was negative in all country groups, but only significant for women in the central European group. Here, being obese reduced wages by 15%. The effect was still significant, and only slightly reduced in magnitude, when including health status. Consequently, it appears as if the effects of obesity on wages among central European women to a large extent had to do with other factors than health. Results supporting the discrimination hypothesis have been obtained in a couple of prior studies (Hamermesh & Biddle, 1994; Harper, 2000; Baum & Ford, 2004).16 Regarding hours worked, the effect of obesity was again only significant in the central European group. Here, the effect had different signs for men and women. Among women, being obese increased the number of hours worked, whereas the opposite effect was obtained for men. The effect was significant for both men and women. The effect for women was still significant, and almost unchanged in magnitude, when including health status, whereas among men, the effect became substantially reduced in magnitude and insignificant. The hypothesis that obesity was exogenous in the regressions was tested, since there are good reasons to suspect that obesity may be endogenous. We were unable, though, to reject the hypothesis of obesity being exogenous in most cases. Similar results were obtained by Cawley (2004), Pagan and Davila (1997) and A`sgeirsdo´ttir (2004). In the case of employment among women, however, we rejected the hypothesis of exogeneity. The effect of treating obesity as endogenous in this case was to increase the effect on employment. Our instruments, the presence of other obese persons in the household, being an oldest child, and having sisters only, were subject to standard econometric tests, which they passed. It should be noted, though, as discussed in the methods section, that ideal instruments, such as the obesity of a parent and child, could not be obtained. Using spousal obesity as an instrument may lead to bias if, for instance, neighbourhood status influences both spousal obesity and occupational attainment of the respondent.

244


Moreover, in some cases, the predictive power of the instruments was weak, suggesting that the results from the instrumental variables regressions should be interpreted with caution. While this study used instrumental variables techniques to deal and test for endogeneity of obesity in our regressions, other techniques have been used in other studies. Consequently, results may to some extent depend on the techniques used. In those studies that have applied more than one of the strategies, it is, however, difficult to conclude if differences in results depend on the choice of strategy. For instance, Averett and Korenman (1996), Behrman and Rosenzweig (2001) and Baum and Ford (2004) all get insignificant effects of BMI when using sibling differences. However, this may be an effect of the limited number of observations in these two approaches. Behrman and Rosenzweig (2001), also failed to find significant effects of BMI when using instrumented BMI, but again this may be caused by their limited sample. In contrast, Cawley (2000, 2004), using a larger sample, found consistent effects across all models. Using sibling differences was not possible in our case, since no information on siblings was present in the dataset. It should be noted that the indicator of obesity used in the present study was based on self-reports of height and weight, which both may be subject to measurement errors. If the latter are randomly distributed, the estimates of the obesity effects may be biased downwards, i.e. our estimates may be conservative. If measurement errors are not random, but, for instance, vary systematically between countries, the expected bias would be harder to predict. We are aware of no studies, however, that have examined variations in the accuracy of self-reported weight and height in different cultural settings. It should be noted, though, that Cawley (2000) found that his results did not change, when correcting for measurement errors in self-reported weight and height. Even though prior studies almost exclusively focused on younger populations, our estimates do not differ radically. In Baum and Ford (2004), for instance, a wage penalty of approximately 5% of being an obese woman was found in the model including individual- and family-fixed effects. Among men, the corresponding effect was 0.7% in the individual fixed-effects model. Larger effects were obtained in Averett and Korenman (1996, 1999), where being an obese white woman was associated with a wage penalty of 17% (Averett & Korenman, 1999). Being man and obese lowered the wage rate by 8% (Averett & Korenman, 1996). Only one prior study analysed the relation between body mass and hours worked (Cawley & Danziger, 2004). The results are not easily comparable, though, since BMI, instead of obesity, was used as an explanatory variable.


245

Here, it was found that among women aged 18–54 the effect of a one-unit increase in BMI was to lower the number of hours worked by 1.6%, suggesting a BMI elasticity of 0.514. The sample was small (n ¼ 651), however, and covered only single mothers receiving cash-welfare benefits in one urban county in Michigan. According to WHO, the prevalence of obesity in European countries is currently about 10–20% in men and about 10–25% in women (WHO, 2000). Moreover, even though there are difficulties in obtaining internationally comparable time-series data, there seems to be a consensus that obesity rates are rapidly increasing all over the world. In Europe, the increase has been about 10–40% during the 1990’s (WHO, 2000). The results of this study suggest that the ongoing rise in the prevalence of obesity in Europe may have a non-negligible effect on the European labour market. Further research should aim at identifying reasons for the north–south difference in the effects of obesity on employment, wages and hours worked obtained in this study. Candidates are differences in the institutional framework between countries, for instance in labour-market regulations, labourmarket structure, anti-discrimination laws and tax schedules. If, for instance, anti-discrimination laws are stricter in Denmark and Sweden, compared with countries in central Europe, this could be part of the explanation as to why obesity showed no significant effect on wages in the former countries. Further work should also try to investigate in more detail the mechanisms by which obesity affects labour-market outcomes in Europe. The results in this study suggests that part of the explanation may lie in the health effects of obesity but that a large part remains to be explained. One candidate for the remaining part is employer discrimination. Investigating discrimination effects using data on occupations may be a fruitful way to proceed. Such data may become available in the SHARE database in due time, when individual occupations are coded into comparable categories across countries.

NOTES 1. BMI is measured by dividing weight by length (in metres) in square. Overweight is usually defined as 25 r BMIo30 and obesity as BMI Z 30. 2. It has been shown that obesity is a risk factor for several health problems such as, hypertension, dyslipidemia, insulin resistance, hyperinsulinemia, abnormal pulmonary function and several types of cancers (Hubert, Feinleib, McNamara, & Castelli, 1983; National Institutes of Health, 1985; Council of Scientific Affairs of the American Medical Association, 1988; Pi-Sunyer, 1991, 1993; Bray, 1992; Abbott et al., 1994; SBU, 2002).

246


3. Exceptions are Loh (1993; no effect on the current wage but a negative effect on wage-growth), Hamermesh and Biddle (1994; no wage effects), Averett and Korenman (1999; positive employment effect for white women) and Garcia and Quintana-Domeque (2006; few significant correlations between obesity and employment/wages). 4. Again, Loh (1993) finds no effect on the current wage, but a negative effect on wage-growth, for the men. 5. Only a few studies, however, include variables that account for differences in health (Gortmaker, Must, Perrin, Sobol, & Dietz, 1993; Averett & Korenman, 1996; Harper, 2000; A`sgeirsdo´ttir, 2004). 6. Obesity may cause discrimination, if employers have preferences for non-obese employees, or if fellow workers have preferences for working with non-obese colleagues, or if customers prefer to be served by non-obese attendants. See, for instance, Hamermesh and Biddle (1994) or Baum and Ford (2004). 7. Reversed causality may be present if, for instance, inferior labour-market outcomes cause depression and depression, in turn, causes obesity (Autrey, Strover, Reatig, & Casper, 1986; Cawley, 2000). Alternatively, inferior labour-market outcomes may cause obesity if fattening foods are relatively cheaper (Cawley, 2004). Further, people who discount the future more heavily are less likely to invest in their human capital (affecting labour-market outcomes negatively) and more inclined to present consumption (increasing the likelihood of weight gain) (Cawley, 2000, 2004; Baum & Ford, 2004). Thus, the observed correlation may also be caused by some third factor. 8. For the non-euro countries, i.e. Sweden, Denmark and Switzerland, we used the average annual exchange rate of 2004 to convert the amounts into euros. The exchange rates used were 9.1243 for Sweden, 7.4399 for Denmark and 1.5438 for Switzerland (ECB, 2005). 9. Other tests are available as well, such as the Breusch and Pagan (1979) and Godfrey (1978) test. These tests, however, will only be valid tests for heteroscedasticity in the IV-regression, if heteroscedasticity is only present in that equation and nowhere else in the system (Pagan & Hall, 1983). The Pagan and Hall test relaxes this requirement and is, therefore, employed in our case. 10. Three strategies have been employed in the empirical literature in order to take into account that the body mass may be an endogenous variable. The first strategy is to use lagged indicators of body mass (Gortmaker et al., 1993; Sargent & Blanchflower, 1994; Averett & Korenman, 1996, 1999; Haskins & Ransford, 1999; Cawley, 2004; Conley & Glauber, 2005, 2006). This assumes that lagged values are uncorrelated with the current wage (or employment probability) residual and that there is no serial correlation in the wage residuals. This strategy removes potential contemporaneous effects of wages on body mass, but does not address problems of lagged third factors (for instance time-preferences) affecting both lagged body mass and current wages. The second strategy (Averett & Korenman, 1996; Behrman & Rosenzweig, 2001; Baum & Ford, 2004; Cawley, 2004; Conley & Glauber, 2005, 2006) is to estimate the wage- and employment-probability equations after taking differences in both outcomes and explanatory variables with another individual with highly correlated genes and environmental background (e.g. a same-sex sibling or twin). This strategy assumes that all unobserved heterogeneity is constant within the pairs of individuals


247

compared and, therefore, is eliminated by the differentiation. It also assumes that wages do not influence body mass, implying that the differenced variable is uncorrelated with the differenced wage residual. However, if there is unobserved heterogeneity that is not shared between siblings (or twins), the latter assumption may not hold. The third strategy is to use variables that are uncorrelated with wages, but affect body mass, to instrument the latter variable (Pagan & Davila, 1997; Behrman & Rosenzweig, 2001; A`sgeirsdo´ttir, 2004; Cawley, 2000, 2004). The main difficulty of using this strategy is to find credible instruments. 11. We also tried an indicator variable of being the only child, but this variable was never significant. This result was similar to the one obtained by Celi et al. (2003). 12. Admittedly, there may be some measurement errors in the variable, since some respondents may have deceased sisters or brothers. It might be expected, though, that since the respondents are at most 64 years old, the overwhelming majority of respondents will have sisters and/or brothers that are still alive. 13. We experimented with other birth-order and family instruments as well, such as being the youngest child, having no siblings and the number of sisters and brothers. None of these, however, predicted obesity among women in the first-stage regression. 14. The variable was constructed by assigning the value 1 to those having at least one obese member of the household, other than the respondent him/herself, and the value 0 to those having no other obese person in the household or having no other household members at all. Obviously, the latter category is not affected by the instrument. However, it is not unusual in the economics literature to have instrumental variables estimates where not every respondent is affected by the instrument. Such instrumental variables provide an estimate of the causal effect for the group whose behaviour is actually affected by the instrument (Imbens & Angrist, 1994). In our case, since we also have additional instruments besides the indicator of the presence of an obese household member, we do not have to rely solely on the former instrument, which only identifies the effect for those having household members. 15. Since only one instrument was used, the C-statistic could not be calculated. Therefore, we used the Durbin–Wu–Hausman test. Consequently, we did not employ GMM-estimation in this case but rather used the standard 2SLS. The test yielded a test statistic of 0.003, and the hypothesis of obesity being exogenous could not be rejected (p ¼ 0.96). 16. Harper (2000) found indications of employer discrimination based on overall appearance, but not because of obesity, for men. However, for women, the results were the opposite, i.e. indicating employer discrimination due to obesity, but not because of general appearance. Baum and Ford (2004) found that indications of employer discrimination for men strongly depended on model specification. For women, they found employer discrimination according to obesity. While Hamermesh and Biddle (1994), found no significant effect of obesity per se, they could not refute the hypothesis of overall appearance/beauty being both positively correlated with individual productivity and negatively correlated with employer discrimination.

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ACKNOWLEDGEMENT This paper uses data from the early release 1 of SHARE 2004. This release is preliminary and may contain errors that will be corrected in later releases. The SHARE data collection has been primarily funded by the European Commission through the 5th framework programme (project QLK6-CT2001-00360 in the thematic programme Quality of Life programme area). Additional funding came from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG4553-01 and OGHA 04-064). Data collection in Austria (through the Austrian Science Foundation, FWF), Belgium (through the Belgian Science Policy Administration) and Switzerland (through BBW/OFES/UFES) was nationally funded. The SHARE data set is introduced in Bo¨rsch-Supan et al. (2005); methodological details are contained in Bo¨rsch-Supan and Ju¨rges (2005). Additional funding for the Swedish participation in the SHARE data collection project came from the Bank of Sweden Tercentenary Foundation, the Swedish Council for Working Life and Social Research and the Swedish Social Insurance Agency. The research reported in this chapter was supported by a grant from the Swedish Council for Working Life and Social Research, which is gratefully acknowledged.

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GENDER, BODY MASS, AND SOCIOECONOMIC STATUS: NEW EVIDENCE FROM THE PSID Dalton Conley and Rebecca Glauber ABSTRACT Previous research provides evidence of a negative effect of body mass on women’s economic outcomes. We extend this research by using a much older sample of individuals from the Panel Study of Income Dynamics and by using a body mass measure that is lagged by 15 years instead of the traditional 7 years. One of the main contributions of this paper is a replication of previous research findings given our differing samples and measures. We compare OLS estimates with sibling fixed effects estimates and find that obesity is associated with an 18% reduction in women’s wages, a 25% reduction in women’s family income, and a 16% reduction in women’s probability of marriage. These effects are robust – they persist much longer than previously understood and they persist across the life course, affecting older women as well as younger women.

From 1960 to 1999 the percentage of obese Americans nearly doubled so that today, more than one-third of all Americans are obese and more than two-thirds are overweight (Flegal, Carroll, Kuczmarski, & Johnson, 1998;


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DALTON CONLEY AND REBECCA GLAUBER

Pastor, Makuc, Reuben, & Xia, 2002; Hedley et al., 2004). This is of concern because increased weight is associated with increased risk of morbidities and increased risk of mortality (Mokdad et al., 2001). It is also of concern because increased weight is associated with reduced economic well being, especially for women. A number of recent studies provide evidence of a negative causal effect of body mass – a measure of weight for height – on U.S. individuals’ economic and marital status outcomes (Averett & Korenman, 1996, 1999; Baum & Ford, 2004; Cawley, 2004; Cawley, Grabka, & Lillard, 2005). All but one of these studies (Cawley et al., 2005) use data from the National Longitudinal Survey of Youth (NLSY) where respondents are at relatively early stages in their lives. Two other studies, published in the current issue, provide evidence of a negative causal effect of body mass on older individuals’ economic outcomes, but these studies focus on the European context (Garcia & Quintana-Domeque, current issue; Lundborg, Bolin, Hojgard, & Lindgren, current issue). In contrast, we build on this research but analyze data from the Panel Study of Income Dynamics (PSID) where U.S. respondents are, on average, 20 years older than NLSY respondents. In addition, we employ a body mass measure that is lagged by 15 years instead of the traditional 7 years. Despite these two substantial changes in our methods relative to previous studies, we report similar results: increased body mass is associated with a reduction in women’s – particularly White women’s – hourly wages, family income, and probability of marriage. Our findings attest to the robustness of the body mass penalty for women – it persists much longer than previously thought, and it persists across the life course, affecting younger women as well as older women.

1. PREVIOUS RESEARCH ON BODY MASS AND SOCIOECONOMIC STATUS Most studies have found that body mass is negatively correlated with women’s socioeconomic status and is insignificantly, or minimally, correlated with men’s socioeconomic status. Register and Williams (1990) used data from the NLSY to compare wage rates and obesity among individuals who were between the ages of 18 and 25 in 1982. Even after correcting for a number of measured factors that affect wage rates – such as union status, race, work experience, and industrial category – the authors found that obese women earn 12% less than their non-obese counterparts. They found

Gender, Body Mass, and Socioeconomic Status

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that obese men, however, earn only 5% less than their non-obese counterparts. Pagan and Davila (1997) also found that women pay an earnings penalty for obesity and that men do not, primarily because men are able to sort themselves into occupations which may offset potential obesity penalties. Cross-sectional associations, however, may be biased. As Cawley (2004) describes in detail, the negative association between women’s economic status and their body mass could reflect one of the three processes: (1) diminished socioeconomic status could cause an increase to body mass through mechanisms such as reduced mental health or reduced quality food options; (2) increased body mass could directly reduce socioeconomic status through mechanisms such as employer discrimination, reduced health, or reduced productivity; or (3) cross-sectional associations could be caused by unobservable characteristics that are correlated with both socioeconomic status and body mass. To disentangle these three processes, researchers have employed a number of techniques which include the use of multiple waves of data with lagged body mass measures; sibling or individual fixed effects models; or most recently, instrumental variable (IV) models. Gortmaker, Must, Perrin, Sobol, and Dietz (1993) use panel data from the NLSY to examine the correlation between obesity and several socioeconomic outcomes including education level, household income, marriage and poverty probabilities, the probability of graduating from college, and self-esteem. With the exception of self-esteem, the authors find that women who were obese between the ages of 16 and 24 suffer from worse outcomes 7 years later, at ages of 23–31. When a number of background measures are held constant, differences in marital status, income, poverty rates, and years of schooling remain significant for women. The only significant negative effect of obesity for men is a reduction in their likelihood of marriage. Other recent studies have paired a lagged measure of body mass with sibling or individual fixed effects to control for unobserved family- or i ndividual-level heterogeneity. Behrman and Rosenzweig (2001) use data from 402 monozygotic twin pairs from Minnesota and find that negative cross-sectional associations between body mass and wages are reflective of unmeasured characteristics. Controlling for these ‘‘earnings endowments’’ with twins reduces the association to zero. However, twin studies are of questionable generalizability to the U.S. population on the whole (see, for example, Bound & Solon, 1999). Averett and Korenman (1996) use the NLSY and employ sibling fixed effects models as a control on unmeasured family background characteristics. The authors examine wage rate differentials as a more direct test of

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discrimination, and they break down income differentials into a component related to the job market – wages – and into a component related to the marriage market – the likelihood of being married and spouse’s earnings. As with other studies, the authors report negative correlations for women, but mild or insignificant correlations for men. Further, these negative correlations are most pronounced for White women, and are primarily concentrated in the marriage market. Differences in marriage probabilities between overweight and healthy weight women account for 50–95% of their lower socioeconomic status. However, sister fixed effects wipe out the correlations in obesity and marriage and economic outcomes for all models except the one which predicts total family income without controls for marital status, children, and age of the youngest child. In a subsequent analysis, Averett and Korenman (1999) use the NLSY to examine mechanisms that might account for Black–White differences in the effect of obesity on women’s economic statuses. They evaluate economic status in 1990 when individuals are between the ages of 25 and 33, and they evaluate body mass in 1982 when individuals are between the ages of 17 and 24. The authors find that self-esteem does not explain racial differences in the obesity penalty. The negative effects of obesity for White women work largely through the marriage market as obesity significantly reduces White women’s likelihood of marriage and significantly reduces their spouse’s earnings. Cawley (2004) also uses a relatively young sample from the NLSY and employs an individual fixed effects model as well as an IV model. Individual fixed effects models, which control for all time-invariant unobservable variables specific to individuals, reveal no significant negative effect of body mass or body weight on Black women’s, Hispanic women’s, or Hispanic men’s hourly wages. These fixed effects models, however, reveal a significant negative effect of body mass on White women’s hourly wages. Using the instrument of sibling’s body mass as a putatively exogenous source of variation in an individual’s body mass, Cawley reports an estimate of body mass on wages that is also only significant for White women. Cawley et al. (2005) extend this research with a comparison of the obesity penalty between German and U.S. individuals. The authors use the PSID and the sibling body mass instrument to model the relationship between body mass and earnings in the U.S., and they find that in both the IV and the ordinary least squares (OLS) models, body mass causes a significant reduction to U.S. women’s earnings. However, it is possible that the instrument of sibling’s body mass fails to meet the exclusion assumption necessary for producing unbiased estimates,


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as it may be directly correlated with an individual’s socioeconomic status. Having an overweight sibling may lead you down certain economic and employment paths. Further, the IV approach does not deal with the problem of unobserved heterogeneity, since the same lurking variable that causes the sibling’s body mass to be associated with the respondent’s in the first stage regression may be associated with the error term in the second stage. But the fact that Cawley’s (2004) and Cawley et al.’s (2005) results are consistent across OLS and IV models (and with corrections for measurement error) makes these studies the most definitive statements on the casual relationship between body mass and economic status to date. Lundborg et al. (current issue) also use an IV approach in their study of the effect of obesity on European men’s and women’s labor supply and wage rates. Their European results echo what others have found for the U.S. population – that obesity has no effect on men’s hourly wages but serves to reduce women’s hourly wages by about 10%. Upon the inclusion of health status variables in their models, women’s obesity wage penalty is reduced by only 1 percentage point, which suggests that the obesity penalty may derive from employer-related discrimination and not from reduced productivity due to diminished health status – to the extent that health status is measured without error. In their wage models, the authors induce exogenous variation in obesity status through the instrument of whether a female respondent has only sisters and no brothers. Lundborg et al. note that this instrument may not satisfy the exclusion assumption as it may be correlated with the error in predicting women’s wages, and with this strategy they are unable to reject the hypothesis of obesity as exogenous to women’s wages. Garcia and Quintana-Domeque (current issue) also provide evidence of a negative correlation between obesity and European women’s wage rates. Though the authors provide a descriptive account only, their estimates of a 7–10% obesity wage penalty, depending upon which European country is under study, are very similar to Lundborg et al.’s estimates.

2. THE CURRENT STUDY While previous studies have used various methods to reduce bias and to approach causal estimates of the effects of U.S. individuals’ body mass on their socioeconomic statuses, all but one of these studies (namely Cawley et al., 2005) are limited in their reliance on samples from the NLSY where individuals are at early stages in their lives. In contrast, we analyze a different survey, the PSID. The PSID obtained respondents’ height and weight in

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1986 and during the latest two survey waves available, 1999 and 2001. We compare siblings from these three survey years who also have valid data available on their education level, wages and earnings (if employed), marital statuses, and total family income. These respondents are, on average, much older than respondents from the NLSY samples previously used, and they allow for a comparison between younger and older individuals, or those at different stages in the life course. Our minimum age for inclusion into the sample is 25 in 1986, the year in which we first measure body mass. In 1986 women and men were, on average, 49 and 46 years old, respectively. In that same year, NLSY respondents were between the ages of 21 and 29. We measure economic and marital status outcomes 13–15 years later when respondents are, on average, around 60 years old (see Table 1). Unlike data from the NLSY, our data on marital status and economic outcomes follow the completion of formal education, and the peak years of earnings and childbearing. We examine body mass within OLS and sibling fixed effects models for marital status outcomes and for three economic outcomes – hourly wages, labor market earnings, and total family income.

3. DATA AND METHODS The PSID began in 1968 with a nationally representative sample of 5,000 American families and has followed them each year since. It is a complicated study design and cannot be done justice in the space allowed here. For a fuller description, see Hill (1992) or Duncan and Hill (1989). By virtue of its complex design, this study has information on the socioeconomic histories of families, as well as on the outcomes of multiple children from the same families who were in the original sample, moved into it, or were born to sample members. We select adult respondents aged 25 and older who were head or wife of their household in any (or all) years for 1986, 1999, and 2001. Further, these individuals had to have a valid person number for their mother; that is, their mother had to have been in the sample at some time. They were then linked to their siblings through this maternal connection. A trivial number (less than 1%) of respondents had a father in the sample but not a mother. The majority had both parents. But since many more of the fathers were missing, we decided to identify siblings based on their mother’s identification. We describe our body mass measures and the variables that we use to capture socioeconomic status below. For sibling fixed effects models, the

Women

Men

Healthy Weight Underweight Overweight Outcome Variables Log wages Log earnings Log family income Proportion married Proportion divorced 1999 Control Variables Labor market experience Age Children under the age of 1 Children under the age of 18 Educational attainment Black Number of observations

Obese

Healthy Weight Underweight Overweight

Obese

2.58 9.82 10.78 0.67 0.29

(0.03) (0.05) (0.04) (0.02) (0.02)

2.47 9.52 10.86 0.71 0.27

(0.10) 2.43 (0.07) 2.34 (0.19) 9.68 (0.11) 9.55 (0.13) 10.25 (0.09) 10.22 (0.06) 0.50 (0.03) 0.46 (0.06) 0.46 (0.03) 0.48

(0.07) (0.15) (0.12) (0.04) (0.04)

3.04 10.58 11.04 0.78 0.17

(0.04) (0.05) (0.04) (0.02) (0.02)

3.37 11.19 10.90 1.00 0.00

(0.24) 3.00 (0.04) 2.77 (0.09) (0.28) 10.58 (0.06) 10.19 (0.15) (0.13) 11.01 (0.05) 10.65 (0.12) (0.00) 0.80 (0.02) 0.75 (0.04) (0.00) 0.18 (0.02) 0.23 (0.04)

2.91 57.76 0.01 0.24 13.22 0.05

(0.06) (0.48) (0.00) (0.01) (0.08) (0.01)

2.83 50.21 0.04 0.42 13.35 0.06

(0.18) 2.71 (0.11) 2.77 (0.16) (1.64) 63.54 (1.00) 59.60 (1.06) (0.02) 0.01 (0.01) 0.01 (0.01) (0.07) 0.19 (0.03) 0.14 (0.03) (0.32) 11.95 (0.18) 11.81 (0.22) (0.03) 0.11 (0.02) 0.18 (0.03)

4.98 55.68 0.02 0.31 13.63 0.04

(0.07) (0.61) (0.01) (0.02) (0.11) (0.01)

4.91 57.60 0.20 0.02 14.20 0.11

(0.85) 5.42 (0.08) 5.20 (0.15) (7.91) 58.01 (0.59) 57.40 (1.05) (0.20) 0.01 0.00 0.01 (0.01) (0.02) 0.01 (0.00) 0.25 (0.04) (1.59) 13.30 (0.13) 12.32 (0.27) (0.11) 0.06 (0.01) 0.09 (0.02)

1,143

96

343

206

742

10

617


Sample Means by Gender and Body Mass (Standard Errors).

