The Economics of the Multilingual Workplace

The Economics of the Multilingual Workplace

Routledge Studies in Sociolinguistics

1. Emergent Lingua Francas and World Orders The Politics and Place of English as a World Language Phyllis Ghim-Lian Chew 2. The Economics of the Multilingual Workplace François Grin, Claudio Sfreddo and François Vaillancourt

The Economics of the Multilingual Workplace

François Grin, Claudio Sfreddo and François Vaillancourt

New York

London

First published 2010 by Routledge 270 Madison Avenue, New York, NY 10016 Simultaneously published in the UK by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN

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This edition published in the Taylor & Francis e-Library, 2010. To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk. © 2010 Taylor & Francis All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Grin, François. The economics of the multilingual workplace / by François Grin, Claudio Sfreddo, and François Vaillancourt. p. cm. — (Routledge studies in sociolinguistics ; 2) Includes bibliographical references and index. 1. Multilingualism—Economic aspects. 2. Diversity in the workplace. 3. Sociolinguistics. I. Sfreddo, Claudio. II. Vaillancourt, François. III. Title. P115.G745 2010 306.44'6—dc22 2009043454 ISBN 0-203-85267-2 Master e-book ISBN

ISBN13: 978-0-415-80018-1 (hbk) ISBN13: 978-0-203-85267-5 (ebk)

Contents

Tables and Figures Acknowledgments Introduction

PART I The Economic Perspective on Multilingualism 1

2

3

ix xiii 1

9

Language at Work: Identifying the Issue

11

1.1 Enduring Concerns, Little-Known Processes

11

1.2 The Economic Analysis of the Firm: A First Approach to the Inclusion of Linguistic Diversity

17

On the Linguistics of the Economy v. the Economics of Language

28

2.1 A Tentative Mapping

28

2.2 Bourdieu and His Heirs: A Glimpse at Some Sociolinguistic Perspectives

30

2.3 Pragmatics-oriented Perspectives

34

2.4 Ethnomethodology and Conversation Analysis

35

2.5 Additional Perspectives on Language in the Economy

37

A Gallery of Empirical Findings

39

3.1 Descriptive Results

39

3.2 Edging Closer to Economic Effects

45

3.3 Different Paths to Estimation

48

3.4 Drawing the Line: Absolute v. Contingent Multilingualism

52

vi Contents 4

Foreign Language Skills and Earnings

55

4.1 Language Skills and the Creation of Value

55

4.2 Defi ning Language for Labour Market Analysis

58

4.3 The Changing Labour Market Value of Languages Over Time

59

4.4 Factoring in Skills Levels

64

4.5 The Value of Immigrants’ Language Skills

69

PART II Foreign Language Skills, Foreign Language Use, and Production

73

5

Language Use and the Production Process

75

5.1 The Relevance of Language Use

75

6

7

5.2 The Determinants of Language Use

78

5.3 The Production Model Revisited

86

From Theory to Measurement

92

6.1 On Modelling, Calibration and Data

92

6.2 Data Collection: Dealing with Multi-faceted Labour

95

6.3 Data Collection: Grouping Goods

100

6.4 Prices at Firm Level

101

6.5 Aggregation at Industry Level

102

The Contribution of Multilingualism to Value Creation

105

7.1

Value Added and the Production Function

7.2 The Data

8

105 106

7.3 Application: Language Skills and Production Functions

110

7.4

115

Extension to Cost and Profit Functions

7.5 Peeking into the Black Box Again

119

Foreign Language Skills and Hiring Strategies

123

8.1 A Neighbouring, Yet Crucial Issue

123

8.2 Foreign Language Requirements and Use

124

8.3 Modelling Recruitment

128

8.4 Towards Linguistic Audits

131

Contents

vii

PART III Policy Implications and Future Prospects

135

9

Policy Implications

137

9.1

137

Bases for Policy Choice

9.2 Link-up with Language Policy: The Role of the State

140

9.3 Language Policy Priorities and Proposals: Contingent Multilingualism

143

10 Multilingualism at Work: A Prospective Glance

152

10.1 Taking Stock

152

10.2 The Ideal Data Set

156

10.3 New Avenues

159

Appendix I: Language-Augmented Production Model 1

The Core Model

163 163

2 Derived Demand for Inputs and Derived Supply for Output

164

3 Comparative Statics

165

4

The Variable Profit Function

166

5

Using the Translog Function

168

Appendix II: Estimation Procedure and Results

171

1

Databases Used

171

2

Value Added, Labour Payment, Capital Payment, Capital Stock and Labour Quantity for Years 1995–2006

172

3

Using the CLES Database: Adjustments and Price Extraction

173

4

Extending CLES-drawn Prices and Quantities Beyond 1995

175

5

Extracting Prices and Quantities of Goods and Services

175

6

Estimation of the Production Function

177

7

Estimation of the Cost Function

181

8

Estimation of the Profit Function

184

Appendix III: A Simple Recruitment Model

187

1 The Context

187

2

Distribution of Speakers

187

3

Costs

188

4

The Firm’s Programme

190

5 Analysis of Results

191

viii Contents Notes Bibliography Author Index Subject Index

193 207 221 225

Tables and Figures

TABLES 4.1

4.2 4.3

4.4 4.5

4.6

4.7

4.8 4.9

Gross Mean Yearly Earnings, Québec, Men and Women 1970 and 2000, Index Values Based on Current Canadian Dollars, Seven Sets of Language Skills

60

Net Impact on Earnings, Québec, Men and Women 1970 and 2000, Percentages, by Skills in Official Languages

62

Net Impact on Earnings, Québec, Men, 2000, Percentages, by Skills in Official Languages, with and without Industry Variable

63

Net Impact on Earnings, Québec, Men,2000, Percentages, by Skills in Official Languages, Eight Industrial Sectors

63

Gross and Net Impact on Earnings of Oral and Written Foreign Language Skills by Skills in Official Languages, Québec, Men, 1971, Percentages

64

Gross Earnings Differentials by Language Skills in National Languages, Switzerland, Men and Women, 1994–1995, Index Values Based on Current Swiss Francs

66

Gross Earnings Differentials by Competence Level in English, Switzerland, Men and Women, 1994–1995, Index Values Based on Current Swiss Francs

67

Net Impact on Earnings of “Excellent” or “Good” Foreign Language Skills, Switzerland, Men 1994–1995, Percentages

68

Gross Earnings Differentials by Competence Level in Turkish, Switzerland, Men and Women, 1997–1998, Index Values Based on Current Swiss Francs

70

x 5.1

Tables and Figures Net Impact on the Use of French in the Workplace, Bilingual Francophones, Québec, 1971, 1979 and 1989, Percentage of Working Time

79

Net Impact on the Use of French at Work, Large Employers, Québec, 1977–1979, in Percentage of Working Time

80

Net Impact of Ownership by Language Groups on Productivity, Unit Costs and Exports, Manufacturing Establishments, Québec, 1978

82

Net Impact on the Use of English at Work, French- and German-speaking Switzerland, 1994–1995, Odds Ratios

84

Net Impact on the Use of English in Percentage of Working Time, Manufacturing Firms in French- and German-speaking Switzerland, 2008–2009 (N = 191), Percentages

85

6.1

Example of Relationship between Inputs and Output

94

7.1

Estimates of Stock of Language Competences Available to Swiss Firms, in Units per 100 Workers, 1995 Data

107

Changes in Price and Quantity of Language Skills and Labour from 1995 to 2004, Switzerland

108

Price Indexes for Sales and Purchases, by Language Used with External Trade Partners, Manufacturing Industries, Switzerland, Selected Years

109

Quantity Indexes for Sales and Purchases, by Language Used with the External Trade Partners, Sectors ‘MET’ and ‘MAC’, Switzerland, Selected Years

109

Elasticity of Value Added with Respect to the Stock of Language Skills, Switzerland

112

Percentage Change in Value Added as a Result of a 100% Drop in the Stock of Foreign Language Skills, Switzerland

114

7.7

Elasticities Derived from a Cost Function, Selected Results

117

7.8

Elasticities Derived from a Profit Function, Selected Results

118

7.9

Share of Communication Time in a Non-local Language, by Language Region and Department within the Firm, Switzerland

121

5.2

5.3

5.4 5.5

7.2 7.3

7.4

7.5 7.6

Tables and Figures xi 8.1

Foreign Language Skills Requirements at Hiring, Switzerland, 1994–1995, by Foreign Language and Language Region, Percentages

125

Use of Foreign Language Skills at Work, Switzerland, 1994–1995, by Foreign Language and Language Region, Percentages

125

Linguistic Skills Under-requirements (Differences between Foreign Language Use and Requirements), Switzerland, 1994–1995, by Foreign Language and Language Region, Percentages

126

English Language Skills Requirements and Use, by Type of Employment, Switzerland, 1994–1995, Odds Ratios

127

Calibrating and Sharing the Cost of Language Education Policy

150

The Ideal Data Set

157

A-II.1 List of Empirical Variables

178

A-II.2 Production Function: Estimation Results

180

A-II.3 Production Function: Scaling Factors

181

A-II.4 Elasticities from a Translog Cost Function

182

8.2

8.3

8.4 9.1 10.1

FIGURES 1.1

Multilingualism at work: general analytic framework.

20

4.1

Foreign language (FL) skills and the production process.

57

5.1

Language-augmented production model.

90

6.1

Breakdown of labour by skills levels.

98

8.1

Distribution of speakers by competence level in language F.

128

8.2

Targeted v. necessary foreign language skills.

130

A-III.1 Distribution of L2 speakers.

188

A-III.2 Distribution of job applicants’ language skills.

189

Acknowledgments

This book is one of the outputs of a three-year research project funded by the Swiss National Science Foundation under National Research Programme No. 56 (grant No. 405640–108630), whose support is gratefully acknowledged. This particular project, which went by the acronym of “LEAP” for “langues étrangères dans l’activité professionnelle” (loosely translatable as “foreign languages in working life”), being the only one in this research programme to be anchored in economic analysis, was— perhaps unavoidably—perceived as a little odd, at least by comparison with other projects hailing, in the main, from applied linguistics. These circumstances have led to heated debates within the research programme, and we found that despite several years’ experience in the application of economic concepts to language issues, particular effort needed to be expended in order to explain what we were doing and why. This has paved the way for the interdisciplinary bridge-building which we have attempted to work into this book. In this context, we have been fortunate to be able to count on the wisdom, intellectual curiosity and support of several experienced scholars, particularly Hugo Baetens Beardsmore, Daniel Coste and Robert Phillipson, to whom we would like to express our heartfelt gratitude. We have also benefited, at various stages of the research project or of the subsequent writing of this book, from the research assistance of Julien Chevillard, Nicolas Desflammes, Till Burckhardt, Patchareerat Yanaprasart, Michele Gazzola and Valeria Cardi, whom we also wish to thank here.

Introduction

There are many ways to approach multilingualism. Most of the perspectives on multilingualism found in the scientific literature are rooted in linguistics and its various subfields, particularly sociolinguistics and psycholinguistics, or fall under the generic heading of ‘applied linguistics’, a label contested by some, but which has nevertheless gained currency among scholars who wished to demarcate themselves from the line of investigation developed, for example, in Chomskyan linguistics. However, other perspectives on multilingualism have appeared. The best known of these avenues may well be the sociology of language, which has benefited from the possibility to “fall [ . . . ] easily into the growing company of sociologies of this and that”.1 We are not dealing with water-tight compartments, and it is not always easy to distinguish between the treatment of multilingualism proposed by the sociology of language and by sociolinguistics, just like the boundaries between some psycholinguistics and the study of language acquisition, though clear in principle, are sometimes hard to pin down in practice. Yet the range of approaches to multilingualism developed over the past forty years is not confi ned to the immediate neighbours of the language disciplines. New perspectives on multilingualism have been opened in fields as diverse as geography, political science (including, in particular, normative political theory), law and economics. Mapping the epistemological and conceptual interrelationships between these perspectives on multilingualism and linguistic diversity would certainly constitute a useful case study in the challenges, pitfalls and rewards of interdisciplinarity and offer fascinating vignettes of intellectual history. Such an endeavour, however, would be well beyond the scope of these opening remarks. The goal of this introduction is simply to explain the philosophy underpinning this book, which approaches multilingualism in a perspective that is quite different from what is generally offered in various strands of the language sciences. Our aim is to investigate multilingualism at work with the concepts and models of economics. At a general level, we are interested in how (and how much) linguistic variables (in particular, those that denote multilingualism) on the one hand, and economic variables on the other hand, influence each

2

The Economics of the Multilingual Workplace

other. As we try to show in this book, this broad theme includes a host of questions that are not only scientifically intriguing but also socially and politically relevant, because answering them can give us powerful levers to make language policies more effective. And we believe that an economic approach, particularly in the perspective of language economics, can make a specific contribution to the issues at hand, formulating and answering questions that other disciplinary perspectives do not address. This claim carries several implications and requires clarification of three important points. Firstly, this book is, in the main, anchored in the epistemological tradition of economics. This tradition, which is as passionately defended by some as it is attacked by others, obviously has strengths and weaknesses— just like any school of thought, methodology or disciplinary perspective. We readily recognise its limitations, which have been discussed elsewhere from different angles (see for example Leibenstein, 1976; Mayer, 1993; Keen, 2001) and which may be seen as the product, in particular, of its relentless quest for generality. At the same time, these limitations do not necessarily amount to shortcomings, since they represent the price to pay for uncovering general analytical principles. It is these general principles that we are interested in. We wish to understand, for example, under what precise conditions an increase in the share of foreign trade taking place with a country where the dominant language is A does, or does not, generally bring about an increase in the need for A-speaking workforce in a country where the dominant language is B. We are not particularly interested, by contrast, in fi nding out whether this has been the case in a particular firm of country B. Likewise, we would like to identify the conditions under which, if the need for A-speaking employees does increase, predominantly B-speaking fi rms in general actually hire more A-speaking employees (as we shall see, this does not follow automatically). But we are not interested in the experience of a specific corporation X or Y, or in the evolution of the linguistic practices of a specific group of bilingual workers in one fi rm or another. Our approach, shared by the overwhelming majority of the economics profession, is based on the belief that the aim of scientific inquiry is ultimately to arrive at statements of general validity, which requires going beyond the description, however detailed and insightful, of individual cases. One crucial epistemological implication is that a sharp, explicit distinction is made between data and variables (Coenen-Huther, 1989). This does not, however, mean that context is overlooked. In fact, economics treats context as a key part of any explanation, because it belongs to the conditions under which, for example, an increase in the share of foreign trade with an A-speaking country will (or will not) increase the demand for employees with A-language skills by fi rms in a B-speaking country. Social, cultural and political norms and values are perfectly legitimate dimensions of context, even in economic perspective. In fact,

Introduction

3

these dimensions need to be taken into account (Boudon, 1991: 50). Context may be incorporated in the analysis either in the form of constraints on human action, or through the specification of actors’ complex, multifaceted goals, or both. Secondly, although we are striving for a fairly high level of generality (which implies that our analysis should at least aim at lending itself to quantitative treatment), we do not see this approach as antithetic to the more descriptive or qualitative approaches favoured in much of applied linguistics, including in the case of studies on multilingualism at work. In fact, we view different approaches as mutually complementary rather than competing with each other. Any given object can be looked at from different angles, with different degrees of detail, and with different questions in mind. Of course, depending on the questions asked, not all approaches will be equally rewarding. Recall that our questions have to do with the mutual influence of linguistic and economic variables on each other (this is our red thread, which we will recall time and again in this book). This implies that not only linguistic but also economic variables must be explicitly featured in our analysis. This is precisely the type of issues that the literature in various strands of applied linguistics does not address: it hardly ever mentions economic variables like productivity, costs and profits, and the causal links through which they might be connected with linguistic variables, such as workers’ linguistic repertoires, are never investigated. But while this justifi es the development of an economic approach to multilingualism at work, it also explains why this type of analysis is in no way meant to replace the type of approach found in other disciplines. Quite simply, the questions asked and the goals of the investigation are not the same. However, distinct approaches can nurture each other. For example, many of the more macro concepts developed in the sociology of language, such as “domain”, for example, can be accommodated into economic analyses, and even the fi ne-grained detail of some interactionist perspectives on multilingual communication can generate information that may sometimes help to construct more relevant variables for inclusion in economic models. We see little justifi cation for the dismissive judgements by some scholars (in economics as well as in applied linguistics) of the work of colleagues hailing from other disciplines, and this observation ties in directly with the third point that needs to be made here. The third point is that the three authors of this book share an interest in language issues stretching over two or three decades, and a continuing commitment to the integration of distinct disciplinary perspectives (particularly from economics on the one hand, and the language disciplines on the other hand) in the study of those issues. This concern for combining the contributions of different disciplines means that particular attention must be paid to matters of accessibility. The terminology, concepts and

4

The Economics of the Multilingual Workplace

methods of economics are not always immediately transparent for practitioners of other disciplines. Of course, the technical apparatus favoured by economists (not always for well-founded reasons) can be abstract and may end up being opaque and exclusionary. However, the difficulties sometimes run deeper, and even seemingly straightforward notions do not carry the same meaning in economics as they do in everyday speech. To wit, the authors have all been, at some point or other, confronted with misunderstandings of core concepts of economic theory (or of its application in policy analysis), like ‘rationality’, ‘efficiency’ or ‘fairness’. This is why every effort is made, in this book, to offer a resolutely non-technical treatment of the questions at hand. It does not mean, far from it, that it eschews all economic reasoning. Quite the contrary, this type of reasoning and the attendant set of concepts are central to the questions at hand, and the reader will get acquainted with tools like production functions, value added or elasticities. We have tried to present these notions (also found in any introductory economics textbook) as transparently as possible, while heeding a piece of advice commonly credited to Albert Einstein: “simplify as much as possible, but not more”. If formal modelling is used in this book, it is quite simply because it is indispensable for handling certain questions, just like certain ingredients are necessary for preparing certain dishes, and some surgery is impossible without using particular instruments. However, the text itself is almost entirely equation-free (a grand total of five appear in the course of ten chapters), and the technical aspects are relegated to the appendices, where the mathematically and statistically inclined readers will fi nd the full detail of our procedures. The emphasis has been placed instead on the logical, yet intuitive presentation of the argument, using plain English, along with the occasional graph or diagram, and, of course, tables with numerical results. One of the key contributions of this book is that it not only puts economic and linguistic variables in relation with each other but also provides measurements of these relations. We hope, therefore, that this book will not only inform readers from the language disciplines about economists’ involvement in the study of language issues but also help them to develop a feel for economic reasoning. Reciprocally, the analytical categories with which linguists study language use at work, or the way in which some arguments in applied linguistics unfold, may sometimes strike economists as markedly different from what they are used to. It is therefore unsurprising that for the most part, economists’ writings about language make little reference, if any, to the contributions of linguists, except in the most general terms. In this book, however, we have tried to go beyond a perfunctory peek over the hedge—to wit, the somewhat eclectic character of the bibliography. We have in particular attempted to take stock of what applied linguistics has to tell us that may be of help in understanding how multilingualism at work affects economic outcomes. This provides us with an opportunity to offer, to the

Introduction

5

economists among our readers, a glimpse into a very different way of looking at multilingualism in economic activity. This concern for accessibility extends to our choice of vocabulary. Although ours is mainly economic, we have tried not to take economic concepts for granted, and have often adapted our terminology: for example, we usually talk about “actors” instead of the “agents” that economists are more familiar with. No matter—after all, both terms mean very much the same thing and can be traced back to the Latin verb agere, to act. Conversely, many notions that linguists would consider self-evident need a bit more explanation for economists. Hence, for example, our (brief) discussion, in Chapter 2, of the subtle concept of “multilingual competence”—a potentially disconcerting notion to some economists who usually view “language X” and “language Y” not only as convenient analytical constructs but as unproblematic representations of reality. Throughout this book, therefore, we are trying to be relevant for two very different groups of readers who do not often meet, namely, those whose approach has mainly been shaped by the disciplines of linguistics and economics respectively. Despite our best efforts, our work will probably be deemed “too economic” by some, and “not economic enough” by others. So be it. For we believe that this book goes further than most in its sincere and sustained commitment to build bridges between these two disciplines. It is for the readers themselves to decide how far we have been successful (or have failed) in this ambition. But we hope at least that our reasoning, as well as the epistemological concerns that motivate it, will be clear to readers, no matter their disciplinary background. Moreover, we hope that reading this book will encourage them to transpose some aspect or other of our approach to their own work. This book contains ten chapters organised in three parts. Part I contains four chapters and is devoted to setting the scene. Chapter 1 develops and discusses the foundations of this book, explaining why multilingualism at work is a relevant question, and what it means to approach it through the prism of economic analysis. En route, we present some fundamental principles of economic theory and language economics. Chapter 2 provides a brief overview of linguistic approaches to multilingualism in the workplace, not as an attempt to offer a complete account of these approaches (a goal which scholars from other disciplines would obviously be much better placed to reach than we are), but rather in order to clarify the contact points and differences between what one might call the ethnography of language use at work and the economics of multilingualism at work. Chapter 3 presents and critically discusses several national and international inquiries on multilingualism at work carried out in recent years, usually with the help of surveys. Although such studies contain a wealth of interesting information, some of their shortcomings help us to identify the most crucial research needs, namely, the development of a robust analytical framework focusing on the relationships between linguistic and

6

The Economics of the Multilingual Workplace

economic variables, and more precisely how the former influence the latter. Chapter 4 concludes our assessment of the current state of affairs in research on multilingualism at work by presenting the theory and measurement of language-based earnings differentials. Such differentials, which have been established econometrically in a number of settings, show that employees’ foreign language skills are often highly rewarded by employers, which presumably implies that employers do fi nd these skills valuable. Our examination of these fi ndings, apart from further establishing the case that languages should be given more attention in economic analysis, shows that the precise source of earnings differentials remains little understood, which is why this book attempts to look beyond these differentials to see where and why they emerge in the process of value creation. Part II is the core of this book. In Chapter 5, we take stock of some results of economic analyses of the determinants of language use at work, before revisiting the theory of the fi rm, showing how it can be expanded in order to explicitly include—for the fi rst time—language skills as determinants of key economic variables such as productivity, costs and profits. To this end, we develop, in line with the normal methodology of economics, a formal model. However, in order to make this tool easily accessible to all readers, the algebraic detail is relegated to an appendix, while the text itself focuses on an interpretive, non-technical presentation of the core ideas of the model. The latter generates a set of explicit causal relationships between language variables on the one hand, and economic variables on the other hand, allowing, for example, predictions to be made regarding the effect that changes in given linguistic variables will have on given economic variables. The following two chapters are devoted to the empirical investigation of language at work. In Chapter 6, we show how the formal model is transposed into an instrument for quantitative research. We discuss the very practical challenges of data gathering and combination, explaining how they can be met in order to move on to empirical estimations. Chapter 7 presents a selection of our fi ndings, providing—to our knowledge, for the fi rst time—quantitative results regarding the effects of linguistically-marked variables on several key economic variables in the theory of production. The results presented do not exhaust the range of questions that may be investigated: an analytical model can be seen as a flexible interface between many variables, allowing us to examine how changes in the value of any of them affect any other one; obviously, not all such relationships could be explored in this book, and we have focused, among the most important ones, on those that can be statistically estimated given the range of data available. Chapter 8 builds on the model in Chapter 5 (through which fi rms’ foreign language skills needs can be explained with reference to their profit-maximising goals) in order to develop a complementary model focusing on fi rms’ hiring strategies. Once a fi rm’s needs for certain foreign language skills have been identified, can we assume that the company will simply go out and hire employees with the corresponding

Introduction

7

profi les? As we shall see, the answer is not quite so simple, and fi rms’ usual concern for cost-effectiveness can lead them to develop hiring strategies that may seem surprising (because these strategies do not fully match their needs) but are nevertheless rational and need to be taken into account if we are to understand how fi rms respond to their linguistic environment. Part III, fi nally, turns to the policy analysis implications. In Chapter 9, we broaden the perspective by turning to language policy. Indeed, one of the motivations for this book also is to provide tools for language policy, by elucidating how fi rms’ behaviour, which we believe to be ultimately responsive to economic considerations in terms of productivity, costs and profits, influences macro-level language dynamics. Understanding such dynamics is essential for effective language policies, including those that concern language education policy, which is traditionally one of the main conduits of language policies. Finally, Chapter 10 is devoted to a set of concluding remarks in which we list pressing data needs and discuss some of the most promising avenues for future research on multilingualism at work. At the end of this volume, the reader will fi nd a set of three appendices covering the mathematical detail of the models and econometric procedures behind the theoretical and empirical results presented in Part II. The empirical work presented in this book uses mostly Swiss and Canadian data. Apart from being the cases that we are most familiar with, Switzerland and Canada (particularly Québec) are, at this time, the two economies for which the role of language has been most extensively studied. But in most countries, the necessary data simply do not exist. However, this book is not about the economics of multilingualism in Switzerland, Québec or anywhere in particular. These two cases are used for illustrative purposes, or as stepping stones in the development of a generally applicable analysis. We hope that this book will encourage data gathering in other economic contexts and contribute, in this way also, to the development of the interdisciplinary venture called language economics.

Part I

The Economic Perspective on Multilingualism

The fi rst part of the book proposes a general introduction to the way in which economists look at language and multilingualism. The emphasis is placed on multilingualism at work, which is only a subset of the wider field of language economics. We address questions such as: why do languages matter economically? How does the economic perspective on language at work differ from other approaches, particularly those developed in applied linguistics? We then critically review existing survey results on multilingualism at work, before taking a closer look at language-based earnings differentials, which constitute exhibit number one in the case that foreign languages are economically valuable.

1

Language at Work Identifying the Issue

1.1

ENDURING CONCERNS, LITTLE-KNOWN PROCESSES

Objective and Subjective Diversity The rising interest in multilingualism, across various disciplines in the social sciences and the humanities, probably reflects widespread but ambivalent perceptions of linguistic diversity and its importance, and the sphere of work is one of the terrains where this ambivalence is most in evidence. Indeed, one question must be settled before we proceed: is multilingualism an increasingly important phenomenon, gaining currency as the bundle of processes often called “globalisation” is gaining ground? Or does globalisation essentially abet the role of one hegemonic language, eroding multilingualism to a mere set of bilingual pairs combining the hegemonic language with a local one? Strangely, there seems to be little interaction between proponents of either view, with the result that two independent lines of discourse are deployed with few opportunities for them to be confronted, let alone reconciled. On one side, many scholars remind us that multilingualism is an ever more common feature of modern societies, and it has become a dominant theme running through sociolinguistics, the sociology of language and much of the literature in the communication sciences, with contributions addressing the issues in very different perspectives (see for example Edwards, 1994; Cigada, Gilardoni and Matthey, 2001; House and Rehbein, 2004; Hellinger and Pauwels, 2007, etc.), not to mention the abundant literature on cultural (as distinct from linguistic) diversity in contemporary societies. Numerous journals are devoted to the study of multilingualism.1 Multilingualism has therefore acquired the position of a central, self-evident theme in the language disciplines, in which research now goes beyond the acknowledgement that languages are always in contact, and questions the very notion of languages as discrete constructs, emphasising instead communication competencies that draw on a continuum of skills straddling many languages (see for example Coste, 2002; Lüdi and Theme, 2002; Heller, 2007a).

12

The Economics of the Multilingual Workplace

On the other hand, several commentators tell us that linguistic diversity is eroding and that languages are disappearing (at least in the form of inherited systems of communication used by native speakers on an everyday basis) at a fast pace. Most lament the fact and call for a greater awareness of the loss this entails (see for example Crystal, 2000; Skutnabb-Kangas, 2000; Martí et al., 2006); the power implications of the linguistic hegemony that gathers pace along with the demise of small languages have long been exposed (Gobard, 1976; Phillipson, 1992, 2003b; May, 2001). Others, however, see the decline of linguistic diversity as a natural, possibly inevitable process (De Swaan, 2001), sometimes insisting that this process has positive aspects (Jones, 2000; van Parijs, 2004b). That linguistic diversity is simultaneously seen as increasing by some and decreasing by others may look like a paradox, which can be resolved by making a distinction between objective and subjective diversity (Grin, 2003a). Objective diversity, as measured by the number of languages currently spoken or by the variety of cultural systems associated with them, is most certainly declining. At the same time, subjective diversity, that is, the diversity that we are confronted with in our everyday lives, is probably higher for a larger proportion of people than ever before. This constant encounter with otherness is the result of the reassertion of long-suppressed local and regional identities, of large-scale migration flows and, again, of what is often referred to as “globalisation”. Although globalisation is a somewhat catch-all word, the intensification of the international trade in goods and services around the world is usually seen as one of its undisputed features. Over the past 25 years, the value of international trade in nominal terms has been multiplied almost ninefold (from USD 2,231 billion in 1983 to USD 19,694 billion in 2008), significantly outpacing the growth of the world economy, which has multiplied about fivefold over the same period 2 . The share of traded goods and services in the world economy has increased from 18.4% in 1983 to 32.4% in 2006. Although this connection has never been explicitly measured, it is very likely that such a massive rise in international trade increases the frequency of contact with people speaking other languages and therefore increases subjective diversity. Thus, linguistic diversity is probably both increasing and decreasing. The former trend is reflected in the rising number of situations where we are confronted with various languages, while the latter can be observed in the rising number of situations where one dominant language, often English, is used instead of other languages for intergroup or international communication. Both trends can be observed in the workplace, which probably explains why issues such as the use of language in multilingual work settings, the way people with different linguistic backgrounds interact when working together, and the various strategies that companies can develop to ensure communication between management and employees have for a long time intrigued researchers from different disciplines.

Language at Work 13

Language in the Workplace: Contrasting Perspectives Against this backdrop, one interesting development, particularly since the early 1990s, is the widely shared acceptance of the notion that language skills are relevant not just as part of a well-rounded education, but as an increasingly critical condition of access to fi nancially and intellectually rewarding jobs—or, in fact, for access to a job at all in a growing range of economic sectors. Let us leave aside for now the descriptive surveys on multilingualism at work, which we review in Chapter 3, and the analytical economics literature, some of which is assessed in Chapters 3 and 4. We may note that although much of the published research about the usefulness of language skills at work focuses on the role of English (Cremer and Willes, 1991; Garzone and Ilie, 2007), many authors stress the importance of languages other than English (Lüdi and Heiniger, 2005), sometimes pointing out that English-speaking countries neglect other languages at their peril (Connell, 2002; Graddol, 2006). The interest in multilingualism at work reaches well beyond academic circles, and the language skills required for the efficient discharge of one’s professional duties is a topic of interest for the media and the general public too. Clearly, the notion that language skills matter in a work context carries major implications for the education system, whether in terms of the choice of languages to teach or how best to teach them, to whom and up to what level of fluency. These issues are taken up again in Part III of this book, where we discuss the policy implications of our fi ndings. But the widely shared interest in language at work probably also reflects the simple fact that language is at the very heart of all human activity and that this fact is particularly evident in the sphere of work, which nearly always involves language-based communication. Most solitary work requires the use of language too, even if it is confi ned to interaction with suppliers and clients. Although it is possible to think of some examples where work is truly and fully silent, they will typically be rather marginal cases.3 It is therefore unsurprising that language at work keeps attracting interest from various corners. However, the existence of this common interest is at the source of an enduring ambiguity, namely, the belief that the question is a straightforward one and that all the scholars studying “language at work” are necessarily interested in the same thing. In fact, they are not, and the realisation that an apparently well-defi ned theme actually takes the form of diverse (sometimes even diverging) concerns and interests provides, in large part, the impetus for this book. Our aim here is to look at language at work in a somewhat unusual manner. Contrary to the bulk of linguistic research that studies multilingualism at work, this book is not chiefly concerned with describing observed language practices in a particular company or team. In the same way, our analysis of these practices is not structured around the strategies and motivations of actual individual actors with first names like Paul or Beatrice, observed in situ when

14

The Economics of the Multilingual Workplace

working with colleagues who speak different languages. We shall not be particularly interested in specific instances of communication in multilingual settings, or in the interplay between the linguistic and non-linguistic dimensions of these processes. Accordingly, the ways in which linguistic and communicative competence is developed (possibly, as claimed by proponents of interactionist perspectives, as a result of a collaborative effort between speakers) are no concern of this book—although we discuss them in Chapter 2, if only to explain how and why our approach is different. And companies’ spending on foreign language teaching to their staff is not central to our approach either. The topics just listed, which attempt to capture, even if roughly, the gist of the questions that inspire much of the published literature on “language at work”, reflect the typical concerns of many applied linguists, which may be characterised (admittedly with some simplification) as an ethnography of language practices at work, where economic variables are treated as merely contextual. In Chapter 2, these strands of research are briefly reviewed, but we may already note that interesting as they are, they do not shed light on another class of questions, which have to do with economic processes and the way in which they may be affected by linguistic processes. It is in fact striking that despite their insistence on revealing the meaning of human action, the perspectives just mentioned leave out economic variables that are supposedly fundamental and constitute the driving force behind all economic activity, and presumably contribute to make human action understandable. For example, variables like market share, cost, revenue and profit are wholly absent from this literature beyond, at best, a mere mention of the fact that such variables do come into play. If we agree that at the end of the day, the business of business is business, and that firms, big or small, are set up in order to generate profits and only survive if they do, it is plausible to assume that this literally essential dimension will have an impact on all aspects of productive activity and that language use at work is no exception. Putting it differently, the study of multilingualism at work raises the question of how multilingualism affects variables like productivity, costs and profits. Accordingly, it is likely that some key dimensions of language use at work will simply be overlooked if the economic processes at hand are not identified, let alone included in the analysis.4 Furthermore, these processes are very likely to influence employers’ demand for particular language skills. If we make the rather straightforward assumption that people’s decisions to learn languages respond, at least in part, to signals from the labour market, including signals regarding the foreign language skills that are in demand at a particular time in a particular place, it seems obvious that economic processes are not only contextual. Rather, we might say that we can hardly expect to understand language learning processes and the corresponding macro-level language dynamics without paying adequate attention to the economic variables at stake. These issues, however, have remained surprisingly under-researched, and the aim of this book is to fill this gap, at least in part. Our approach, therefore,

Language at Work 15 deliberately departs from the mainstream applied linguistics research on language at work. It is rooted in economics, with distinct concerns and goals. This book is concerned with understanding how language variables (particularly those that reflect the fact that a work setting is, in some way or other, multilingual) affect economic variables such as costs and profits, as well as the processes, first and foremost production, through which the values taken by those variables are defined. These core economic variables constitute our “entry point” into the issue of languages at work, and they will lead us to identify questions that are fundamentally different from those that the applied linguistics literature and its many specialisations usually address.

Specificity of the Economic Approach Economic analysis introduces a clear hierarchy of concerns in the study of the behaviour of actors, and the firm is assumed to be ultimately interested in profit, all other matters being instrumental in the maximisation of profit. Obviously, a company is composed of men and women of flesh and blood, harbouring all kinds of interests, passions and values, operating in a politically, socially and economically defined context which typically takes the form of norms or expectations constraining the actors’ actual degree of autonomy. Though these points are well-taken, they are also regarded, from an economic standpoint, as rather self-evident. Reminding readers of the existence of social relations or cultural constraints is fine and well, but it is so obvious that it does not, per se, greatly increase our understanding of the fundamental processes at hand. Furthermore, all the attention typically lavished, in some fields of specialisation, on the case-specific, possibly idiosyncratic features of multilingual communication in a particular setting (like a particular work meeting at the headquarters of one particular company) often gets in the way of a distinct, arguably overarching goal, namely, the attempt to tease out, from the daunting complexity of facts, a more general and synthetic understanding of these processes (Pool, 1991a). The quest for generality is a permanent, almost obsessive concern of economic analysis (Becker, 1976, Chap. 1), which can also be found in relatively far-flung branches of the discipline, such as the language economics which concerns us here. Acres of forests have been sacrificed to the discussion of the relative merits of the distinct epistemologies that underpin these different approaches to social research: on the one hand, an emphasis on providing true accounts of social reality—idiosyncratic as manifestations of this reality may be—and, on the other hand, a concern for elucidating general (ideally causal) patterns—simplified as the resulting models may appear. In this book, we shall not enter this debate, since it is the object of an ample literature, whether in terms of a general epistemology of the social sciences (Winch, 2007 [1958]; Elster, 1989), in the discipline of economics proper (Blaug, 1992; Mayer, 1993; Keen, 2001), or in the specific case of the application of economic analysis to issues in which language plays a major

16

The Economics of the Multilingual Workplace

part (Grin, 1996a, 2003b). Rather, this book attempts to address some of the main questions that arise if language at work is approached with the toolkit of the economist. As a result of this choice of emphasis, we shall stay away from most of the questions that applied linguists tend to focus on and design our analysis with the help of relatively simple notions of language and communication. For example, competence in one or more foreign languages will be taken as a given, as a type of skill that workers may possess to a higher or lower degree. This, of course, does not imply an assumption that language competence does not change over time or that such change happens independently of interaction with coworkers having different language repertoires. Nor does it mean that one necessarily views languages as “whole, bounded systems, associated, moreover, with whole, bounded communities” (Heller, 2007b: 11). It only means that this book is not chiefly interested in the minutiae of how language skills evolve and how they are enmeshed in the multifaceted process usually called “communication”, and that we believe that this finegrained detail, which is relevant with respect to some questions one may legitimately ask about language and how language is used, is not essential to understanding the type of relationships that we focus on here. Nevertheless, it remains important to ensure that the deliberately, purposefully different form of analysis developed in this book is clearly positioned vis-à-vis some of the main concepts of sociolinguistic or applied linguistic research and that it is not in contradiction with the latter’s fi ndings—unless, of course, we have explicit reasons for wanting to disagree with given propositions put forward in these strands of research. In fact, some sociolinguistic and applied linguistic findings can be incorporated in the analytical design of language economics, because the latter provides a general framework into which many types of analyses can be fitted; these matters of compatibility and incorporation are addressed later on in this chapter. Adopting an economic approach, however, is indispensable for studying, or even formulating, important questions about which mainstream sociolinguistics and applied linguistics do not tell us very much. In order to offer a flavour of the range of issues at hand, let us single out a few questions: how does the linguistic diversity reflected in the language skills of the workforce affect profits? Is the productivity of labour higher if all employees are requested to interact in one “official” company language only, or if they are allowed to express themselves in their respective native language, while coworkers draw on a receptive competence in other languages in order to understand each other? Can a company significantly increase its market share by using the language of its customers in advertising, or are the extra sales not worth the cost? Does the answer to this question depend on the type of goods and services provided—or perhaps on the languages concerned? How does the linguistic make-up of the market for the output affect the profitability of given products, given the latter’s linguistic intensity⎯that is, the extent to which these products are linguistically or culturally marked?