Table 1.

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unit of analysis is the sibling and we have averaged economic outcomes from 1999 and 2001. Mean values – which generally conform to national averages – are presented in Table 1.

3.1. Economic Outcomes The three economic outcome variables that we use in this study are hourly wage rates, labor market earnings, and total family income. All three variables are logged to the base e to correct for skewness.

3.2. Marital Status Outcomes We examine the effects of body mass on the likelihood of being married or being divorced, separated, or widowed in 2001. In this year, about 62% of women are married and about 78% of men are married. On average, healthy weight women are more likely to be married than overweight or obese women, but healthy weight men are no more likely to be married than overweight or obese men. The same holds true for divorce. About 29% of healthy weight women are divorced in 2001, whereas about 46% of overweight women are divorced and 48% of obese women are divorced. The proportion of divorced men does not vary by body mass – about 17% of healthy men are divorced in 2001, whereas about 18 and 23% of overweight and obese men are divorced in 2001.

3.3. Body Mass Body mass index (BMI) is a ratio of weight (in kilograms) to height (in meters squared). The PSID collects weight (in pounds) and height (in inches). We converted these measures but excluded all extreme values (weight greater than 400 lb or less than 70 lb and height greater than 84 in. or less than 45 in.). In most models, we compare obese, overweight, and underweight individuals to healthy weight individuals. To determine these categorical statuses, we rely on the clinical classifications where an individual is obese if her BMI is at or above 30, overweight if her BMI is at or above 25 but below 30, underweight if her BMI is below 18.5, and at a healthy weight if her BMI is at or above 18.5 but below 25. Though our primary 1986 predictor variables are obese, overweight, or underweight


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compared with healthy weight, we use a continuous measure of BMI logged to the base e in some models.1 In 1986, women had an average body mass of 24.20 and men had an average body mass of 25.82.

3.4. Control Variables All models control for respondent’s age (models are robust to the inclusion of a quadratic age term as well). Models also control for educational attainment. This is measured as the total number of years of formal schooling completed – a continuous variable from 1 to 17, with the topcode representing any graduate work, regardless of whether a degree was received. (The PSID does not, unfortunately, distinguish between various levels of graduate schooling.) Models also control for labor market experience, which is measured using PSID data on weekly work hours for all years from 1968 to 2001. Additionally, models control for respondent’s parenthood status with three dummy variables for no children living in the household, children under age 1 living in the household, and children between the ages of 2 and 18 living in the household. Coefficients remain relatively robust to these control variables and robust to the inclusion of either lagged 1986 control variables or non-lagged 1999–2001 contemporaneous variables.

3.5. Statistical Approach To estimate the effect of body mass on socioeconomic status, we would ideally like to control for all factors that affect body mass. However, there are likely a host of genetic and environmental factors that are unmeasured and unobserved in the PSID (and in any survey sample). To the extent that these unobserved factors are associated with body mass and with socioeconomic and marital statuses, OLS estimates would produce inconsistent results. For example, if we take the case of parent’s body mass – an unobserved factor – that is positively associated with an individual’s body mass and negatively associated with an individual’s socioeconomic status, then OLS would produce upwardly biased estimates of a body mass effect. It is unclear if unobserved variables serve only to produce upwardly biased body mass estimates or if there are unobservable variables which may also lead to downwardly biased estimates. For example, certain parents may foster a sedative learning environment for their children and this unobserved factor would be positively associated with an individual’s body mass and

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also positively associated with their socioeconomic status. This would lead to a downwardly biased OLS estimate. As discussed above, researchers have used sibling comparisons to deal with the issue of unobservable variable bias, as siblings are more alike on genetic and environmental factors than are a random sample of individuals. Differencing estimates across siblings effectively reduces many of the unobserved, confounding environmental and genetic factors. The sibling fixed effects model is specified in Eq. (1): Yij ¼ bXij þ ai þ mij

(1)

where Yij is our outcome variable of interest for sibling j in family i (namely socioeconomic and marital status outcomes), X is a vector of body mass and explanatory variables, and our error term is broken into two components: ai, the family fixed effect, and mij, the error specific to each sibling j in family i. When we difference across siblings in each family, the unobserved family fixed effect is eliminated. This method does not, however, eliminate factors that are specific to each sibling j in family i. To the extent that these nonsibling-constant errors are correlated with the explanatory variables, the estimates from sibling fixed effects models may still be biased. Sibling fixed effects models capitalize only on the variation in explanatory and outcome variables that occurs within families and between siblings. As such, they return strict and inefficient estimators, and variation between families is lost. Therefore, we compare sibling fixed effects models with more efficient OLS models which analyze variation between families. For all models, we report results from a Hausman (1978) test of the hypothesis of no systematic differences between OLS and fixed effects estimates. In most of our models, we find few systematic differences between OLS and fixed effects coefficients. Ideally, we would like to estimate the causal effect of body mass on socioeconomic and marital status outcomes without bias due to unobserved family heterogeneity (as discussed above) and without bias due to a specific form of endogeneity – that of reverse causality. Sibling fixed effects models alone do not effectively eliminate bias due to reverse causality. In fact, it is likely that reverse causality is especially problematic in the current study as socioeconomic and marital status outcomes could be significant causes of body mass. We follow the lead of previous research that uses a lagged body mass variable to deal with bias due to reverse causality. Instead of using a lag of 7 years – as do Averett and Korenman (1996), Cawley (2004), and Gortmaker et al. (1993) – we use a lag of 13–15 years. We examine the effect of 1986 body mass on averaged 1999–2001 socioeconomic outcomes and on


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2001 marital status outcomes. Though our use of a 15 year lag is largely by default – the PSID only includes weight and height variables for the years 1986, 1999, and 2001 – it nevertheless provides a substantively important test of the persistence of the obesity penalty across a much longer time period than has been previously examined.

4. FINDINGS We begin with an initial analysis of the extent to which socioeconomic status, height, weight, and body mass cluster within families. To estimate sibling resemblances, we use a variance decomposition method that follows the strategy for income used by Mazumder and Levine (2003) and Solon, Corcoran, Gordon, and Laren (1991). See Conley and Glauber (2005) for a thorough discussion of this variance decomposition method for an unbalanced survey design. Sibling correlations for height, weight, and body mass are calculated for same-sex siblings and are presented in Table 2. We report a 0.359 correlation in sisters’ height, a 0.292 correlation in sisters’ weight, and a 0.332 correlation in body mass. For brothers, we find a 0.529 correlation in height, a 0.371 correlation in weight, and a 0.318 correlation in body mass. These figures imply that there is a significant amount of sibling variation in body mass – even more than the amount of intra-family variation in most Table 2. PSID Sibling Correlations in Socioeconomic Status Using 1983–2001 Waves and Sibling Correlations in Height, Weight, and Body Mass for Sisters and Brothers Using 1986, 1999, and 2001 Waves (Fisher’s z transformation, Standard Errors of z, Number of PersonYears, Number of Individuals, and Number of Families Below). Ln Earnings

Ln Income

Height: Sisters Only

Height: Brothers Only

Weight: Sisters Only

Weight: Brothers Only

BMI: Sisters Only

BMI: Brothers Only

0.376 0.395 0.035 20,792 1,876 801

0.458 0.495 0.035 18,144 1,871 806

0.359 0.376 0.023 5,042 2,188 1,847

0.529 0.589 0.026 3,926 1,849 1,505

0.292 0.301 0.023 4,949 2,171 1,833

0.371 0.389 0.026 4,385 1,904 1,536

0.332 0.345 0.023 4,873 2,165 1,830

0.318 0.330 0.026 3,916 1,849 1,505

po0.01.

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socioeconomic measures. The degree of variation on these indicators within families is relevant to the current study because our sibling fixed effects models capitalize only on variation that occurs within families and between siblings.

5. THE BODY MASS SOCIOECONOMIC STATUS PENALTIES BY RACE AND GENDER Table 3 presents the effect of obesity, overweight status, and underweight status in 1999–2001 on average log hourly wages, log labor market earnings, and log family income. We report both OLS and sibling fixed effects models and include in all of our models educational attainment, labor market experience, age of youngest child, and age. We include race only in the OLS models, as race does not vary between siblings. The bottom two rows in Table 3 present the chi-square test statistic of the Hausman test and the probability of obtaining this test statistic. Where the Hausman test leads us to reject the null hypothesis of no systematic differences between OLS and sibling fixed effects models, we report fixed effects estimates. In most models, there are few systematic differences between OLS and fixed effects estimates. In line with previous research, we find that obesity is associated with a 17.51% reduction in women’s wages and a 25.06% reduction in women’s family income. Overweight status, as compared with healthy weight status, causes a 20.87% reduction in women’s family income. The negative association between women’s body mass and labor market earnings may be due to unobserved heterogeneity as sibling fixed effects models do not result in significant point estimates. Table 3 also reveals that for men, being obese or overweight does not come with any economic penalties. Table 4 reports estimates from models which include a continuous measure of body mass and an interaction effect between race and body mass. The continuous measure of body mass is necessary for our sibling fixed effects models which rely on inter-sibling differences. Our results from Table 4 parallel results presented in Table 3 – body mass negatively affects women’s wages, earnings, and family income, although the standard errors are higher for the wage and earnings models which result in statistically insignificant estimates. These findings also indicate that body mass penalties are amassed by White women and not by African American women. Black–White differences are most pronounced for family income. For women’s family income, the OLS estimates do not appear to be biased by unobserved heterogeneity. The coefficients for wages and earnings work in similar

OLS and Sibling Fixed Effects Models Predicting 1999–2001 Log Wages, Log Earnings, and Log Family Income from 1986 Body Mass (Standard Errors). Women Log Wages OLS

Obese Overweight Underweight Number of individuals Number of siblings Chi-square test of difference in OLS and FE is not systematic Probability > chi-square

17.51 (0.07) 2.79 (0.07) 0.00 (0.09) 623

Men

Log Earnings

Sib FE

OLS

27.30 (0.24) 23.84 (0.18) 0.00 (0.24) 623

25.16 (0.14) 12.07 (0.11) 0.40 (0.17) 630

Log Family Income

Log Wages

Log Earnings

Log Family Income

Sib FE

OLS

Sib FE

OLS

Sib FE

OLS

Sib FE

OLS

Sib FE

40.46 (0.37) 72.21 (0.27) 4.69 (0.37) 630

25.06

4.29 (0.26) 17.14 (0.19) 3.32 (0.30) 1,052

13.42 (0.09) 2.10 (0.05) 0.84 (0.21) 712

10.42 (0.20) 15.35 (0.12) 5.62 (0.83) 712

24.41 (0.14) 0.76 (0.07) 1.12 (0.25) 719

19.75 (0.20) 22.62 (0.11) 0.98 (0.82) 719

18.82 (0.13) 3.89 (0.06) 1.27 (0.25) 1,023

43.68 (0.41) 6.56 (0.23) 15.97 (1.81) 1,023

(0.13) 20.87 (0.09) 0.84 (0.14) 1,052

553

560

970

592

600

884

15.39

41.10

11.20

7.20

14.13

10.10

0.052

0.000

0.191

0.515

0.078


Table 3.

0.258

265

Notes: All models include control variables for 1999–2001 educational attainment, labor market experience, age of youngest child, and age. Race is included in OLS models but not in sibling fixed effects models because it does not vary between siblings. All models report the percent change in wages, earnings, or family income due to obesity, overweight, or underweight as compared with recommended weight. To obtain this percent change, we used the following transformation: 100*(exp(BS.E.*S.E./2)1). Robust standard errors are reported for OLS models. po0.05. po0.01.

Women Log Wages

OLS Log body mass

Black log body mass Number of individuals Number of siblings Chi-square test of difference in OLS and FE is not systematic Probability>chisquare

Sib FE

OLS

(0.12) 0.10 (1.33) 0.00

0.56 (0.43) 0.00 (0.00) 0.75

0.35 (0.22) 0.16 (1.88) 0.04

(0.41) 624

(0.95) 624

(0.59) 631

Sib FE

Men Log Family Income OLS

Sib FE

(1.49) 631

(0.63) 1,053

Log Earnings

Log Family Income

OLS

Sib FE

OLS

Sib FE

OLS

Sib FE

(0.45) 0.00 (0.00) 0.53

0.17 (0.24) 3.70 (2.22) 1.24

0.52 (0.40) 0.00 (0.00) 4.09

0.43 (0.28) 0.37 (2.86) 0.16

0.12 (0.39) 0.00 (0.00) 0.92

0.18 (0.26) 0.62 (1.95) 0.21

0.16 (0.80) 0.00 (0.00) 4.56

(1.06) 1,053

(0.68) 713

(4.31) 713

(0.88) 720

(4.29) 720

(0.59) 1,024

(9.57) 1,024

0.71 0.44

0.17 (0.68) (0.22) 0.00 4.67 (0.00) (2.08) 1.43 1.36

Log Wages

554

561

971

592

600

884

15.68

31.96

10.15

7.31

13.92

10.07

0.028

0.000

0.184

0.605

0.125

0.345

Notes: All models include control variables for 1999–2001 educational attainment, labor market experience, age of youngest child, and age. Robust standard errors are reported for OLS models. po0.10. po0.05. po0.01.


Black

0.27

Log Earnings

266

Table 4. OLS and Sibling Fixed Effects Models Predicting 1999–2001 Log Wages, Log Earnings, and Log Family Income from a Continuous Measure of 1986 Log Body Mass, Race, and Interactions (Standard Errors).


267

directions, although their standard errors are higher which result in insignificant estimates. We are not the first to report race differences in the effects of body mass on economic outcomes. Averett and Korenman (1996) also find that the negative effects of obesity are more pronounced for White women than those for African American women. In fact, they find that overweight Black women earn about 8% more per hour than obese White women. We report similar results although we find only significant positive effects of body mass on African American women’s family income. For a variety of social, political, and historical reasons, there seem to be fewer social and economic penalties for overweight Black women. However, we do not take this to mean that Black women’s life outcomes are somehow less dependent on health or physical attractiveness. Instead, a comparably important measure of beauty for Black women is that of their skin color, as lighter skinned African Americans are still more likely to be considered physically attractive than are their darker peers and enjoy higher SES (Hughes & Hertel, 1990). For men, we find that body mass has no deleterious economic effects, but with one exception. African American men pay a wage penalty for their body mass. The same does not hold for White men. Findings reported in Table 5 indicate that body mass reduces women’s likelihood of marriage and increases women’s likelihood of divorce, separation, or widowhood. At the sample means of explanatory variables, obesity leads to a 0.16 reduction in women’s probability of marriage, and overweight status leads to a 0.12 reduction in women’s probability of marriage. For the probability of marriage, body mass OLS coefficients are not biased due to sibling-constant unobservable variables. For divorce, however, unobserved sibling heterogeneity produces upwardly biased OLS coefficients. The OLS model indicates that on average, obesity leads to a 0.13 increase in the probability of divorce and overweight status leads to a 0.11 increase in the probability of divorce, but estimates from sibling fixed effects models are not significantly different from zero. In summary, findings that we have presented thus far replicate results from previous studies. Women, White women in particular, pay economic and marital status penalties for being obese and overweight, and men do not. Given that our samples and measures differ substantially from previous studies, our findings attest to the robustness of the body mass penalty for women as it persists across the life course. We next turn to an analysis of variation in the body mass penalty between older and younger women. Here we capitalize on the older age structure of the PSID as compared with the NLSY and present new research findings.

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Table 5. Marginal Effects of Women’s 1986 Body Mass on Women’s 2001 Marital Statuses from Logit and Logit Sibling Fixed Effects Models (Standard Errors). Married

Obese Overweight Underweight Number of individuals Number of siblings Chi-square test of difference in OLS and FE is not systematic Probability>chisquare

Divorced

Logit

Sib FE

Logit

Sib FE

0.16 (0.05) 0.12 (0.05) 0.70 (0.08) 1,161 1,042

0.09 (0.14) 0.12 (0.12) 0.17 (0.16) 1,161 1,042 11.89

0.13 (0.05) 0.11 (0.05) 0.10 (0.08) 1,161 1,042

0.07 (0.16) 0.20 (0.12) 0.09 (0.18) 1,161 1,042 16.50

0.156

0.036

Notes: All models include control variables for 1999–2001 educational attainment, labor market experience, age of youngest child, and age. Race is included in OLS models only. Robust standard errors are reported for Logit. Results for men are insignificant and not reported. po0.05. po0.01.

6. BODY MASS EFFECTS OVER THE LIFE COURSE Table 6 presents the proportion of younger and older women in healthy, overweight, or obese body mass categories in 2001 by the proportion of women in these categories 15 years earlier. Younger women are between the ages of 25 and 34 in 1986 and older women are between the ages of 35 and 44 in 1986. We compare the proportions between younger and older women to determine if women’s movement into and out of healthy weight, overweight, or obesity categories differs by age. Our general conclusion is that it does. About 67% of younger women who were of a healthy weight in 1986 are of a healthy weight 15 years later, whereas only 61% of older women are at a healthy weight 15 years later. Further, of older women who were of a healthy weight in 1986, 9% are obese 15 years later, whereas of younger women who were of a healthy weight in 1986, only 6% are obese 15 years later. Compared with younger women, older women are also less likely to move from


269

Table 6. Changes in Women’s Body Mass by Age: Body Mass Proportions in 1986 by Body Mass Proportions in 1999–2001, by Age (Standard Errors). Healthy BMI, 1999–2001 Younger women: healthy BMI, 1986 Older women: healthy BMI, 1986 Difference in proportions Younger women: overweight, 1986 Older women: overweight, 1986 Difference in proportions Younger women: obese, 1986 Older women: obese, 1986 Difference in proportions

Overweight, 1999–2001

Obese, 1999–2001

0.67 (0.03)

0.25 (0.03)

0.06 (0.01)

0.61 (0.03)

0.30 (0.03)

0.09 (0.02)

0.07

0.04

0.03

0.13 (0.05)

0.41 (0.08)

0.44 (0.08)

0.02 (0.02)

0.50 (0.07)

0.48 (0.07)

0.11

0.09

0.04

0.00 (0.00)

0.13 (0.06)

0.88 (0.06)

0.04 (0.04) 0.04

0.15 (0.07) 0.03

0.81 (0.08) 0.07

Note: Younger women are of ages 25–34 in 1986 and older women are of ages 35–44 in 1986. po0.10. po0.05.

overweight to healthy weight. Clearly, changes in body mass are conditioned by stage in the life course. What is less than clear, however, is if the body mass socioeconomic penalties are conditioned by stage in the life course. Table 7 presents some evidence that body mass penalties vary across women’s stage in the life course. Our age restrictions limit samples sizes and our ability to carry out sibling fixed effects. We rely on a continuous measure of body mass (to reduce problems induced by a small sample size) and we report OLS estimates of the effect of body mass on economic and marital status outcomes. In the analyses reported above, we found little systematic variation between OLS and sibling fixed effects models; nevertheless, we use caution in our interpretation of these OLS models, as unobservable variable bias may differ between older and younger women. Table 7 reveals that body mass has a stronger negative association with younger women’s labor market outcomes. A 1% increase in younger women’s body mass leads to a 0.46% reduction in younger women’s hourly wages and a 0.67% reduction in women’s labor market earnings. The OLS estimates for older women are not significantly different from zero.

270

Table 7. OLS Models Predicting Women’s 1999–2001 Log Wages, Log Earnings, Log Family Income, and Logit Models Predicting Women’s 2001 Marital Statuses from a Continous Measure of Women’s 1986 Body Mass, by Age Group (Standard Errors).

Younger Body mass Number of individuals

0.46 (0.17) 263

Log Earnings

Older

Younger

0.26 (0.19) 223

0.67 (0.25) 266

Log Family Income

Married

Divorced

Older

Younger

Older

Younger

Older

Younger

Older

0.22 (0.21) 225

0.71

1.15

0.28

0.44

(0.54) 273

(0.15) 298

(0.18) 270

0.07 (0.12) 298

0.24 (0.15) 270

(0.27) 298

Notes: Younger women are of ages 25–34 in 1986 and older women are of ages 35–44 in 1986. All models include control variables for 1999– 2001 educational attainment, labor market experience, age of youngest child, age, and race. Robust standard errors are reported. Marginal effects are reported for logit marital status models. po0.10. po0.05. po0.01.


Log Wages


271

Conversely, body mass has a stronger negative effect on older women’s marriage market outcomes. For family income, a 1% increase in older women’s body mass leads to a 1.15% reduction in family income. And for marriage, a 1% increase in older women’s body mass leads to a 0.44% reduction in the probability of marriage. The negative effects of body mass on family income and on the probability of marriage are less strongly negative for younger women.

7. DISCUSSION AND CONCLUSION Previous research has found that U.S. women pay an obesity wage penalty and men do not (Averett & Korenman, 1996, 1999; Baum & Ford, 2004; Cawley, 2004; Cawley et al., 2005). While all of these studies employ sophisticated techniques aimed at estimating causal effects, all but one of these studies (Cawley et al., 2005) provide estimates that are generalizable only to individuals at the early stages of their lives. Further, studies that have used lagged body mass variables to reduce endogeneity bias have all relied on a 7 year lag. We change the research design of these studies in two respects: we use a much older sample of individuals from the PSID and we employ a body mass measure that is lagged by 15 years. Given these substantial methodological changes, our similarity in findings attests to the robustness of the body mass effect. Sibling fixed effects models, which we have employed in this study, provide estimates of the body mass effect that are robust to specific forms of unobserved heterogeneity. All unobserved characteristics that siblings share are differenced out of the model, but characteristics that siblings do not share are not differenced out of the model. To the extent that these unobserved, sibling-specific characteristics are correlated with our lagged body mass measure, they could be biasing both traditional OLS estimates as well as our sibling fixed effect estimates. Thus, as with previous research, we can conclude that our estimates approach causality but that they are not the definitive final statement on causality. Compared with healthy women, obese women pay a 17.51% wage penalty and a 25.06% family income penalty. Assuming that our identification strategy is valid, these are substantial negative causal effects of obesity on women’s economic well being. Further, marriage, as one route toward women’s economic well being, is less probable for overweight and obese women. Obesity reduces women’s probability of marriage by 0.16, compared with healthy weight women, and overweight status reduces women’s

272


probability of marriage by 0.12. As with previous research, we find that men pay no economic or marital status penalties for obesity, with one exception – obese African American men pay a small wage penalty. Finally, we capitalize on the PSID age distribution and find that younger women pay higher labor market penalties for their increased body mass relative to older women, whereas older women pay higher marriage market and family income penalties relative to younger women. As with any study of women’s labor market outcomes, our estimates are subject to a certain amount of selection bias, as women may move into and out of the labor force due to marriage, childbearing, and retirement. Our finding of a stronger obesity penalty for younger women’s labor market outcomes, compared with older women’s labor market outcomes, may be due to certain older women (namely those with higher socioeconomic statuses and lower body mass indices) selecting out of the labor force and out of our models. On the same token, it is possible that those with higher socioeconomic statuses and lower body mass indices are selecting out of the labor market to bear and rear children when they are younger. The PSID provides a wealth of data for all stages of the life course (unlike the NLSY which is specifically fielded to one cohort). Nevertheless, disaggregating age and cohort effects within the PSID is difficult. By design, our models control for period effects as we measure body mass and socioeconomic status at standard time periods. Observable age differences, however, could be partially attributable to the fact that our older sample of women came of age more than a decade before our younger sample of women. A final question worthy of consideration is: how substantial are the effects of increased body mass on women’s economic well being? Using estimates reported in Table 3 and sample means on all independent variables, we find that a White obese woman has a predicted wage of about US$ 8.67, whereas a White healthy woman has a predicted wage that is 20% more – US$ 10.48. This difference due to obesity is equivalent to the difference due to almost 2 years of education. The magnitude of the obesity penalty is even greater for family income, as a White obese woman has a predicted value of family income that is 33% less than that of a White healthy woman. This difference in family income due to obesity is equivalent to 2.5 years of education – a substantial difference on any count. What are the potential causal mechanisms driving significant body mass effects for women? Labor market discrimination may be one causal mechanism that accounts for this relationship. Employers may discriminate against job seekers for both rational and non-rational reasons related to physical appearance. Employers may rationally expect workers who suffer


273

from obvious health disadvantages to be less productive workers since they may miss work time due to ill health or they may be hampered on the job by a particularly disadvantageous condition. Since health status is not a legally protected category under civil rights law, employers may use some markers of ill health as a proxy for potential productivity – regardless of whether these perceptions are accurate. Marriage market dynamics may also be predicated on a double standard – heavier women are penalized in the marriage market, as they are less likely to marry than women of a healthier weight, but heavier men are not penalized in the marriage market, as they are equally likely to marry or divorce as men of a healthier weight. These negative effects of body mass on women’s likelihood of marriage and divorce become more pronounced as they age. Though our study does not provide definitive evidence as to why women pay these penalties, we do provide evidence of a robust negative effect of increased body mass on women’s socioeconomic status. The negative effects of obesity or overweight status persist much longer than previously understood, and they persist across the life course, widening the gender gap in economic well being.

NOTE 1. In addition to body mass, we explored an analysis of height, a different measure of physical appearance. However, all of our estimates using height as a predictor variable were insignificant, and we do not present them.