Language at Work 17 All these are genuinely economic questions, in the sense that they focus our attention on economic variables like costs, sales, profit, productivity or market share. They are “language economics questions”, in that they ask how these economic variables are affected by language-related variables. Our goal in this book is precisely to address some of these neglected questions, and this requires a methodology rooted in language economics. However, just like applied linguistics, even when studying language at work, largely ignores economic issues, mainstream economics typically ignores the role of language. While this may not be a problem if the processes analysed are independent of language, this oversight becomes problematic if they are not. In fact, a number of important economic questions cannot be adequately treated if language variables are not included. Recent advances in language economics have helped to explicitly bring language into some economic analyses, but much work remains to be done, and many important economic processes, particularly those that have to do with the production and consumption of market goods and services, have never been analysed with proper allowance for the role that linguistic diversity may play in them. The basic strategy of this book, therefore, is to revisit some core elements of economic theory, in particular those that are part of what is known as the (microeconomic) theory of production, in order to spell out and analyse at closer range the linkages between some linguistic variables, treated as independent or “explanatory”, and some economic variables, treated as dependent or “explained”. In accordance with the hierarchy of issues that so strongly structures mainstream economic analysis, however, the questions of this book are organised around the key variables of the theory of production, such as cost, price and profit, because maximising profit under a set of constraints defi ned by the cost of inputs and the price of the output is assumed to be the driving concern of the entrepreneur, across contexts and across more or less idiosyncratic representations, norms and “values”. This also means that we do not examine specific production processes, as one would in industrial engineering or operations management. Our goal, rather, is to provide a systematic link between indicators of multilingualism on the one hand, and of economic performance on the other hand. The rest of the examination fans out from this starting point.

1.2 THE ECONOMIC ANALYSIS OF THE FIRM: A FIRST APPROACH TO THE INCLUSION OF LINGUISTIC DIVERSITY

Basics of Production Theory The economic analysis of the fi rm is one of the core chapters of mainstream microeconomic theory, and it is presented in every introductory textbook.5 The fi rm is assumed to seek to maximise profits from the sale of a particular good or service that it produces, but it can also be a range of goods and

18 The Economics of the Multilingual Workplace services. Depending on market structure, the fi rm will have little or no control over some variables, which means that it must choose the optimal level of other variables, over which it has some degree of control, in order to maximise profits. “Perfect competition” is usually taken as the benchmark case, and we shall do the same here. This is standard practice in economic analysis, particularly when the priority is on examining a novel topic. The advantage of doing so is that a number of complex issues can be put aside. We can, for example, ignore most of the difficulties arising from strategic interaction between competing fi rms. Under perfect competition, this problem hardly arises. Since perfect competition implies, among other features, “freedom of entry” (that is, no-one may be prevented from producing the good), it follows that if an established producer reaps attractive profits, competitors may launch their own product on the same market, thereby increasing supply and driving down the price of the good. Ultimately, the market price will stabilise at a level that allows a fi rm to cover its costs and generate a profit that constitutes an incentive for entrepreneurs to be present on this market at all, but no more. On this view, competition can be seen to minimise not just selling price but production costs as well, which is all to the consumers’ advantage. Let us note, however, that truly perfect competition is relatively rare outside of the stock market. Even if, apart from situations of monopoly, fi rms are in competition with each other, markets usually present features of oligopoly, in which a few producers (instead of a very large number, as in perfect competition, or just one, as in monopoly) compete for market share. Because of their modest numbers, oligopolists are in a position to influence the market price of the good or service. By contrast, under perfect competition, fi rms are “atomised”, that is, they represent too small a fraction of aggregate supply to have any influence on price. It follows that the market price is a given to which the fi rm must adapt. Another important consequence of perfect competition is that all fi rms must adopt the same technology, or at least a technology no less efficient than that of its competitors. The reason is simply that an entrepreneur who fails to adopt the most up-to-date technology available will sooner or later fall behind his more cost-effective competitors and be forced out of the market. Now, if all fi rms use a similar technology, and if they all face the same price for their output, they still have a number of choices to make. One is the combination of inputs. In the most general model of the fi rm, these inputs are summarised as “labour” and “capital”, and the fi rm will have to make a choice between more or less “labour-intensive” and “capital-intensive” versions of the technology. If we assume that fi rms are in competition not only on the output market but also on the markets for inputs, they will be confronted with similar production costs. More specifically, we could say that the cost of capital they have to acquire for investments (normally, some interest rate i) is the same for all fi rms. The same applies to the cost of the labour they hire at any given level of skill, and all fi rms must pay the

Language at Work 19 same wage rate w. Under the assumptions made so far, fi rms will not only use the same technology but use it in the same way; that is, they will adopt more or less the same factor combination. Although the basic textbook model of production identifies two factors (capital and labour), its logic applies equally well to a broader range of factors.6 Ultimately, one choice the fi rm does make is how much to produce. It is easily shown that the optimal output level Q* is reached precisely at the point where the cost of producing one more unit of output exceeds the (given) market price for the output. Lower output levels would be possible, but they would not maximise profit (there is still profit to be made on additional units, as long as they cost less to produce than the market price at which they can be sold); conversely, higher levels of output are also possible, but would entail a per-unit loss that reduces total profit. It is, of course, possible to refi ne this basic model in a number of ways. The extensions proposed in the economics literature logically focus on aspects of the problem which are assumed to be important, that is, to make a genuine difference to the understanding of production processes. For example, economists examine what happens when the technology changes, or when it departs from the usual assumption of increasing followed by diminishing returns, or when output levels cannot be smoothly adjusted and must, for technical reasons, make big jumps from a level Q1 to a significantly higher level Q2 . We may also analyse the markets for production factors, or focus on market structure, investigating what happens when a fi rm’s decisions affects, and is affected by, the decisions made by another fi rm, which is the case under oligopoly. The range of questions addressed in the economic analysis of the fi rm, and in the corresponding scientific literature, is endless.

Introducing Language However, across all the thousands upon thousands of papers published on some aspect or other of the theory of production, one general assumption appears to hold, namely, that language is not a relevant variable. Either it is (implicitly) assumed to make no difference at all on the production processes considered, or if it does make a difference, the latter is assumed to be outside the normal sphere of interest of the economist. The result is a language-less theory of production. At most, mainstream economists are prepared to concede that language competences are part of the skills that employers seek and are willing to reward, but beyond the econometric analysis of language-based earnings differentials (to which we shall return in Chapter 4), the way in which language affects what goes on in the fi rm or, more specifically, how it influences production and sales, has remained largely unexplored. There again, it is important to avoid confusion with the applied linguistics literature, in which the use of language on the workplace may also be studied. However, the linguistics of

20 The Economics of the Multilingual Workplace language at work, which tell us a lot about patterns of language use, tell us next to nothing about the role of language in economic processes. Studying the latter requires us to revisit standard economic theory and to add one or many linguistic variables to the basic model and to develop extensions to this basic model. Although Chapter 5 is devoted precisely to this task, it is useful to posit a general analytical framework from the start. To this end, we shall begin by identifying the basic elements of all economic models, that is, variables and relationships between them. They can be represented as a diagram which will provide a bird’s-eye view of the framework (Figure 1.1).

Figure 1.1

Multilingualism at work: general analytical framework.

Language at Work 21 This framework starts out from the actors’ “linguistic attributes”. The notion of linguistic attributes is well established in the language economics literature, and normally refers to actors’ fi rst language as well as their skills in other languages, which are jointly taken into account in modern analyses of the returns on their language skills (Grin and Vaillancourt, 1997). Let us note once again that linguistic attributes need not be defi ned in terms of skills in “discrete”, sharply separate languages, but may easily be seen in terms of multilingual repertoires drawing on many languages. It is also possible, in more refi ned versions of the model, to view linguistic attributes as contingent, in the sense that some resources can be more easily activated in some interactional contexts. However, at the level of generality at which our approach is deliberately located, these refi nements are not essential. Our focus being elsewhere, we prefer to stick to admittedly simplified concepts of language skills, which, taken together, make up the linguistic attributes. These are usually assigned to individual actors, but they may also characterise groups of actors, like potential employees or buyers, as well as organisations like a fi rm. We take these linguistic attributes as given in order to focus on the ways in which the fi rm will respond to the linguistic environment, defi ned, in large part, by the fact that potential employees, suppliers and consumers on target markets “speak” certain languages.7 Let us now confront these language variables with economic questions. Some of them have been sketched out at the beginning of this section; let us note that they proceed directly from the core economic paradigm (elegantly characterised by Wheelan, 2002). According to this paradigm, one of the most pervasive elements of human experience is the need to make choices. Because resources are limited and have alternative uses, choices have to be made in order to ensure the best use of these resources, and this, in turn, implies answering the three basic questions of any economics textbook, namely, “what to produce?”, “how to produce?” and “for whom to produce?”.8 The fi rst two questions are part of what is known as “resource allocation” and relates to economic efficiency, while the third one harks back to “resource distribution” and relates to fairness. In this book, we shall not address the last one, even though its importance is not in doubt; we will, instead, focus on efficiency. This choice could be discussed at length, but we can simply sum it up as follows: efficiency is the heart of the economic paradigm; it is the side of economic analysis that specialists and laypeople alike immediately think about when pondering economic matters; and, perhaps most importantly, it is the presence of efficiency issues that ultimately sanctions the “economic” nature of a particular problem. This is particularly true when studying fi rms. Firms are not concerned with fairness, nor do they have any reason to be. Their concern is profit (or, as pointed out earlier in this chapter, the business of business is business), and maximising profit is a consequence of efficient resource allocation, quite

22

The Economics of the Multilingual Workplace

independently of the fairness (or lack thereof) of the process involved or of the resulting outcomes. The question “what to produce?” will be affected by the fi rm’s linguistic environment in at least one way: the latter will cause the fi rm to produce goods meeting the needs of buyers that have given linguistic attributes. It is likely that the choice of the range of goods that a fi rm produces is also affected by the availability of staff with given linguistic attributes, but this variable is, from an economic analytical standpoint, of a different logical order: the presence of certain language skills will not, by itself, impel the fi rm to produce certain goods and services, while by contrast the linguistic make-up of the target market will. Deciding what to produce means that many fi rms will have to make at least one of two types of choices. Firstly, they must defi ne the language characteristics of essentially non-linguistic products. For example, a textiles company with international sales will have to decide whether the logos appearing on a given line of clothing (say, sweatsuits) will use language and, if so, what language. Secondly, if it offers intrinsically linguistic products (say, accounting and auditing services, where the use of one language or another is necessarily part of the product bought by the client), it will have to decide in what language or languages to offer them. The language characteristics of the good or service produced, however, is not a major issue for all fi rms. A company selling, say, ball bearings does not have to worry about intrinsic linguistic characteristics. Yet language will play a part in its operations, as we shall now see when turning to the second question, that is, “how to produce”. The question of “how to produce” refers to the choice of a factor combination, a problem already discussed earlier, where we have seen that fi rms will tend to differ less in their level of technological acumen than in the way in which they apply a broadly shared technical know-how, if they are confronted with different factor prices. For example, some fi rms will chose to produce a particular commodity (say, rice) with expensive agricultural machinery and little manpower, while others will produce the same commodity with a considerable amount of manpower but very little automated equipment. Any closer examination of production processes will require identifying narrower, more specific categories of production factors. The fi rst example that naturally springs to mind is that of the distinction between skilled, semi-skilled and unskilled labour. We may also divide labour in terms not only of a generic, somewhat opaque “skill”, of which a worker may possess more or less, but also in terms of specific competences, including foreign language skills. Therefore, the question of “how to produce” may well lead the fi rm to select one factor combination over another owing to the linguistic characteristics of these factor combinations. It may decide that internal communication should take place in language j, or language k, or both. Actual language practices are another matter; what we are discussing here are the

Language at Work 23 decisions made by the fi rm. Some jobs within the company may require more or less fluency in a particular language. The fi rm will then have an incentive to seek to “match” people to jobs—or sometimes the reverse, by adapting a job description to an employee’s linguistic attributes. Hence, the linguistic dimensions of “how to produce” are not primarily influenced by the language characteristics of the target market. What goes on linguistically during the production process within the fi rm need not be affected by the language of its clients. However, the use of different languages in the production chain will be affected by two facets of the linguistic environment: fi rst, the language skills of the employees (or, in the long run, the linguistic attributes present in the recruitment basin); second, the language skills (or possibly the lack thereof) of suppliers of intermediate goods and services, ranging from raw materials to semi-fi nished goods and services sold to the fi rm by another fi rm. Thus, re-examining the core economic questions by paying attention to the linguistic environment of firms helps us to identify several areas where language may indeed make a difference in production processes and is therefore worth looking at, contrary to the implicit assumption of most production theory. This will be useful, in Chapter 5, to formulate not only a full-fledged theoretical model but also a range of testable assumptions that will pave the way for empirical work. Let us now turn to another area of the fi rm’s activity which takes place after production and which surrounds market exchange—that is, it may precede, accompany or follow the actual delivery of a good or service and the payment for the good or service received. The area of activity in question can be somewhat informally defi ned as external communication with the fi rm’s clients, and includes in particular advertising and after-sales support services. These questions are somewhat peripheral in economics. The key variables around which they are organised are not only quantity, market price or factor intensity, but a loosely defi ned set of variables often lumped together in textbooks as “non-price variables”. Otherwise, they are considered to be the province of distinct specialisations, like marketing or business administration in a broad sense; they are often studied and taught in business schools rather than economics departments. However, language plays a major part in external communication and needs to be included in our analysis of how language may affect the behaviour of fi rms. Here again we may assume, as we did in the case of the question “what to produce?”, that the language of external communication, for a profit-maximising fi rm in competition with others, will be influenced by the linguistic attributes of the target market. Quite simply, if a Western European car maker wants to increase its share of the Turkish market, it will have to advertise in Turkish and set up a network of local dealerships able to provide after-sales services in Turkish. Of course, the decision to target a particular market may, in part, reflect the language skills of the workforce (and hence the linguistic attributes present in the recruitment

24

The Economics of the Multilingual Workplace

basin), but from the perspective of a more general, fundamental analysis, the main criterion for the language in which a fi rm advertises its goods and services is the language of its clients. So far, we have positioned three major exogenous variables (the linguistic attributes of employees, suppliers and clients), as well as three major questions defi ning a fi rm’s operations (“what to produce?”, “how to produce?” and “how to communicate with the outside?”). These questions can be respectively formulated in terms of variables such as the quantity of goods and services produced in different languages, or indicators of convergence between the language profi le of a post and the linguistic attributes of the person holding it, or the language used in advertising campaigns. The relationships between these variables are symbolised by arrows 1a, 1b, 1c and 1d in the upper part of Figure 1.1. What happens within the fi rm is symbolised by the boxes and arrows that are entirely contained inside the large box in the middle of the figure. It is useful to distinguish between two classes of relationships. First, the three questions just outlined (and the variables in terms of which these questions are formulated) are interrelated in various ways. For example, the language(s) in which goods and services are sold is linked to the linguistic characteristics of the product (this is represented by the broken horizontal arrow that connects “what to produce?” with “how to communicate with the outside?”). However, this set of relationships may be seen as a logical consequence of the deeper structure already discussed: as we have seen (and as shown by the fi rst set of arrows in the figure), the questions “what to produce” and “in what language to communicate with the outside” are primarily influenced by the linguistic attributes of markets. Thus, the apparently direct link between these two questions may be handled through our treatment of the role of the linguistic attributes of the market. For this reason, we shall not discuss in detail these “horizontal” connections symbolised by arrows 2a, 2b and 2c in Figure 1.1. It is important, however, to identify another set of relationships symbolised by arrows 3a, 3b, 3c and 3d in the figure, which have to do with language needs and recruitment practices. One of the points investigated in this book is how a fi rm’s language needs come about. Whereas much of the empirical research published to date, as we shall see in Chapter 3, attempts to collect information about these needs independently of any explicit identification of fundamental economic processes, we assume that such needs do not appear haphazardly and do, in fact, proceed from economic, profitmaximising behaviour. Since the causal relationships that link patterns of language use with variables such as profit or market share are still little understood (hence again, incidentally, the need for this book), it is unlikely that each and every fi rm has developed an explicit procedure to infer language needs from production processes. Nevertheless, common sense is enough to suggest that in the long run, fi rms will develop recruitment and hiring practices that take into account, even if indirectly or approximately,

Language at Work 25 these language needs. If they did not, this would be at the expense of their cost structure and ultimately imperil their position with respect to their competitors. Typically, fi rms who survive must be doing something right or at least better than those who do not survive, including in terms of recruiting staff with the required language skills. The relationships between economic processes (summed up by the three question areas “what to produce”, “how to produce” and “how to communication with the outside”) and recruitment are symbolised by arrows 3a, 3b, 3c and 3d in Figure 1.1. Since we have just mentioned the possibility that fi rms have no explicit procedure for deriving needs (and hiring practices) from economic considerations, we must acknowledge the likely existence of a more direct (though not necessarily stronger) influence of the linguistic environment on recruiting practices. This influence is symbolised by the arrows 5a and 5b running down either side of Figure 1.1. Let us now take stock of the analytical relationships singled out so far. The issues investigated in this book are, in the main, captured by the sets of arrows numbered “1” and “3” in Figure 1.1. While this does not mean that other causal relationships described in the figure are irrelevant, they are less central to our analysis. The arrows numbered 5a and 5b denote linkages that can probably be observed empirically but are, in a sense, precisely what we intend to move away from. Putting it differently, we are not chiefly interested in the direct, and unavoidably blunt or derivative, linkage between the demolinguistic make-up of a fi rm’s target market and its recruitment practices. Rather, our concern is to elucidate how this demolinguistic context impacts on recruitment through economic processes (or, in terms of our imagery, through the type “1” and “3” arrows of Figure 1.1). The “type 2” arrows, despite the fact that they point to interesting and probably empirically relevant relationships, are beyond the concerns of this book. Apart from the fact that, as we have already observed, they could in part be inferred from the “type 1” arrows, which are much closer to our research questions, the type of analytical relationships that certainly exists between, say, factor combination (“how to produce”) and external communication (“how to communicate with the outside”) is likely to be much more case-specific, possibly even idiosyncratic. In other words, investigating the “type 2” arrows could constitute an appropriate topic for case studies. We still believe, however, that such case studies should bank on a body of more general, theory-based results of the kind proposed in this book. Finally, the reader will have noted that the analytical framework also features arrows numbered 4a, 4b and 4c, leading to a box called “effects on language dynamics”. Language dynamics are well outside the scope of this volume, although we have elsewhere tried to show (e.g. Grin, 1992, 2005b) that such dynamics can be analysed through language economics. However, the processes studied in this book have a direct bearing on micro- and macro-level language dynamics. A language spreads (or declines), among

26

The Economics of the Multilingual Workplace

other reasons, because goods and services in this language are produced (or are not); because goods and services, independently of their intrinsic linguistic characteristics, are (or are not) advertised in it; and because fi rms do (or do not) require people equipped with skills in this language. These causal relationships are symbolised by arrows 4a, 4b and 4c in Figure 1.1, which attempt to summarise that part of language dynamics that is channelled through fi rms operating in a market economy. The foregoing discussion shows that in the context of productive activity, linguistic and economic variables are connected in a number of ways, but most of them are barely explored. This provides much of the justification for this book. However, this investigation is not quite a shot in the dark either, since well-established fi ndings suggest that these connections do exist and that they have major importance. In the main, these fi ndings take the form of language-based earnings differentials: we know that language skills are rewarded on the labour market, and multivariate analysis shows that depending on the languages and economic sectors concerned, competence in foreign languages can generate sizeable wage premiums, sometimes edging close to 30%, even when key determinants of labour income, such as education and work experience, are held constant. These statistically robust fi ndings tell us that foreign language skills do contribute to productivity and that using these skills (or, at least, the capacity to use them) generates value in the economic sense. If this were not the case, it is unlikely that employers would be willing to pay someone more just because he happens to have higher foreign language skills. But in order to understand how this value is created, we need to crack open, at least a little, the black box of the production process. This is not the only possible strategy. One alternative would have been to go down a different avenue and to use a managerial approach, in which a sizeable body of work has been devoted to cultural diversity and also, though to a lesser extent, multilingualism. The label of “managerial approaches” is admittedly a bit of a simplification. What we are referring to is a set of studies originating, for the most part, in management schools (which, depending on institutional structure, may be part of economics faculties, institutes or departments or, on the contrary, be sharply distinct from the latter). A string of papers by Marschan-Piekkari, Welch and Welch (1999), Feely and Harzing (2002, 2003), Frederiksson, Barner-Rasmussen and Piekkari (2006) or Harzing and Feely (2007), for example, look at the linguistic diversity that characterises multinational companies as a problem to be solved, and they discuss ways of solving it. This approach is, in a number of ways, related to ours; however, it still differs from it on at least three counts. Firstly, in those studies, language is immediately recast as communication, and the problems to be solved are communication problems. While we obviously do not dispute the notion that language is used for communication, we also believe that language serves other functions. The point here is not to revisit the well-known Jakobsonian functions of language; but (as we

Language at Work 27 shall see in Chapter 4) economic analyses of language insist on the need to jointly take into consideration a communicational and an identity-related function of language. Secondly, the dependent variable in the managerial studies of multilingualism is, precisely, communication. However, one of the points made in the foregoing discussion is that communication itself is not our concern; communication matters to the extent that it affects the variables most directly connected with value, such as productivity and profits (or its opposite, costs). A focus on communication would therefore, with respect to our questions, constitute a detour. Thirdly (also as a logical corollary of their focus on communication), these studies examine communication problems as they arise in multinational corporations; a frequently mentioned issue, in this literature, is that of relations between headquarters and subsidiaries abroad. There is no doubt that multinationals offer particularly stimulating cases for the analysis of the communication problems linked to linguistic diversity. However, we believe that language and linguistic diversity are important in more fundamental ways. Language problems may arise in all companies, including those whose production takes place in one country only and where everyone in the company shares the same mother tongue: it is enough for them to have clients or suppliers who normally use another language for the issue of linguistic diversity to arise—and to deploy potential economic effects. For all these reasons, “managerial perspectives on linguistic diversity”, rather than a repository of answers to the questions addressed in this book, must be considered a neighbouring field focusing on a specific subset of language economics questions. We shall return to the management literature in Chapters 8 and 10. But since our goal in this book is to examine the relationship between linguistic and economic variables, and more specifically between linguistic diversity on the one hand, and value creation on the other hand (without, however, moving into the case-specific processes studied in industrial engineering or operations management), we shall mostly leave managerial approaches aside until then, and use fundamental economic analysis instead as our starting point; it is in this sense that we talk about “opening a black box”. Opening this black box, therefore, requires taking a closer look at production processes and at the role that languages may play in them. Before we embark on this enterprise with our economists’ toolkit, we shall begin, in Chapter 2, by reviewing what specialists from the language disciplines have to say on multilingualism at work and how the linguistic and economic approaches can be positioned with respect to one another.

2

2.1

On the Linguistics of the Economy v. the Economics of Language

A TENTATIVE MAPPING

This book belongs to a field of research usually called the ‘economics of language’, or ‘language economics’. Some features of language economics have been mentioned in Chapter 1, but a more formal perspective will be useful for the following stages of our discussion. Let us therefore defi ne it as follows: The economics of language rests on the paradigm of mainstream theoretical economics and uses the concepts and tools of economics in the study of relationships featuring linguistic variables. It focuses principally, but not exclusively, on those relationships in which economic variables also play a part. This defi nition, or close variants of it, has been discussed extensively elsewhere (Grin, 1996a, 1996b, 2003b, 2007, in press), and for the purposes of this book, only some of its implications, which are directly relevant to our investigation, will need to be highlighted. For the same reason, a comprehensive review of the literature in language economics would constitute somewhat of a detour from the argument proposed here, and the interested reader may turn instead to one of several presentations of the main strands of research in language economics available in the contributions just mentioned. What matters, for our purposes, is that one of the main uses of language economics is to explain and measure the economic effects of language-related processes—or, putting it more informally, addressing questions such as: “if the level of a given linguistically-marked variable changes, what will be the economic consequences, if any?” This requires spelling out causal relationships that flow from linguistic to economic variables and doing so more specifically than we did when commenting on Figure 1.1 in the preceding chapter. This exercise is quite different from the questions typically studied in various branches of the language disciplines. However, it will be useful for us to start by reviewing the main linguistic perspectives on “multilingualism at work”, both in

On the Linguistics of the Economy v. the Economics of Language 29 order to further clarify what this book is about—and what it is not about— and in order to identify relevant results from linguistic research that can be accommodated into the economic analysis of how multilingualism affects economic outcomes. This task amounts to a review of what might be called the linguistics of economy, and the rest of this chapter is devoted to such a review. Let us however begin with three qualifications. Firstly, what follows is not intended as a comprehensive account of the work of linguists who have, in some way or other, referred to the economy in general or to the workplace in particular, either as the context of speech acts or as a reality influenced by language.1 Rather, what we propose here is a critical discussion of selected linguistic perspectives on language at work. We make no claim to exhaustiveness; however, the perspectives discussed in the following pages include (without being limited to) those that language specialists keep referring to in relation to the study of multilingualism at work. Our chief aim here is to check what linguists have written that has direct bearing on the question at hand, namely, how multilingualism affects economic variables. Secondly, the expression in the title of this chapter, linguistics of the economy, though it is of course meant to evoke symmetry, is used in full awareness of the semantic distinction between “the economy” and “economics”. The point is that the former is only a part of the latter, and that “economics” can have two different meanings, either as a discipline or as a part of human action and human experience broader than what is normally understood as “the economy”. Economics as a discipline studies how people use their scarce resources to achieve a great variety of purposes. There is no a priori limit on the nature of the ends pursued, nor on the type of resources used—hence again, incidentally, the procedural rather than substantive nature of the concept of “rationality” in economics (see Section 1.1). Economic analysis therefore encompasses non-material (sometimes also called “symbolic” or even “psychic”) ends and means, which, from an analytical standpoint, are no less relevant than the more usual material or fi nancial ones. What makes a problem economic, then, is simply the need to make choices in the face of the scarcity of the resources available (Becker, 1976). This means that the range of objects of study of economics as a discipline (or, putting it differently, economics as an object of study) is much broader than “the economy”—if the latter term is used (as it usually is) to refer to the range of activities deployed in the spheres of production, consumption and exchange that are typically totted up in a macroeconomic aggregate such as a country’s gross domestic product, or GDP. Thus, a more appropriate mirror image for the economics of language might be linguistics of economics, provided we interpret the term “economics”, as Becker suggests, as a broad object of study. We have, however, chosen to stick to “the linguistics of economy”, precisely because for the most part, linguists whose contributions refer to economic processes tend to have a rather traditional

30

The Economics of the Multilingual Workplace

view of economics as being essentially concerned with the production and consumption of material goods traded on a market. 2 Thirdly, our discussion will deliberately leave aside a fascinating area of research, namely, the language of economics (which might also be called, in light of the discussion in the preceding paragraph, the “linguistics of economics”, where economics must be understood as a discipline and the associated type of discourse). The language of economics is mainly anchored in rhetoric and the theory of argumentation (Henderson, Dudley-Evans and Backhouse, 1993), but it has also been approached by economists from within their discipline (McCloskey, 1990; Brémond, Couet and Salort, 1996). Research in this area mainly studies the ways in which economic narratives unfold, thus building on the epistemology of economics, 3 as well as the effect such narratives are intended to achieve and how they may be received, thus linking this field of study with discourse analysis. However, the language of economics, because its focus is on economics as a type of discourse rather than on economic variables themselves (whether these are part of the wide range of human activities that constitute “economics”, or of a more narrowly defi ned “economy”), remains outside the scope of this book, and our overview of linguists’ perspectives on economic matters will not include work on the language of economics. In the rest of this chapter, we shall discuss contributions found in three strands of research. The way in which we propose to structure them, by identifying three strands instead of two, four or more, may of course be disputed, and because of the manifold linkages between them, we readily admit that contributions could have been categorised differently. Yet we have not, in the abundant but rather fragmented literature in the language disciplines, found any truly integrative account of linguists’ contributions about language at work (with the possible exception of a survey by Roberts, 2007), and we therefore had to put forward our own typology. For the purposes of our argument, it will be useful to characterise these three broad families as pertaining to sociolinguistics, pragmatics and ethnomethodologically-inspired conversation analysis.

2.2 BOURDIEU AND HIS HEIRS: A GLIMPSE AT SOME SOCIOLINGUISTIC PERSPECTIVES At the meeting point between sociology and the language disciplines, the vast field of sociolinguistics is one in which the issue of languages at work is, of course, frequently raised. For example, Janet Holmes (1992: 24) mentions “employment” as one of “five domains which can be identified in many communities”, and the workplace is defi ned as the “setting” associated with this domain.4 However, with some exceptions (for example McGroarty, 1990), there are relatively few contributions that specifically focus on the interplay between languages and economic

On the Linguistics of the Economy v. the Economics of Language 31 activity, and there would be no point in reviewing, in this chapter, dozens of sociolinguistic studies on the use of one language or another, just because they may also happen to describe language use in a work setting. If, however, we look for contributions asking how patterns of language use might be causally related to economic processes, they are far less numerous, and when this issue is addressed, it tends to be embedded in broader accounts, focusing for example on the effects that “economic development” may have on minority language maintenance, or on the emergence of a range of economic activities that have specifi c linguistic or cultural characteristics. This is often referred to as “ethnic business”, but although it is likely that the development of an ethnic business sector (that is, an economic process) has an impact on the maintenance of given languages (that is, a linguistic process), it is not always clear that the reverse is true, namely, that the use of a language in a particular ethnic business does, in itself, give rise to noteworthy economic consequences that would not have arisen if the sector in question did not possess those particular linguistic or cultural traits. In what follows, we shall therefore leave aside sociolinguistic contributions whose chief concern is not language at work per se but matters of language use and language dynamics, work being but one of the domains in which such dynamics unfold.5 We shall instead review some contributions that not only place a more explicit emphasis on multilingualism in the workplace but also establish, or at least postulate, a specific form of analytical link between the economic and linguistic variables at hand. Let us, however, start with a detour in the direction of the work of the sociologist Pierre Bourdieu, because it figures prominently among the references regularly quoted in sociolinguistic writings about language at work— particularly his famous 1982 essay, Ce que parler veut dire.6 As is well known, Bourdieu argues that language is used by social actors as an instrument in the production and reproduction of social structures. Language thus serves as an instrument of power, and this can apply to multilingual skills too—a fi nding also put forward by some work in the management literature. This process, of course, also occurs in the sphere of work. Furthermore, his analysis makes abundant use of economic terms, particularly “profit” and “market”, and the second chapter of Ce que parler veut dire is promisingly titled “La formation des prix et l’anticipation des profits” (“Price formation and profit anticipation”). It is no wonder that this has led many to believe that Bourdieu’s is an economic analysis of the role of language. However, closer examination (as well as basic familiarity with economic analysis) is enough to show that even if Bourdieu offers excellent sociology of language, it is quite remote from any economics of language. Let us consider his use of the term “market”, enthusiastically embraced by linguists referring to the “linguistic market”.7 In economics, a market is a real or virtual setting for the exchange of a given good or service. A market presupposes the existence of a supply function and a demand function

32

The Economics of the Multilingual Workplace

for the good or service concerned. The dependent variable, both for supply and demand, is the amount of the good or service respectively supplied by producers or demanded by consumers, as a function of other variables,8 mainly, but of course not only, its market price. In short, a market is generally defi ned by supply and demand schedules positioned in a price-quantity space.9 However, neither supply, nor demand, quantity or price are ever identified in Bourdieu’s work, whose use of the term ‘market’ turns out to be entirely metaphorical. Metaphors are perfectly respectable tools for intellectual work, but they only take us so far—and not necessarily where we believe we are going. Thus, the concept of “linguistic market” is useful to the extent that it refers to the fact that language use indexes social, political and economic inequality and that different variants of a language (or, by extension, of languages seen as different) do not enjoy the same degree of prestige in a given place at a given time—but it has very little to do with a market in the economic sense.10 Economic concepts seem to enjoy a recurrent appeal with some authors in the language disciplines, who have considered the possibility of using them in the study of language issues. For example, Rudi Keller (1994) has proposed using the concept of “invisible hand” generally attributed to the economist Adam Smith, who mentions it in his 1776 book, The Wealth of Nations. Since the 18th century, the metaphor of the invisible hand has been invoked by countless authors, arguably giving it much more importance than Adam Smith intended. Therefore, it is useful to recall what it actually says. Its core idea is that although social actors pursue their individual interests in an uncoordinated fashion, their decentralised actions coalesce into an orderly pattern conducive to the greater good.11 Keller interprets the evolution of language as governed by the same logic. An earlier example of the direct import of a linguistic concept is Rossi-Landi’s intrepid analogy between communication and markets, where he claims that “a linguistic community presents itself as a sort of immense market, in which words, expressions and messages circulate in the same way as commodities do” (Rossi-Landi, 1968: 29; our translation). Unfortunately, this parallel also rests on a serious misunderstanding of the economic concept of market. On a market in the economic sense, goods and services are traded for other goods and services (in a barter economy) or traded for money (in a monetised economy). That the baker normally hands over a loaf of bread in return for money illustrates the fact that exchange will take place if both partners agree to it. Because it implies no reference to an explicit or an implicit price for what is being “exchanged”, the “exchange” of words and sentences in conversation is of a completely different nature. The Bourdieusian heritage informs several orientations in recent research on language at work. One potentially interesting orientation focuses on the importance of language as an aspect or “component” of work intrinsically bound up with work as product and process—what authors in Borzeix and Fraenkel (2001) call “la part langagière du travail” (“the language part of

On the Linguistics of the Economy v. the Economics of Language 33 work”). However, their analyses (which also tap into the interactionist perspective that animates conversation analysis, addressed later in this chapter) are of limited use here for two reasons. Firstly, they investigate the role of language rather than languages or linguistic diversity. Secondly, their concerns turn out to be fundamentally different from ours: they aim at a deeper understanding of communication processes as they arise at work (see Lacoste, 2001, for a survey), but without ever engaging the type of issues around which economic analysis is built. Much of their effort seems to be devoted to reminding the reader that work can hardly take place without language (not a very surprising point); and that, as Bourdieu pointed out, language is used by actors to produce or reproduce social structure; let us observe, incidentally, that this is not incompatible with actors’ faculty to use language in political struggles aiming to change social structures, a fact which reduces the import of the notion that language abets the reproduction of social inequality, and hence the analytical or informational value of some of the contributions discussed in this section. Some work in modern critical sociolinguistics, exemplified by contributions in Heller (2007a) shares similar concerns and uses the workplace as one of its privileged terrains of investigation, claiming the Bourdieusian heritage while recognising its metaphorical drift (Heller and Boutet, 2006: 7). These contributions examine language practices and ideologies as they unfold in the context of employment, and in some ways, they are closer to the concerns of this book. First, they explicitly take account of linguistic diversity, studying for example the respective position of French and English in Canada or Catalan and Spanish in Barcelona.12 Secondly, they strive to incorporate, in their analyses, phenomena that have economic import, making frequent reference to “globalisation” (generally left undefi ned) and to “the new economy”. This latter term, however, seems to take on two different meanings in these writings—either that of a recently emerged economic sector selling a certain type of goods and services, of which informational content is a defi ning characteristic, or an overall economic system in which information-oriented consumption and production are gaining increasing importance by comparison with traditional sectors of economic activity.13 However, this literature fails to derive the implications of its professed belief that language is a resource. For the proof that language is a resource, and is used as such, must logically be sought not merely in the rather unsurprising observation that certain results are achieved through certain uses of the languages present in the actors’ linguistic repertoires; it is also necessary to show that these results are valuable, given a set of objectives. This requires a clearly-argued definition of what counts as valuable and the identification of an explicit cause-and-effect relationship between these uses of language and the value created (even if this value is of a symbolic, rather than narrowly monetary, nature). And this, in turn, requires the analyst to show why these (valuable) results would not have been achieved if the observed language strategies had not been used.

34

The Economics of the Multilingual Workplace

We are therefore led to conclude that the chief concern of this line of research is quite different from ours, and it results in a very interesting ethnography of language at work rather than in an elucidation of how language and economics affect each other, although some contributions move closer to this question and are relevant to our investigation in that they draw attention to potentially important contextual aspects of production, consumption and exchange. Examples of useful contributions are Dubois, LeBlanc and Beaudin (2006), who discuss the case of a call centre set up in the bilingual region of Moncton (New Brunswick) precisely because of its bilingual recruitment basin, or Lamarre and Lamarre (2006), who examine how trade liberalisation is reflected in the language needs of a Montrealbased company in the field of video postproduction.

2.3

PRAGMATICS-ORIENTED PERSPECTIVES

Another strand of research places somewhat less emphasis on the macro-social conditions of language use, and focuses more on language itself and on the language forms actually used in work settings, taking explicit account of the tasks to be performed; this is why it can be said to be centred on pragmatics, that is, on speech acts in actual communication (Crystal, 1987).14 This allows researchers to investigate issues related to code-switching, code-mixing, variation, terminology and translation in work contexts (Behr, Hentschel, Kauffmann and Kern, 2007). It is, however, difficult to delineate the precise extent of this orientation, which opens up on a host of related issues, some of which (like the importance of context and ideologies in what actors themselves defi ne as their work) were raised in the preceding section, while others (for example on the ways in which interaction actually unfolds) are addressed in the next section. The range of issues that arise is potentially endless. For example, some contributions underscore the importance of the “multimodality” of communication, in which gestures and the exchange of looks participate in the construction of meaning embedded in communicational practices at work. Others look at the reciprocal influence between narrowly-defi ned production activities, on the one hand, and communication about work already carried out or about to be undertaken, on the other hand. In multilingual settings, these mutual relationships influence the choice of language at work, and this choice may be renewed in every interaction, taking account of its specific circumstances. This, in turn, raises the question of what linguistic competence actually is, leading many authors to stress the notion of multilingual competence: actors are not merely competent in language A and language B; rather, they develop “a body of knowledge and capabilities that enables them to mobilise, when necessary and under given circumstances, the resources of a multilingual repertoire, and that also contributes to the construction,

On the Linguistics of the Economy v. the Economics of Language 35 evolution and possible reconfiguration of this repertoire” (Coste, 2002: 117; our translation). Many of the contributions we are subsuming under the heading of pragmatics-oriented perspectives address the dominant position of English in multinational corporations, for international advertising, or in international trade. For example, Cremer and Willes (1991, 1994) have looked at the language of deals in particular business settings in the Far East, while contributions in Garzone and Ilie (2007) also include European and US examples of how the choice of language for particular forms of interaction responds to context, given a set of business objectives—and actors’ language skills. To what extent is the line of work described here relevant to the questions addressed in this book? There again, the link is an indirect one. Let us observe that all of these contributions focus on language in the context of work; however, they do not attempt to identify the economic effects of patterns of language use (rather, they look at how language use is affected by the nature of the work performed). As such, their concerns are distinct from ours, because we are interested in economic variables whose value is determined by the interplay of a host of processes; work is but one of these processes. Therefore, because they often contain a very detailed examination of how language is used in a work setting, pragmatics-oriented perspectives offer pointers regarding how different ways of using language, or using different languages, may be related to many of the economic processes sketched out in Figure 1.1 in the preceding chapter. For example, simple, direct sentences seem preferable in contact with clients (Schmäle, 2007), and frequent code-switching among staff (between German and French) at a bilingual bank allows for quicker, more fluid sharing of information within the company (Lüdi and Heiniger, 2005). But the richly textured detail of pragmatics-oriented approaches is something that matters to the questions of this book only to the extent that it influences economic variables such as productivity, costs and profits, and this point is one that still needs to be established.