ACKNOWLEDGMENTS The authors would like to thank the editors of Advances in Health Economics and Health Services Research, Vol. 17, John Cawley and Kristian Bolin; the participants in workshops at New York University, University of Southern California, Harvard, Yale, Mt. Sinai School of Medicine, and Montefiore Hospital; as well as anonymous reviewers for helpful comments and suggestions.

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Pastor, P. N., Makuc, D. M., Reuben, C., & Xia, H. (2002). Chartbook on trends in the health of Americans. Health, United States. Hyattsville, MD: National Center for Health Statistics. Register, C. A., & Williams, D. R. (1990). Wage effects of obesity among young workers. Social Science Quarterly, 71, 130–141. Solon, G., Corcoran, M., Gordon, R., & Laren, D. (1991). A longitudinal analysis of sibling correlation in economic status. Journal of Human Resources, 26, 509–534.


PART IV: OTHER COSTS AND CONSEQUENCES OF OBESITY


HEALTH INSURANCE AND THE OBESITY EXTERNALITY Jay Bhattacharya and Neeraj Sood ABSTRACT If rational individuals pay the full costs of their decisions about food intake and exercise, economists, policy makers, and public health officials should treat the obesity epidemic as a matter of indifference. In this paper, we show that, as long as insurance premiums are not risk rated for obesity, health insurance coverage systematically shields those covered from the full costs of physical inactivity and overeating. Since the obese consume significantly more medical resources than the non-obese, but pay the same health insurance premiums, they impose a negative externality on normal weight individuals in their insurance pool. To estimate the size of this externality, we develop a model of weight loss and health insurance under two regimes – (1) underwriting on weight is allowed and (2) underwriting on weight is not allowed. We show that under regime (1), there is no obesity externality. Under regime (2), where there is an obesity externality, all plan participants face inefficient incentives to undertake unpleasant dieting and exercise. These reduced incentives lead to inefficient increases in bodyweight, and reduced social welfare. Using data on medical expenditures and bodyweight from the National Health and Interview Survey and the Medical Expenditure Panel Survey, we estimate that, in a health plan with a coinsurance rate of 17.5%, the obesity externality imposes a welfare cost of about $150 per capita. Our The Economics of Obesity Advances in Health Economics and Health Services Research, Volume 17, 279–318 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1016/S0731-2199(06)17011-9

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results also indicate that the welfare loss can be reduced by technological change that lowers the pecuniary and non-pecuniary costs of losing weight, and also by increasing the coinsurance rate.

1. INTRODUCTION Adult obesity is a thorny problem. Several studies document rising obesity prevalence in the U.S. (See Mokdad et al., 1999, 2003). Economists have argued that the primary cause of increasing obesity prevalence are: (1) a falling relative price of food; (2) a technologically induced shift away from physically demanding work; and (3) a decline in time spent on food production at home (see Lakdawalla & Philipson, 2002; Cutler, Glaeser, & Shapiro, 2003; Anderson, Butcher, & Levine, 2003).1 As most view these fundamental changes in the economy as desirable and would not want to undo them, developing public policy to address the root causes of rising obesity prevalence is difficult, if not entirely problematic. Nonetheless, the health care and other accounting costs associated with obesity are enormous. For example, Wolf and Colditz (1994) estimate that over $68 billion annually are lost annually in increased health care costs and job absenteeism as a result of obesity in the U.S. The morbidity and accounting costs associated with obesity have led public health experts (such as Nestle, 2003; Brownell & Horgen, 2003; Sturm, 2002) to advocate vigorous public intervention, including regulation of fast food establishments and taxes on nutritionally questionable foods. In this paper, we develop the theme that measures of the direct (such as medical costs) or indirect (such as productivity loss) costs due to obesity are not germane to the debate over whether public actions to curb obesity are justified. In accordance with traditional economic practice, we argue that it is the costs of bodyweight decisions not borne by an adult making those decisions (hereafter, external costs) that are most relevant to public policy. If external costs are high, then public welfare can be improved by interventions that change the incentives adults face when making decisions about bodyweight. If external costs are small, then adults pay fully for their bodyweight decisions and public interventions aimed at decreasing bodyweight can play only a limited role in improving public welfare.2 The primary mechanism by which obesity induces external costs is through health insurance. Perhaps not surprisingly, there is a large literature measuring differences in expected medical expenditures by obese and nonobese populations. Authors in this literature typically compare yearly

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medical expenditures by obese individuals with non-obese individuals in public health insurance programs. We will argue here that such calculations are incomplete. To our knowledge, no one has measured the external costs of obesity caused by health insurance. Measuring the obesity externality involves more than just measuring the subsidy to obese individuals induced by health insurance. The welfare loss due to the obesity externality depends upon both the size of the subsidy and upon the extent to which bodyweight decisions are distorted on the margin by the subsidy. If Homer Simpson would eat the same number of jelly donuts regardless of the generosity of his health insurance coverage, then in his case, the subsidy does not generate any welfare loss. The subsidy would simply represent a transfer to Homer from the thinner individuals in his insurance pool, with no net effect on social welfare. To measure the welfare loss from the obesity externality, we take a different approach. We develop a simple model of optimal weight in the presence of insurance. We show that the welfare loss caused by health insurance externalities depends critically on whether obese and non-obese individuals face pooled premiums. In other words, health insurance by itself, does not lead to an externality. The externality arises only if health insurance premiums do not reflect enrollee weight, such as when heterogeneous (obese and non-obese) enrollees are lumped into a single risk pool. When premiums are risk adjusted for bodyweight, the insured will have appropriate incentives to limit medical care expenditures through weight loss – after all, obese insured individuals will pay for their higher bodyweight through higher health insurance premiums. We show formally that the welfare loss caused by pooled health insurance is proportional to the product of the subsidy to obese individuals times the elasticity of bodyweight with respect to the subsidy. Finally, we calibrate a version of this model using data from the Medical Expenditure Panel Survey (MEPS). We estimate that the welfare loss due to the obesity externality in the U.S. is about $150 per-capita (in 1998 dollars). This estimate of the welfare loss is much smaller than the difference in medical expenditures between the obese and non-obese that is typically found in the literature.

2. BACKGROUND Americans are increasingly overweight or obese.3 The proportion of adults classified as obese increased from 12.0% in 1991 to 20.9% in 2001 (Mokdad et al., 1999, 2003). Obesity is associated with an increased risk for a number of serious chronic conditions, including diabetes, hypertension, heart disease, and

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stroke (Sturm, 2002). In this section, we provide a brief review of the large literature on the consequences of obesity for expected health care expenditures. We also provide a review of the smaller literature on the external costs of obesity induced by health insurance. Finally, we discuss why a traditional economic approach to measuring the welfare loss from the obesity externality, such as the one we advance, is preferable to a behavioral economic approach.

2.1. Obesity and Health Care Expenditures Not surprisingly, expected health care expenditures are higher for obese individuals than for normal weight individuals. A large number of studies document this fact. The vast majority of these studies use convenience samples consisting of individuals from a single employer or a single insurer (Elmer, Brown, Nichols, & Oster, 2004; Bertakis & Azari, 2005; Burton, Chen, Schultz, & Edington, 1998; Raebel et al., 2004). There are also studies of obesity-related medical expenditure differences in an international setting. Both Sander and Bergemann (2003), in a German setting, and Katzmarzyk and Janssen (2004), in a Canadian setting, find higher medical expenditures for obese people. There are a few studies that use nationally representative data. Finkelstein, Flebelkorn, and Wang, (2003) use data from the linked National Health Interview Survey (NHIS) and MEPS. They estimate that annual medical expenditures are $732 higher for obese than normal weight individuals. On an aggregate level, approximately half of the estimated $78.5 billion in medical care spending in 1998 attributable to excess bodyweight was financed through private insurance (38%) and patient out-of-pocket payments (14%). Sturm (2002), using data from the Health Care for Communities (HCC) survey, finds that obese individuals spend $395 per year more than non-obese individuals on medical care. Thorpe, Florence, Howard, and Joski (2004) also use MEPS data, but they are interested in how much of the $1,100 increase between 1987 and 2000 in per-capita medical expenditures is attributable to obesity. Using a regression model to calculate what per-capita medical expenditures would have been had 1987 obesity levels persisted to 2000, they conclude that about $300 of the $1,100 increase is due to the rise in obesity prevalence. This is a large literature, which space constraints prevent us from surveying in more detail. The many studies that we do not discuss here vary considerably in generality – some examine data from a single company or from a single insurance source – though they all reach the same qualitative conclusion that obesity is associated with higher medical care costs.4


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2.2. External Costs of Obesity Associated with Health Insurance Despite the lavish literature attention on medical expenditure differences, very few studies attempt to estimate the degree to which health insurance coverage leads to subsidies for the obese. Some studies have attempted to estimate how much of obesity-related medical costs are subsidized by public insurance. Finkelstein, Ruhm, and Kosa (2005), in a literature review of the causes and consequences of obesity, estimate that ‘‘the government finances roughly half the total annual medical costs attributable to obesity. As a result, the average taxpayer spends approximately $175 per year to finance obesity related medical expenditures among Medicare and Medicaid recipients.’’ To arrive at this conclusion, they rely on a study by Finkelstein, Fiebelkorn, and Wang (2004), who calculate state and federal level estimates of Medicare and Medicaid expenditures attributable to obesity. Another study, conducted by Daviglus et al. (2004), links together data from a sample of Chicago area workers in the labor force between 1967–1973, to Medicare claims records from the 1990s. They estimate substantial obesityrelated differences in Medicare expenditures. For example, women workers who were obese between 1967 and 1973 spent $176,947 in the 1990s on Medicare, while analogous non-obese, non-overweight female workers spent $100,431 in undiscounted costs. Obese male workers spent $125,470, while non-obese non-overweight male workers spent $76,866. However, estimating how much of obesity-related medical costs are financed by public insurance is not sufficient for calculating the subsidy for obesity. Conceptually, calculating the size of the subsidy also requires estimating payments by obese and non-obese individuals for enrolling in health insurance in addition to the expected benefits of enrollment. Roughly speaking, obese and non-obese people alike pay for Medicare when they are under 65 and spend (receive benefits) when they are older.5 Since obese people work, earn, are taxed, and die at different rates than non-obese people, looking at Medicare expenditure differences alone will paint a misleading picture of the Medicare subsidy for the obese. Calculating the obesity subsidy induced by private insurance also requires estimating both payments for health insurance and medical expenditures. Since private insurance is typically provided in an employment setting, it is not enough to look at premiums for health insurance paid by employers and employees.6 The key question is whether employers adjust the cash wages of obese workers with health insurance in order to account for the higher cost of insuring these workers. Although theory predicts that employers would have incentives to do so (Rosen, 1986), in practice, it is not clear that they

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would be able to make these adjustments.7 According to Gruber (2000), ‘‘ythe problems of preference revelation in this context are daunting; it is difficult in reality to see how firms could appropriately set worker specific compensating differentials.’’ As is the case with Medicare, however, there is very little research on obesity-related payment differences in a private insurance settling. An important exception is Bhattacharya and Bundorf (2005), who find some evidence that obese workers receive lower pay than non-obese workers primarily at firms that provide health insurance. In related work, Keeler, Manning, Newhouse, Sloss, and Wasserman (1989) and Manning, Keeler, Newhouse, Sloss, and Wasserman (1991), using data from the RAND Health Insurance Experiment (RAND HIE) and from the NHIS, report estimates of lifetime medical costs attributable to physically inactivity (rather than obesity): ‘‘At a 5 percent rate of discount, the lifetime subsidy from others to those with a sedentary life style is $1,900.’’ Though they label this estimate the ‘‘external cost of physical inactivity,’’ like the rest of the literature they focus on physical inactivityrelated medical expenditure differences, while ignoring payment differences that occur outside experimental settings in their calculation of the subsidy. Finally, one of the major themes of this paper is that estimating the welfare loss caused by the obesity externality also requires estimating the effect of health insurance subsidies on bodyweight decisions. Though, as we have seen, many studies examine health expenditure differences, there are no studies that measure the effect of health insurance subsidies on bodyweight decisions.

2.3. Justifying a Traditional Economic Approach This paper takes a traditional economic approach to social welfare measurement. Despite the widespread acceptance of Grossman’s (1972) model of health capital by economists, this traditional approach may be seen by some as inappropriate in the context of obesity. The primary criticism of the traditional approach concerns its reliance on a rational model of decisionmaking. Well-known perceptual biases, such as myopic discounting of future outcomes, may make rational calculation difficult.8 It is thus unclear whether people are capable of the kind of rational calculation that plays a prominent role in traditional economic models when they are deciding what to eat and how much to exercise. We offer three reasons why the traditional economic approach is nonetheless justified when thinking about obesity externalities. First, even if


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the rationality assumptions underlying the traditional approach are not quite right, they provide a good approximation of how equilibrium bodyweight adjusts in the face of changing economic circumstances. The economic literature on the obesity epidemic, as we argue in the introduction, points to changing prices of food, work, and leisure as the primary causes of increasing bodyweight. So there is clear evidence that the budget constraints people face have changed over time. Traditional economic models clearly predict that such changes in the constraints should lead to increasing bodyweight, which is exactly what has happened. In contrast, there is no evidence that we are aware of that implicates preference changes as an explanation for bodyweight trends. Such preference changes seem to us an unlikely explanation for obesity trends – are people really more myopic about bodyweight now than their parents? Of course such evidence does not rule out the possibility that perceptual distortions play an important role in determining bodyweight levels. At the same time, such evidence suggests that a traditional economic approach might not be such a bad place to start.9 A second reason to adopt a traditional economic approach is that an obesity externality would still cause social harm whether or not people are entirely rational when making bodyweight choices. Homer Simpson’s presence in a health insurance pool would be a cause for alarm by others in the pool since he is obese and hence he is likely to have high medical costs; there will be an obesity subsidy from the others to Homer. There would be cause for alarm whether or not he or anyone else in the pool behaves rationally with respect to their bodyweight. If Homer reacts to the lower price of obesity that is induced by the subsidy by eating more jelly donuts, there will be social harm from the obesity externality, again whether or not Homer is acting strictly rationally. It thus seems reasonable to us, at least as a first approximation, to measure the size of this social harm using the traditional economic approach to social welfare. The third reason to adopt a traditional approach is that it makes available a philosophically coherent approach to the measurement of social welfare that is well accepted by economists, policy-makers, philosophers, and others.10 By contrast, behavioral economic approaches raise important and difficult issues about social welfare measurement that are far from settled, even within the confines of the behavioral economics literature.11 The thorniest problem, in our view, concerns the definition of individual welfare. The main distinction between behavioral economic and traditional economic models is that behavioral models posit that a single individual may have multiple and incompatible preference systems.12 In traditional models, each individual has a single and consistent preference ordering that has a

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one-to-one relationship with that individual’s welfare. The hard problem of social welfare measurement in traditional models involves interpersonal welfare comparisons across individuals within a society. Social welfare analysis in behavioral economic settings requires both intrapersonal (across the incompatible preference systems within a single individual) and interpersonal welfare comparisons. To say anything about social welfare, behavioral economic models thus face a strictly harder problem than the one that traditional models face. Economists have approached the problem of intrapersonal welfare comparison in a variety of ways, each incompatible with the others. For example, Bhattacharya and Lakdawalla argue for a Pareto ‘‘self-improvement’’ criterion, wherein an intervention is adjudged to improve an individual’s welfare if and only if the intervention would improve welfare for all preference systems possessed by the individual.13 Other economists have chosen to depart from the Pareto criterion in various ways. For instance, in their analysis of smoking and cigarette taxation, Gruber and Koszegi (2001, 2004) advocate a ‘‘dictatorship of the present’’ that privileges the preferences of the current period self over all future selves; the same approach is suggested by Caplin and Leahy (2000) in the context of policy evaluation. Alternatively, O’Donoghue and Rabin (1999) choose to privilege the ‘‘long-run self’’ who is constituted by equally weighting the preferences of all temporal selves. How the problem of intrapersonal welfare comparison is solved matters. Gruber and Koszegi (2001, 2004) and O’Donoghue and Rabin (1999) both argue that targeted taxes on addictive goods might improve the welfare of myopic individuals (though the ‘‘dictatorship of the present’’ and ‘‘long-run self’’ approaches lead to different measures of the optimal tax), while Bhattacharya and Lakdawalla (2004) showed that time-invariant taxes alone are too blunt to be Pareto self-improving. In the presence of such fundamental disagreement about social welfare measurement, it seems prudent to us to adopt a traditional economic approach here.

3. A MODEL OF THE OBESITY EXTERNALITY INDUCED BY HEALTH INSURANCE In this section, we develop a simple economic model of weight choice to characterize the obesity externality. The model highlights two important facts. First, the deadweight welfare-loss due to the obesity externality depends on both the responsiveness of medical care expenditures to bodyweight and the responsiveness of weight choice to the obesity externality. Second, health


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insurance, by itself, does not lead to an externality. The externality arises only if health insurance premiums are not risk-adjusted to reflect enrollee weight, such as if heterogeneous (obese and non-obese) enrollees are lumped into a single risk pool. In that case of pooled premiums, we show that because individuals do not bear the medical care costs of weight gain, bodyweight choices will not be optimal. However, if premiums are adjusted to reflect weight gain (or loss) then plan enrollees internalize the external effects of weight change. Even if individuals are fully insured, as long as premiums are risk-adjusted they will still have an incentive to decrease expected medical care expenditures through weight loss. Unlike the pooling case, consumers recover lower medical expenditures through lower premiums.14 In this section, we consider a model with full insurance coverage. We relax this assumption in subsequent sections to permit incomplete insurance through the introduction of copayments to the insurance contract.

3.1. A Model of Optimal Bodyweight The model timeline in Fig. 1 illustrates the basic setup of the model. Each consumer starts with an initial endowment of weight W0. This endowment might be seen as reflecting the consumer’s genetic propensity to be overweight or obese, and in any case it cannot be chosen by the consumer. In the first stage consumers decide how much weight to lose, o. Weight loss (exercising, dieting) gives consumers some disutility but has two associated benefits: (1) it increases productivity, consequently raising consumer income and (2) it improves health (more precisely, it decreases the probability of falling sick).15 Falling sick entails additional medical care expenditures, but since consumers are insured, they are reimbursed for all of these additional medical care expenditures. In the second stage, nature reveals a health shock

Choose weight loss:

Choose consumption: C

time Initial Weight W0, received

Insurance Starts Health Observed Earn Income

Fig. 1.

Model Timeline.

Insurance Ends

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with i ¼ 1yN points of support.16 Each type of health shock entails additional medical expenses, Mi. Consumers first observe this health shock and then decide how much to consume. The consumers’ problem is to maximize expected utility by jointly choosing weight change (o) and a consumption plan fC i gN i¼1 for each of the N possible health states: max EU ¼

o;fC i gN i¼1

N X

pi ðW 0 oÞ UðC i Þ FðoÞ

(1)

i¼1

where U(Ci) represents utility from consumption; pi ðW 0 oÞ is the probability of health state i given weight ðW 0 oÞ; Ci is the consumption in health state i; and, F(o) is the disutility from weight loss. We divide our analysis now into two cases: (1) health insurance pools risk across people with heterogeneous risk (so that premiums do not change with bodyweight); and (2) people pay the risk-adjusted premiums for their own bodyweight. The primary difference between these cases manifests itself in consumer budget constraints. 3.2. Risk Pooling In this case, health risk is pooled across people of different bodyweight. As long as the pool size is large enough, a single individual’s medical expenditures will ¯ charged to everyone in the have a negligible effect on the common premium, P; pool. Hence, from the point of view of each individual, premiums are taken as fixed, and the budget constraint is: ¯ 8i IðW 0 þ oÞ ¼ C i þ P

(2)

In Eq. (2), I(W0+o) is the income earned by an individual who weighs W0+o. By allowing income to depend on weight, we are modeling the effect of health on labor-market productivity. We assume that I0 ( )>0. The budget constraint specifies that in each health state i, income equals expenditures on consumption and health insurance premiums. An immediate consequence of Eq. (2) is that consumption is identical in each health state, which makes sense since consumers are fully insured against medical expenditures. The consumer’s problem is to maximize expected utility, (1), subject to the budget constraint, (2). We solve the consumer’s problem using standard discrete numerical programming methods. In the first step, taking the amount of weight and as given, we calculate the optimal demand for consumption in each health state. Inputting the optimal consumption plan in the utility


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function gives the maximum utility attainable in each health state. In the second stage, we choose weight to maximize expected utility given optimal consumption in each health state. Plugging the budget constraint into Eq. (1), we reformulate the consumers’ problem in the second stage: ¯ FðoÞ max EU ¼ U½IðW 0 oÞ P o

(3)

The first-order condition for the consumer’s maximization problem is: ¯ F0 ðo Þ ¼ 0 I 0 ðW 0 o ÞU 0 ½IðW 0 o Þ P

(4)

Here, o* is the consumer’s optimal weight in the pooling case. The first term in Eq. (4) is the marginal gain from weight loss; it is entirely due to the marginal increase in income from increased productivity arising from weight loss (scaled by the marginal utility of consumption). In equilibrium, consumers will lose weight until the marginal gain from weight loss equals the marginal disutility from weight loss. If the insurance market is in competitive equilibrium, then premiums will be actuarially fair. They will equal the expected medical expenses for individuals in the insurance pool: ¯ ¼ P

N X

pi ðW 0 o ÞM i

(5)

i¼1

Eq. (4) also shows that since consumers are fully insured against medical expenses, the only incentive for weight loss is the increase in income due to weight loss. Thus, when insurance premiums do not depend on weight, consumers do not view the reduction in medical expenditures as an additional benefit of weight loss when making decisions about bodyweight. Insurance induces a form of moral hazard with respect to weight loss incentives since the benefits of weight loss are not fully internalized by the consumer. As a consequence, weight loss creates a positive externality for everyone else in the insurance pool, since it lowers their health insurance premiums.17 Because this benefit is not fully captured by the consumer losing the weight, insured people will tend to lose less weight than would be socially optimal. By contrast, the productivity benefits of weight loss are fully internalized as changes in productivity lead to an increase in consumer income.

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3.3. Risk-Adjusted Insurance We now turn to the case where health insurance premiums adjust to reflect the weight choice of consumers. In contrast to the previous case, where the premium is taken as fixed, consumers now face a risk-adjusted schedule of health insurance premiums that depends on their own bodyweight. In the context of employer provided insurance this could be achieved by wage reductions for obese employees, or simply by offering premium rebates to individuals who lose weight. In this case, the budget constraint is given by: IðW 0 oÞ ¼ C i þ PðW 0 oÞ 8i

(6)

Here, PðW 0 oÞ is the health insurance premium for an individual who weighs W0–o. Again, if the insurance market is competitive, premiums will be actuarially fair. Hence, they will be an increasing function of weight, reflecting the increase in expected medical expenses: " # N X PðW 0 oÞ ¼ pi ðW 0 oÞM i (7) i¼1

The consumers’ problem in this case can be reformulated as: max EU ¼ UðIðW 0 oÞ PðW 0 oÞÞ FðoÞ o

(8)

The first-order condition for the consumer’s maximization problem is: ½I 0 ðW 0 o Þ P0 ðW 0 o Þ U 0 ½IðW 0 o Þ PðW 0 o Þ F0 ðo Þ ¼ 0

ð9Þ

Here, o** is the consumer’s optimal weight in the risk-adjusted case. Clearly, Eq. (9) is necessary for o** to be individually optimal, but whether it is also socially optimal depends on what is meant by social optimality. Suppose EU is the expected utility of the representative consumer in the economy, and all individuals start with the same initial weight, W0. In that (unrealistic) case, o** can be said to be socially optimal, since the full social costs of bodyweight decisions are internalized. In Appendix A, we consider a more realistic case where W0 differs across individuals in the population. We show that, aside from transfers that do not depend on final weight, W0– o**, Eq. (9) is a necessary condition for the social optimum. It is instructive to compare the first-order condition in Eq. (9) with the analogous condition in Eq. (4) when there was a single risk pool. Both equations have a single term reflecting the marginal costs of weight loss: F0 ( ). However, Eq. (9) has two terms, I0 ( ) and P0 ( ), reflecting the


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marginal benefit of weight loss accruing from an increase in productivity and a decrease in the health insurance premium. By contrast, Eq. (4) has only a single term reflecting the marginal productivity benefit of weight loss: I0 ( ). Thus, when premiums reflect individual health risk, consumers have two incentives for weight loss – productivity gains and lower health insurance premiums. In this case, there is no moral hazard induced by health insurance and consumer bodyweight decisions. In Appendix B, we consider what implications the model has for optimal weight loss under pooled and risk-adjusted premiums. The effect of moving from pooled to risk-adjusted premiums depends on whether an individual receives an ex-ante subsidy from health under pooled premiums. There are three types of people: those at the margin, who receive no subsidy; supramarginal people whose initial weight is more than the average person in the risk pool and hence receive a positive subsidy; and inframarginal people whose weight is less than the average person in the risk pool and hence pay the subsidy. Our main finding is that people at the margin will optimally lose more weight under risk-adjusted premiums than they will under pooled premiums. The story is a bit more complicated for inframarginal and supramarginal people. Intuitively, moving from pooled to risk-adjusted premiums alters weight loss incentives in two distinct ways: through a reduction in the price of weight loss and through a change in net income since the subsidy is eliminated. For individuals at the margin, the only force operating in the switch from pooled to risk-adjusted premiums is the price reduction, which leads to an unambiguous decline in optimal weight. For inframarginal individuals, switching from pooled to risk-adjusted premiums leads to a removal of a negative subsidy and hence to an increase in income. While the reduction in price still encourages weight loss, the increase in income encourages weight gain. Hence, the net effect of the switch on the optimal weight of inframarginal individuals is theoretically ambiguous. By contrast for supramarginal individuals, the switch also eliminates a positive subsidy for weight gain, which decreases income. Since weight is a normal good in our model, the income and price effects work in the same direction – toward a lower optimal weight under risk-adjusted premiums for supramarginal individuals.