2.4

ETHNOMETHODOLOGY AND CONVERSATION ANALYSIS

Some linguists hail from an interactionist perspective (Gumperz and Hymes, 1972) which has branched out into several schools of thought and analytical approaches, including ethnomethodology and conversation analysis.15 They deserve mention in this chapter because a significant body of research on multilingualism at work comes from these two approaches. Since ethnomethodology and conversation analysis are viewed quite critically by some segments of the linguistics profession itself, and are simply unknown to most social scientists, let us begin by characterising them, leaning on work by Silverman (1998), Coulon (2002) and Mondada (2007), whose respective

36

The Economics of the Multilingual Workplace

bibliographies point to numerous additional references. Ethnomethodology, which is associated with the sociologist Harold Garfi nkel, aims to uncover the “methods” which actors (referred to, somewhat oddly, by the affi x “ethno”) use to make sense of social life and perform their everyday activities (Coulon, 2002: 24), particularly through interaction, since language “simultaneously expresses, describes and constitutes social reality” (ibid.: 4). Scholars studying actors’ “ethnomethods” claim not to impose their own, predefined analytical concepts to account for observed behaviour, but to allow them to emerge from the detailed observation of behaviour. As to conversation analysis, “it takes a specific stance, stressing the importance of the situated moment-by-moment organization of interaction, of the intelligibility it has for the participants, and of the membership categories that are achieved and made relevant within the interaction itself” (Mondada, 2007: 297). A speech act can therefore only be fully understood if due account is taken of participants’ situation in the interaction (indexicality) and of each participant’s personal (particularly cultural) experience (reflexivity). We shall not discuss the considerable epistemological problems raised by an approach that makes the rather tall claim (as Coulon does; see 2002: 3) to revolutionise the social sciences, and to free it from the strictures of researcher-imposed analytical categories. Let us however try to assess what the “ethnomethodologically-inspired conversation analytical stance” (Mondada, 2007: 299), for which the workplace is a privileged terrain, can contribute to the understanding of how language processes affect economic outcomes. To some extent, this very question might be considered a non-starter, since ethnomethodology insists on the unique character of every single interaction, thereby calling into question the relevance, or even the very feasibility of developing more broadly applicable categories, often denounced as “topdown” and therefore unacceptable. It follows that “the sense of the plurilingual resources used by speakers can neither be mechanistically related to a set of predetermined factors, such as identities or social structures, nor associated with imputed intentions, strategies or goals of the participants” (ibid.: 297). Denying that “intentions, strategies or goals” can be made out in actors’ behaviour is of course antithetic to the economic approach, which not only assumes that actors do something for a reason, but also that this reason always comes down to making the most of one’s limited resources, both material and symbolic, in order to achieve certain goals, also both material and symbolic.16 Whether actors’ statements regarding the nature of their goals should be taken at face value as the only proper emic approach to understanding behaviour, or whether these might be reconfigured and fitted into broader, synthetic, and presumably more widely applicable motivations defined by the researcher, is a separate question.17,18 It follows from the preceding discussion that whatever ethnomethodology and conversation analysis may have to offer is likely to be of indirect relevance to us. Firstly, these approaches may have heuristic value. Just like more standard ethnographic perspectives, they collect detailed

On the Linguistics of the Economy v. the Economics of Language 37 observations about communication processes, and it is possible that such observations may, for example, help economists flesh out the analytical relationships between linguistic and economic variables presented in Figure 1.1 in the preceding chapter. In particular, ethnomethodological sensitivity to the fact that a process may be perceived quite differently depending on whether it is approached from the outside (for example, by a researcher studying bilingualism at work) or experienced from the inside (for example, by bilingual workers themselves) might help economists identify important language-economics links that could have gone unnoticed when using only the (deliberately simplified) concepts of economic modelling. Secondly, because conversation analysis relies on the careful examination of the detail of verbal exchanges, it helps to avoid building plainly incorrect assumptions into the analysis, thereby providing what might be called, in reference to Popperian epistemology, a very close falsification check—the fact that ethnomethodology-inspired approaches should turn out to be useful in this perspective being, of course, somewhat of an irony. For example, the high frequency of code-mixing and code-switching in interaction, which conversation analysts keep reporting from all kinds of multilingual settings, can have very direct implications for the study of the economic effects of linguistic processes. In particular, it alerts us to the fact that even if informants in a survey may tell us that they manage client contacts in language A and internal work meetings in language B, far more complex patterns emerge in practice, suggesting (in line with the fi ndings by Hauschildt and Vollstedt quoted in section 3.3) that communicational efficiency in multilingual settings, as well as the ensuing effects on productivity, costs and profits, should not be assumed to be best served by the adoption of “pure” language regimes mandating the use of one language only for a given range of tasks.

2.5 ADDITIONAL PERSPECTIVES ON LANGUAGE IN THE ECONOMY The preceding sections certainly do not do justice to the range of linguists’ contributions about the relationships between language and the economy. Mention has been made at the beginning of this chapter of the long-standing history of mutual curiosity between linguists and economists, but let us remember that our concern here is with a more specific set of questions, dealing with the ways in which linguistic and economic variables influence each other, and more precisely how the former affect the latter. Even without an interest in the fortunes of the fi rm, linguists are often intrigued by language economics questions, often because they are seen as the key to understanding some issues that are of central importance to language, such as the dynamics of language spread, maintenance and shift, or patterns of language learning. For example, some studies specifically

38 The Economics of the Multilingual Workplace focus on the role of language (usually a minority language) in commerce (Ramallo and Rei Doval, 1997), or on its effects on regional economic development (van Langevelde, 1999); another line of work investigates the need for foreign language skills in large corporations, particularly those based in English-speaking countries (for example Fixman, 1990; Lambert, 1990; Reeves, 1990; Connell, 2002). We have already pointed to the “managerial” literature as a possible contact point between linguistic and economic processes, even if the management literature, given its usual focus on communication, remains at one remove from core economic variables like productivity or optimal input levels. Such linkages are exemplified in the concept of linguistic audit (Reeves and Wright, 1996) designed to generate knowledge about the relationship between language variables on the one hand, and economic measurements of productivity and costs on the other hand. Their function is to identify language needs as experienced by workers and management, and it stands to reason that such needs are, in turn, related to the way in which the fi rm performs economically. We shall return to the notion of linguistic audit in Section 8.4. Not only are such contributions closer to our concerns, but their view of what qualifies as data also is epistemologically directly compatible with the type of approach normally used in economic analysis; they blend almost seamlessly with work by economists with a specific interest in language issues (for example, on the economics of Scottish Gaelic, Chalmers, 2005) or by scholars sitting astride disciplinary boundaries (for example Walsh, 2008, in press on Irish). This line of work often relies on quantitative or qualitative surveys, and the data collected provide an informational background which does not necessarily explain why and how the practice of multilingualism at work, or workers’ multilingual repertoires, create value in the economic sense. However, the analytical treatment with which such questions may be tackled can bank on these fi ndings, which is why instead of discussing these issues here, they will be addressed in the next chapter, which is entirely devoted to an assessment of this essentially empirical line of research.

3

3.1

A Gallery of Empirical Findings

DESCRIPTIVE RESULTS

As far back as the 1970s, surveys have been carried out to assess the foreign language needs of business and the economic consequences for businesses or, more rarely, for national economies, of having—or lacking—foreign language skills. The economic value of foreign language skills from the standpoint of individuals is a related but distinct issue that we examine in Chapter 4. Interest in the economic value of language skills from the standpoint of business has picked up since the 1990s, with a steadily growing number of case studies or, occasionally, more ambitious quantitative surveys. In this chapter, we take a closer look at these surveys, which differ from one another in several important ways: the samples may be large or small, representative or not; some of them focus on a certain type of business (for example, export-oriented ones), while others consider the entire economy; the information is not always collected from persons holding similar positions; and the degree of statistical sophistication with which the data are analysed also varies greatly. Most importantly, the actual thrust of the questions asked is not always the same. Nevertheless, studies carried out in Australia, Belgium, Canada, Denmark, France, Germany, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, Sweden, Switzerland, the United Kingdom, the United States and the European Union as a whole share three important traits: fi rstly, they are intended to fi nd out about businesses’ perceived needs, rather than developing a sociological or linguistic interpretation of the role of languages in business, as most of the studies reported in the preceding chapter do. Secondly, they turn for information to decision-makers—usually at the level of corporate or human resources management—rather than interviewing or observing workers themselves and focusing on the detail of the latter’s use of languages as they perform their tasks. Thirdly, these studies generally lean towards quantitative approaches or combine qualitative and quantitative methodologies, instead of the exclusively qualitative orientation that characterises most of the work discussed in Chapter 2.

40 The Economics of the Multilingual Workplace In this chapter, we do not review this vast range of studies in detail, but we attempt to provide, through a presentation of selected fi ndings, a synthetic overview of the field. In this opening section, we focus on essentially descriptive approaches organised in two groups: (1) the “Australian studies” of the late 1980s and early 1990s, which afford insights into the issue as it presents itself in a predominantly English-speaking country; and (2) a cluster of surveys carried out more recently in the Francophonie, which will help us to assess how the issue of language at work is addressed in countries and regions that often display, along with the shared use of French, a high awareness of language issues. In Section 3.2, we present selected fi ndings from three relatively more recent surveys in Switzerland, Belgium and Catalonia, which move beyond description and generate more explicit assumptions about the interaction between the linguistic and economic processes at hand. These three studies also provide glimpses into cases where languages other than English or French play a major role (the languages concerned being German, Dutch, Spanish, Catalan and, more distantly, Italian). In Section 3.3, we report on a few studies that display a more formal analytical orientation and offer estimates of, or at least pointers to the economic consequences involved. In Section 3.4, however, we also explain why these studies do not actually address the questions that lie at the centre of this book, and to clinch this particular point, we introduce a crucial distinction, not discussed in the literature so far, between “absolute” and “contingent” multilingualism. Although the review presented in this chapter cannot claim to be exhaustive (the number of studies carried out in various parts of the world, particularly in Europe, is surprisingly high, and some hitherto unnoticed reports regularly turn up), we have attempted to provide, in this chapter, a balanced coverage of empirical research about multilingualism at work in contrasted linguistic environments.

The Australian Studies Several surveys have been carried out in Australia in the 1980s and 1990s. These surveys, however, did not appear in a vacuum and should rather be seen in relation with different stages in societal perceptions of the meaning of linguistic and cultural diversity. According to Lo Bianco’s (1988) entertaining account of successive shifts in emphasis in public discourse about diversity and multiculturalism, a fi rst period coinciding (in Australia) with the early to mid 1970s set great store by issues of rights and equity, as well as by their linguistic implications for adults in the workforce. These considerations stressed the need to help immigrants maintain competence in their respective languages, because such competence could be useful for the acquisition of English—itself a necessary condition for successful participation in the Australian labour market. The second phase is described by Lo Bianco as characterised by a culturalist discourse which “in its worst

A Gallery of Empirical Findings

41

manifestation [ . . . ] almost became a new fetish for the delectation of the public mind with its almost transcendentally argued virtues of inter-cultural tolerance and harmony. This period is replete with the cotton-wool vigour of a new language—all about ‘rich mosaics’ and the ‘multi-hued texture and fabric’ of our society” (Lo Bianco, 1988: 88). But although some positive economic effects of multiculturalism could be assumed, there was little interest in proving or measuring them. In a third phase, the approach to diversity became, in Lo Bianco’s words, “more pragmatic, somewhat self-interested”. This ushered in a series of studies which asked if one aspect or another of Australia’s “economic performance” could be improved through the multilingualism embodied in immigrants’ language repertoires. The most targeted study is probably the one by Stanley, Ingram and Chittick (1990), working with data on 451 exporting companies’ language attitudes and practices. Their most important fi nding may be that at the time of data collection, Australian businesspeople were not particularly concerned about foreign language skills, which come out in ninth (and last) position among the factors that could inhibit a company’s exports to non-English speaking countries; in fact, only 28% of companies mentioned this as a hindrance. However, the authors of the report hasten to add that “highly language-related factors are rated as major obstacles to trade” (1990: 17; our italics). Participants at a conference convened in the same year by the Australian Advisory Council on Languages and Multicultural Education (AACLAME; see National Languages and Literacy Institute of Australia, 1991) concluded that foreign language skills (particularly in nine languages most often mentioned by their respondents) can enhance international competitiveness, without, however, putting forth hard evidence to this effect. A separate report by the Australian Language and Literacy Council (1994), which reviews previous surveys and reports on face-to-face interviews with business people, sums up the gist of the Australian studies by noting that “facility in a language other than English is not pre-eminent in determining the success of business and industry”, adding the non-committal observation that “it [language] is, however, one of the many factors that can enhance their performance” (1994: 3). Finally, Stanton and Lee (1995) used macroeconomic data to test the relationship between the demolinguistic growth of some ethnic groups in Australian society and the growth of exports (in volume and value) to their respective countries of origin, fi nding only weak, or not statistically significant association. By and large, therefore, no clear pattern emerges, making it difficult to conclude that in the case of Australia, foreign language skills really matter for economic performance. However, this may be linked to the perception, by some respondents or researchers involved in these studies, that if Australia has little use for foreign language skills, it may be because it is a predominantly English-speaking society. For example, the authors of the report published by the Australian Language and Literacy Council

42

The Economics of the Multilingual Workplace

write that “it is a deliciously Australian irony [ . . . ] that Australian governments should discover the economic importance of languages at a moment in our history when languages have never been less important on purely economic determinist grounds” (1994: 9; our emphasis). Comparable perceptions of English as trumping all other linguistic skills and making the latter somewhat redundant are reported by Fixman’s (1990) work on a sample of 32 US-based corporations. However, this is precisely the type of assumption that more recent research would challenge, not least from the perspective of individual users themselves. For example, a vast majority of respondents from a sample of 581 alumni of a renowned US-based school of international management acknowledged that foreign language skills, together with familiarity with foreign cultures, provided them with a defi nite competitive advantage, presumably reflecting the fact that they had more to offer to their employers than did graduates who did not possess those skills (Über Grosse, 2004).

The Case of the Francophonie “Francophonie” is the term used to describe not only a “cultural, media, economic and political space” (Organisation internationale de la francophonie, 2007: 5) but also the set of countries who have joined the OIF (Organisation internationale de la francophonie) and who, according to official wording, “share the use of French”. These countries’ political approach to language policy has shifted, particularly since the mid 1990s, from a focus on the exclusive promotion of French to an unequivocal endorsement of multilingualism as a guiding principle. This change reflects recognition of the fact that globalisation abets a form of linguistic dominance that mainly favours English and that presenting a logically consistent alternative implies promoting a multi-polar, and hence multilingual world. This focus on multilingualism is now fi rmly established at the centre of official discourse about the Francophonie, particularly in the organisms representing the “Francophonie du Nord”—that is, France, Québec, and the Frenchspeaking parts of Belgium and Switzerland. This has led, in the late 1990s, to a renewed interest in multilingualism itself as well as in the role of French as a constituent part of multilingualism, not least in business and commerce. Investigation into these questions could build on Québec’s distinguished research tradition in the economics of language—a point we shall return to in the next chapter. This interest has taken the form, across French-speaking regions, of a collection of studies, many of which are presented in Secrétariat à la politique linguistique (2004) or mentioned in a series of reports published by the Délégation générale à la langue française et aux langues de France (DGLFLF 2004, 2007a, 2007b, 2008). For the most part, these studies provide descriptive information about the use of different languages in the businesses investigated (Bouchard,

A Gallery of Empirical Findings

43

2004). The information gathered covers a wide range of variables, including in particular employees’ foreign language skills, the languages mainly used by clients on target markets, managers’ views regarding the relative usefulness of language skills, their awareness of language-related barriers in business transactions, the possibility of using linguistic identity (including the French language itself) to sharpen the profile of a product and increase sales, and fi rms’ language training practices. Several studies go one step further and mention ways in which multilingualism, apart from being an observable reality in the context of work, is likely to have economic implications. Some contributions mention, for example, the role of language in the competitive positioning of individual companies (on a market, with respect to other businesses), or its impact on flexibility, innovation, legitimacy and interaction (usually with buyers). Yet for the most part, these studies, which convincingly make the case that in principle, multilingualism does matter, fail to articulate a set of explicit causal relationships flowing from linguistic variables to economic ones, although the language-related information gathered is sometimes commented on with respect to general economic information—usually, the relative importance of the export sector, the respective shares of a country’s main trading partners in export flows, company size, the economy-wide distribution of workers across types of jobs, and so on. However, all this stops short of the key tests needed to establish that multilingualism does affect (and affects positively) the economic performance of fi rms, as reflected in “hard” variables such as productivity, costs and profits. Instead, the focus of the investigation often shifts to related questions, such as whether fi rms have a “language policy” and what this latter might be. By contrast with the looser wording often used in the contributions discussed in Chapter 2, the francophone studies commendably abstain from overusing the term “policy” and blurring its meaning. For example, Baldi notes that the businesses investigated in these qualitative and quantitative studies rarely have “a language policy in the sense of an in-depth reflection generating strategic choices” (DGLFLF, 2004: 33; our translation); nevertheless, allusions to policy often turn out to concern the narrower question of the arrangements made by companies, internally or through outsourcing, for the foreign language training of staff members.1 These studies are however useful in spotting several interesting patterns that can serve to orient more detailed model-building. For example, Laur (2004) notes that: • as regards internal communication, the dominant language of corporate headquarters influences language choice with subsidiaries much more than between subsidiaries. In other words, linguistic diversity will tend to be less in the former than in the latter type of communication; in the former, the language of the “centre” dominates, and this dampens the role of English as a language of communication in major

44

The Economics of the Multilingual Workplace companies when headquarters are mainly non-Anglophone (as is the case for most of the companies surveyed in the studies presented in this section); • foreign languages make their way into a company’s communication practices not only for reasons of sales and marketing, but also through contacts with suppliers. Interestingly, in the case of Québec fi rms, interaction with non-Francophone suppliers appears to have a stronger effect on language practices than interaction with non-Francophone clients—a result also found in Switzerland (see Chapter 5); company ownership and control are major determinants of its internal linguistic hierarchy—there again, an observation congruent with results from other studies in Québec, showing that the language of owners plays a key role in internal language practices (see Chapter 5).

A survey commissioned by France’s Observatoire de la formation, de l’emploi et des métiers (OFEM, 2003) reports that the managers of internationally-oriented French companies generally view English as the main language of international trade and consider competence in it as a source of competitive advantage. This perception is consistent with the fact that almost a third of the 501 companies polled had either fi nanced or organised English-language training for staff members. The use of English appears to increase when “initial elements of communication” (such as meeting minutes or internal memos) circulate in English, or when conceptual references or norms are culturally anchored in English-speaking countries, for example in the case of some legal documents (DGLFLF, 2004). Of particular interest owing to sample size (over 900 export-oriented small and medium enterprises, or SMEs), which makes it possible to look into more detailed questions, the Ubifrance (2004) study reports that communication with non French-speaking clients largely relies on written information, with promotional brochures and leaflets in other languages being used by 89% of respondents, while 87% offer foreign language training to staff members; by contrast, reliance on translation and interpretation is limited. English is the most widely used of these foreign languages, followed by German and Spanish, and the level of competence required in English rises with a staff member’s position in the corporate hierarchy. Of the total number of SMEs, 82% report expecting a “good to excellent” command of English from top management, as compared to 53% for middle managers and 37% for clerical staff. Requirements tend to be higher in sectors like transport and communications, as well as in the generic category of “business services”, and, at the other end of the spectrum, to be lower in the food, beverages and tobacco sector. The use of English is positively correlated with staff size, with the share of exports going to non French-speaking markets and with the relative share of sales to Eastern Europe, South Asia and the Far East, while French is the fi rst language of communication with Africa, Latin America (excluding the Caribbean) and the Near East. 2 These

A Gallery of Empirical Findings

45

effects, however, are not the same in internal communication (for example, between a parent company and its local subsidiaries) versus external communication (with buyers and suppliers). Of the respondents, 46% report that the ability to operate in French can be part of a comparative advantage for foreign sales, usually in the case of French-speaking export markets.

3.2

EDGING CLOSER TO ECONOMIC EFFECTS

It is difficult to estimate with any degree of precision the number of studies that have been carried out to date on multilingualism at work and the foreign language needs of businesses. Much depends, of course, on how wide the net is cast. For example, the 2002 “Reflect” report on foreign language and cultural training needs in the United Kingdom, Ireland, Poland and Portugal3 includes about one hundred bibliographical references. However, the range narrows considerably if we focus on studies that examine economic effects. Of course, the extent to which these effects are actually addressed is not always easy to assess. Many contributions (often one-off policy reports intended to inform governments’ foreign language education strategies) start out by announcing an economic-based analysis of fi rms’ language needs, but quickly dodge the issue and branch off into a description of these fi rms’ language training policies. The extent to which the latter reflect objective needs and mesh with the economic processes that give rise to those needs is not always clear.4 But the mere fact that a study provides information on fi rms’ declared foreign language needs (for example, in the case of Italy, the 2007 “Let It Fly” study5) or foreign language use at work does not mean that it actually examines, let alone quantifies, the economic effects arising from given patterns of foreign language use.6 Ultimately, we are left with a relatively small clutch of studies edging closer to the set of issues at the centre of this book, of which three more recent ones will be briefly presented here in chronological order. These studies concern non English-speaking countries where investment in foreign language acquisition is generally viewed as an economically sensible strategy and advocated as such.

Switzerland One of the most extensive reports available to date is the survey by Andres et al. (2005) on foreign languages in Swiss businesses, working from a representative sample of over 2,000 fi rms and 1,000 employees in the three language regions where German, French and Italian respectively are the main languages spoken (the case of businesses in the very small Romanche-speaking areas in eastern Switzerland was not investigated). In the Swiss context, all languages other than the locally dominant one count as foreign, even in the case of other national languages. Thus, for example,

46

The Economics of the Multilingual Workplace

French and German, along with English (which is not one of Switzerland’s national languages) are foreign languages in the Italian-speaking parts of the country.7 Beyond detailed data on language use, which provide a relevant backdrop to the models and estimates presented in Part II of this book, the survey identifies reasons for resorting to foreign languages and specific activities in which they are particularly useful. For example, Andres et al. report (2005: 14; our translation) that the primary reasons why inadequate skills can be problematic in a work context are the “slowing up of internal work processes” (mentioned as a problem by 9% of businesses), “misunderstandings or conflicts affecting collaboration” (8%), “slowing up of work processes with persons outside the fi rm” (6%) and “qualitatively inadequate realisation of a task” (5%). By contrast, the loss of orders for lack of foreign language skills, which is considered by several studies across Europe as crucial (as we shall see in the following section), comes last of seven possible factors and is mentioned by only 2% of respondents. Foreign languages are widely used in Swiss fi rms, and 41.5% of them report a frequent use (weekly or more) of English; the figure is 58.9% for German and 49.5% for French, confi rming the relevance of Switzerland’s national languages in economic life, even outside the regions where they are respectively dominant. Among these frequent users, a strong majority expects foreign language skills to take on increasing importance in the future (79.7% in the case of English, 90.9% for German, 86.2% for French and 62.3% for Italian). A relative majority of 41.2% of frequent users of Spanish expect its importance to rise. The need for other, rarer languages is also mentioned, with Albanian quoted by 3.5% of respondents, followed by Serbian (3.4%), Portuguese (3.3%), Russian (3.0%), Turkish (2.9%), Chinese (2.5%) and Croatian (2.2%) (ibid., 2005: 16). The relative importance of different languages, however, varies by economic sector. The percentage of fi rms reporting frequent use of a foreign language generates sector-specific rank orderings of languages: in tourism, for example, the sequence is German-English-French, whereas it is German-French-English in transport and communications, and FrenchGerman-English in public administration (ibid., 2005: 29).

The Brussels Region Many of these patterns are echoed in Mettewie’s (2006) thorough investigation of the language needs of 357 businesses in the Brussels region. In this context, where the trio of languages that comes to the fore includes Dutch, French and English, the researchers have focused on the three following sets of issues, each leading to a series of more specific questions: (1) what is the relationship between the supply of and demand for foreign language skills among private sector businesses in the “Bruxelles Capitale” region? (2) What are the costs associated with the need for and/or lack of

A Gallery of Empirical Findings

47

multilingualism for the businesses of the region? (3) What do business leaders themselves suggest in order to improve the matching between the supply of and demand for foreign language skills? The data indicate the prevalence of Belgium’s two official languages. The ability to use English is seen as very important, but mostly in conjunction with skills in Dutch and French; employers report having little difficulty in recruiting competent users of English plus either of the other two languages, but the most sought-after profile is trilingualism, and the most problematic segment of the labour market may be that of French-Dutch bilinguals, who are much in demand. More than half of respondents report that they could not find job applicants with this specific combination, with some functions in the company being particularly affected by this lack of skills (Mettewie, 2006: 67 ff.). Good language skills facilitate access to jobs and promotion to more rewarding positions. However, fi rms report often having to make a compromise between language skills and professional skills—an issue that we analyse more formally in Chapter 8 of this book. In this study, 41% of respondents admit to having lost contracts for want of adequate language skills (ibid.: 79); this high figure may of course be related to the fact that before looking abroad, Belgian companies would naturally first turn to Belgian suppliers, only to be confronted with the relative lack of skills in French and Dutch among Flemings and Walloons respectively.

Catalonia Let us fi nally mention an original study by Alarcón (2007) who, working with a sample of 754 SMEs in Catalonia, tries to get to the heart of the tasks performed at work in order to assess the “linguistic intensity” of those tasks, following a suggestion made by Harris (1998). Linguistic intensity is the component of communication required for the production and commercialisation of products that determines the linguistic costs of internal [ . . . ] and external [ . . . ] transactions. [. . . It] can be defined through three elements: 1) the number of languages required for job performance (‘intensity by diversity’), 2) the need of language or languages as an instrument for working in the business activity (‘intensity by extension’) and 3) excellence in use understood as the evolution of requirements regarding knowledge of the language (‘intensity by quality’). (Alarcón, 2007: 3) The concept of linguistic intensity is potentially very relevant, because it squarely addresses the issue of how language enters production processes. Five different worker profiles are defi ned in terms of linguistic intensity, generating five company profi les in accordance with the dominant worker profi le within each. Generally, Catalan is by far the most frequently used

48 The Economics of the Multilingual Workplace language for various work tasks, before Spanish and English. However, higher linguistic intensity is correlated with a greater use of English and Catalan, to the detriment of Spanish.

3.3

DIFFERENT PATHS TO ESTIMATION

The papers reviewed in the preceding sections reflect a progressively greater attention to the multiple ways in which language use meshes with economic processes; however, the economic implications are hinted at or assumed, rather than formally established or empirically estimated. In this section, we discuss studies that provide orders of magnitude of the economic consequences of language. These studies illustrate three different ways of coming up with figures.

The ELAN Study The ELAN Study (Hagen, 2006), produced under the auspices of Britain’s National Centre for Languages (CILT) constitutes a significant milestone in the debate about the economic implications of multilingualism. It was commissioned by the European Union, its conclusions have been enthusiastically embraced by the European Commission’s Business Forum for Multilingualism (2008) and it has enjoyed considerable visibility as a result; ELAN, therefore, may well be the most frequently quoted study on the economic consequences of the lack of foreign language skills in European businesses. The study works with a sample of 1,989 SMEs from 29 European countries. In addition to the 27 countries that are members of the European Union at the time of the writing of this book, it includes Iceland and Turkey. The largest contributor of interviews is Spain with 109 responses, and the smallest the Netherlands, with 8 questionnaires. The coverage of respective countries’ economic fabric varies considerably, with large countries like Germany having supplied only 28 questionnaires while Iceland has returned 60 questionnaires.8 ELAN’s chief interest, however, lies in the attempt made to put a figure on the losses incurred by businesses because of inadequate language skills. 11% of respondents (195 businesses) reported having lost potential contracts. The ELAN study (Hagen, 2006: 5) reports losses for 101 respondents—understandably, many businesses are unable or unwilling to provide figures, which appear to range, over one contract or more, from 1 million to 23.5 million Euros. The reliability of any calculation based on such numbers is, of course, open to question. However, extrapolating from these figures, the authors venture a weighted average loss per SME, which can then be projected across all SMEs in Europe—under the assumption that the percentage of SMEs having suffered losses, as well as the order of magnitude of these losses, is comparable across respondents and in the European economy as

A Gallery of Empirical Findings

49

a whole. The ELAN study concludes that “at least 945,000 SMEs may be losing trade as a result of lack of language competence. The average loss per business over a three year period is € 325,000” (ibid.). To get an approximation of the total loss per year, the triennial figure must be divided by three, and then multiplied by the estimate of the number of businesses affected; this generates a figure of a little over 102 billion Euros per year (quoted for example in DGLFLF, 2008: 6). The aim of the ELAN study is to offer the fi rst macro-level, surveybased estimates of the cost of insufficient skills, thereby offering glimpses, in the negative, as it were, of the contribution of multilingualism to economic value creation. However, its results need to be handled with caution. Even abstracting from methodological problems with the sampling procedure, which does not ensure structural representativeness and may result, given the small sample relative to population size, in a distorted picture, and assuming away the potentially considerable self-selection bias in the sample, two major issues remain. Firstly, the figure of 102 billion Euros may not be all that impressive, considering that the European Union’s gross domestic product (GDP) in the same year was of the order of 13,000 billion. Thus, the estimated loss represents 0.83% of the European Union’s GDP, and even less if a correction is made to take account of the presence of non-EU countries in the original sample. In any event, less than 1% of aggregate GDP, though not negligible in the absolute, is not a strikingly high ratio. Secondly, one may doubt whether the extrapolation used in the ELAN study is actually valid as an indicator of actual losses. If, say, a German company approaches a British one, in German, with a view to place an order for some supplies, and if its inquiry gets ignored (whether because of the absence of German language skills in the British company or for any other reason), it will fi nally turn to another company for this order. Thus the order will, ultimately, most probably be honoured by another supplier, presumably one that can handle contacts in German. Hence, at the aggregate level, inadequate language skills may cause no economic loss at all—it could be a mere case of trade diversion.9 It is, of course, conceivable that some potential clients give up, and that some transactions that could have taken place never happen because of a lack in foreign language skills. However, the uncertainties surrounding the figures put forward in the ELAN study and doubts about their interpretation are significant enough to suggest that other avenues should be followed in order to assess the economic value of multilingualism.

Economic Implications of “Linguistic Intensity” In a thorough analysis of the economics of the Spanish language, García Delgado, Alonso and Jiménez (2007) explore a set of theory-based approaches to estimate its value. Their work proposes a very detailed procedure resorting to figures from Spain’s national accounts. Like the study by Alarcón

50 The Economics of the Multilingual Workplace discussed earlier, their approach uses the notion of the linguistic intensity embedded in products, which is based on an estimate of a language coefficient for individual goods and services, reflecting the importance of language in their respective production process. The coefficient is multiplied by the amount of the goods and services produced, and the resulting figures can be aggregated to cover the entire economy. Since the production of goods and services implies production processes with a number of successive stages, care must be taken to avoid computing the same value several times over, and the authors present a sophisticated technique to estimate the added value attributable to language at each production stage. Further adjustments are necessary in order to eliminate the distorting effect, on the estimation of value creation, of taxes and subsidies. The adjusted sum of language-based added values across sectors yields the contribution of language to GDP (see García Delgado et al., 2007: 100–102, for a presentation of the methodology). A comparable procedure had been applied by Martín Municio (2003), who also uses language intensity coefficients and concludes that the Spanish language contributes to 15% of Spain’s GDP. Esperança (2008) replicates Martín Municio’s calculations for Portugal, retaining the same coefficients for economic sectors but applying them to the Portuguese language and confronting them to the corresponding figures on value added by sector in the Portuguese economy. This generates a roughly similar ratio of 17% of GDP.

Inside the Multinational Firm We have already mentioned another line of research, which is mainly anchored in management studies. For example, Hauschildt and Vollstedt (2002) analyse the switch from German to English as the “official” language of internal communication in multinational companies, showing that sweeping decisions of this kind are difficult to implement and generate new problems along the way. They suggest that organisational efficiency (which one assumes to be positively correlated with economic outcomes) requires differentiated language policies that take account of the variability of communicational situations within the company; that differentiated practices must nonetheless be seen as belonging to an integrated approach to managing multilingualism; and that corporate language policy must earn the acceptance of employees and be seen as a long-term endeavour, which in turns calls for ongoing internal training within the fi rm and the setting up of an internal office or centre entrusted with planning and coordination. Feely and Harzing (2003) list a number of channels through which language differences among staff can exert negative impacts on communication (and, subsequently, on performance): the communication situations they mention, however, are not confi ned to strictly internal ones. They point to possible problems in buyer-seller relationships, foreign

A Gallery of Empirical Findings

51

market expansion, joint ventures, headquarters-subsidiary relationships and staffi ng policies. Few studies, however, have also investigated these issues quantitatively. A paper by Gómez-Mejia and Palich (1997) scrutinises the link between cultural diversity and performance for 228 of “Fortune 500” fi rms in the 1985–1989 and 1990–1994 periods. They build on a longer research tradition that focuses on cultural, rather than linguistic, diversity, whose results have regularly been inconclusive. They hypothesise that “high cultural heterogeneity in [a multinational fi rm]’s global portfolio of business units may offset the purported economic benefits of international diversification” (ibid.: 310). Heterogeneity may (1) hamper the flow of technical knowhow between business units; (2) slow down the transfer of organisational innovations; (3) limit the extent to which some technologies appropriate to production in given cultural contexts can be implemented in others; (4) restrict the potential of horizontal integration that cultural relatedness makes possible; (5) exacerbate problems linked to culturally-based interpersonal dynamics; (6) increase control costs (particularly between culturally different headquarters and subsidiaries; and (7) complicate the implementation of human resource programmes.10 This leads the authors to assume that international diversification (that is, operating on different national markets) will be positively associated with fi rm performance if these different national markets are culturally related, while a negative effect on performance is expected if these markets are “unrelated”.11 Various statistical analyses using different ways of representing cultural diversity yield ambiguous results, and diversity indexes are not related to market performance: cultural diversity does not appear to be an asset, but it is not a hindrance either (conversely, there seem to be no “salutary effects for cultural similarity” either). Other contributions from the management literature are somewhat closer to our concerns, in particular when they investigate the implications of linguistic instead of cultural diversity. Nevertheless, just like GómezMejia and Palich, they often start out from the notion that diversity is a problem to be overcome (Feely and Harzing, 2002, 2003; Harzing and Feely, 2007). It is not clear, however, that striving for linguistic uniformity is an appropriate solution, as shown by Frederiksson et al. (2006), who use a set of 36 interviews with Siemens (a Fortune 500 company, as it happens). Though a deeply international company with over 430,000 employees in 190 countries, Siemens has witnessed a trend towards using English as a de facto corporate language. One key fi nding (which dovetails with applied linguists’ observations reported in Chapter 2) is that “the mere introduction of English as the corporate language does not automatically lead to its adoption, nor does it make it ‘shared’ throughout the organization” (ibid.: 407). Again, the focus on communication (rather than measures of performance) as a dependent variable somewhat detracts from the relevance of these approaches to the questions addressed in this book.