3.4. Deadweight Loss from the Obesity Externality In this section, we show that the size of the loss in social welfare from the obesity externality under pooled premiums depends on both the fact that expected health expenditures are higher for the obese and also on how

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responsive people would be in their weight loss decisions to a switch from pooled to risk-adjusted premiums. This calculation is important because, while there is a lot of empirical evidence that obese people are more likely to have higher medical care expenditures than non-obese people, there is no empirical evidence on whether pooled insurance causes obesity or weight gain. Whether the rise in obesity prevalence is a public health crisis, or merely a private crisis for many people, depends on the evidence on both quantities. We start with the expression for expected utility, evaluated at the optimum under risk-adjusted insurance: EU ðo Þ ¼ U½IðW 0 o Þ PðW 0 o Þ Fðo Þ

(10)

We have imposed the condition that consumption does not vary with health outcome since consumers are fully insured under both cases. Next, we consider a first-order Taylor series approximation of Eq. (10) around o*, which is optimal weight loss under pooled insurance: @EU EUðo Þ EUðo Þ þ ðo o Þ (11) @o o The deadweight loss (DWL) from the obesity externality is the change in expected utility resulting from pooling. Eq. (11) suggests an approximation to this quantity: @EU DWL ¼ EUðo Þ EU ðo Þ Do (12) @o o Here, Do o o is difference between optimal weight under riskadjusted and pooled risk cases. Since weight is socially optimal in the riskadjusted case, Do also reflects the degree to which weight choice differs from socially optimal when pooling pertains. Using a first-order Taylor series approximation, the DWL in expected utility terms due to the obesity externality is: DWL fU 0 ½IðW 0 o Þ PðW 0 o Þ ½I 0 ðW 0 o Þ þ P0 ðW 0 o Þ F0 ðo ÞgDo

ð13Þ

Substituting the first-order condition in Eq. (4) in Eq. (13) yields a simple expression for the DWL from the obesity externality: DWL U 0 ðÞP0 ðW 0 o ÞDo

(14)

Eq. (14) shows that the DWL is proportional to two crucial factors: the extent to which bodyweight deviates from the optimal due to pooled health insurance,


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Do, and the responsiveness of medical care expenditures to changes in weight, P0 (W0–o*). The DWL from the obesity externality is zero if individual weight choice does not respond to subsidies for obesity or if medical expenditures do not change with bodyweight. While it is widely recognized in the public health and economics literatures that P0 ( ) is an important component of the obesity externality, there is no work attempting to quantify Do.

4. CALIBRATING THE MODEL We turn now from our theoretical analysis of the obesity externality to a calibration exercise designed to generate dollar estimates of the size of the externality. The calibration exercise relies on a version of our model that is slightly altered to permit incomplete insurance contracts. Our basic strategy involves three steps. First, we manipulate the parameters of a particular utility function so that the model predictions about bodyweight distribution under pooled premiums matches the observed bodyweight distribution in a nationally representative data set.18 Second, using those same parameter estimates, we solve for optimal utility and bodyweight under risk-adjusted insurance for each individual in the data. Utility under risk-adjusted premiums will clearly exceed utility under pooled premiums, given the presence of the obesity externality in the latter case. Finally, we calculate the level of additional income that, if given to each individual in the data set, would equalize social welfare under risk-adjusted and pooled premiums.

4.1. Medical Expenditure Panel Survey Data For this exercise, we use nationally representative survey data from the MEPS because it contains all the data elements we need. In particular, we use the 1998 MEPS, linked to the 1997 NHIS sample.19 We restrict our analysis to insured individuals over 25 years old. We measure bodyweight in body mass index (BMI) units, which equals weight (measured in kilograms) divided by height (measured in meters) squared. Table 1 shows some key characteristics of our MEPS sample. There are approximately 6,900 individuals in the sample, with about 2,900 normal weight, 2,500 overweight, and 1,500 obese individuals. Median annual medical care expenditures are rising in bodyweight, though there is only a small difference between normal and overweight individuals, and in fact mean expenditures are higher for normal weight than overweight individuals. Overweight individuals

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Table 1. Medical Care Expenditures and Demographic Characteristics by Weight. Sample Size

1998 Expenditures Mean Median Age in years Income Insurance Private Medicaid Medicare Uninsured

Normal (BMI 18.5–24.9)

Overweight (BMI 25–29.9)

Obese (BMI>30)

All BMI Categories

N ¼ 2,962

N ¼ 2,458

N ¼ 1,484

N ¼ 6,904

$1,987 $476 46.2 years $28,084

$1,976 $502 48.2 years $28,968

$2,804 $837 48.9 years $26,107

$2,132 $542 47.4 years $28,040

60.7% 4.9% 19.0% 15.4%

61.5% 3.8% 20.8% 13.9%

57.0% 7.9% 21.5% 13.6%

60.3% 5.1% 20.1% 14.5%

Source: Authors calculations using linked 1998 MEPS-1996/7 NHIS population.

earn about $900 more on average per year than normal weight individuals, while obese individuals earn about $2,000 less. Insurance coverage is similar for obese, overweight, and normal weight individuals – about 60% have private insurance, 5% have Medicaid, 20% have Medicare, and 15% are uninsured. We exclude the uninsured from further analysis in this study, as they do not face the obesity externality.

4.2. Calibrating Utility Function Parameters We pick a particularly simple form for the utility function to minimize the number of unknown parameters: Uðc; oÞ ¼ ln c go2

(15)

In Eq. (15), the only parameter we will need to choose in the utility function is g, which is the utility cost per BMI unit squared of weight loss. To further simplify the calibration exercise, we restrict individuals to one of three bodyweight choices: normal weight (BMI ¼ 20), overweight (BMI ¼ 25), and obese (BMI ¼ 30). We need information on how the distribution of medical expenditures changes with bodyweight decisions. Since the change in distribution of medical expenditures due to bodyweight might vary with demographic variables, we estimate different expenditure distributions for obese,


295

overweight, and normal weight individuals for four different demographic subgroups: (1) males, aged 25–39, (2) females, aged 25–39, (3) males, age 40+, and (4) females, age 40+. We calibrate the model separately for each demographic subgroup. In Appendix C, we describe our methodology for estimating these expenditure distributions from the MEPS data. Fig. 2 shows that these distributions have three salient features. First, for all subgroups, the medical care expenditure distribution shifts to the right with an increase in bodyweight, so that obese individuals are more likely to spend more on medical care than normal weight individuals. Second, the gradient of medical care expenditures with respect to bodyweight is much higher for females. In other words, the increase in expected medical expenditures due to weight gain is much higher for females as compared with males. Finally, the gradient of medical care expenditures with respect to bodyweight also increases with age. Our next step is to estimate the single utility function parameter, g, for each demographic subgroup such that the model’s prediction under pooled premiums about the proportion of obese individuals matches the observed proportion for that subgroup. We want to match the data to the model with pooled premiums because: (1) the premiums paid by people with public insurance, to the extent any premiums are paid, are not risk adjusted for obesity; and (2) the nominal premium paid by people with employer provided private insurance is also typically not risk adjusted for obesity.20 In Section 3, consumers were completely insured against medical risks. In reality, under most health insurance plans in the U.S., consumers must pay coinsurance for medical expenditures. Let m represent the coinsurance rate. For each person in the data, k ¼ 1yK, let Mik and Cik represent medical expenditures and consumption associated with health shock i ¼ 1yN. Let Ik represent consumer k’s income. Consumer k’s budget constraint under pooled premiums is: ¯ þ mM ik I k ¼ C ik þ P

for i ¼ 1 . . . N

and

k ¼ 1...K

(16)

In our calibration exercise, each individual picks consumption and weight loss so that utility – Eq. (15) – is maximized, subject to the budget constraint. We assume that a certain proportion of the population is ‘‘genetically normal weight.’’ For these individuals weight is not a choice variable and is solely determined by their genetic endowment. We choose the value of this parameter to be the proportion of normal weight individuals under pooled premiums. In other words, we assume that people who are normal weight under pooled premiums must be endowed with normal weight, as there are few incentives to choose a normal weight with pooled

Female, Age 25-39

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Obesity and the Empirical Medical Expenditure Distribution.

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premiums. In the remaining population everyone starts out obese (i.e., with a BMI of 30) and then decides whether to lose 0, 5, or 10 BMI points. For our main results we assume that the copayment rate is 17.5%, which corresponds roughly with the average level of out-of-pocket expenditures for health care among the insured population in the U.S.21 Given these assumptions, it is simple to calculate optimal weight using the same backward recursion algorithm that we describe in Section 3.2. The MEPS data give us the distribution of M under alternate bodyweight choices, as well as income, Ik, for each individual. Consumption and ¯ There is bodyweight, ok, are endogenously determined by the model, as is P: only one unknown parameter – the cost of weight loss, g. Recall that we would like to find a value for g such that the predicted weight distribution matches that observed in the MEPS data. We have one main computational problem. To solve the model for any ¯ but both variables depend on the given individual, we need to know g and P; optimal choices made by all other individuals in the pool. Our approach is as follows. First, we fix a trial value for the cost of weight loss, g0. Second, we follow a four-step procedure to calculate optimal choices given this value of g: (1) we guess an initial premium level that is consistent with our assumption about the initial weight distribution; (2) we calculate optimal weight for each individual in the data based on this guess; (3) we recalculate the risk-adjusted premium that is associated with this new weight distribution; and (4) we iterate steps 2 and 3 until P¯ converges. Convergence here means that the pooled premium level implied by step (3) yields the same (or rather, sufficiently close) premium level in the next iteration. This procedure generates a predicted bodyweight for all individuals in our sample, W k ðg0 Þ; which is a function of our initial guess for g. From this we calculate Obesek ðg0 Þ ¼ 1 ðW k ðg0 Þ ¼ 30Þ and Overweightk ðg0 Þ ¼ 1 ðW k ðg0 Þ ¼ 25Þ; which are indicators of whether each individual is optimally obese or overweight. Third, we calculate the following loss function: Loss ¼

K X ½Obesek Obesek ðgÞ2 þ ½Overweightk Overweightk ðgÞ2 (17) k¼1

Here, Obesek and Overweightk are an indicator of whether individual k in the sample is obese or overweight. Using a first and second difference approximation to the derivatives of Eq. (17) with respect to g, we calculate a Newton–Raphson update for our estimated value of g. Finally, using this updated guess, g1, we iterate on calculating equilibrium optimal choices and updating the loss function until we have a converged value of the costs of

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weight loss, g*. Using this converged value, we calculate the predicted probability of obesityPin the population under pooling that corresponds with K the observed data, K1 P k¼1 Obesek ðg Þ; as well as social welfare under pooled K premiums, SW ðg Þ ¼ k¼1 EU k ðg Þ: 4.3. Estimating Welfare Loss from the Obesity Externality To find the welfare loss from the obesity externality, we will need to know what bodyweight and utility would have been absent the externality. Consequently, we calculate what bodyweight choices and utility would be if individuals faced risk-adjusted insurance premiums, rather than pooled premiums. Since we already have a value for the costs of weight loss, g*, this is a substantially easier problem than the one we have already solved. As before, we start with a initial bodyweight distribution, and then individuals choose a BMI of 20, 25, or 30 to maximize utility – Eq. (15). In the risk-adjusted case, however, individuals maximize the following budget constraint: I i ¼ C ik þ Pðok Þ þ mM ik

(18)

Here, P(ok) represents the risk-adjusted premium associated with weight loss ok. We calculate this premium level for the various weight loss choices (0, 5, and 10 BMI points) using Eq. (7; adjusted for the presence of copayments in the insurance contract). We can calculate this using the medical expenditure distribution information that we derived from the MEPS. For each individual, this optimization yields a predicted bodyweight under risk adjusted insurance, W k ðg Þ and an indicator of predicted obesity, Obesek ðg Þ: It also yields a measure of social welfare under risk-adjusted insurance. SW ðg Þ ¼

K X

EU k ðg Þ,

k¼1 EU k ðg Þ

is the expected utility under risk-adjusted insurance for each where individual. The final step in our calibration exercise involves measuring the welfare loss from the obesity externality in dollar units. Let SW ðY ; g Þ be the optimal level of social welfare under pooled insurance when each individual k in the population has income equal to Ik+Y. In that case, individuals maximize Eq. (15) subject to the budget constraint: I k þ Y ¼ C ik þ P¯ þ mM ik .


299

SW ðY ; g Þ can be calculated in exactly the same way as SW ðg Þ for any given value of Y. A measure of the welfare loss from the obesity externality is the dollar amount that, if given to each individual in the pooled case, would equalize social welfare under the pooled and risk-adjusted cases. This compensating differential, Y*, is defined by the following equation: SW ðY ; g Þ SW ðg Þ ¼ 0

(19)

Y*>0 is guaranteed to exist since social welfare is clearly increasing in Y and since SW ð0; g ÞoSW ðg Þ (i.e., social welfare under risk-adjusted insurance must exceed social welfare under pooling at the same level of income). We find Y* using a univariate bisection search that involves recalculating SW ðY ; g Þ for various levels of Y until Eq. (19) is satisfied.

5. CALIBRATION RESULTS Table 2 presents the main results from our calibration exercise.22 We estimate the welfare loss from the obesity externality to be $149 per person. The welfare loss for the obesity externality varies substantially by demographic subgroups with the greatest losses for women in the 40+ age group and smallest losses for men in the 25–39 years age group. This wide variation in the welfare loss due the obesity externality can be explained by the two factors highlighted in Eq. (14): the extent to which bodyweight deviates from the optimal due to pooled health insurance, and the responsiveness of medical care expenditures to changes in weight. Fig. 2 showed that medical care expenditures of women and older persons were most responsive to weight and, therefore, these demographic groups are likely to suffer a higher welfare loss. Table 2. Group

Age 25–39 Males Females Age 40+ Males Females All groups

Welfare Loss from the Obesity Externality.

Change in Distribution of Weight due to Pooled Premiums

Welfare Loss from Obesity Externality (Y)

Normal (%)

Overweight (%)

Obese (%)

5 0

9 16

14 16

$7 $78

0 7 3

19 14 15

19 21 19

$80 $304 $149

300


Table 2 also shows the extent to which weight choices deviate from the optimal (risk-adjusted insurance) under pooled premiums. Despite the theoretical possibility that risk-adjusted insurance might induce people to weigh more (if the income effect outweighs the direct price effect), for all groups we find that pooled insurance increases the prevalence of obesity. Again, we find significant differences by demographic subgroups with the weight choices of women and older persons deviating more from the optimal under pooled premiums than the weight choices of men and younger persons. Thus, the pattern of weight choice deviations also implies that women and older persons are likely to suffer a higher welfare loss due to the obesity externality. We turn next to a series of figures designed to graphically illustrate two important comparative statistics results from the model. In particular, we look at how the prevalence of obesity and the welfare loss from obesity change with the coinsurance rate and with the utility cost of losing weight. These comparative statics are important, in part, because they are policy relevant. The coinsurance rate, at least for public insurance, is directly amenable to government control. The utility costs of weight loss depend strongly on prevailing dietary and exercise technology, as well as on the availability of pharmaceutical products that promote weight loss. These are also often amenable to government policy; directly through investment in scientific research and indirectly through the regulation of the market for weight loss products. These figures also give some sense of how sensitive our main results are to our assumptions. Because the results are qualitatively similar for all our subgroups, we illustrate the comparative statics only for men 40 years of age or older. Fig. 3 displays the effect of changing the copayment rate, holding all else fixed.23 Panel A shows that the prevalence of obesity declines strongly with increase in the coinsurance rate. Panel B shows that the welfare loss from the obesity externality declines with increases in the coinsurance rate. The most striking feature of this figure is that the welfare loss remains relatively stable up to coinsurance rates of about 15% but then declines sharply for increases in coinsurance rates beyond this level. At 15% copayment, the welfare loss is about $260 per person; at 25% it is about $115 per person; at 35% it is about $50 per person; at 45% it is about $25 per person; and at 55% it is nearly zero. At 100% coinsurance, of course, there is no welfare loss due to the obesity externality, though presumably there would be welfare losses from sources that we have not modeled. Clearly, imposing modest coinsurance can be an effective way of controlling the welfare loss due to the obesity externality under pooling.


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Panel A: Change in Obesity Prevalence (Pooled vs. Risk-adjusted) 1

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Fig. 4 displays the effect of changing the utility cost of weight loss, holding all else fixed. Panel A shows that decline in the costs of weight loss leads to a decrease in the prevalence of obesity. Panel B of Fig. 4 shows that the welfare loss from the obesity externality declines steadily with decrease in the costs of weight loss. Reducing the costs of weight loss to half of its initial baseline level (see Appendix Table A.1) reduces the welfare loss to about a quarter of its original value (from $80 per capita to $20 per capita). Similarly increasing the costs of weight loss by 50% from its baseline level increases the welfare loss by a 150%. Thus, our findings suggest that improvements in weight loss technology can play an important role in limiting the welfare loss from the obesity externality. They also suggest that any new developments (such as tastier junk food) that increase the costs of weight loss can dramatically increase the welfare loss from the obesity externality.

6. CONCLUSIONS This paper represents a first step in understanding what is known and not known about whether the obesity crisis is truly a public health crisis. There is little question that obesity is a serious issue for personal health.24 But should obesity be a public health concern, or should adults decide their bodyweight unimpeded by public intervention? On this point, the debate is fierce. On one side, legislators in Congress are considering bills like the ‘‘Personal Responsibility in Food Consumption Act’’ that would limit lawsuits that hold restaurants responsible for obesity. On the other side, public health specialists urge interventions, such as taxing junk food and subsidizing healthier foods.25 The notion of externalities suggests a useful framework to transform this contentious debate into a scientifically addressable problem. Decisions about bodyweight in the absence of government intervention can only be optimal if individuals face the full costs of their decisions about eating and physical activity – that is, in the absence of externalities. Cawley (2004) summarizes a well-known lesson of public economics: ‘‘Without a market failure, there is no economic justification for government intervention. A high prevalence of obesity is not in itself proof of market failure.’’ Given this background, we explore whether health insurance that subsidizes medical expenditures for the obese leads to an obesity externality. One of our most important findings is that health insurance, by itself, does not lead to an externality. The externality arises when health insurance


303

Panel A: Change in Obesity Prevalence (Pooled vs. Risk-adjusted) 1 0.9

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200

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Fig. 4. Response to Changes in the Cost of Losing Weight. Panel A: Change in Obesity Prevalence (Pooled vs. Risk-Adjusted). Panel B: Welfare Loss from Pooling.

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premiums do not adjust to reflect the weight choices of individuals so that individuals do not bear the full costs of their weight choice. However, if premiums adjust to reflect weight gain (or loss) then the change in premiums internalizes the medical care costs of weight gain. Therefore, even if individuals are fully insured, they will still have an incentive to decrease expected medical care expenditures through weight loss as consumers recover lower medical expenditures through lower premiums. We also show that the welfare loss from the obesity externality is proportional to the product of the difference in medical expenditures between the obese and non-obese and the extent to which the health insurance subsidy induced by pooled insurance causes distortions in bodyweight decisions. The estimates from a simple calibration of our model using nationally representative data suggest that the obesity externality induced by health insurance imposes a welfare cost of $150 per capita. This is lower than the literature estimates of the transfer induced by insurance to obese individuals. Women and older people are hit the hardest by this externality as they have a higher medical-expenditure elasticity with respect to bodyweight and pooled insurance causes more severe distortions in bodyweight decisions of these people. Our model also suggests some feasible ways of mitigating the external harms caused by the obesity crisis, to the extent that they exist. An important implication of our model is that obesity can have external effects through health insurance coverage only if premium setting ignores obesity. An obvious way to capitalize on this implication is to risk rate premiums to take bodyweight changes into account. This information is either readily available to insurance plans through medical records, or could be collected cheaply. In public insurance settings, subsidies could be given to individuals who maintain healthy bodyweight. Our model results suggest two more policies that limit the social welfare harm from the obesity externality under pooled insurance. First, even moderate levels of cost sharing dramatically reduces the welfare harm. Second, technological developments that aid people in losing weight can substantially reduce welfare losses. Such developments potentially include the development of products like low calorie food substitutes, improved and more enjoyable exercises and exercise equipment, and pharmaceutical products to control over-eating. Developments could also include interventions in workplaces and homes to alter the environment in which dietary and exercise choices are made. As long as the public costs of such interventions are less than the external costs of obesity, it will be worth it to invest in them.


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NOTES 1. There are, of course, many other non-economic determinants of bodyweight, including genetic predispositions to obesity and non-rational impulses (such as myopic decision making and lack of self-control) that prevent optimal bodyweight control. These are unlikely explanations for the observed trends in bodyweight, even if they help explain baseline levels. There is certainly no evidence that we are more irrational or have different genes than our parents or grandparents. 2. Cawley (2004) provides a detailed discussion of possible market failures related to obesity. 3. BMI is the standard of bodyweight in the medical literature. BMI is weight, measured in kilograms, divided by height, measured in meters, squared. Individuals with a BMI between 25 and 30 are considered overweight, while those with a BMI of 30 or more are considered obese (National Institute on Health, 1998). Henceforth, we use BMI and bodyweight interchangeably. 4. Some of the studies we reviewed, but arbitrarily do not discuss here include Bungam, Satterwhite, Jackson, and Morrow (2003), Musich, Lu, McDonald, Champagne, and Edington (2004), Quesenberry, Caan, and Jacobson (1998), Thompson, Brown, Nichols, Elmer, and Oster (2001), and Wang et al. (2003). 5. For example, McClellan and Skinner (1999, 2006) and Bhattacharya and Lakdawalla (2005), in estimating Medicare progressivity, estimate lifetime profiles of tax receipts for Medicare as well as Medicare expenditures. 6. For employees enrolling in the same insurance plan, premiums do not depend on bodyweight (see Keenan, Buntin, McGuire, & Newhouse, 2001 Keenan et al., 2001), so in that case, there are no obesity-related payment differences. However, when employers offer multiple health plans, obese workers may tend to select into a different set of plans than their thinner colleagues. In that case, premiums may differ. 7. The literature on medical expenditure associated obesity costs has a parallel and often intersecting literature on the labor-market productivity costs associated with obesity (often these latter costs are called ‘‘indirect’’ costs of obesity). The theory of compensating wage differentials has important implications for whether these labor market costs are external; that is, whether obese individuals pay for lower productivity levels (such as through more sick days) associated with their bodyweight, or someone else pays. This theory suggests that obese workers will pay for lower productivity through reduced wages. The economics literature on obesity-related wage differences – for example, Register and Williams (1990), Pagan and Davila (1997), and Cawley (2000) – unanimously finds that obese workers earn lower wages than their thinner colleagues, and that these differences are equal to or greater than the wages differences that would arise from measurable productivity differences. Hence, both theory and evidence suggest that these ‘‘indirect’’ costs of obesity are not external. 8. With one exception, no authors have specifically applied behavioral economic models to explaining bodyweight choices. Cutler et al. (2003) hint that bodyweight choices might fruitfully be modeled using a model of intertemporal myopia, but do not develop the point. Nonetheless, there has been a lot of promising work in the behavioral economics literature that might be applied to the analysis of bodyweight decisions. See, for example, Lowenstein and Prelec (1992) for a good review of this literature. While the analysis of myopia has a long history in economics, Laibson

306


(1994, 1997) rekindled interest in it as a means of understanding individual decisionmaking. See Strotz (1955) and Phelps and Pollak (1968) for early work by economists on myopic behavior. 9. In a famous essay entitled ‘‘De Gustibus Non Es Disputandum,’’ Stigler and Becker (1977) urge social scientists to carefully whether constraints explain patterns of behavior before considering tastes as an explanation. They argue that an appeal to tastes to explain puzzling behavior patterns is often an ad hoc expedient that should be cut by Occam’s razor. Furthermore, they argue that behavior that at first appears to be explainable only an appeal to tastes is often explainable by a deeper analysis of the interaction between constraints and tastes. A similar point could be made in this context. 10. Economists have relied on such this traditional approach to social harm measurement in the context of externalities since (at least) Samuelson’s (1954) seminal paper on the topic. Of course, we do not mean to say that there are no philosophical controversies surrounding the traditional economic approach to social welfare. We just mean that this traditional approach remains reasonably popular among social scientists despite these controversies. 11. See, for example, Bernheim and Rangel (2005). 12. See, for example, Bernheim and Rangel (2002) or Bhattacharya and Lakdawalla (2004). 13. To our knowledge, Phelps and Pollak (1968) are the first authors to explicitly adopt a Pareto criterion in an analysis of myopic preferences, although they focus on an intergenerational version. Goldman (1979) implicitly assumes such a criterion for time-inconsistent agents, while Laibson (1994) discusses it in the context of a myopic savings model. 14. This result is similar in nature to Ehrlich and Becker’s (1972) theoretical finding that when insurance premiums reflect risk, individuals have incentives to expend resources on self-protection. 15. The model can also be interpreted as a model of weight gain, with a reinterpretation of the source of disutility from this gain (diminished body image, perhaps). The main point is that a change in weight away from the optimum choice induces disutility. 16. The results of the model are similar when health shocks are permitted to be continuous, but the solution technology is less transparent. 17. This argument is developed in more detail in Appendix A. 18. Unlike the theoretical model, we take income as fixed and exogenous. The empirical literature suggests that bodyweight has only a small effect on productivity at the workplace – see Cawley (2004) and Bhattacharya and Bundorf (2005). 19. This is the same sample of individuals analyzed by Finkelstein, Flebelkorn, and Wang (2004) Finkelstein et al. (2004). The also excludes pregnant women. 20. Although it is likely that premiums do not depend on weight we do not know the true extent of risk pooling among the insured population and on what characteristics the pooling occurs. We assume that health insurance premiums vary by gender and broad age categories only. Therefore, we conduct the analysis for each demographic subgroup separately. Population estimates of welfare loss and obesity are obtained by taking a weighted average (based on population proportions) of estimates for each demographic group. 21. See Cohen et al. (1996).


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22. Appendix Table A.1 lists the relative costs of weight loss for individuals in different demographic subgroups. These results show that women and older persons have higher costs of losing weight. 23. This simulation estimates the welfare loss due to obesity when coinsurance varies from 0 (full insurance) to 1 (uninsurance). Therefore, this simulation only includes individuals with income greater than $50,000. This is necessary because incomes less than $50,000 would result in negative consumption if individuals are uninsured and receive a health shock that requires $50,000 or more in health expenditures. 24. However, recently, Flegal, Graubard, Williamson, and Gail (2005) have found that overweight individuals (with a BMI between 25 and 30) live slightly longer than normal weight individuals (with a BMI between 18.5 and 25). There is no controversy about the fact that, all else equal, obese individuals (with a BMI over 30) tend to die before normal weight individuals. 25. There is some dissent among public health experts about the desirability of higher food taxes. Drewnowski (2004) and Drewnowski and Darmon (2005) find that the price of unhealthy energy dense foods is less than more healthy, less energy dense alternatives. They argue that the reason why poorer individuals are more likely to be obese is that it is cheaper to buy energy dense foods. They caution that imposing additional taxes on these foods would have malign distributional consequences. 26. Eq. (B.7) implies that poolðW 0j ; o j ; S j Þ increases with Sj, since the marginal utility of income is declining in income. 27. We have experimented with adding additional points of support above $50,000 with no substantive difference in our final results. 28. We set y7 equal to N.

ACKNOWLEDGMENTS We are grateful to the USDA and to the National Institute on Aging for financial assistance. We thank seminar participants in the Departments of Economics at Rice University and Texas A&M University for spirited and constructive discussions. We thank Kate Bundorf, Dana Goldman, Eric Finkelstein, Darius Lakdawalla, Grant Miller, and William Vogt for helpful conversations. We thank an anonymous referee for suggesting that we place the paper in the context of non-economic explanations for obesity and for other valuable editorial advice. Finally, we thank Catherine Cruz for assistance in preparing the manuscript. Despite all this help, we are solely responsible for the opinions expressed in this paper and for any errors.

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APPENDIX Table A.1

Estimated Utility Cost of Weight Loss.

Group

Cost of Weight Loss (g)

Age 25–39 Males Females Age 40+ Males Females

0.000009 0.000140 0.000081 0.000449

APPENDIX A. A CHARACTERIZATION OF THE SOCIAL OPTIMUM In this section, we derive necessary conditions characterizing the socially optimal level of weight loss for a society of j ¼ 1yJ individuals. Each has the following expected utility, taken from Eq. (1): EU j ¼

N X

pi ðW 0j oj Þ UðC ij Þ Fðoj Þ

(A.1)

i¼1

; as the sum of expected utilities over all

We define total social welfare, individuals in the society:

A

A

¼

J X

gj EU j

(A.2)

j¼1

In (A.2), gj represents the Pareto weight that individual j has in the social welfare function. In the social budget constraint, total income equals total expenditures on consumption plus total medical expenditures over all individuals. Both income and the distribution of medical expenditures depend on bodyweight decisions: ( J X j¼1

IðW 0j oj Þ

N X i¼1

) pi ðW 0j oj ÞðM i þ C ij Þ

¼0

(A.3)

312


Eq. (A.3) builds in our assumption that expectations about the distribution of medical expenditures in the population correspond to the observed distribution of expenditures. The social problem is to pick consumption and bodyweight for all individuals in every state of the world – fC ij ; oj g 8i; j – to maximize subject to the social budget constraint. To this end, we construct the following Lagrangian function, where l is the multiplier associated with the social budget constraint, (A.3):

A

L¼

J X N X

gj pi ðW 0j oj Þ UðC ij Þ gj Fðoj Þ

j¼1 i¼1

l

( J X

IðW 0j oj Þ

j¼1

N X

) pi ðW 0j oj ÞðM i þ C ij Þ

ðA:4Þ

i¼1

There are two sets of first-order conditions: @L ¼ gj U 0 ðC ij Þ þ l ¼ 0 8i; j @C ij

(A.5)

and N X @L ¼ p0i ðW 0j oj Þgj UðC ij Þ gj F0 ðoj Þ @oj i¼1

þ l I 0 ðW 0j oj Þ þ

N X

!

p0i ðW 0j oj ÞðM i þ C ij Þ

¼ 0 8j

ðA:6Þ

i¼1

An immediate implication of (A.5) is that at the social optimum, each individual j in the society must set his (or her) consumption level to the same value, say C j ; across all the N different health states: C ij ¼ C j 8i; j

(A.7)

Applying (A.7) to (A.6) yields the following: ðgj UðC j Þ þ lC j Þ

N X

p0i ðW 0j oj Þ gj F0 ðoj Þ

i¼1 0

þ l I ðW 0j oj Þ þ

N X i¼1

! M i p0i ðW 0j

oj Þ

¼ 0 8j

ðA:8Þ

PN PN 0 By definition, i¼1 pi ðW 0j oj Þ ¼ 1; so we have i¼1 pi ðW 0j oj Þ ¼ 0: Furthermore, differentiating Eq. (7), which defines the risk-adjusted


313

premium, PðW 0j oj Þ; yields the fact that: P0 ðW 0j oj Þ ¼

N X

p0i ðW 0j oj ÞM i 8j.

(A.9)

i¼1

These equations and (A.5) permit a further simplification of Eq. (A.8): F0 ðoj Þ U 0 ðC j Þ½I 0 ðW 0j oj Þ P0 ðW 0j oj Þ ¼ 0 8j

(A.10)

Hence, the social optimum requires each individual to equate the marginal (utility) costs of weight loss with the marginal (utility) benefits from the weight loss – an increase in income and a reduction in expected medical costs. One feasible allocation that meets (A.10) would set consumption for each individual equal to income, less the risk-adjusted premium given weight: C j ¼ IðW 0j oj Þ PðW 0j oj Þ 8j

(A.11)

It is easy to show that this allocation would be optimal for some distribution of initial bodyweight, {W0j}, and some set of Pareto weights, {gj}. In this allocation, there are no transfers between individuals with different initial bodyweights. Other optimal and feasible allocations are possible, but these would involve fixed transfers between individuals that do not depend on final bodyweight (though they might depend on initial bodyweight). Optimal transfers would clearly vary with {gj}, though all optimal allocations would need to obey condition (A.10).

APPENDIX B. OPTIMAL WEIGHT LOSS UNDER RISK-ADJUSTED AND POOLED INSURANCE In this section, we compare optimal weight loss under pooled with riskadjusted health insurance pricing. As in Appendix A, we consider an economy where there are J individuals, each with an initial weight, W0j. Throughout, we assume that J is large. As before, we let oj represent the jth individual’s optimal weight loss under pooling, while we let o j represent j’s optimal weight loss under risk-adjusted insurance. It will be useful to divide these individuals P into three groups based on their final weight, W0,j–oj. Let PðW 0j oj Þ ¼ N i¼1 pi ðW 0j oj ÞM i be the expected of individual j under pooling, and let P¯ ¼ PJ medical expenditures ð1=JÞ j¼1 PðW 0j oj Þ be the average pooling premium in the economy. We define an individual j to be at the margin if he would receive no ex ante

314


¯ An subsidy under pooled health insurance – that is, if PðW 0j oj Þ ¼ P: individual j is said to be inframarginal if his expected medical expenditures would be less under pooling than the average pooling premium PðW 0j ¯ Clearly, inframarginal individuals are thinner than those at the oj ÞoP: margin. Finally, an individual j is said to be supramarginal if he would ¯ These individuals are receive a subsidy under pooling: PðW 0j oj Þ4P: heavier than those at the margin. We first consider individuals at the margin. We will need the following function, based on the first-order condition in the pooling case: poolðW 0 ; oÞ ¼ I 0 ðW 0 oÞU 0 ½IðW 0 oÞ PðW 0 oÞ F0 ðoÞ

(B.1)

Clearly, for those at the margin, the first-order condition under pooling, Eq. (4), implies that poolðW 0j ; oj Þ ¼ 0: We now consider poolðW 0j ; o j Þ evaluated at the optimal weight under risk-adjusted pricing: 0 0 poolðW 0j ; o j Þ ¼ ½I ðW 0j oj Þ þ P ðW 0j oj Þ U 0 ½IðW 0j o j Þ PðW 0j oj Þ 0 F0 ðo j Þ P ðW 0j oj Þ U 0 ½IðW 0j o j Þ PðW 0j oj Þ

ðB:2Þ

In (B.2), we have added and subtracted P0 ( )U0 ( ) from the right-hand side of (B.1). Next, we apply Eq. (9), which is the first-order condition in the riskadjusted case, to (B.2). This yields: 0 0 poolðW 0j ; o j Þ ¼ P ðW 0j oj ÞU ½IðW 0j oj Þ PðW 0j oj Þ (B.3)

We assume that utility increases with consumption, so U0 ( )>0. Intuitively, expected medical expenditures should increase with weight, so that P0 (1)>0. With these assumptions, we have poolðW 0j ; o j Þo0; and hence: poolðW 0j ; o j ÞopoolðW 0j ; oj Þ ¼ 0

(B.4)

The second-order condition for the pooled premium case implies that: @ poolðW 0 ; oÞ o0 (B.5) @o (B.4) and (B.5) together imply that o j 4oj : Hence, individuals at the margin will optimally lose more weight under risk-adjusted premiums than they will under pooled premiums. We next consider the inframarginal and supramarginal cases. The logic of the analysis is similar to the logic in the marginal case, with a crucial difference – individuals in these cases are subsidized for their bodyweight


315

decisions in the pooled premium case. For inframarginal individuals, who are thinner, the subsidy is negative, while for supramarginal individuals, who are heavier, the subsidy is positive. Intuitively, moving from pooled to risk-adjusted premiums alters weight loss incentives in two distinct ways: through a reduction in the price of weight loss and through a change in net income by eliminating the subsidy. For individuals at the margin, the only force operating in the switch from pooled to risk-adjusted premiums is the price reduction, which leads to an unambiguous decline in optimal weight. For supramarginal individuals, the switch also eliminates a positive subsidy for weight gain, which decreases income. Since weight is a normal good in our model, the income and price effects work in the same direction – toward a lower optimal weight under risk-adjusted premiums for supramarginal individuals. By contrast, for inframarginal individuals switching from pooled to risk-adjusted premiums leads to a removal of a negative subsidy and hence to an increase in income. While the reduction in price still encourages weight loss, the increase in income encourages weight gain. Hence, the net effect of the switch on the optimal weight of inframarginal individuals is theoretically ambiguous. Let Sj ¼ PðW 0j oj Þ P¯ represent the amount of the subsidy under pooled premiums. We need an altered version of our pool function that includes the subsidy as an additional argument: poolðW 0 ; o; SÞ ¼ I 0 ðW 0 oÞU 0 ½IðW 0 oÞ PðW 0 oÞ þ S F0 ðoÞ (B.6) As before the first-order condition under pooled premiums, Eq. (4), implies that poolðW 0j ; oj ; Sj Þ ¼ 0: We consider the value of pool under riskadjusted premiums: 0 0 poolðW 0j ; o j ; S j Þ ¼ I ðW 0j oj ÞU ½IðW 0j oj Þ 0 PðW 0j o j Þ þ S j F ðoj Þ

ðB:7Þ

Because of the subsidy, U0 ( ) is no longer evaluated at the full income level that would pertain under risk-adjusted premiums – IðW 0j o j Þ PðW 0j o Þ: Hence we cannot exactly mimic our analysis for marginal individuals. j However, since U is concave by assumption so that U00 ( )o0, we have: poolðW 0j ; o j ; 0ÞopoolðW 0j ; oj ; S j Þ for inframarginal individuals (B.8)

and poolðW 0j ; o j ; 0Þ4poolðW 0j ; oj ; S j Þ for supramarginal individuals (B.9)

316


By the same logic as that preceding Eq. (B.3), we have: 0 0 poolðW 0j ; o j ; 0Þ ¼ P ðW 0j oj Þ U ½IðW 0j oj Þ PðW 0j oj Þ

(B.10) Further, by the logic preceding Eq. (B.4), we have: poolðW 0j ; o j ; 0Þo0 ¼ poolðW 0j ; oj ; S j Þ

(B.11)

Combining (B.9) and (B.11), we have: poolðW 0j ; oj ; S j Þ4poolðW 0j ; o j ; S j Þ for supramarginal individuals (B.12) As before, by the second-order condition of the pooled premium problem, we have that pool is declining in o: @ poolðW 0 ; o; SÞ=@oo0: Consequently, (B.12) implies that o j 4oj for supramarginal individuals. Thus, optimal weight is lower under risk-adjusted premiums for supramarginal individuals. By contrast, the combination of (B.8) and (B.11) place no restriction on the relative values of poolðW 0j ; oj ; Sj Þ and poolðW 0j ; o j ; S j Þ for inframarginal individuals. There are two possibilities (excluding the knife edge equality case): poolðW 0j ; o j ; 0ÞopoolðW 0j ; oj ; S j ÞopoolðW 0j ; oj ; S j Þ

(B.13)

poolðW 0j ; o j ; 0ÞopoolðW 0j ; oj ; S j ÞopoolðW 0j ; oj ; S j Þ

(B.14)

and

If (B.13) holds, then o j 4oj and inframarginal individuals will lose weight in the switch from pooled to risk-adjusted premiums. Since poolðW 0j ; oj ; S j Þ ¼ 0 for all values of S j ; while poolðW 0j ; o j ; S j Þ increases with Sj, this is most likely to be true for smaller values of Sj.26 In contrast, if (B.14) holds, then oj 4o j and inframarginal individuals will gain weight in the switch from pooled to risk-adjusted premiums. This latter case is most likely to be true when the subsidy, Sj, is largest. A first-order Taylor series expansion of the expression for poolðW 0j ; o j ; S j Þ in (B.7) around S j ¼ 0 lends some intuition for our results: 0 00 poolðW 0j ; o j ; S j Þ poolðW 0j ; oj ; 0Þ I ðW 0j oj ÞU ½IðW 0j oj Þ

PðW 0j o j ÞS j

ðB:15Þ

The first term in this approximation, poolðW 0j ; o j ; 0Þ; is always negative by (B.11), and represents the pure price effect of a switch from pooled to riskadjusted premiums. The second term in the approximation represents the


317

income effect of the switch. Its sign will depend on whether the subsidy induced by pooling is positive, negative, or zero, since I0 ( )o0 and U00 ( )o0. For individuals at the margin, there is no subsidy so Sj ¼ 0 and no income effect. For them, the switch induces a pure decrease in the price of weight loss, which leads to weight loss. By definition supramarginal individuals, who tend to be heavy, receive positive subsidies under pooling, so Sj>0. For them, the income effect term is negative, which reinforces the negative price effect from the first term. The total effect is negative, and supramarginal individuals will lose weight in the switch. Finally, inframarginal individuals, who tend to be lighter, receive negative subsidies. For them, Sjo0 and the income effect term in (B.15) is positive. The price and income effects work in opposite directions – the former promotes weight loss in the switch, while the latter promotes weight gain. The model produces no prediction about which effect will dominate.

APPENDIX C. ESTIMATING THE MEDICAL EXPENDITURE DISTRIBUTION BY BODYWEIGHT In this Appendix, we describe how we estimate the distribution of medical care expenditures for obese, overweight, and normal weight individuals in the MEPS data. Our goal here is to estimate a discrete distribution over medical expenditures, PðM ¼ yi jweightÞ; where M is medical expenditures and yi, i ¼ 1yN, are the points of support in the distribution. We want a discrete distribution so that our empirical work is consistent with our theoretical treatment. We permit six points of support: {$0, $50, $100, $1,000, $10,000, $50,000}. The probability of expenditures greater than $50,000 for this mostly working age population is small.27 We estimate this distribution separately for each demographic subgroup in the data. For convenience in the notation, we suppress the conditioning on demographic subgroups. We start by estimating a standard two-part model of medical expenditures: PðM40Þ ¼ Fða0 þ a1 overweight þ a2 obeseÞ

(C.1)

ln M ¼ b0 þ b1 overweight þ b2 obese þ if M40

(C.2)

Here, overweight is an indicator of whether an individual in the sample has a BMI between 25 and 30, while obese is an indicator of a BMI over 30. We estimate Eq. (16) on the entire subgroup, while we estimate Eq. (17) on the subsample of people who have positive medical expenditures.

318


We next take our estimates a^ and b^ from the two-part model and derive a discrete empirical distribution function for medical expenditures for obese, overweight, and normal weight individuals within each covariate subgroup. We assume that Nð0; s^ 2 Þ: s^ 2 is the estimated variance from the regression in (C.2). Since we estimate Eqs. (C.1) and (C.2) separately for individuals from different covariate subgroup, we are effectively allowing the variance of the error to be heteroskedastic. Eq. (C.1) implies a simple estimate of the probability of zero expenditure: PrðM ¼ 0jweightÞ ¼ 1 Fðâ0 þ a^ 1 overweight þ a^ 2 obeseÞ For i>1, we calculate:

(C.3)

28

PðM ¼ yi jweight; M40Þ ¼ Pðln M ¼ ln yi jweight; M40Þ ln yi þ ln yi1 ^ ob0 þ b^ 1 overweight þ b^ 2 obese P 2 ln yi þ ln yiþ1 þo weight 2 9 8 ln y þln y i iþ1 > > b^ 0 þ b^ 1 overweight þ b^ 2 obese = < F 2 ¼ > yi1 ; : F ln yi þln b^ 0 þ b^ 1 overweight þ b^ 2 obese > 2

ðC:4Þ

The second step in (C.4) follows from an approximation to the cumulative density function of e taken at the midpoint of the intervals between the points of support. The third step follows from our normality assumption. The Law of Conditional Probability implies the following identity: PðM ¼ yi jweightÞ ¼ PðM ¼ yi jweight; M40ÞPðM40jweightÞ

(C.5)

We estimate the first term in (C.5) using the expression in Eq. (C.4), while we estimate the second term using the expression in Eq. (C.3). Together, the Eqs. (C.3) and (C.5) gives us empirical estimates of the discretized distribution over medical expenditures, PðM ¼ yi jweightÞ; which is what we set out to find in this appendix in the first place.

OBESITY, HOSPITAL SERVICES USE AND COSTS$ Nana Bro Folmann, Kristine Skovgaard Bossen, Ingrid Willaing, Jan Sørensen, John Sahl Andersen, Steen Ladelund, and Torben Jørgensen ABSTRACT Objective. To quantify the association between obesity and somatic hospital costs and number of overall somatic hospital contacts – number of inpatient admissions, number of outpatient visits, and number of emergency department visits – based on anthropometric measurements of waist circumference (WC) and information from The National Patient Registry and The Danish Case-Mix System (DRG).

$

Contributors: Baseline data from the Inter99 study was borrowed from TJ. NF and KB are responsible for the design of the study, analysis, interpretation and drafting. IW and SL participated in the design of the study, interpretation of data, and drafting the article. JS, JSA and TJ took part in the interpretation of data and the drafting of the article. All authors approved the final manuscript. NF and KS are the guarantors. Funding: The Inter99 Study has been funded by the Danish Medical Research Council, The Danish Centre for Evaluation and Health Technology Assessment, NovoNordisk, Copenhagen County, Danish Heart Foundation, The Danish Pharmaceutical Association, Augustinus Foundation, Becket Foundation and Ib Henriksens Foundation. This work was independent of the funders.


319

320

NANA BRO FOLMANN ET AL.

Participants. The study population consisted of two sub-samples from the Inter99 study at Research Centre for Prevention and Health in 1999– 2001. One sub-sample used WC as an indicator for obesity (n ¼ 5,151), whereas the other used BMI as an indicator for obesity (n ¼ 4,048). Using WC, obesity was defined as WC > 102 cm for men and > 88 cm for women. Normal weight was defined as circumference o 94 cm for men and o 80 cm for women. Using BMI, obesity was defined as BMI > 30 kg/m2, whereas individuals with BMI ¼ 18.5–24.9 kg/m2 were defined as normal weight. Individuals with BMI o 18.5 kg/m2 were excluded from both sub-samples. Design. We undertook a 3-year retrospective study of the relationship between obesity and use of hospital resources. Data on hospital contacts and costs were obtained from The National Patient Registry and DRG. Analyses were performed using two-part models and Poisson regression. Outcome variables were costs and hospital contacts. Results. This study has demonstrated that obese individuals have a greater use of hospital services and greater hospital costs compared with normal weight individuals. When using WC as an indicator for obesity, mean hospital costs were 33.8% greater among obese women and 45.3% greater among obese men in a 3-year period but the differences were not significant. When using BMI to measure obesity, obese men had significantly greater costs (57.5%) than normal weight men. Furthermore, obese men and women (indicated by WC) had an increased number of hospital contacts compared with normal weight individuals (rate ratio 1.32, 95% CI 1.21–1.43 for men and 1.20, 95% CI 1.11–1.28 for women) including inpatient admissions, outpatient visits, and emergency department visits. The same trends were seen when obesity was indicated by BMI.

1. INTRODUCTION In Western countries, including Denmark, there has been a major increase in the prevalence of obesity in recent years. According to self-reported data, about 12% of the adult Danish population is obese. This is a conservative estimate as it is known that obese people tend to underreport their weight (Bendixen et al., 2004). Concerns about the rising prevalence have stimulated research in the health consequences of obesity as it is well established that obesity is an independent risk factor for a variety of diseases – among

Obesity, Hospital Services Use and Costs

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them type 2 diabetes, coronary heart disease, gall bladder disease, musculoskeletal diseases, and different types of cancers (Bray, 2004; Kenchaiah et al., 2002; Calle & Thun, 2004). Surprisingly little is known about the impact on present and future health services use and related costs based on empirical data (Cornier, Tate, Grunwald, & Bessesen, 2002; Thompson, Brown, Nichols, Elmer, & Oster, 2001; Quesenberry, Caan, & Jacobsen, 1998; Heithoff, Cuffel, Kennedy, & Peters, 1997). The purpose of this study was to quantify the association between: (1) obesity and hospital costs and (2) obesity and number of overall hospital contacts – number of inpatient admissions, number of outpatient visits, and number of emergency department visits.

2. METHODS 2.1. Study Population The analyses were performed using baseline data from the Danish Inter99 population study conducted at Research Centre for Prevention and Health, Copenhagen County. Further details of the Inter99 study have been published elsewhere (Jørgensen et al., 2003). In brief, the Inter99 study population comprised 61,301 individuals born in the years 1939–1940, 1944–1945, 1949–1950, 1954–1955, 1959–1960, 1964–1965, and 1969–1970 who lived in 11 municipalities in the southwestern part of Copenhagen on December 2, 1998. The study population therefore consisted of individuals between 30 and 60 years of age at baseline. The individuals were drawn from the Civil Registration System which registers all inhabitants in Denmark. From the study population, an age- and sex-stratified random sample comprising 13,016 individuals was drawn and the individuals were invited to a health examination at the Research Centre in the period 1999–2001. Of the 13,016 individuals invited, 82 individuals were non-eligible, as they had died or could not be traced. Of the remaining, a total of 6,906 (53.4%) turned up for examination. Of these, 122 were excluded due to alcoholism, drug abuse, or linguistic problems, leaving 6,784 (52.5%) for analyses. The present study used two sub-samples of the Inter99 study population including only participants who were obese or with normal weight (Tables 1A and 1B). Using WC (n ¼ 5,151), obesity was defined as waist circumference (WC)>102 cm for men and >88 cm for women. Normal weight was defined as circumference o 94 cm for men and o 80 cm for women. Using

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Table 1A.


Distribution of Obesity and Normal Weight using WC as an Indicator for Obesity by Sex. Men

Normal weight (WC o 94/80 cm for men and women ) Frequency 1,848 Row (%) 50.2 Column (%) 74.7 Obese (WC Z 102/88 cm for men and women) Frequency 626 Row (%) 42.6 Column (%) 25.3 All 2,474

Women

Total

1,832 49.8 68.4

3,680

845 57.4 31.6 2,677

1,471

a

a

5,151

Excluding individuals with BMI o 18.5 kg/m2.

Table 1B.

Distribution of Obesity and Normal Weight using BMI as an Indicator for Obesity by Sex. Men

Women

Total

1,745 61.1 73.9

2,855

615 51.5 26.1 2,360

1,193

a

Normal weight (BMI ¼ 18.5–24.9 for men and women ) Frequency 1,110 Row (%) 38.9 Column (%) 65.8 Obese (BMI Z 30 kg/m2 for men and women) Frequency 578 Row (%) 48.5 Column (%) 34.2 All 1,688 a

4,048

Excluding individuals with BMI o 18.5 kg/m . 2

BMI (n ¼ 4,048), obesity was defined as BMI > 30 kg/m2, whereas individuals with BMI 18.5–24.9 kg/m2 were defined as normal weight. Individuals with BMI o 18.5 kg/m2 were excluded from both sub-samples. The chosen cut-offs for WCs and BMI are consistent with WHO recommendations (WHO, 2000).