52 The Economics of the Multilingual Workplace 3.4 DRAWING THE LINE: ABSOLUTE v. CONTINGENT MULTILINGUALISM The work of Spanish and Portuguese scholars mentioned in the preceding section points towards some of the most promising avenues in empirical language economics, and the concept of linguistic intensity, as well as its empirical estimation in different work contexts, provides an original meeting point for economists and linguists to combine their respective analytical tools: in fact, the detailed investigations of language practices conducted by conversation analysts can help to justify empirically the numerical value of linguistic coefficients in various productive activities. Let us also observe, however, that these estimates of the share of language in a country’s GDP actually bear upon the value of communication in production, and more specifically of communication through the medium of the locally dominant language. As such, they do not address quite the same question as ours, which concerns the contribution of multilingualism to value creation. This is why a new methodology, which must be both theoretically founded and empirically operational, had to be developed, since only relatively disconnected elements of such a methodology are available in the literature. Summing up, we note that although the contributions directly germane to our questions are few, the body of literature surrounding them is quite substantial, and casting the net just a little wider immediately yields a rich harvest of titles on related issues. The question then becomes how wide the net should be cast, or how far from our initial concerns we should go. For example, one could raise the related question of the effects of language differences on trade between nations (see e.g. Helliwell, 1999; Melitz, 2008; Ku and Zussman, 2008; Fidrmuc and Fidrmuc, 2009). These authors conclude that the absence of a common language limits the volume of international trade and that, as a consequence, trading partners forgo considerable gains. Such observations are certainly relevant to this book’s core question, namely, the value of multilingualism in the context of productive activity, but only up to a point. Let us fi rst note that this core question already is a very complex one and that a line must be drawn, lest we were to include the whole range of possible connections between language and the economy and ultimately find ourselves confronted with the task of addressing the entirety of the field of language economics (the bulk of which investigates issues other than language in productive activity). But there is also a specific analytical reason for deliberately not following some leads. It has to do with the often overlooked distinction between absolute and contingent multilingualism. On the one hand, we may wonder about the value of multilingualism in the absolute, where the implicit counterfactual is the absence of multilingualism—or a severely marginalised version of it. This is the question raised by Helliwell, or Fidrmuc and Fidrmuc, as well as by numerous authors writing on diversity from the perspective of economics (Jones,

A Gallery of Empirical Findings

53

2000; Alesina and La Ferrara, 2004; Ottaviano and Peri, 2004; Ginsburgh and Weber, 2005), political science (De Swaan, 2001) or normative political theory (Laitin and Reich, 2003; Pogge, 2003; van Parijs, 2004b): is diversity good or bad in itself? Their shared point of view on this question is that linguistic difference increases communication costs or social fragmentation, leading some to conclude that it is therefore a fundamentally negative trait. Others make allowance for beneficial aspects of linguistic diversity, possibly in line with Will Kymlicka’s well-known work, as a repository of cultural resources offering a “context of choice” (Kymlicka, 1995; Maclure, 2003). Ottaviano and Peri (2004), who only use cultural but no linguistic indicators, show that US-born individuals earn more if they live in cities where the share of foreign-born is higher, suggesting some positive effects on production and consumption. Alesina and La Ferrara (2004) are careful to present the issue as a weighing of positive and negative implications of diversity. At a theoretical level “the potential benefits of heterogeneity come from variety in production. The costs come from the inability to agree on common public goods and public policies” (ibid.: 7); however, their regression results suggest that linguistic fractionalisation has a negative and statistically significant impact on the growth of per-capita GDP (ibid.: Tables 1 through 4). The dominant view is that diversity is (or is likely to be) a problem. Despite the inconclusive or partial character of most empirical results, several authors (particularly Jones, Laitin and Reich, De Swaan and van Parijs) feel confident enough to conclude that achieving higher welfare calls for the adoption, in a limited or generalised way, of a common language. They usually advocate English for this purpose, and they are quite willing, despite (sometimes half-hearted) protestations to the contrary, to contemplate a world in which other languages have little use other than as a cultural ornament or a nostalgic link to a distant past (for a synthetic and critical discussion of this position, see May, 2003); hence, the logical counterfactual to multilingualism is uniformity, which is why we may characterise this line of investigation as being concerned with absolute multilingualism. On the other hand, we may be concerned, as is the case here, with the value of contingent multilingualism: starting out from the observation that the world is, at this point, linguistically diverse, the question is not whether multilingualism is economically advantageous or not in the absolute, but whether, with reference to economic criteria, it is advisable for social actors to operate multilingually, which requires at least some of them to be bilingual—or more. The answer to this question is contingent upon the fact that the world is diverse, and we are therefore not considering linguistic uniformity as a potential counterfactual. The counterfactual to multilingualism, then, is unilingualism in the modes of operation of actors. And the actors we are focusing on in this book are businesses as key players in economic life. The distinction between absolute and contingent multilingualism is important because the conclusions that may be reached regarding the net

54

The Economics of the Multilingual Workplace

economic value of one do not necessarily carry over to the net economic value of the other. For example, the fact that it is advisable, in a multilingual work environment, to possess multilingual skills and to operate multilingually does not automatically imply that multilingualism is good in the absolute. Although there is a degree of overlap between the two questions, the analytical tools needed to establish the latter case are quite different from those that are needed for the former. The fact that multilinguals (as we shall see in more detail in the following chapter) often earn substantially more than monolinguals does not automatically mean that multilingualism is economically profitable in the absolute; putting it more bluntly, the fact that the world is multilingual does not logically imply that it is a good thing that it should be so. This confusion, however, is apparent in much official discourse about the value of multilingualism, not least in very official publications (European Commission, 2003 or more eloquently, Business Forum for Multilingualism, 2008). The ways in which the value of absolute multilingualism may be approached economically have been discussed elsewhere, leading to the provisional conclusion that absolute multilingualism is economically valuable too, independently of the human rights–based considerations usually brought to bear on the matter. We return to the linkages between contingent and absolute multilingualism in Chapter 9; let it be clear, however, that this book is primarily concerned with contingent multilingualism, even if many of our conclusions have broader applicability. Contingent multilingualism may be approached in terms of practices and skills, at the level of the individual or of an organisation such as a company. But in any case, these various manifestations of multilingualism can only occur if some individuals possess multilingual skills, a topic to which we now turn.

4

4.1

Foreign Language Skills and Earnings

LANGUAGE SKILLS AND THE CREATION OF VALUE

Economists have long been interested in the relationship between language and income, and more specifically in the effect that actors’ language skills may have on their earnings, or “labour income”. In fact, this question is central in the development of language economics as a field of specialisation, and much of its history is organised around it. This issue also has considerable importance for this book, because the questions addressed here are directly connected to the value of foreign language skills on the labour market. This opening section discusses this link before we track, in Section 4.2, progressive changes in the way in which language skills have been thematised and operationalised in economic analysis. In Sections 4.3 and 4.4, we present a set of figures on the value of foreign languages in Québec and Switzerland respectively. The statistical estimation of the value of foreign language skills is a vast area of inquiry into which we shall only take a quick peek; however, these two cases are interesting for mutually complementary reasons. The Québec data enable us to look at the evolution of the value of multilingualism over time, while the Swiss data help us not only to illustrate the value of multilingualism (as opposed to unilingualism) at a particular point in time but also to assess the value of higher language skills (as opposed to more basic ones). Section 4.5 addresses the particular case of the value of immigrant languages—which is not to be confused with the more usual question of the value, for immigrants, of learning the main language of the country in which they have settled. Before we embark on this discussion, a point of definition is in order on a question already mentioned briefly in Section 3.2. By “foreign language”, we mean languages other than the locally dominant language. Thus, acquiring a foreign language means for example learning German (for any nonspeaker of German living in Britain or in the United States, whether his first language is English, Spanish or Marathi), or learning English (for, say, a French-speaking resident of France or a Dutch-speaking resident of the

56

The Economics of the Multilingual Workplace

Netherlands). This extends to officially multilingual countries with de facto language territoriality, even if this arrangement is not enshrined in law, and it applies to any locally non-dominant language, even if the latter is recognised as an official or national language. Let us consider the cases of Belgium, Canada and Switzerland: Dutch will count as a foreign language in the predominantly French-speaking city of Liège in Wallonia, and French will count as a foreign language in the predominantly Dutch-speaking city of Antwerp in Flanders; English will be considered a foreign language in Québec, just like French in Alberta. In the same way, German in Geneva, French in Zurich and Italian in both cities count as foreign languages, although German, French and Italian are official languages in Switzerland. Let us begin with a general observation: foreign language skills are widely seen as an advantage, for example in official publications on multilingualism by the European Commission or by the state agencies in charge of language matters in all multilingual countries. Even in places where one language exerts hegemonic dominance over a large region (English in the British Isles and most of North America, Spanish in Central and South America except Brazil), competence in a foreign language is, if nothing else, acknowledged as a cultural asset often associated with above-average education, particularly if this competence is the result of deliberate investment in a prestigious variety. This is considered perfectly obvious in most European countries, where the European Commission insists that such skills are a defi nite plus for the labour market, because it guarantees access to a wider range of positions or to more interesting and rewarding jobs, in terms of both social image and monetary rewards. Language skills are a must for a candidate to be merely considered for an increasing number of positions, even for some in which, a few decades ago, there would have been no explicit requirement of this kind. These widely shared perceptions, reflected in media discourse about language, are borne out by the data collected in numerous studies, including those reviewed in the preceding chapter. However, a gap remains between widely shared perceptions and measured facts; and when facts can be measured, they may constitute only an indirect trace of deeper processes. This is the case for language-based earnings differentials, also known as wage premiums. Let us examine this point with a three-tier diagram (Figure 4.1). What can be directly observed without particularly involved data treatment is the fact that on average, persons with foreign language skills earn more (top tier of Figure 4.1). This relationship can be quantified by computing, for example, the average monthly earnings of people with modest, average or high foreign language skills. This observation is only a starting point, because people with higher earnings may possess not only better language skills but also other assets, such as more advanced education, which may be at least as important in explaining why they earn more. Therefore, it is useful to analyse the processes at hand more closely. Depending on the range and reliability of the data available, it

Foreign Language Skills and Earnings 57

FL SKILLS, TOGETHER WITH OTHER ATTRIBUTES

GROSS EARNINGS DIFFERENTIALS

FL SKILLS, NET

NET EARNINGS

OF OTHER

DIFFERENTIALS

ATTRIBUTES

FL SKILLS

Figure 4.1

PRODUCTION PROCESS

Foreign language (FL) skills and the production process.

may indeed be possible to go one step further and remove (or, in statistical terms, “control for”) the influence of other determinants of earnings, such as educational level or work experience, in order to estimate the net earnings differentials accruing to persons who possess foreign language skills. These effects are sometimes called, a little loosely, “private rates of return” on foreign language skills, although in technical terms, they are not, strictly speaking, rates of return. They capture the fact that even with similar education and experience, persons with foreign language skills earn more (middle tier of Figure 4.1). However, wages are structurally correlated to productivity. According to basic economic theory, a person’s salary must be equal to (or at least tend towards) the contribution to value creation of the last person hired. This does not mean that wage setting is completely impervious to the respective power positions of employers and workers, but that in a market economy, it remains primarily influenced by workers’ contribution to value creation. In technical terms, one would say that the wage rate is equal to the market value of the marginal productivity of labour; this proceeds from the simple fact that employers will, in general, keep hiring workers only as long as the latter’s contribution to value creation exceeds (or is at least equal to) the cost to the employer of having them on the payroll. Part of the wage rate, therefore, rewards that part of productivity that arises from language skills. Putting it another way, foreign language skills are rewarded with wage premiums. But then our estimations of the wage premiums accruing to people with foreign language skills are merely an echo of the actual process of value creation. Value creation emerges from the production process itself, and we may assume that it is the actual

58

The Economics of the Multilingual Workplace

or potential use of language skills by workers that improves productivity, as suggested in the bottom tier of Figure 4.1.1 Thus, the causal chain connecting language skills with earnings differentials cannot be entirely contained in the top tier, or even in the two top tiers of Figure 4.1. In this chapter, we discuss, theoretically and empirically, the top and middle tiers. But the rationale of this book can, to a large extent, be interpreted as exploring the deeper, bottom tier, in order to shed some light on the role that language plays in the “black box” of value creation. A fourth and deeper tier, which we do not attempt to examine, would take us away from the general line of reasoning applied in economic analysis and into the process-specific considerations of industrial engineering or operations management, where the investigation refers to the production of a given good or service.

4.2

DEFINING LANGUAGE FOR LABOUR MARKET ANALYSIS

Most of the work on the relationship between language and earnings, particularly in the early years, comes from Canada, and more specifically from Québec. This interest grew in the 1960s out of concern over ongoing income inequalities between native speakers of English and French in the province of Québec and the search for the reasons behind them and for policies to remedy this state of affairs. Traditionally, the reasons for differences in socioeconomic status between ethnic groups (without, at fi rst, necessarily including an explicit reference to language) has been sought in the existence of attitudinal phenomena such as xenophobia or racism (Becker, 1957), in particular aspects of social dynamics such as exclusion from some important social networks (Migué, 1970; Lavoie, 1983) or in unequal levels in standard determinants of income such as education and work experience (Becker, 1964; Mincer, 1974). When such variables turned out to account for only a small part of observed earnings gaps, language was, of course, a prime candidate for explaining such gaps, and this prompted a sustained research effort to probe the connections between language traits on the one hand, and socioeconomic success on the other hand. This, of course, required operationalising language in a way that would make it possible to investigate this connection statistically. Over the years, economists have used three different approaches to achieve this, as we shall see in the following brief review.2 The first one treats language as an ethnic attribute or marker similar to race. It was inspired by Becker’s (1957) theoretical model of racial discrimination and by the empirical work undertaken in the United States in the 1965–1970 period to explain earnings differences between black and white Americans. This line of work focuses on people’s first language as a determinant of their socioeconomic success. This approach was used by Fogel (1966) in the United States and by Raynauld, Marion and Béland (1969) in Québec. While usefully highlighting issues of social stratification and its economic

Foreign Language Skills and Earnings 59 consequences, this approach overlooks the role of language as a tool for communication which people can choose to acquire, thus increasing their human capital, and bypasses the question of foreign language skills. The second approach does exactly the reverse and views language as a form of human capital, thus highlighting the communication function of language while neglecting its function as a vehicle for the expression of identity. The reference to human capital may be implicit, as in Hočevar (1975), or explicit, as in Carliner (1976) or Breton (1978). This constituted an important development, since it allowed economists to compute the returns to language skills— something that had not been tried in other disciplines—much in the same way as returns on financial or educational investment can be estimated. In a third wave of research initiated by Vaillancourt (1980), both the identity and communication functions of language were taken into account, and this has remained standard practice in most empirical work since then. This means that when trying to explain labour income as a dependent variable, analysts use, as an independent variable alongside other determinants of earnings, actors’ linguistic attributes, comprising both their fi rst language (which serves as an indicator of their ethnic identity) and their second or foreign language skills (which serve as indicators of the linguistic components of their human capital). This does not mean, of course, that a person’s fi rst language is not part of her human capital: it clearly is. However, this approach makes it possible to distinguish the respective contribution to earnings of different components of her linguistic repertoire. While the statistical methods applicable to these calculations are well established, and the resulting estimates of rates of return statistically robust, a clear distinction must be maintained between what these estimates can and cannot tell us. In particular, these estimates do not provide a very dependable guide for decisions regarding long-term investment in foreign language skills, because they do not take into account the fact that language can be seen as a “supercollective good”, whose usefulness for communication increases with the number of speakers (Sabourin, 1985; Dalmazzone, 1998; Church and King, 1993) but whose labour market value may decline for the very same reason (Grin, 1999b).

4.3 THE CHANGING LABOUR MARKET VALUE OF LANGUAGES OVER TIME Although Québec is not the only part of the world where returns on foreign or second language skills have been estimated, it is the case where investigation has been most extensive and most sustained over the years. At this time, comparable results exist for Canada in general, as well as for Switzerland and Luxembourg, but in the case of the latter two, for specific years only. The absence of other countries from this list is due to the fact that empirical estimates of rates of return require very specific data, bearing upon people’s

60

The Economics of the Multilingual Workplace

language skills, age, gender, education, labour income, plus as many additional control variables as may reasonably and dependably be gathered. The countries that have such data, whether from censuses or surveys, are few, and attempts to replace the necessary data by proxies (see e.g. Ginsburgh and Prieto-Rodriguez, 2007, on the European Union) can be informative, but they do not provide estimates of language-based wage premiums. Some countries, notably the United States, Germany, Israel and Australia, have data bases that include the variables required. However, these data concern immigrant groups and their mastery of the locally dominant language—a question analytically rather different from that of the value of foreign language skills.3 Let us begin by reviewing some fi ndings about Québec, for which figures are sufficiently detailed to allow disaggregation by industry, allowing for fi ner-grained analysis. The fi rst and easiest way to examine the labour market value of language is by computing mean earnings by language group, as shown in Table 4.1. However, the standard Canadian typology of language groups should fi rst be recalled. Essentially, three groups are defi ned on the basis of their mother tongue, which may be English (Anglophones), French (Francophones) or another language (Allophones). The official defi nition of “mother tongue” is “the fi rst language learned at home during childhood and still understood by the individual at the time of the census”. Anglophones and Francophones can be unilingual if they do not speak the other official language of Canada, and bilingual if they do. Allophones will, however, be defi ned as anglophone or francophone respectively, depending on which of the official languages of Canada they have learned and in which they have “the ability to conduct a conversation”; and a “bilingual Allophone” is in fact a trilingual who knows English and French in addition to her mother tongue.

Table 4.1

Gross Mean Yearly Earnings, Québec, Men and Women 1970 and 2000, Index Values Based on Current Canadian Dollars, Seven Sets of Language Skills

Year and sex → Language group ↓

Men 1970

Men 2000

Women 1970

Women 2000

Unilingual Anglophones

159.1

114.9

123.8

110.7

Bilingual Anglophones

174.0

130.6

127.7

126.3

Unilingual Francophones

100

100

100

100

Bilingual Francophones

143.4

131.0

124.1

128.2

Anglophone Allophones

125.8

91.7

107.5

91.4

Francophone Allophones

105.7

71.6

104.6

74.8

Bilingual Allophones

145.7

111.6

125.3

115.6

Source: Adapted from Vaillancourt, Lemay and Vaillancourt (2007), Tables 1 and 2.

Foreign Language Skills and Earnings 61 These figures show that: • differences in mean labour income between Anglophones and Francophones are smaller for women than for men; • there is a convergence in the labour income of Anglophones and Francophones over the 1970–2000 period; • the relative position of Allophones, often immigrants, with respect to Francophones deteriorates over the period. The changes in the gross earnings differentials (corresponding to the top tier in Figure 4.1) associated with a given set of language skills may not refl ect changes in the returns to language skills as such. They could be explained by changes in the education or experience of individuals with these language skills. This is why economists set greater store by the net impact of language skills, that is, the net earnings differentials located in the middle tier of Figure 4.1, which can be computed using multivariate analysis. The methodology used to obtain them is, in general, as follows: 4 • relevant data must be secured for the range of variables listed at the beginning of this section. They may be obtained from public use samples from censuses (Canada, including Québec) or survey data (Québec, United States, Israel, Germany, Switzerland, Australia); • more or less narrow samples are analysed, usually focusing on people in the usual working age range (20–65), and sometimes restricted to men only;5 • ordinary least squares (OLS) regressions are used to estimate the link between a dependent variable (usually, the natural logarithm of labour income) and a set of independent variables; • the set of independent (or “explanatory”) variables used as control variables typically includes age, education, experience and its square6, and weeks worked (if the sample is not restricted to full-time workers or if no other adjustment has been made to convert part-time incomes into full-time equivalents). This corresponds to the standard specification of Mincerian equations, named after the economist Jacob Mincer (1974). They were initially developed to evaluate the rates of return to schooling. Additional controls such as marital status, region of residence, type of employment and so on, are also commonly used, depending on the availability of data; • the key independent variables, however, are respondents’ linguistic attributes, including their fi rst language (or mother tongue, or L1) and their second or foreign language skills, and OLS regressions provide estimates of the coefficients expressing the contribution of each independent variable to the value of the dependent variable.7 The net impacts are presented in Table 4.2.

62 The Economics of the Multilingual Workplace Table 4.2

Net Impact on Earnings, Québec, Men and Women 1970 and 2000, Percentages, by Skills in Official Languages

Year and sex → Language group ↓

Men 1970

Men 2000

Women 1970

Women 2000

Unilingual Anglophones Bilingual Anglophones Bilingual Francophones Anglophone Allophones Francophone Allophones Bilingual Allophones

10.11 16.99 12.61 ns ns 6.03

-18.06 ns 12.20 -30.10 -33.88 -11.78

ns ns 9.73 ns 22.82 11.10

ns 7.40 17.04 ns -19.10 5.32

Source: Vaillancourt, Lemay and Vaillancourt (2007), Tables 3 and 4. All reported values are significant at the 5% level. ns = not statistically significant at the 5% level. Reference category: unilingual Francophones.