2.2. Data Collection At baseline, participants were clinically examined by trained staff who obtained anthropometric measurements. WC was measured in cm, height was


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measured without shoes to the nearest cm, weight was measured without shoes and overcoat to the nearest kg, and body mass index was calculated (kg/m2). Furthermore, information about employment/education and lifestyle factors (smoking habits, physical activity, and alcohol consumption) was collected from self-administrated questionnaires. Information about sex and age was extracted from the Civil registration system which registers all inhabitants in Denmark. Data on the use of hospital services were obtained through linkage to the National Patient Registry which contains information on all patient contacts with somatic hospital departments in Denmark. The register contains information about the total number of hospital contacts and the number of contacts at inpatient, outpatient, and emergency departments. The Danish Case-Mix System (DRG) was used to cost hospital care including the cost of outpatient visits, operation, medication, and length of stay, and the cost was converted to US$ (using currency rate 100 DKK ¼ US$ 16.25).

2.3. Study Design By using a retrospective study design, we examined the relationship between obesity and hospital costs and hospital contacts. Information about hospital costs and hospital contacts was obtained in 1996–1999 and WC, height, and weight were measured in 1999–2001. Hospital costs and hospital contacts were compared for obese participants and participants with normal weight.

2.4. Statistical Analysis We conducted analyses on hospital costs and total number of hospital contacts and separate analyses of the specific type of hospital contacts. In the analyses of hospital cost, we used a two-part model. The model is appropriate when analyzing skewed data, which is the case in these analyses. Logistic regression was used to predict the probability of having at least one hospital contact in the 3-year period of investigation. Afterwards, the mean costs given hospital contact(s) were estimated. The predicted hospital costs were then calculated by multiplying the probability of hospital contact with the mean costs. Finally, a linear regression using the predicted costs was performed using predicted costs as the explained variable. Bootstrapping with 3,000 random samples was used to estimate 95% confidence intervals (CI) as the usual procedure for estimating CI is inadequate when analyzing

324


predicted values. The variable for hospital contacts approximately followed a Poisson distribution, and therefore Poisson regression models were used in analyzing hospital contacts. The analyses were adjusted for age, employment/education, smoking, physical activity, and alcohol consumption. Normal weight was used as the reference category and data were analyzed separately for men and women.

3. RESULTS Tables 1A and 1B present the distribution of obesity and normal weight in the two study samples, using WC and BMI as indicators for obesity. It appears that women were over-represented in both samples. Table 2 presents the distribution of baseline characteristics of the study sample using WC as an indicator for obesity. Obese participants were more likely to be older (po0.0001), have sedentary lifestyle (po0.0001), of lower employment/education (po0.0001 for women and ¼ 0.04 for men), and drink more alcohol (po0.0001 for women and ¼ 0.0015 for men) than participants with normal weight. Smoking was more common among women with normal weight (po0.003) compared with obese women but no difference was seen for men (p ¼ 0.37). Table 3 presents the mean/median number of hospital contacts and mean/ median hospital costs for the whole population using WC as an indicator for obesity. Obese participants, especially women, had significantly higher hospital costs compared with participants with normal weight (p ¼ 0.005 for women and 0.02 for men). On average, obese participants had a higher frequency of hospital contacts compared with participants with normal weight (po0.0001). Furthermore, obese men had a significantly higher frequency of inpatient admissions and outpatient visits (p ¼ 0.0032 and 0.0002) and obese women had a significantly higher frequency of emergency department visits (p ¼ 0.0015) compared with those with normal weight. Data are skewed. The majority of median values for hospital contacts are zero and median values for hospital costs are either zero or especially low. Relative to individuals with normal weight, mean predicted hospital costs in relation to WC were 33.8% greater among obese women and 45.3% greater among obese men but the associations were not significant (Table 4). The predicted difference in mean costs between obese and normal weight individuals was US$ 453 for men and US$ 484 for women during a 3-year period.

Distribution of Baseline Characteristics on Age, Employment/Education, Smoking, Physical Activity, and Alcohol using WC as an Indicator for Obesity by Sex. Men Obeseb (%)

Normal Weighta (n)

Obeseb (%)

Normal Weighta (n)

Obeseb (%)

107 217 388 404 364 235 133 0

5.8 11.7 21.0 21.9 19.7 12.7 7.2 0

17 28 85 118 160 155 63 0

2.7 4.5 13.6 18.9 25.6 24.8 10.1 0

116 242 427 368 343 220 116 0

6.3 13.2 23.3 20.1 18.7 12.0 6.3 0

30 67 140 166 195 177 70 0

3.6 7.9 16.6 19.6 23.1 21.0 8.3 0

All Employment/education Employed, Z4 years education Employed, Z1–3 years education Employed, no education Unemployed, Z1 year education Unemployed, no education Missing All

1,848

100

626

100

1,832

100

845

100

926

50.1

296

47.3

393

21.5

152

18.0

430

23.3

119

19.0

902

49.2

351

41.5

205

11.1

84

13.4

205

11.2

112

13.3

98

5.3

45

7.2

120

6.6

80

9.5

54

2.9

20

3.2

63

3.4

65

7.7

135 1,848

7.3 100

62 626

9.9 100

149 1,832

21.5 100

85 845

10.1 100

Normal Weighta (n)

325

Obeseb (%)

Normal Weighta (n) Age (years) 30 35 40 45 50 55 60 Missing

Women


Table 2.

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Table 2. (Continued ) Men Normal Weighta (n)

Women

Obeseb (%)

Normal Weighta (n)

Obeseb (%)

Normal Weighta (n)

Obeseb (%)

Normal Weighta (n)

Obeseb (%)

622 435 61

33.7 23.5 3.3

198 196 27

31.6 31.3 4.3

694 446 58

37.9 24.3 3.2

346 191 29

41.0 22.6 3.4

712 18 1,848

38.5 1.0 100

198 7 626

32.0 0.5 100

623 11 1,832

34.0 0.6 100

268 11 845

31.7 1.3 100

Physical activity Sedentary Moderate Hard Very hard Missing All

329 977 433 52 57 1,848

17.8 52.9 23.4 2.8 3.1 100

200 338 70 0 18 626

32.0 54.0 11.2 0 2.9 100

305 1,237 253 12 25 1,832

16.7 67.5 13.8 0.7 1.4 100

244 513 61 2 25 845

28.9 60.7 7.2 0.2 3.0 100

Alcohol o21/14 unitsc >21/14 unitsc >35 units Non-drinkers Missing All

1,333 207 112 124 72 1,848

72.1 11.2 6.1 6.7 3.9 100

412 93 59 41 21 626

65.8 14.9 9.4 6.5 3.4 100

1,363 185 17 177 90 1,832

74.4 10.1 0.9 9.7 4.9 100

546 75 6 141 77 845

64.6 8.9 0.7 18.0 9.1 100

a

WCo94/80 cm for men and women. WCZ102/88 cm for men and women. c 21 units for men and 14 units for women (limitations recommended by National Board of Health). b


Smoking Never smoked Ex-smoker Occasional smoker Daily Missing All


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Table 3. Mean/Median Hospital Costs and Mean/Median Number of Hospital Contacts using WC as an Indicator for Obesity by Sex, 1996– 1999. Men a

Normal Weight

Hospital cost (US$) Hospital cost (US$) Contacts to hospital Inpatient admissions Outpatient visits Emergency department visits a

Women b

Obese

Mean/median 1,123/0 1,600/99 75th percentile/95th percentile 392/5,309 944/9,841 Mean/median 1.31/0 1.70/1 0.22/0 0.34/0 0.38/0 0.58/0 0.63/0 0.65/0

Normal Weighta

Obeseb

1,548/99

2,216/99

1,603/6,845

2,093/10,402

1.64/1 0.40/0 0.63/0 0.46/0

1.97/1 0.49/0 0.71/0 0.59/0

WCo94/80 cm for men and women. WCZ102/88 cm for men and women.

b

Table 4.

Mean Predicted Hospital Costs using WC as an Indicator for Obesity by Sex, 1996–1999. Men Crude

Normal weight (reference) Mean costs (US$) 1,110 95% CI 902–1,384 Obese Mean costs (US$) 1,586 95% CI 1,260–1,941 Difference (US$) 476 % 42.8 a

Women a

Adjusted

Crude

Adjusteda

1,013 811–1,295

1,534 1,371–1,717

1,430 1,273–1,598

1,467 1,135–1,832 453 45.3

2,190 1,802–2,666 655 42.7

1,940 1,597–2,254 484 33.8

Age, employment/education, smoking, physical activity, and alcohol consumption.

In relation to BMI, mean predicted hospital costs were 33.7% greater among obese women and 57.5% greater among obese men compared with normal weight individuals (Table 5). The association was only statistically significant for men. The predicted difference in mean costs between obese and normal weight individuals was US$ 574 for men and US$ 481 for women during a 3-year period.

328

Table 5.


Mean Predicted Hospital Costs using BMI as an Indicator for Obesity by Sex, 1996–1999. Men Crude

Normal weight (reference) Mean costs 1,019 95% CI 858–1,194 Obese Mean costs 1,665 95% CI 1,312–2,062 Difference 647 % 63.5 a

Women a

Adjusted

Crude

Adjusteda

1,000 837–1,182

1,547 1,375–1,733

1,427 1,263–1,606

1,574 1,216–1,918 574 57.5

2,279 1,806–2,899 732 47.3

1,908 1,558–2,301 481 33.7


Table 6. Rate Ratios Estimates for Total Hospital Contacts, Inpatient Admissions, Outpatient Visits, and Emergency Department Visits using WC as an Indicator for Obesity by Sex. Men

Normal weight (reference) Obese Total hospital contacts 1.30 Inpatient admissions 1.55 Outpatient visits 1.53 Emergency department visits1.04 a

Women

Crude

Adjusted

Crude

Adjusteda

1

1

1

1

(1.21–1.40)1.32 (1.31–1.83)1.37 (1.35–1.74)1.47 (0.93–1.17)1.16

a

(1.21–1.43)1.21 (1.13–1.67)1.23 (1.27–1.70)1.13 (1.02–1.32)1.29

(1.14–1.28)1.20 (1.09–1.38)1.24 (1.02–1.25)1.16 (1.15–1.44)1.23

(1.11–1.28) (1.08–1.43) (1.04–1.30) (1.08–1.40)


Using normal weight as a reference, Table 6 presents the rate ratio estimates for total hospital contacts, inpatient admissions, outpatient visits, and emergency department visits in relation to WC by sex. Obese men had an increased number of hospital contacts (rate ratio 1.32; 95% CI 1.21–1.43) such as inpatient admissions, outpatient visits, and emergency department visits compared with men with normal weight. Obese women also had an increased number of total hospital contacts (rate ratio 1.20; 95% CI 1.11– 1.28). In relation to BMI, obese men had an increased number of hospital contacts (rate ratio 1.32; 95% CI 1.21–1.43) such as inpatient admissions,


329

Table 7. Rate Ratios Estimates for Total Hospital Contacts, Inpatient Admissions, Outpatient Visits, and Emergency Department Visits using WC as an Indicator for Obesity by Sex. Men

Normal weight (reference) Obese Total hospital contacts 1.32 Inpatient admissions 1.64 Outpatient visits 1.44 Emergency department visits1.10 a

Women

Crude

Adjusteda

Crude

Adjusteda

1

1

1

1

(1.22–1.43)1.30 (1.35–1.98)1.37 (1.25–1.66)1.32 (0.97–1.25)1.23

(1.18–1.42)1.23 (1.10–1.71)1.24 (1.12–1.56)1.17 (1.07–1.41)1.32

(1.15–1.32)1.18 (1.08–1.42)1.18 (1.05–1.31)1.14 (1.17–1.49)1.29

(1.09–1.28) (1.00–1.39) (1.00–1.30) (1.12–1.49)


outpatient visits, and emergency department visits compared with men with normal weight (Table 7). Obese women also had an increased number of total hospital contacts (rate ratio 1.18; 95% CI 1.09–1.28). The results do not differ substantially from the results in relation to WC.

4. DISCUSSION This study demonstrated that mean hospital costs using WC to categorize obesity were 33.8% greater among obese women and 45.3% greater among obese men compared with normal weight persons, but the associations were not significant. When using BMI to categorize obesity, hospital costs were significantly greater among obese men (57.5%) compared with normal weight men. Furthermore, obese men and women had an increased number of hospital contacts compared with individuals with normal weight –when both WC and BMI were used as indicators of obesity. The literature supports some of the findings in this study. Studies have shown a positive association between obesity and hospital costs (Cornier et al., 2002; Thompson et al., 2001; Quesenberry et al., 1998; Heithoff et al., 1997). However, the literature is inconsistent in relation to hospital contacts. One study finds the reverse association between obesity and inpatient admissions (Trakas, Lawrence, & Shear, 1999) while other studies support the association between obesity and increased outpatient visits (Thompson et al., 2001; Quesenberry et al., 1998; Reidpath, Crawford, Tilgner, & Gibbons, 2002). A study indicates a positive association between obesity and

330


inpatient admissions and emergency department visits (Reidpath et al., 2002). It should be noted that it is difficult to compare the findings. First, this study is based on anthropometric measurements of obesity and register data on hospital contacts, whereas most studies use self-reported information about height, weight, and hospital contacts. Other studies have shown that obese individuals compared with normal weight individuals have an increased use of medication and an increased number of visits to GP (Thompson et al., 2001; Quesenberry et al., 1998; Narbro et al., 2002; Dzien et al., 2003; Reidpath et al., 2002). The remaining question is whether there is a causal relationship between obesity and hospital contacts. Although the associations for hospital costs in the relation to WC were not significant, there was a clear tendency of greater costs among obese individuals. Furthermore, when using BMI, the association for men was significant. To strengthen our analyses, we adjusted for several confounders including age, socioeconomic status, smoking, physical activity, and alcohol. However, there are some remaining factors to consider when addressing the causal relationship. It was not possible to undertake a prospective study as obese participants were offered life style counseling following the baseline health examination and life style interventions could have biased a prospective study design. By using the retrospective design, we assumed that weight, height, and WC of the participants measured in 1999– 2001 remained constant during the 3-year study period. The duration of obesity is also an important factor since obesity-related health complications increase over time. A study has shown that hospital costs increase with the number of years obesity is present (Thompson et al., 2001). Some studies have shown that the positive association between obesity and hospital costs is weakened when the analyses are adjusted for obesity-related diseases like type 2 diabetes and coronary heart disease (Quesenberry et al., 1998; Heithoff et al., 1997; Fontaine & Barlett, 2000). Therefore, much indicates a causal relationship between obesity and increased hospital contacts and between obesity and increased hospital costs. The associations were stronger for men compared with women. This may indicate that obese men have an increased risk of disease compared with obese women. Two studies support this finding (Thompson et al., 2001; Heithoff et al., 1997) but in general very few studies have focused on gender differences in the relationships between obesity and hospital resources. The difference may possibly be due to differences in the abdominal fat distribution between men and women. Fat distribution in men is more likely to be situated around the abdomen, while in women around the hips. Large hip circumference in women seems to have independent and protective effect on


331

CVD and CHD morbidity and mortality, whereas abdominal fat is a health hazard for both sexes (Heitmann, Frederiksen, & Lissner, 2004). However, for men, BMI appeared to be a bit stronger associated with hospital costs compared with WC. For women, BMI and WC showed the same results. Due to the low participation rate in the Inter99 study (52.5%), there is a potential risk of selection bias. However, analyses showed that non-participants had no more hospital contacts compared with participants (Jørgensen et al., 2003). It is uncertain whether the findings can be generalized to other settings. First, there may be differences in the degree and duration of obesity in another population. Secondly there might be differences in the organization of the treatment of obesity-related diseases. For instance, the treatment of type 2 diabetes is to a higher degree managed by the GP in some counties instead of the hospital services. Furthermore, the participants were 30–60 years of age and therefore the results should not be generalized to children, young adults, or the elderly. The results have shown that obese individuals have a greater use of hospital services and greater hospital costs compared with normal weight individuals. Given the high and increasing prevalence of obesity, we probably must expect that future health care costs will continue to increase due to obesity-related diseases.

REFERENCES Bendixen, H., Holst, C., Sorensen, T. I., Raben, A., Bartels, E. M., & Astrup, A. (2004). Major increase in prevalence of overweight and obesity between 1987 and 2001 among Danish adults. Obesity Research, 11(7), 1464–1472. Bray, G. A. (2004). Medical consequences of obesity. Journal of Clinical Endocrinology and Metabolism, 89(6), 2585–2589. Calle, E. E., & Thun, M. J. (2004). Obesity and cancer. Oncogene, 23(38), 6365–6378. Cornier, M. A., Tate, C. W., Grunwald, G. K., & Bessesen, D. H. (2002). Relationship between WC, body mass index, and medical care costs. Obesity Research, 10(11), 1167–1172. Dzien, A., Pfeiffer, K. P., Dzien-Bischinger, C., Hoppichler, F., & Lechleiner, M. (2003). The influence of obesity on the frequency and distribution of medication. Acta Medica Austrica, 2, 51–54. Fontaine, K. R., & Barlett, S. J. (2000). Access and use of medical care among obese persons. Obesity Research, 8(5), 403–406. Heithoff, K. A., Cuffel, B. J., Kennedy, S., & Peters, J. (1997). The association between body mass and health care expenditures. Clinical Therapy, 19(4), 811–820. Heitmann, B. L., Frederiksen, P., & Lissner, L. (2004). Hip circumference and cardiovascular morbidity and mortality in men and women. Obesity Research, 12, 482–487.

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Jørgensen, T., Borch-Johnsen, K., Thomsen, T. F., Ibsen, H., Glu¨mer, C., & Pissinger, C. (2003). A randomized non-pharmacological intervention study for prevention of ischaemic heart disease: Baseline results Inter99. European Journal of Cardiovascular Prevention and Rehabilitation, 10, 377–386. Kenchaiah, S., Evans, J. C., Levy, D., Wilson, P. W., Benjamin, E. J., Larson, M. G., Kannel, W. B., & Vasan, R. S. (2002). Obesity and the risk of heart failure. New England Journal of Medicine, 347(5), 305–313. Narbro, K., Agren, G., Jonsson, E., Na¨slund, I., Sjo¨stro¨m, L., & Peltonen, M. (2002). Pharmaceutical costs in obese individuals. Archives of Internal Medicine, 162, 2061–2069. Quesenberry, C. P., Caan, B. J., & Jacobsen, A. (1998). Obesity, health services use, and health care cost among members of a health maintenance organization. Archives of Internal Medicine, 158, 466–472. Reidpath, D. D., Crawford, D., Tilgner, L., & Gibbons, C. (2002). Relationship between body mass index and the use of healthcare services in Australia. Obesity Research, 10(6), 526– 531. Thompson, D., Brown, J. B., Nichols, G. A., Elmer, P. J., & Oster, G. (2001). Body mass index and future healthcare cost: A retrospective cohort study. Obesity Research, 9(3), 210– 218. Trakas, K., Lawrence, K., & Shear, N. H. (1999). Utilization of health care resources by obese Canadians. Canadian Medical Association Journal, 160(10), 1457–1462. WHO. (2000). Obesity: Preventing and managing the global epidemic – Report of a WHO consultation. World Health Organization, Geneva.

HOW MUCH DOES OBESITY MATTER? RESULTS FROM THE 2001 CANADIAN COMMUNITY HEALTH SURVEY William MacMinn, James McIntosh, and Caroline Yung ABSTRACT A five category self-reported health indicator together with the self-reported prevalence of diabetes and heart disease for older Canadians, are examined using data from five cohorts of men and women from the 2001 Canadian Community Health Survey. Consistent with other studies we find that smoking and dietary behaviors are highly correlated with general self-reported health, diabetes, and heart disease. Individual standardized weight, the body mass index, was negatively associated with health outcomes for all age groups, but became less important with age as socioeconomic variables became more important. Socioeconomic variables explained more of the variation in health outcomes than the combined effects of tobacco use and excessive weight problems. In addition, there is compelling evidence that obesity could overtake smoking as the leading cause of health problems in Canada.


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WILLIAM MacMINN ET AL.

1. INTRODUCTION Obesity is a major health problem in many developed countries including Canada. Using measured height and weight values from 2004, Statistics Canada recently reported that 23.1% of Canadians were obese, and another 36.1% of Canadians were overweight. This means that almost 60% of Canadians face possible negative health effects associated with being overweight or obese (Tjepkema & Shields, 2005a). Obesity is negatively related with self-assessed health (Okoson, Choi, Matamoro, & Allen, 2001; Contoyannis & Jones, 2004). Obese individuals face higher risks of diseases such as type 2 diabetes mellitus, hypertension, coronary artery disease, stroke, osteoarthritis, depression, and certain types of cancer (Canadian Medical Association, CMAJ, 2005). There are a large number of studies which examine the determinants of obesity and its effects on mortality and other health outcomes. In spite of the large body of evidence linking obesity to lower self-rated health and higher risks of diabetes, heart disease, and other prevalent diseases its place in the hierarchy of causes of poor health is not completely understood. In this paper we ask how important obesity is in comparison to alcohol and tobacco use as a cause of morbidity and reported poor health when appropriate procedures are used to control for variation in age, gender, lifestyle, and unobservable characteristics. Two types of variables are examined in this study. The first is a five category self-reported health indicator, an appropriate choice because there is considerable variation even for younger age groups. The other key variables of interest are the self-reported prevalence of diabetes and heart disease. Relationships between these two diseases, the two most common serious illnesses reported by older Canadians, and a set of covariates are examined for two age groups: 50–59 and 60 and over.1 In our analysis, we consider a range of potentially useful normalized regressors and we determine the relative contributions of standardized body weight, smoking, and alcohol use in explaining health outcomes and the prevalence of diabetes and heart disease among older Canadians. In particular, we will be able to determine whether the problems associated with being overweight or obese have overtaken those associated with smoking to become Canada’s number one health concern. By normalizing the covariates (zero means and unit variances) we are able to determine the relative importance of each individual variable in relation to each health outcome examined: health status, diabetes, and heart disease. Consistent with other studies that examine health outcomes, we find that

How Much Does Obesity Matter?

335

dietary behaviors (as characterized by what and how much individuals eat), smoking, and alcohol use are highly correlated with general self-reported health. Estimated coefficients derived from mixed ordered probability models, show that individual standardized weight, the body mass index (BMI), was negatively associated with self-reported health for all age groups, but became less important with age as socioeconomic variables became more important. Smoking behavior variables were large and highly significant for the younger age groups and estimated smoking effects declined in older age groups. Alcohol consumption was found to be significantly positively associated with good self-assessed health but heavy drinking was associated with poor outcomes for most male age groups and young women.2 Our results indicate that individual characteristics: income, education, and employment status, rather than modifiable lifestyle behaviors: smoking, physical activity, and dietary behavior, are more important in determining selfassessed health. Similar results were found for diabetes and heart disease, in that higher socioeconomic status (SES) and good health behaviors are related to a lower likelihood of heart disease and diabetes. Comparing the self-assessed health and disease models, the main difference is that socioeconomic variables play a smaller role in explaining the probability of either chronic disease than for self-assessed health, but dietary behavior, alcohol use, and BMI are more important for disease prevalence. A brief review of the relevant literature follows below, together with a discussion of methodological issues and the approaches used in this study. Statistical procedures are outlined in Section 3; Section 4 describes the data used in the analysis. The results for health outcomes are presented in Sections 5. Section 6 contains a discussion of the results. The paper concludes with an outline of potential implications for health policy based on the results.

2. LITERATURE AND METHODOLOGICAL ISSUES: A BRIEF REVIEW A large and growing literature deals with the estimation of ordered probability models of self-rated health outcomes. Recent surveys of this work and how it relates to objective measures of health are found in Idler and Benyamini (1997), Idler, Leventhal, McLaughlin, and Leventhal (2004). A few recent studies using self-rated health are Miilunpalo, Like, Pokka, and Opponent (1997), Cott, Gignac, and Bradley (1999), Ford, Moriarity, Zack,

336


Mokdad, and Chapman (2001), Okoson et al. (2001), He and Barker (2004), and Bolin, Lindgren, and Ro¨ssner (2006). Self-rated health is a widely-used measure of health. A survey of over 27 studies which examine self-ratings of health is found in Idler and Benyamini (1997). The literature consistently indicates that self-rated health reliably predicts mortality even after controlling for individual characteristics and risk factors, including chronic conditions (Borg & Kristensen, 2000). Because they predict comorbidities in addition to mortality, self-ratings may capture a broader dimension of health. Self-ratings differ by gender and social position; self-reported outcomes for men are generally found to be better predictors of health, possibly because of the higher number of gender-specific conditions women experience through their lifetimes (Idler & Benyamini, 1997). The literature finds that obesity is related to negative perceptions of selfrated health (Okoson et al., 2001; Contoyannis & Jones, 2004). Using the Third National Health and Nutrition Examination Survey Okoson et al. (2001) estimate gender and ethnic-specific logistic regression models to examine the association between self-rated health and obesity. They find that the proportion of subjects reporting excellent health declines in higher classes of obesity (Class I, II, and III) for whites and blacks; the decline was less for Hispanics. Although they disaggregate by gender and race, the authors did not control for SES. Contoyannis and Jones (2004) use U.K. data from 1984 to 1991 to estimate a health production function and reduced forms of lifestyle equations with discrete indicators of lifestyle choices and self-rated health. Controlling for observable and unobservable factors, the authors identify interactions between health behaviors and self-rated health. The authors find that non-obese individuals are more likely to report better health status. However, Hirdes and Forbes (1993) did not find obesity significantly related to moving out of good self-rated health in a longitudinal sample of 2,000, 45-year-old individuals interviewed from 1959 to 1978. Their data comes from the Ontario Longitudinal Study of Aging. In addition to the higher prevalence of illness and lower self-rated health, individuals with obesity may also experience negative psychological effects and discrimination. Body-image dissatisfaction (Rosen, 1996) and bingeeating disorders (Yanovski, 1993) are two conditions more common among the obese. Do obese individuals face weight-based discrimination? US and European studies show the relationship between weight and labor outcomes differ by gender and ethnicity (Cawley, 2004; Garcia & Domeque, 2005; Bhattacharya & Bundorf, 2005). Obese women earn significantly less than non-obese women, but the same is not found for men (Cawley, 2004; Garcia & Domeque, 2005). The relationship between wages and weight in men


337

appears to be caused by unobserved heterogeneity (Cawley, 2004). Bhattacharya and Bundorf (2005) argue that obese women in the United States earn lower wages because of the higher health insurance costs of being obese passed on from employers. Unobserved heterogeneity in individual health and behaviors can bias results. Contoyannis and Jones (2004) fail to reject the exogeneity of health behaviors. Controlling for observable and unobservable factors, they find that, failing to account for unobserved heterogeneity underestimates the effects of lifestyle factors on individual health, relative to estimates by randomized treatment of lifestyle behaviors. The results are similar to earlier findings of Rosenzweig and Schultz (1983) and Mully and Portney (1990), that unobservable heterogeneity bias leads to underestimates of the beneficial effects of health inputs on health. There are alternatives to self-reported health outcomes. The health utility index (Horsman, William, Feeny, & Torrance, 2003; van Doorslaer & Jones, 2003) is an example. This is an index based on the self-assessed quality of individual attributes including vision, hearing, speech, and four others. The advantage of the index is that it is numerically valued and, therefore, can be analyzed by standard regression methods. However, its construction does involve arbitrary scaling assumptions whereas the self-reported health status variable does not. 2.1. Methods in the Literature and Alternative Approaches The literature discussed above is often characterized by somewhat arbitrary use of data. For example, a dummy variable that takes the value one for BMI greater than 30, is often used to represent obesity, overweight is represented by BMI between 25 and 30, and so forth. There are two problems with this approach. Our tests indicate that the BMI variable always gives a significantly higher likelihood value than an obesity dummy alone. If studies do not add a control for being overweight, they assume that being overweight is not a health problem. Second, using dummy variables as obesity or overweight indicators precludes testing for non-linear effects which can lead to a loss of valuable information. Recent results show that the relationship of BMI to mortality follows a J- or U-shaped curve. A follow-up of 1 million respondents in the US Cancer Prevention Study II found a U-shaped relationship between BMI and mortality, while Katzmarzyk, Craig, and Claude (2001) found a J-shaped pattern in females and a U-shaped pattern in males. Such non-linear effects are also important in the relationship between self-rated health and alcohol, where evidence has shown that

338


moderate use may be beneficial but excessive use is harmful (Standridge, Robert, & Adams, 2004). Researchers examining correlations between health outcomes and obesity also tend to focus on specific issues. While legitimate in some circumstances, proper comparisons of the importance of various contributing factors to health outcomes are not usually made and important variables may be excluded. Many studies use inappropriate controls for SES, either by omitting socioeconomic variables (Okoson et al., 2001) or using only one measure, like education, as a proxy for other variables. Additionally, some studies that do use socioeconomic variables as regressors carry out analysis in a way that prevents a determination of the relative importance of these variables (Tremblay, Ross, & Berthelot, 2002). Our study makes a number of important contributions to the extensive literature regarding obesity and health outcomes. The large size of the Canadian Community Health Survey 2001 (CCHS) – a national survey with 130,880 observations – allows for analyses on disaggregated subsamples. Disaggregation by age and gender is not typical in other studies using smaller surveys; it also provides some valuable new insights into the interplay of age, gender, and health, and is necessary on statistical grounds. Estimating the models on different age groups is also essential because of selection problems. The coefficients of the smoking behavior variables decline with age, and these estimates will be contaminated if older age groups are pooled with younger groups and a simple age variable is used. Moreover, age is a strong factor associated with obesity, as BMI generally increases until age 65 and decreases thereafter, providing further support for using separate age groups (Scali, Siari, Grosclaude, & Gerber, 2004; Soriguer et al., 2004). The main methodological contribution of this paper is the application of methods, which deal with problems stemming from unobserved heterogeneity in behaviors. Explanatory variables like smoking behavior, the person’s BMI, and alcohol use are likely to be correlated with attitudes toward health risks and the degree to which an individual exercises prudence and care in managing his or her lifestyle. Like ‘ability’ in the labor literature on earnings functions, and ‘time-preference’ in the education and health literature, these unobserved variables affect the outcome. Not accounting for omitted information will lead to biases in the estimates of parameters associated with the regressors in any probability model of reported health status. Our approach to this problem, following Heckman and Singer (1984), is to deal with this unobserved heterogeneity across individuals, by assuming that a finite number of different types of individual exist. Sample likelihood


339

functions arise as mixtures of individual probability density functions, with one density function for each type of individual. Finite mixture models are quite common in statistical literature and are shown to work well, usually requiring a small number of different types to fully capture the effects of unobservables. Applications of this procedure are seen in Cameron and Heckman (1998), Wedel, Desarbo, Bult, and Ramaswamy (1993), and seminal work by Heckman and Singer (1984). To date, there are few applications of finite mixture models in the field of health. Although others have looked at the effects of unobservables heterogeneity on health (Kenkel, 1991; Berger & Leigh, 1989; Contoyannis & Jones, 2004), none to our knowledge have used mixture models to examine health outcomes and their relationship to obesity.

3. STATISTICAL PROCEDURES Self-reported health status is a key variable of interest. Respondents reported whether their health was poor, fair, good, very good, or excellent. The approach we use to analyze these responses is an ordered probability model which depends on a latent variable crossing a set of threshold points. Let hi ¼ X i b þ pi þ i

(1)

hi

where is continuous numerical representation of how individual i regards his or her health status. Xi is a vector of covariates which includes the characteristics of the individual such as age or educational attainment as well as some observable behavioral patterns such as smoking habits or alcohol use. The variable, pi, represents the level of ‘prudence’ of the individual. People with high levels of prudence are more likely to be nonsmokers, more likely to exercise caution in their use of alcohol, and more likely to attend to health issues associated with overeating and physical inactivity. ei is a unit variance random disturbance term whose cumulative distribution function is F( ).3 The researcher can not observe pi, ei, and hi : However, categorical health outcomes are observed, which represented by the variable hi, takes the values 1, 2, up to 5. We assume that the probabilities of these outcomes are generated by the health status variable crossing a set of thresholds. This generates a relationship between hi and hi which satisfies the following equation: hi ¼ j if hi 2 ½aj1 ; aj Þ j ¼ 1; 2; . . . ; 5

(2)

340


where the a coefficients are an increasing sequence of threshold points and (a1, a5) ¼ (–N, N). The probabilities of health outcome, j conditional on (Xi, pi) is, therefore Prfhi ¼ jjX i ; pi g ¼ F ðaj X i b pi Þ F ðaj1 X i b pi Þ

(3)

To deal with the unobservable prudence variable suppose that there are K distinct types so that pi can take on values (q1, q2y,qK ) with probabilities (p1, p2,y,pK). The resulting sample log likelihood function is " # N K X X ln L ¼ ln pk Prfhi ¼ jjðX i ; pi ¼ qk Þg (4) i¼1

j¼1

where N is the sample size. This procedure was developed by Heckman and Singer (1984) who were the first to use finite mixture models to treat unobservable characteristics. If health outcomes are determined by such a prudence variable, then the estimates that arise from probability models will be biased if no procedure is used to correct for its absence, even if smoking, drinking, and consumption behaviors are uncorrelated with the prudence variable. This is because coefficients in probability models are a function of the variance, and not accounting for the unobservables underestimates the variance biasing all coefficients.

4. DATA SOURCES AND VARIABLES Tables 1 and 2 contain summary statistics of characteristics for individuals. Data for the analysis comes from the 2001 Canadian Community Health Survey (CCHS) public use dataset. CCHS is a cross-sectional survey that collects information related to health status, health care utilization, and health determinants for the Canadian population. Information was collected for 136 health regions4 with 130,880 observations covering all provinces and territories. Responses were collected from individuals aged 12 or older, living in private occupied dwellings. People living on Indian Reserves and on Crown Lands, institutional residents, full-time members of the Canadian Armed Forces, visible minorities, and residents of certain remote regions were excluded. All variables are self-reported and the sample was restricted to individuals aged 20 and above, and non-visible minorities; (final sample size was 95,685, after dropping missing values for variables). Individuals were asked their height and weight. From this self-reported information the BMI, weight/


Table 1.

341

Variable Sample Means for Men by 10-Year Age Groups.

Variable Dependent variables Poor health (%) Fair health (%) Good health (%) Very good health (%) Excellent health (%) Diabetes (%) Heart disease (%) Independent variables Cigarettes smoked daily Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Education level 1 Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Physical activity level 3 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Sample size

20–29

30–39

40–49

50–59

60+

0.7 3.9 19.9 41.8 33.6 0.7 0.6

1.3 5.0 23.1 41.8 28.7 1.3 0.1

2.6 8.4 28.0 36.6 24.5 2.9 2.5

5.1 12.0 28.2 32.8 22.0 7.0 8.3

7.1 17.7 30.7 27.9 16.5 14.6 21.2

5.26 0.28 2.66 0.12 1.30 0.19 25.08 0.14 0.25 0.14 0.47 43.43 0.29 0.22 0.49 1.29 0.60 0.38 0.69 0.16 5,701

6.11 0.24 5.63 0.18 1.41 0.13 26.13 0.15 0.20 0.07 0.58 46.24 0.22 0.23 0.54 1.27 0.24 0.67 0.77 0.25 8,461

6.92 0.19 8.88 0.28 1.64 0.12 26.34 0.19 0.20 0.07 0.54 46.73 0.20 0.23 0.57 1.26 0.16 0.69 0.76 0.26 9,638

5.87 0.15 9.71 0.41 1.88 0.10 26.68 0.27 0.15 0.06 0.52 45.45 0.20 0.21 0.59 1.31 0.10 0.72 0.64 0.26 7,241

2.89 0.09 7.59 0.55 1.84 0.04 25.87 0.50 0.11 0.05 0.34 37.20 0.21 0.22 0.57 1.36 0.08 0.68 0.14 0.06 10,164

Note: The dependent variables are the five categories of self-assessed health increasing from poor to excellent, together with two dummy variables indicating if the respondent reported having diabetes or heart disease.

(height)2, was computed and used to categorize individuals. Using standard definitions, those with a BMI of less than 25 were regarded as having normal weight, those between 25 and 30 were overweight, and those above 30 were regarded as being obese. Body mass index is the most convenient and commonly used measure of adiposity and obesity. Other less frequently used measures include the waist to hip ratio, and other techniques such as skinfold thickness, hydrostatic weighting, and isotope dilution are used but not

342

Table 2.


Variable Sample Means for Women by 10-Year Age Groups.

Variable Dependent variables Poor health (%) Fair health (%) Good health (%) Very good health (%) Excellent health (%) Diabetes (%) Heart disease (%) Independent variables Cigarettes smoked daily Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Education level 1 Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Physical activity level 3 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Sample size

20–29

30–39

40–49

50–59

60+

1.2 4.8 23.2 42.8 28.1 0.8 0.7

1.5 5.4 22.1 40.1 30.9 1.6 0.9

3.0 7.9 24.6 38.8 25.7 2.8 2.2

4.9 11.9 27.5 35.1 20.5 5.4 4.7

6.6 20.2 33.5 27.9 11.8 10.8 17.6

3.87 0.24 2.52 0.14 0.51 0.05 23.68 0.11 0.20 0.14 0.55 40.51 0.23 0.25 0.52 1.42 0.47 0.49 0.60 0.21 6,691

4.41 0.18 5.04 0.21 0.70 0.03 24.36 0.10 0.21 0.08 0.61 44.44 0.21 0.26 0.53 1.43 0.17 0.70 0.64 0.27 9,341

5.06 0.14 7.59 0.27 0.95 0.03 24.93 0.14 0.23 0.08 0.54 45.06 0.19 0.26 0.55 1.42 0.12 0.68 0.67 0.27 9,756

4.17 0.11 8.10 0.30 0.94 0.02 25.97 0.25 0.20 0.06 0.49 41.94 0.19 0.24 0.57 1.46 0.08 0.64 0.51 0.23 7,486

1.92 0.08 5.70 0.27 0.75 0.00 25.22 0.52 0.15 0.05 0.28 32.01 0.14 0.20 0.66 1.49 0.05 0.38 0.07 0.03 13,940

Note: The dependent variables are the five categories of self-assessed health increasing from poor to excellent, together with two dummy variables indicating if the respondent reported having diabetes or heart disease.

widely available and lack consistency (Racette, Designer, & Designer, 2003). BMI as a measure of adiposity does not distinguish fat mass from muscle mass, and is also subject to measurement error in self-reporting. Both factors contribute to a degree of inaccuracy of BMI and represent a limitation to our analysis, but BMI is still generally accepted as a valid measure (WHO, 1997). As mentioned earlier, important information is lost when ‘obesity’ and ‘overweight’ dummies are used instead of the BMI itself. In


343

this analysis, we use actual BMI derived from reported height and weight from the survey. Smoking behavior was characterized as being an occasional or daily smoker. In each case the number of cigarettes smoked per day was used. We also utilized the number of years smoked as a daily smoker and if the respondent was not a current smoker, whether he or she had been a former daily smoker and for how long. Drinking behavior variables are based on consumption in the week prior to the interview, given the respondent drank within the last 12 months. The variable labeled alcohol consumption is the number of times per week that alcoholic beverages were consumed. Heavy drinking is defined as having more than 5 drinks per day, at least once a week. A number of variables which describe the SES of the respondent were included as regressors, in both the self-rated health and disease prevalence models. Socioeconomic status variables include, the respondent’s highest level of educational attainment with four categories: less than secondary school, secondary school graduate, some post-secondary school, and postsecondary school graduate with the first category being the reference category. Three categories of physical activity: active, moderate, and inactive which is the reference category. Household income is total pretax income from all sources from all family members in thousands of Canadian dollars and vegetable consumption is the number of vegetable servings eaten per day. Marital status has three categories: married or common law, widowed, divorced or separated, and single being the third and reference category. Respondents were considered employed if they held a job in the 12 months prior to the survey and occupation is a dummy variable indicating a managerial or other high-level occupation.

5. CORRELATES OF SELF-REPORTED HEALTH, DIABETES, AND HEART DISEASE In this section we report the results of our findings, first for reported health outcomes, and then for the two diseases most prevalent among older respondents. Comments and explanations are deferred to Section 6. In Tables 3–6 the results from the statistical model described above are shown for various age groups of males and females who participated in the 2001 CCHS. With the exception of the female age groups 20–29 and 30–39, three mixing distributions were used for all groups; only two mixtures were used for the two youngest groups of females.5 The choice of three was based on

344

Table 3.


Ordered Logit Parameter Estimates for Self-Assessed Health Outcomes in Men by 10-Year Age Groups.

Variable Cigarettes smoked daily {Cigarettes smoked daily}2 Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index {Body mass index}2 Smoking BMI interaction Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Significant health region effects Significant provincial effects Sample size

20–29

30–39

40–49

50–59

60+

0.552 0.137 0.077

0.428

0.393

0.050 0.115 0.203 0.111 0.103 0.066 0.273 0.095 0.056 0.208 0.004 0.236 0.406 0.417 0.139 0.134 0.182 0.026 0.677 0.101 Yes Yes 8,461

0.016 0.043 0.285 0.188 0.117 0.069 0.467 0.003 0.198 0.184y 0.056y 0.214 0.537 0.368 0.144 0.112 0.159 0.033 0.847 0.104 Yes Yes 9,638

0.071 0.012 0.030 0.282 0.191 0.101 0.029 0.338 0.042 0.039 0.187 0.067 0.244 0.554 0.355 0.157 0.056y 0.169 0.071y 0.978 0.150 Yes Yes 7,241

0.085 0.007 0.001 0.119 0.120 0.214 0.051 0.089 0.021y 0.012 0.132 0.036 0.205 0.379 0.457 0.310 0.051 0.036 0.009 0.907 0.043y Yes Yes 10,164

0.108 0.114 0.017 0.035 0.095y 0.129 0.042 0.153 0.161 0.284 0.231 0.253 0.086 0.282 0.116 0.053y 0.004 0.102 No No 5,701

Notes: These are maximum likelihood estimates based on Eq. (4) where the mixing distributions are logistic. Two mixtures are used for the first age groups and three for the remaining age groups. y Indicates a p-value less than or equal to 0.10. Indicates a p-value less than or equal to 0.05. Indicates a p-value less than or equal to 0.01, respectively.

the Akaike Information Criterion (AIC) or the impossibility of using four mixing distributions. In addition to the variables listed in Tables 1 and 2, an age variable, 10 provincial dummies, and the proportions of respondents reporting poor, fair, very good, or excellent in each health region were also included as regressors. The estimation method was un-weighted maximum likelihood.6 All of the regressors were normalized so that means were zero and variances were one. As a result, the size of the estimated coefficients reflects their individual importance in explaining health outcomes.


Table 4.

345

Ordered Logit Parameter Estimates for Self-Assessed Health Outcomes in Women by 10-Year Age Groups.

Variable

20–29

Cigarettes smoked daily Cigarettes smoked daily2 Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Body mass index2 Smoking BMI interaction Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Significant health region effects Significant provincial effects Sample size

0.531 0.065 0.060y 0.060 0.047 0.108 0.085 0.420 0.017 0.148 0.188 0.132 0.336 0.136 0.186 0.091 0.132 0.176 0.000 0.090 0.011 No Yes 6,691

30–39 y

0.301 0.000 0.085 0.147 0.056 0.010 0.029 0.371 0.015 0.135 0.197 0.109 0.284 0.171 0.275 0.165 0.126 0.206 0.004 0.316 0.067 No No 9,341

40–49

50–59

60+

0.415

0.114 0.047 0.111 0.124y 0.052y 0.225 0.010 0.391 0.010 0.153 0.210 0.063 0.236 0.342 0.406 0.211 0.047y 0.276 0.003 0.710 0.104 No No 7,486

0.038 0.018y 0.059 0.120 0.099 0.203 0.021 0.090 0.027 0.008 0.143 0.074 0.214 0.236 0.340 0.286 0.055 0.066y 0.035 0.605 0.040 Yes Yes 13,940

0.030y 0.061 0.173 0.060y 0.123 0.011 0.467 0.044 0.007 0.366 0.129 0.378 0.355 0.445 0.276 0.030 0.266 0.057 0.892 0.092 Yes No 9,756

Note: These are maximum likelihood estimates based on Eq. (4) where the mixing distributions are logistic. Two mixtures are used for the first age groups and three for the remaining age groups. y Indicates a p-value less than or equal to 0.10. Indicates a p-value less than or equal to 0.05. Indicates a p-value less than or equal to 0.01, respectively.

5.1. Self-Reported Health Status The parameter estimates for self-reported health outcomes appear in Tables 3 and 4. These coefficients have the following interpretation. A positive coefficient means that an increase is the variable associated with it increases the probability of excellent health and lowers the probability of poor health. Marginal effects appear in Tables 5 and 6. These are computed as the partial derivatives of the probability of having excellent health with respect to a particular regressor. They have the same sign as the corresponding

346


Table 5.

Ordered Logit Marginal Effects for Self-Assessed Health Outcomes in Men by 10-Year Age Groups.

Variable Cigarettes smoked daily Cigarettes smoked daily2 Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Body mass index2 Smoking BMI interaction Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Significant health region effects Significant provincial effects Sample size

20–29

30–39

40–49

50–59

60+

0.099 0.024 0.016

0.045

0.043

0.003 0.013 0.023 0.013 0.008y 0.006 0.037 0.009 0.003 0.024 0.002 0.023 0.036 0.043 0.013 0.015 0.0–21 0.004 0.026 0.010 Yes Yes 8,461

0.002 0.005 0.028 0.019 0.008 0.006y 0.052 0.001 0.021 0.017 0.003 0.018 0.048 0.035 0.012 0.014 0.016 0.005 0.034 0.010 Yes Yes 9,638

0.007 0.001 0.001 0.029 0.017 0.008 0.003 0.038 0.003 0.004 0.021 0.009 0.026 0.049 0.032 0.015 0.007 0.019 0.004 0.039 0.013 Yes Yes 7,241

0.007 0.000 0.000 0.001 0.008y 0.016 0.004y 0.010 0.001 0.001 0.009 0.003 0.015 0.025 0.030 0.020 0.004 0.004 0.004 0.021 0.001 Yes Yes 10,164

0.016 0.018 0.002 0.007 0.035 0.007 0.013 0.029 0.027 0.047 0.037 0.042 0.012 0.0372 0.022 0.007 0.002 0.015 No No 5,701

Note: These are maximum likelihood estimates of the marginal effects of each variable which are the derivatives of the probability of having excellent health evaluated at sample means. y Indicates a p-value less than or equal to 0.10. Indicates a p-value less than or equal to 0.05. Indicates a p-value less than or equal to and 0.01, respectively.

coefficients reported in Tables 3 and 4. The information content of these two sets of tables is very similar so only the first set of results will be discussed. The results in Tables 3 and 4 reveal similar patterns of importance and statistical significance. For both genders, individual characteristics as well as behavioral variables are significant in explaining reported health outcomes. For the youngest age groups daily cigarette smoking is the single most important variable in explaining how well individuals feel. For males in the youngest age group this coefficient is –0.552, and is by far the largest coefficient indicating the dominance of this variable over other characteristics and behaviors. This variable becomes less important as age increases and is


Table 6.

347

Ordered Logit Marginal Effects for Self-Assessed Health Outcomes in Women by 10-Year Age Groups.

Variable Cigarettes smoked daily Cigarettes smoked daily2 Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Body mass index2 Smoking BMI interaction Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Significant health region effects Significant provincial effects Sample size

20–29 0.831 0.010 0.011 0.008 0.008 0.015 0.012 0.065 0.003 0.021 0.026 0.017 0.047 0.019 0.027 0.012 0.021 0.025 0.001 0.004 0.002 No Yes 6,691

30–39 y

0.053 0.000 0.009 0.022 0.008y 0.013 0.005 0.065 0.002 0.022 0.035 0.019 0.049 0.026 0.042 0.025 0.023 0.033 0.000 0.023 0.010 No No 9,341

40–49

50–59

60+

0.039

0.010 0.006 0.011 0.015y 0.004 0.023 0.002 0.049 0.003y 0.016 0.023 0.007y 0.026 0.036 0.043 0.022 0.006y 0.030 0.000 0.035 0.012 No No 7,486

0.004 0.002y 0.004 0.009 0.008 0.014 0.002 0.008 0.002 0.000 0.011 0.006 0.016 0.018 0.025 0.021 0.005 0.005 0.002 0.011 0.003y Yes Yes 13,940

0.002 0.006 0.022 0.006y 0.011 0.001 0.049 0.007 0.003 0.039 0.014 0.040 0.032 0.045 0.027 0.005 0.028 0.009y 0.039 0.009 Yes No 9,756

Note: These are maximum likelihood estimates of the marginal effects of each variable which are the derivatives of the probability of having excellent health evaluated at sample means. y Indicates a p-value less than or equal to 0.10. Indicates a p-value less than or equal to 0.05. Indicates a p-value less than or equal to 0.01, respectively.

replaced by the number of years smoked. While the importance of this variable also declines; at least one smoking variable is always significant for each age group. The result that current smoking was insignificant in the health of older age groups may be an unintuitive result; we will return to this point in Section 6.2. Our models include the positive square of cigarettes smoked on a daily basis to identify non-linearities in this relationship.7 We find the relation between cigarettes smoked and the reported health outcome is significantly concave for the first two age groups and linear thereafter, except for females aged 50–59 where the relation becomes significantly convex. A concave relation is one in which smoking two packs of cigarettes

348


a day is more harmful than smoking one pack a day, but less than twice as harmful. The BMI coefficients were also large and significant for all age groups. Non-linear BMI terms were negative and significant for several age groups implying a convex relationship that excessive overweight or obesity is much worse than just being overweight or obese. In contrast, alcohol use was found generally beneficial and significantly so for older age groups. Physical activity was significantly beneficial for all age groups and benefits also increased with frequency of exercise. Additionally, the consumption of green vegetables is associated with better health. Individual characteristics like education, household income, and having a job were also associated with better reported health status. The results from Tables 3 and 4 show substantial differences across both gender and age groups. Looking at the first age group, for example, it is clear that both the absolute values of the coefficients as well as their significance vary between genders. Alcohol use and heavy drinking are significant variables for women but not for men. The differences in the parameters as a group were captured by likelihood ratio tests which rejected the hypothesis that the coefficients are the same for both genders. When tested, pooling the data led to likelihood values which were significantly less than the sum of the likelihood values arising from separate estimation by gender. This result held for all age groups. 5.2. Heart Disease and Diabetes Tables 7 and 8 contain parameter estimates of unmixed probability models for having diabetes and heart disease in the two oldest age groups, where higher prevalence occurs. Here a positive coefficient is associated with an increased probability of having the disease. Perhaps because of the binary nature of the outcome variable we were not able to estimate mixture distributions for these two diseases. This means that there is no control for unobservable effects, a limitation on the analysis. These models use the same set of covariates that were employed in the ordered probability models for health outcomes. Health region variables were used but were never significant and not reported. Results on diabetes and heart disease in the age groups 50+, these are similar to what was found for reported health outcomes; however, socioeconomic variables tend to be less important than they were for reported health outcomes. In contrast, the estimated coefficients for the BMI are very large for both diseases. At least one of the smoking variables is positively associated with the probability of having diabetes or heart disease


Table 7.

349

Logit Parameter Estimates for Diabetes in Older Respondents.

Variable

Men

Cigarettes smoked daily Cigarettes smoked daily2 Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Body mass index2 Smoking BMI interaction Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Significant provincial effects

Women

50–59

60+

50–59

60+

0.302 0.031 0.038 0.220 0.112 0.227 0.001 0.567 0.010 0.087 0.032 0.009 0.060 0.122 0.028 0.020 0.062 0.015 0.014 0.155 0.011 No

0.071 0.006 0.005 0.106 0.147 0.251 0.064 0.475 0.043 0.062 0.061y 0.017 0.053 0.059y 0.114 0.052y 0.049 0.023 0.024 0.137 0.006 No

0.374y 0.020 0.053 0.158 0.227 0.528 0.066 0.863 0.098 0.006 0.004 0.036 0.119y 0.120y 0.062 0.005 0.046 0.151 0.033 0.228 0.047 Yes

0.112 0.024 0.036 0.070 0.090 0.292 5.553 0.592 0.068 0.026 0.042 0.035 0.130 0.136 0.152 0.096 0.056y 0.012 0.011 0.138 0.009 Yes

Notes: These are maximum likelihood estimates derived from an unmixed ordered logistic probability model. y Indicates a p-value less than or equal to 0.10. Indicates a p-value less than or equal to 0.05. Indicates a p-value less than or equal to 0.01, respectively.

but, as in the case with reported health outcomes, the reported frequency of alcohol use is associated with a lower probability of these two diseases. Like the results for self reported health status, gender differences are significant.

6. DISCUSSION OF THE RESULTS 6.1. Obesity and Health In the 20–29 group, the relationship for females between BMI and negative health status was very large relative to other determinants, but this relationship

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Table 8. Logit Parameter Estimates for Heart Disease in Older Respondents. Variable

Cigarettes smoked daily Cigarettes smoked daily2 Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Body mass index2 Smoking BMI interaction Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Significant provincial effects

Men

Women

50–59

60+

50–59

60+

0.178 0.024 0.032 0.334 0.381 0.056 0.088y 0.188 0.008 0.202 0.035 0.028 0.096y 0.135 0.049 0.084y 0.054 0.210 0.273 0.252 0.067 Yes

0.050 0.016 0.063 0.100 0.204 0.094 0.099 0.183 0.020y 0.076 0.040 0.001 0.019 0.078 0.122 0.061 0.094 0.150 0.092y 0.252 0.064 Yes

0.238 0.079 0.041 0.185 0.117 0.147 0.010 0.180 0.022 0.030 0.117y 0.026 0.080 0.252 0.221 0.146 0.134 0.171 0.357 0.204 0.022 Yes

0.123 0.016 0.030 0.093y 0.136 0.070 0.069y 0.092 0.003 0.020 0.058 0.002 0.006 0.186 0.171 0.142 0.007 0.054 0.131 0.165 0.006 Yes

Notes: These are maximum likelihood estimates derived from an unmixed ordered logistic probability model. y Indicates a p-value less than or equal to 0.10. Indicates a p-value less than or equal to 0.05. Indicates a p-value less than or equal to 0.01, respectively.

is weaker in males. The female BMI coefficient was –0.420, the second largest contributor to self-rated health after daily smoking, but for males of a similar age, BMI was –0.095 and significant only at the 10% level. This result, similar to the smoking result, indicates a strong need to disaggregate analysis by gender and age due to variation in the contribution of BMI to self-rated health across genders and age groups. Okoson et al. (2001) also find a negative interaction of age and obesity in each racial/ethnic groups confirming the association between obesity and health is different across age groups. For other age groups, BMI had a large, negative, and significant contribution to health status across genders, but decreased in the 60+ age


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group. This result may be caused by obese/overweight people dying prematurely, or other health concerns related to age taking over. Other studies also found obesity related to lower self-rated health (Okoson et al., 2001; Contoyannis & Jones, 2004). Okoson and colleagues studied obesity and self-rated health disaggregating by gender and ethnicity. They found obesity was associated with lower self-rated health black and white males and females, but less in Hispanics. 6.2. Smoking and Health The estimated coefficients associated with the number of cigarettes smoked daily shows a pattern of declining values as age increases, with at least one of the smoking variables having an adverse effect on reported health outcomes for both genders in all age groups. These results indicate that major errors of inference would arise if pooled estimates were used to indicate the effects of smoking on reported health outcomes since daily smoker coefficients were different across age groups. Although cessation and duration variables, years smoked, or being a former daily smoker were significant for the older age groups, smoking had no ‘apparent’ adverse effect on the age group 60+. In addition to demonstrating the importance of examining specific age groups, these results raise questions about the underlying causal mechanisms which determine how smoking affects health. This appears to be contrary to the literature that finds smokers generally have lower self-rated health status than non-smokers, even in studies controlling for chronic conditions (U.S. Department of Health and Human Services: Surgeon General’s Report, 2004). There is some support to indicate smoking may have a stronger impact on individuals at younger ages. Higher utilization of outpatient and inpatient services among smoking adolescents and young adults relative to never smokers suggest negative effects of smoking are not only due to smokinginduced diseases that occur later on in life. Additionally, similar utilization differences were not present or reversed in older age groups (U.S. Department of Health and Human Services, 2004). Longitudinal studies also indicate evidence of lung growth impairment from active smoking in adolescents. There are three processes that determine effects of smoking in older age groups. First, older age groups are subject to a selection process. Smoking increases mortality risks from smoking related diseases, many premature deaths are likely to be associated with smoking related diseases. Individuals who begin smoking early may not live long enough to report smoking or previous smoking related poor health. Second, there may be a reverse causal

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relation between smoking and reported health, causing confounding due to time of cessation (Leffondre, Abrahamowics, Siemitycki, & Rachet, 2002). Some long-term smokers quit after experiencing serious adverse effects from smoking, an effect not captured by the current smoking variable in the upper age groups. Several studies have observed this cessation effect, where greater differences in poor health are observed between former smokers and non-lifetime smokers, than between current smokers and non-lifetime smokers (U.S. Department of Health and Human Services, 2004). Finally, there may be a segment of the population who can smoke and feel no adverse effects. A combination of these processes may imply that older age groups in the data have smaller proportion of individuals who smoke and show poorer health, suffer as a result. Two consequences follow from these results. First, studies which concentrate on higher age groups are not likely to be informative about the population as a whole. In particular, they are likely to understate the debilitating effects of smoking on individual health for lower age groups. Second, it would be incorrect to conclude from the small and insignificant smoking coefficients that 50-year-old never smokers or former smokers can safely start smoking. 6.3. Alcohol Use and Health Benefits of moderate alcohol consumption are better documented in the area of cardiovascular disease, but a review of the literature on moderate alcohol consumption finds other benefits that also include lowered risks of dementia and metabolic illnesses, including decreased risks of developing type 2 diabetes and obesity (Standridge et al., 2004). Our results on the average alcohol consumption variable are consistent with much of the literature which indicates that moderate alcohol consumption is positively associated with good health and negatively related to prevalence of diabetes and heart disease. However, our results differ from Contoyannis and Jones (2004) who while controlling for unobservable heterogeneity found prudent alcohol consumption had little effect. The authors indicate their finding may be due to using an alcohol dummy that cannot differentiate between heavy alcohol consumption and abstinence.8 6.4. Ageing vs. Health Behaviors It is well known that health declines with age. Age was included as a regressor in all of the models and always had a significantly negative


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coefficient within each age group. The coefficients differed in size across age groups, so these estimates cannot be used to calculate the effects of ageing on reported health outcomes over long periods of time; they also cannot be used to compare the relative impact of ageing with impacts of various types of behavior. Instead, we conduct a simple exercise in Tables 9 and 10 by Table 9.

Ageing vs. Health Behavior Related to Health Outcomes for Males. 20–29

Percent in category Average health indicator Poor health (%) Fair health (%) Good health (%) Very good health (%) Excellent health (%) Diabetes (%) Heart disease (%)

50–59

Bad

Good

Bad

Good

47.0 3.845 0.7 3.9 19.9 41.8 33.6

25.4 4.281 0.5 2.2 13.0 41.2 43.1

43.4 3.286 5.1 12.0 28.2 32.8 22.0 17.1 21.2

10.6 3.944 2.9 8.8 22.2 33.8 32.3 10.5 15.9

Note: Good health behavior is characterized by not smoking and having a body mass index of less than 25.0 and bad behavior is characterized as being obese or a daily smoker. The percentage of individuals with the bad and good health behaviors by age group, are shown in the columns.

Table 10.

Ageing vs. Health Behavior Related to Health Outcomes for Females.

Health Category

Percent in category Average health indicator Poor health (%) Fair health (%) Good health (%) Very good health (%) Excellent health (%) Diabetes (%) Heart disease (%)

20–29

50–59

Bad

Good

Bad

Good

43.1 3.681 1.8 7.5 30.2 41.6 18.8

35.2 4.162 0.6 2.3 16.2 42.0 38.8

41.0 3.253 7.5 16.6 31.9 31.4 12.7 14.5 18.3

21.3 3.833 2.7 6.8 23.8 37.7 28.9 7.2 17.3

Note: Good health behavior is characterized by not smoking and having a body mass index of less than 25.0 and bad behavior is characterized as being obese or a daily smoker. The percentage of individuals with the bad and good health behaviors by age group, are shown in the columns.

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considering two age groups and health outcome differences between them. In each age group we identify what we call ‘good’ and ‘bad’ health behaviors. These are somewhat arbitrary but they illustrate the basic concept. Good health behavior is characterized by not smoking and having a BMI of less than 25.0. Bad behavior is characterized as being obese or a daily smoker. Of course, other definitions which include dietary and exercise activities could be used but their inclusion does not lead to dramatic changes in the results. Due to the cross-sectional nature of the data, the effects of age on health status will, by necessity, involve cohort comparisons since the data does not allow us to follow the same individuals over time. The effect of ageing is computed as the percentage change in the reported excellent health shares for ‘good’ individuals as they move from the age group 20–29 to 50–59. For example, the proportion of males reporting excellent health in the ‘good’ health category declines from 43.1 to 32.3%. The effect of ageing together with the consequences of being a ‘bad’ health type are obtained by comparing the individuals with good health behavior reporting excellent health in the 20–29 age group with the bad health types reporting excellent health in the 50–59 age group. Here the change is from 43.1 to 22.0%. Decomposing the total change into ‘age’ and ‘behavioral’ determined components attributes 51.2% of the total decline to the process of ageing and the rest to behavioral causes. A similar exercise can be carried out using average health scores. Here the results are more dramatic in that only 33.9% of the decline in the average health indicator can be attributed to the ageing process. In each case, however, it is clear there are substantial health benefits which accrue to individuals from not smoking or overeating, even though their health will deteriorate as they age. 6.5. Individual Characteristics vs. Health Behaviors The question of which type of variable contributes most to explaining reported health outcomes is an important issue that is addressed in Tables 11 and 12. Looking at the results in this way is important since the success of health policy depends more in the short run on what individuals do, as opposed to who they are. In what follows we will refer to individual socioeconomic and demographic characteristics as Group I variables. These are education, household income, marital status, province of residence, and variables which are determined by the health region in which the respondent lives. Group II variables are the modifiable behavioral variables, cigarettes smoked, BMI, physical activity, and fruits and vegetables consumption. For each age group, three models were estimated. The first model contains no


Table 11.

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Contribution to Explained Variation for 10-Year Age Groups for Men by Type of Variable.

Log-Likelihood ln(L0) ln(LI) ln(LI+II) Percentage explained by Group I variables

20–29

30–39

40–49

50–59

60+

6920.135 6852.524 6425.070 13.7

10730.989 10410.316 10200.948 61.1

13201.859 12632.654 12348.629 66.7

10560.550 9957.827 9783.615 78.2

15395.807 14843.914 14569.704 66.8

Notes: The likelihood function, L0, uses only an intercept term. Group I variables include socioeconomic and demographic characteristics of the respondent. Group II variables represent the behavior of the respondent such as smoking and physical activity. LIuses only Group I variables and LI+II uses both Group I and Group II variables.

Table 12.

Contribution to Explained Variation for 10-Year Age Groups for Women by Type of Variable.

Log-Likelihood ln(L0) ln(LI) ln(LI+II) Percentage explained by Group I variables

20–29

30–39

40–49

50–59

60+

8400.434 8244.812 8033.919 42.3

11993.785 11650.985 11330.242 51.7

13336.336 12807.531 12444.825 59.2

10850.007 10344.755 10052.709 63.4

20599.184 19978.626 19572.457 60.3

Notes: The likelihood function, L0, uses only an intercept term. Group I variables include socioeconomic and demographic characteristics of the respondent. Group II variables represent the behavior of the respondent such as smoking and physical activity. LIuses only Group I variables and LI+II uses both Group I and Group II variables.

explanatory variables and generates the baseline value of the log-likelihood function. The second model contains all of the variables which describe the individual’s characteristics. Finally, the third model contains all of the variables that are available. Our presumption is that some of the Group II variables might be correlated with some of the Group I variables but the causal relationship runs from Group I to II. As a result, it is better to run the model with the Group I individual SES and demographic variables first, because Group II variables are likely to depend on Group I variables. The results for males and females, which appear in Table 11 and 12, indicate that individual characteristics as opposed to modifiable behaviors account for the largest part of the explained variation in reported health outcomes in all but the first age group.

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6.6. Smoking vs. Obesity Our results provide some information on the relative effects of obesity problems compared with those caused by smoking behavior. This is important because it provides perspective on the potential costs of obesity if weight continues to increase at the current rate. Katzmarzyk and Ardern (2004) reported the national percentage of attributable risk (PAR) from overweight and obesity for Canadian adults 20–64 years of age increased from 5.1 to 9.3% between 1985 and 2000, representing almost 10% of all deaths in Canadians. In a separate study, Kazmarzyk and Janssen (2004) derived estimates for direct and indirect costs of inactivity and obesity in 2001. They estimated the total economic costs associated with obesity was $4.3B ($1.6B in direct and $2.7B in indirect costs). Recently, it has been suggested in the United States that problems associated with obesity are about to overtake smoking as the ‘prevailing underlying cause of death by 2005’ (Obesity to overtake smoking as leading cause of death, 2004). In Tables 13 and 14, we evaluate this claim for Canada in relation to heart Table 13.

The Importance of Smoking vs. Dietary Behavior for Diabetes.

Log-Likelihood Function

BMI variables Smoking variables

Males

Females

50–59

60+

50–59

60+

2593.010a 2678.304

3152.106a 3201.863

1377.253a 1462.129

4436.802a 4584.600

Note: The likelihood function for BMI (smoking) includes all regressors excluding only smoking (BMI) variables. a Indicates the more important type of variable, either BMI or smoking.

Table 14.

The Importance of Smoking vs. Dietary Behavior for Heart Disease.

Log-Likelihood Function

BMI variables Smoking variables

Males

Females

50–59

60+

50–59

60+

2948.860 2924.484a

4032.419 4026.480a

1332.401 1330.141a

6154.832 6145.512a

Note: The likelihood function for BMI (smoking) includes all regressors excluding only smoking (BMI) variables. a Indicates the more important type of variable, either BMI or smoking.


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disease and diabetes, both of which include obesity as an important risk factor. Our approach is to determine whether it is individual BMI or the smoking variables that contribute to the largest increase in the likelihood functions for the probability models of the two diseases. In these two tables we calculated the values of the likelihood functions for each disease when excluding one type of variable. The first row contains the values of the maximized likelihood functions using all of the variables except those associated with smoking. Similarly, the second row excludes the BMI variables. For diabetes it is clear that the variables which matter most are BMI variables, which means being overweight or obese explains more of the variation in the probability of having diabetes than smoking. For heart disease, the ranking was reversed but the differences are quite small for three of the four cases, indicating that both behaviors appear to have similar effects on the risk of heart disease. This is an important finding which requires additional research in the future. Although smoking is currently named the leading preventable cause of death in North America, future research should put more effort into distinguishing the relative importance of individual risk factors for specific diseases. 6.7. Statistical Issues As we mentioned earlier it is important to use methods which account for unobservables. To see the importance of treating the problem of unobserved heterogeneity, we compare the parameter estimates that arise when three distributions are used to those which come from a single logistic model. These results appear in Table 15 for the age group 40–49. This age group was chosen because it was the most responsive to the treatment of unobserved heterogeneity. In addition to fitting the data better in terms of having a higher likelihood function, corrected for the number of estimated parameters, the estimated coefficients are on average significantly larger in absolute value in the mixture models. For women, the effect of cigarette smoking increases by a factor of two when mixtures are used. There are also important differences in inference between the two models. For example, the effects of smoking cigarettes occasionally, becomes significant when the mixed model is used for women. Our result that controlling for unobserved heterogeneity (although with different methodology) increases the effects of health behaviors on self-rated health is similar to previous findings by Contoyannis and Jones (2004). Our results also support Rosenzweig and Schultz (1983) and Mully and Portney (1990) who find that unobservable heterogeneity bias leads to underestimates of the beneficial effects of health inputs on health.

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Table 15. Logit Parameter Estimates for Mixed and Unmixed Distributions for Men and Women Aged 40–49. Variable

Cigarettes smoked daily Cigarettes smoked daily2 Cigarettes smoked occasionally Years smoked Former daily smoker Alcohol consumption Heavy drinking Body mass index Body mass index2 Smoking BMI interaction Education level 2 Education level 3 Education level 4 Household income Physical activity level 1 Physical activity level 2 Vegetable consumption Marital status 1 Marital status 2 Employed Occupation Significant health region effects Significant provincial effects

Males

Females

Mixed

Unmixed

Mixed

Unmixed

0.393 0.016 0.043 0.285 0.188 0.117 0.069 0.467 0.003 0.198 0.184 0.056y 0.214 0.537 0.368 0.144 0.112 0.159 0.033 0.847 0.104 Yes Yes

0.233 0.008 0.029 0.175 0.112 0.054 0.038y 0.309 0.006 0.113 0.102 0.017 0.108 0.288 0.209 0.071 0.085 0.095 0.031 0.462 0.058 Yes Yes

0.415 0.030y 0.061 0.173 0.060y 0.123 0.011 0.467 0.044 0.007 0.366 0.129 0.378 0.355 0.445 0.278 0.030 0.266 0.057 0.892 0.092 Yes Yes

0.219 0.012 0.027 0.127 0.035 0.066 0.001 0.292 0.029y 0.003 0.223 0.087 0.234 0.187 0.255 0.156 0.027 0.161 0.046y 0.464 0.053 Yes No

Note: Unmixed distributions arise from ordered logistic models. Mixtures involve three logistic distributions with different intercepts. Terms and threshold points. y Indicates a p-value less than or equal to 0.10. Indicates a p-value less than or equal to 0.05. Indicates a p-value less than or equal to 0.01, respectively.

Our findings that having a job and being physically active are conducive to reporting good health are what many researchers have found. However, there are some potential inference problems here. Both having a job and being able to participate in a reasonable amount of physical activity requires a reasonable standard of health. One reason for retirement is the presence of serious health problems. Thus, it is possible for those in the 60+ age category, that the high correlation between good health and having a job is due to healthy individuals that can and want to work, as opposed to respondents feeling good because they are working. For younger age groups, being without a job can be stressful


359

and is a well known cause of health problems. One plausible explanation is that at the upper end of the age distribution causation runs both ways. The very large coefficients for the 60+ age group, probably overestimate the effect of being employed on health status. The same holds for physical activity.

7. POLICY IMPLICATIONS AND CONCLUSION While many of our results are consistent with previous findings, they also reveal a number of important age and gender differences in health outcomes. Our findings reinforce concerns of health policymakers over the smoking and dietary behaviors of Canadians. More specifically, across Canada, both males and females are much heavier than the cohort born 25 years earlier. Despite lower smoking rates, the current cohort of 20–29 years old are likely to face higher obesity rates when they reach 50–69, compared with the current generation. Tremblay, Katzmarzyk, and Willms (2002, p. 540) provide compelling evidence of a progressive increase in being overweight and obese over the last 15 years, a problem which is even more pronounced in children. Tjepkema and Shields (2005b) find that in 2004/ 2005, children and adolescents between 2 and 17 years of age had measured overweight and obesity rates of 18 and 8%, up from previous measured rates of 12 and 3%, respectively in 1978/1979. Furthermore Reilly et al. (2003) found that obese children are likely to remain obese as adolescents and adults. Adult Canadians have also exhibited large increases in obesity, with measured obesity increasing almost by 50% in the last 25 years (Tjepkema & Shields, 2005a). Given the importance of excess body weight on the health of individuals in these age groups, this situation should be viewed with considerable alarm. One of our principal findings is that health outcomes are as much a consequence of individual attributes, as they are a consequence of individual behavior. Specifically, we noted that low income and low educational attainment are important barriers to good health. As it is now structured, the health care system offers little direct incentive to individuals for taking full responsibility for the health consequences of their behavior. Federal health policy focuses on providing information and coordinating activities that encourage the uptake of healthy behavior. Perhaps policies introducing incentives for behavioral change could be effective. In addition, out of equity concerns, a number of specific programs are targeted toward low socioeconomic or otherwise disadvantaged individuals. Our research does indicate a need to target the disadvantaged.

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Some additional research to support evidence based policy recommendations can be informative. Can the schooling system play a part? Is it possible for schooling to share in the traditional role of the family, as the institution which shapes attitudes and encourages lifestyles that promote good health? Many anti-smoking and sex education programs already exist and are successful. These could be enhanced to promote the adoption of better diets and more physical activity. However, the type of schooling program may matter. Veugelers and Fitzgerald (2005) find that students in schools with a specific comprehensive program (Annapolis Valley Health Promoting Schools Project), emphasizing healthy food choices and encouraging physical activity, had significantly better diets, were more physically active, and less likely to be obese then students from schools without nutrition programs. More research is needed on the effectiveness of child interventions programs, as other results from the literature on this are mixed. Income was one of the most important variables in determining reported health outcomes. Can financial incentives be effective in getting individuals to improve their lifestyles? At the individual level, incentives might include tax deductible fees at sports and health clubs. At the firm level, companies could receive tax credits for investing in fitness and health improvement programs and facilities. Low-income households might also receive additional subsidies for health and fitness related programs. Will targeted programs for older individuals be effective? From our results, the large coefficients associated with being employed in the 60+ age groups suggest potential health benefits to people retiring at later ages. Later retirement can also lead to increased tax revenues and fewer people on pensions. Furthermore, those who continue working past the normal retirement age were generally found healthier, and they will demand less from the health care system than non-workers of a similar age. The large physical activity coefficients for the oldest age group also indicate potential for improving the health of seniors through targeted programs to improve physical activity. Recent reports in the press highlight the ballooning health care costs to US firms arising from employee health care obligations (see Waldie, 2005, March 22). While Canada differs from the US by providing universal coverage for medically necessary services, a large proportion of private health care costs are incurred by the firm after the employee has retired. Providing access to recreation facilities after retirement can have long-term benefits to both firms and their employees. Our results, like others, point to serious problems associated with high BMI values. Unlike the data collected on smoking behaviors, the survey did not ask respondents about their BMI histories. Similar to smoking, obesity


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has long-term effects, especially for those who are overweight or obese from childhood or adolescence. Further research is needed to determine not only the extent to which individuals recover from being obese, but also how weight loss depends on age at first obesity. It would be useful for future policy design if health surveys asked respondents about their weights at earlier ages. This point is also stressed by Bolin et al. (2006, p. 15). Understanding the dynamics between obesity and health outcomes could aid in formulating effective policies and programs that encourage healthy behaviors.

NOTES 1. Examining younger age groups is not particularly a revealing exercise since the incidence of these maladies is quite low for younger age groups. 2. Detailed description of the alcohol consumption variables are found in Section 4. 3. The estimates in Section 5 are based on a logistic cumulative distribution function. This was chosen to achieve faster convergence rates. There are no qualitative differences when a normal cumulative distribution is used. 4. Health care delivery institutions are administered at the level of the health region. The survey has a health region indicator for each respondent. Means of important variables can be constructed at the health region level. We use the averages of the reported health outcomes as regressors to see whether communities or health regions have an impact on individual outcomes. 5. For this group the probability of the third distribution converged to zero so that only two mixing distributions could be used. 6. Statistics Canada provides a set of weights to make the sample reflect national characteristics. We found no significant differences between weighted and unweighted estimates. 7. The positive square of x is fx2 I ðx0Þ þ 0 I ðxo0Þ g: 8. Prudent alcohol consumption in their analysis was defined as weekly consumption of 0–14 units for females and 0–21 unit for males.

ACKNOWLEDGMENT The authors wish to express their thanks to the editors and the two anonymous referees, and to Charles Mallory, Zachary Jacobson, Dan Harvey, and Xuzhen Zhang for comments on earlier versions. The analyses and conclusions expressed in this paper are those of the authors and should not be taken as reflecting the official position of Health Canada.

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