The net impacts of language skills are as follows: • the returns to English unilingualism for anglophone men have gone from being positive in 1970 (with a net premium over francophone men of 10.11%) to being negative in 2000, where ignorance of French is associated, on average, with a wage rate 18.06% below that of francophone men; • bilingual anglophone men used to earn, on average, almost 17% more than unilingual francophone men in 1970; by 2000, that advantage had vanished, and the wage rate of bilingual anglophone men was no longer significantly different from that of unilingual francophone men. Let us note, however, that if bilingual anglophone men are compared with their unilingual counterparts, competence in French delivers a premium of 18%; • the returns on bilingualism for anglophone women are positive in 2000, with a premium of 7.40% over the wage rate of unilingual francophone women; • the returns on bilingualism for francophone men and women are positive at all times, with sizeable wage premiums of 12.20% and 17.04% respectively in 2000; • the returns on English and French language skills accruing to allophones have generally deteriorated from 1970 to 2000. These results, however, tell us little about the impact of specific sectoral or fi rm policies on the use of language and whether they may have an impact on rates of return. Yet if “economic sector” is added as an independent variable in the earnings regressions, as done by Lemay (2005), this barely changes the impact of language skills on earnings. Consider Table 4.3, which fi rst recalls, in the top row, the results of the second column of Table 4.2 concerning men in 2000, and reports, in the bottom row, estimates for the same coefficient, obtained this time with an industry variable.

Foreign Language Skills and Earnings 63 Table 4.3

Net Impact on Earnings, Québec, Men, 2000, Percentages, by Skills in Official Languages, with and without Industry Variable Unilingual Anglophones

Bilingual Anglophones

Bilingual Francophones

Not present

-18.06

ns

12.20

-30.10

-33.8

-11.78

Present

-15.44

ns

9.28

-32.97

-34.63

-12.93

Industry variable

AngloFranco- Bilingual phone phone AlloAllophones Allophones phones

Source: Table 4.2 earlier and Lemay (2005: 19, Table 6). All reported values are significant at the 5% level. ns = not statistically significant at the 5% level. Reference category: unilingual Francophones.

Let us now consider the net impact of language skills by industry, focusing again on the men in the sample for the year 2000. The results, shown in Table 4.4, indicate that rates of return on language skills can vary quite substantially between sectors. This confi rms again the intuition summarised in Figure 4.1 at the beginning of this chapter, namely, that it is worthwhile examining the relationship between economic and linguistic variables at closer range.

Table 4.4 Net Impact on Earnings, Québec, Men, 2000, Percentages, by Skills in Official Languages, Eight Industrial Sectors Language Skills → Sector ↓ Primary sector* Manufacturing

Unilingual Anglophones

Bilingual Anglophones

Bilingual AngloFrancoBilingual Francophone phone Allophones Allophones Allophones phones

-28.69

ns

14.62

0

ns

12.58

Construction

-33.16

TCU**

-21.46

-21.20

ns -34.39

-35.0 -38.77 ns

ns -16.78

ns

-45.81

ns

7.83

-32.90

-35.01

-13.45

ns

12.86

-30.55

-22.60

-8.67 ns

Commerce

ns

Finance

ns

17.10

11.11

-33.07

-51.21

Services

ns

9.44

19.67

-17.62

-18.58

Public sector

ns

ns

16.21

ns

ns

-21.80

ns 16.28

Source: Lemay (2005, Table 20). *Primary Sector = Agriculture, forestry, fishing and mining. **TCU = Transport, communications, utilities. All reported values are significant at the 5% level. ns = not statistically significant at 5% level. Reference category: unilingual Francophones.

64 The Economics of the Multilingual Workplace Let us now discuss the rates of return on language skills in a different perspective, in order to assess the impact of skills levels on earnings.

4.4

FACTORING IN SKILLS LEVELS

To our knowledge, the first study to have examined the impact of the level of foreign language skills on earnings is by Vaillancourt and Pes (1980). It explains the 1971 wages of a sample of Québec men using education, experience and language skills as independent variables, and its key results are summarised in Table 4.5, which affords a new perspective on the economic value of language skills. This time, we are looking at a different counterfactual: instead of comparing bilingualism with unilingualism, we can also compare the respective situations of agents with higher or lower skills in a foreign language. These results show that: • wage premiums are correlated with skills levels; • gross earnings premiums are significantly higher than the net impacts of language skills;

Table 4.5

Gross and Net Impact on Earnings of Oral and Written Foreign Language Skills by Skills in Official Languages, Québec, Men, 1971, Percentages Oral Gross

Written Net

Gross

Net

Francophones with competence in English at level . . . Excellent

77

28

83

25

Good

50

18

48

14

Weak

16

ns

19

5

Nil (ref. categ.)

—

—

—

—

Anglophones with competence in French at level . . . Excellent

104

38

123

37

Good

116

41

137

47

Weak

130

41

109

26

Nil

117

16

78

27

Source: Vaillancourt and Pes (1980), Tables 1 and 2. All reported values are significant at the 5% level. ns = not statistically significant at the 5% level. Reference category: unilingual Francophones with no skills in English.

Foreign Language Skills and Earnings 65 • wage premiums rewarding good or excellent foreign language skills are similar when using either written or oral skills for both Anglophones and Francophones; • wage premiums for good or excellent language skills (in 1971) are much higher for Anglophones than Francophones. Highlighting the role of the level of one’s foreign language skills in the determination of earnings is not just important in itself; it bears out our assumption that language skills generate value because they are used (a point to which we return in Section 5.1), which would explain why the level of competence achieved is relevant. One would expect this type of observation to have encouraged extensive data gathering on skills levels in relation with earnings. Surprisingly, detailed data bases are very few, and in what follows, we discuss the results obtained using one of the only such data sets, which was collected in Switzerland in the mid 1990s. Switzerland traditionally represents an interesting case for the study of multilingualism because of its unique institutional arrangements regarding language. The latter need to be presented as a backdrop to information on their economic value, in which, as we shall see, the level of people’s foreign language skills has a signifi cant impact on their earnings. Switzerland is a constitutionally quadrilingual country where German, French, Italian and Romanche are recognised as national languages.8 Because of the country’s federal structure, regional (cantonal) and local (municipal) authorities retain sizeable areas of competence, including in terms of language policy, which is, in the main, under the responsibility of the 26 cantons.9 The crucial feature of the Swiss arrangement is language territoriality. With the exception of a small number of bilingual municipalities, the country is offi cially monolingual: one and only one language is regarded as official on any given point of the territory, giving rise to four language regions, which are not institutional entities as such and have no legal standing whatsoever (in fact, the language boundary may run right across the territory of a canton), but they represent important and readily identifi able sociolinguistic realities. It is in this sense that we may speak of “German-speaking”, “French-speaking” and “Italian-speaking” Switzerland. As a result of language territoriality, the implications of trading with clients or suppliers in another language region of the country are not very different, in linguistic terms, from trading with a foreign country where a different language is spoken. Language skills are therefore relevant for large segments of the economy, even for fi rms that only trade domestically. Although Swiss multilingualism has received abundant attention in legal, political and educational research, interest in its economic dimensions is a relatively recent development. However, Switzerland is one of

66 The Economics of the Multilingual Workplace the few countries where statistically representative data on language skills and earnings are available, making it possible to compute language-based earnings differentials. In this section, we shall again focus on second (or “foreign”) language skills.10 As in the Québec case, we may start with gross differentials between the mean earnings of groups defi ned in terms of their linguistic attributes. However, since the necessary data have been gathered only once, it is not possible to monitor the evolution over time of these differentials. Conversely, the Swiss data include information on the level of respondents’ foreign language skills by taking four levels into account (excellent, good, basic, none). They also make a distinction between listening, speaking, reading and writing skills. This results in a 4 × 4 matrix of language skills for each respondent. Respondents were assigned to a particular competence level for each of the four skills according to their self-assessed positioning on a grid of language tasks, directly inspired from the Council of Europe’s Common European Framework of Reference for Languages,11 with due adaptation for computer-assisted telephone interviewing. At the time of writing, therefore, Switzerland has the most detailed statistically representative data base on foreign language skills. Table 4.6 reports gross earnings differentials between language-defined groups, where foreign language skills are computed as the average of Table 4.6

Gross Earnings Differentials by Language Skills in National Languages, Switzerland, Men and Women, 1994–1995, Index Values Based on Current Swiss Francs Men

Women

100 93.4 78.5

100 107.3 86.8

Unilingual Germanophones Unilingual Francophones Unilingual Italophones

91.2 86.0 71.8

94.7 106.4 73.4

Bilingual Germanophones 2 Bilingual Francophones 3 Bilingual Italophones 3 Bilingual Italophones 2

114.6 109.3 78.4 81.5

108.0 114.1 95.2 92.0

Total sample or subsample

95.1

101.1

Germanophones 1 Francophones 1 Italophones 1

Source: Adapted from Grin (1997: 39, Table 5.8). 1 By L1, irrespective of foreign language skills, if any. 2 “Excellent” or “good” skills in French. 3 “Excellent” or “good” skills in German or Swiss-German dialect.

Foreign Language Skills and Earnings 67 self-reported competence in the four skills. In order to ensure comparability, earnings have been converted to full-time equivalents for respondents working part time. Instead of presenting figures in current Swiss francs, these have been converted in index values, using as a reference point the mean labour income of native speakers of German in general (that is, irrespective of their second or foreign language skills). For reasons explained in the preceding section, the values are reported separately for the male and female subsamples. The figures in Table 4.6 indicate that bilinguals, whether men or women, always earn more than unilinguals. The gradient of the relationship between foreign language skills and labour income may in fact be quite steep, even exceeding 27% ((109.3—86)/86) in the case of French-speaking men. Let us now consider the case of English, which is not one of Switzerland’s official languages and is the mother tongue of 1% of residents (according to 2000 Census data), making the role of English in Switzerland fundamentally different from its role in Québec. In the following table, we use the data on self-reported skills in English, also obtained with reference to the 4 × 4 matrix of language tasks, in order to illustrate the link between earnings and the level of respondents’ skills in the language. The fi ndings are reported in Table 4.7 with index values. The reference value (100) is assigned to respondents who report no skills in the foreign language. The figures in Table 4.7 show that in terms of gross earnings differentials, competence in English is very profitable. On average, men with “excellent” skills earn 50% more than men with no competence at all in the language. The corresponding earnings gap is 43% for women. Even “good” skills (which fall short of native-like fluency) are associated with a wage differential of 29% for men and 32% for women. For respondents who already possess “good” skills, reaching excellence is associated with a 16.3% increase in earnings for men (16.3 = (150—129)/129) and 8.3% for women (8.3 = (143—132)/132). Can we then take these figures as indicative of the private impact of language skills on earnings? Not quite, since language skills are correlated, in particular, with education, and education remains the primary determinant of labour income. The respective effects of these two variables must be disentangled and there again, the solution is to turn to

Table 4.7

Gross Earnings Differentials by Competence Level in English, Switzerland, Men and Women, 1994–1995, Index Values Based on Current Swiss Francs Men

Women

Excellent

150

143

Good

129

132

Basic

116

110

None

100

100

Source: Adapted from Grin (2001a: 72).

68

The Economics of the Multilingual Workplace

Table 4.8

Net Impact on Earnings of “Excellent” or “Good” Foreign Language Skills, Switzerland, Men 1994–1995, Percentages

Foreign Language → Language Region ↓ French-speaking area

French -

German-speaking area

14.1

Italian-speaking area

17.2

German 13.8 16.9

English 10.2 18.1 ns

Source: Adapted from Grin (1999a, Chap. 8). All reported values are statistically significant at the 5% level. ns = not statistically significant at the 5% level. Reference category is “basic” or “no” foreign language skills.

multivariate analysis. As before, a vast array of different regressions can be run according to the precise question asked and depending on the range of available data. For the purposes of this paper, let us begin by comparing the net earnings differentials accruing to people with French, German and English as foreign languages in the three main language regions of Switzerland (Table 4.8), by controlling for the core variables of Mincerian equations, that is, education and experience (both measured in years) and experience squared.12 Clearly, having adequate foreign language skills (as opposed to having none, or only basic skills) is handsomely rewarded on the labour market. For example, the profitability to the individual of acquiring foreign languages exceeds the average value of one additional year of schooling, which, depending on the period, country and tier of the education system concerned, usually falls somewhere in the 5% to 7% range. It is interesting to note, however, that: • wage premiums for skills in the country’s main other language (respectively German and French) are roughly similar at about 14% in the French- and German-speaking regions; • the premiums for these two languages lay around 17% in the Italianspeaking part of the country, probably reflecting the acutely minoritarian status of the Italian language in Switzerland; • competence in English is unevenly rewarded across regions, with a much higher net impact in German-speaking than in French-speaking Switzerland (about 18% versus 10%).13 This last fi nding, however, is the most intriguing of the lot with respect to the concerns of this book. The figures in Table 4.8 suggest that differences in the profitability of skills may be related to the locally dominant language (which is precisely what defi nes Switzerland’s language regions).

Foreign Language Skills and Earnings 69 However, if we retain the a priori reasonable assumption that language skills are rewarded because they contribute to productivity in the context of economic activity, what would matter is not the locally dominant language per se but the nature of the economic activities in which people are involved, which may, in turn, be unevenly distributed across the country’s language regions. In order to address this question, one would ideally need to take account not only of education, work experience, language region and gender, but also of economic sector (as was done for Québec in Table 4.4) and the type of activity performed. However, the size of the Swiss sample (2,400 respondents, of which 80% could be used for these estimations) is not sufficient to settle this point.14 Furthermore, as noted in the very fi rst pages of this book, even if the data base were larger, it would not necessarily yield a direct relation between language skills and value creation in the economic sense. This is why a closer examination of the role of language in actual economic activity is required. Before we address this question, however, let us take a detour in the direction of a related question, namely, the value of immigrants’ language skills.

4.5

THE VALUE OF IMMIGRANTS’ LANGUAGE SKILLS

In keeping with our defi nition of foreign languages as languages other than the locally dominant one, we are quite naturally led to address the case of immigrant languages. This question, however, requires some preliminary qualification. What matters then are not immigrants’ skills in a locally dominant language, even if this language is initially foreign to them, and this is why the considerable literature on the rates of return on, say, investment in English by Hispanic immigrants to the United States is no concern of this book. This line of research has given rise to econometrically sophisticated analyses, as evidenced (in the case of United States) by Grenier (1984), Chiswick (1978, 1991), Chiswick and Miller (1995, 2007), Kossoudji (1988), McManus (1985, 1990), McManus, Gould and Welch (1983), Rivera-Batiz (1990) and Tainer (1988). Comparable studies have been carried out with immigrant populations in Germany (Dustmann, 1994), Australia (Chiswick and Miller, 1985) and Israel (Chiswick and Repetto, 2001). However, these studies originate in the subfi eld of labour economics rather than language economics. Their concern is with understanding what goes on in the labour market (including wage rate determination), rather than with assessing the economic value of language. With respect to the latter question, the fi nding that knowing English is profitable for people who wish to work in the United States does not come as a major surprise. From a language economics standpoint, this line of work is akin to the investigation of the value of literacy in their

70 The Economics of the Multilingual Workplace native language among native speakers; we are therefore quite far away from the central question of this book, that is, the value of foreign languages; therefore, the value of immigrants’ skills in a locally dominant language will not be discussed further. The economic value of immigrants’ skills in their native language has, by contrast, remained surprisingly under-researched. This may be due to the scarcity of data or to the implicit assumption that such skills are worthless anyway. Efforts have been made to question this perception, but they often take the form of qualitative approaches with essentially descriptive content, thus meeting the same limitations as many of the contributions reviewed in Chapter 2. As to the abundant research on ethnic business (Ward and Jenkins, 1984; Light and Karageorgis, 1994; Piguet, 1999), it mainly focuses on the effects of ethnicity-based networks on the economic advancement of given immigrant communities, but even if, in practice, ethnicity is proxied by language, the role of the latter is not explicitly analysed. A small number of studies examine, at a general conceptual level, the economic implications of linguistically different ways of managing immigration while also taking immigrants’ L1 into account (Grin and Vaillancourt, 2001; Esser, 2006). However, to our knowledge, the only published estimates of the labour market value of immigrant languages are in Grin, Rossiaud and Kaya (2003), in a comparative pilot study on Italian-speaking and Turkish-speaking immigrants in the French-speaking part of Switzerland. Since Italian is one of Switzerland’s national languages, the situation of Italian immigrants in French-speaking Switzerland is less emblematic of the situation of what is generally understood as “immigrant language”. Let us therefore focus on the results for Turkish-speaking immigrants (who include not only Turkish residents in Switzerland but also respondents of Turkish origin who have obtained Swiss citizenship). Gross earnings differentials are reported in Table 4.9, where the defi nition of skills levels is similar to that used in the preceding section, and the sample size is 296.

Table 4.9

Gross Earnings Differentials by Competence Level in Turkish, Switzerland, Men and Women, 1997–1998, Index Values Based on Current Swiss Francs Men

Women

Excellent

108

105

Good

102

95

Basic or None

100

100

Source: Adapted from Grin, Rossiaud and Kaya (2003: 420).

Foreign Language Skills and Earnings 71 The data suggest a weak relationship between competence in Turkish and labour income in the case of men, and no clear pattern in the case of women. When the data are analysed in greater depth with Mincerian earnings equations as in the preceding section, the regression coefficients for competence in Turkish are not statistically significant, making it impossible to draw conclusions in this regard (Grin, Rossiaud and Kaya, 2003: 424– 426).15 Qualitative research carried out alongside the quantitative survey, however, indicates that many speakers of Turkish not only use their native language at work but often engage in deliberate strategies to make the best use of this skill. In some cases, employers reportedly agree to redefi ne the tasks entrusted to Turkish-speaking employees in order to give them opportunities to use Turkish—usually for the benefit of the company as well, for example in order to develop contacts and provide service to Turkishspeaking clients. No fi rm conclusions can be drawn at this stage about the value of immigrant languages on the labour market. Larger data sets are indispensable to assess whether immigrant languages are valuable in the same sense (though probably not with the same order of magnitude) as skills in major foreign languages. In the meantime, circumstantial evidence points in the direction of niche effects, that is, specific jobs in which particular language skills are in demand—and accordingly rewarded. It is important, however, not to stretch these observations too far. Firstly, a collection of individual examples does not amount to statistically significant facts. Secondly, the widespread concern for beating back xenophobia has led many commentators to insist that the presence of immigrants on the labour market is economically beneficial to the host society (a complex issue on which economists generally agree, but only after engaging in econometrically demanding investigations; see e.g. Faini, de Melo and Zimmermann, 1999). However, a logical leap is often made, leading some to assert that the linguistic and cultural traits characterising immigrants are somehow economically valuable too. If so, one would expect this to be reflected, among other effects, in unambiguous economic effects. But as we have just seen, no positive net earnings differentials for speakers of those languages can be reported; at the time of writing, it is more prudent to say that the jury is still out on this matter. However, what little is known about the economic significance of immigrant language skills once again confronts us with one question: should we focus on the presence of language skills, or on their actual use at work? To examine this point, let us move on to the second part of this book.

Part II

Foreign Language Skills, Foreign Language Use, and Production

After having set the scene in the fi rst part of this book, we have concluded that existing theoretical and empirical research answers but a small part of the question: why are foreign languages valuable, and just how valuable are they? In Part II of this book, we develop entirely new theoretical models before confronting them with data. The analysis shows how linguistic and economic variables can be systematically related at the very heart of the production process, yielding novel implications on the economic effects of various changes in variables that have linguistic dimensions. The analysis of “language-augmented” production processes is complemented by an exploration of the strategies of employers when recruiting staff with foreign language skills.

5

Language Use and the Production Process

5.1

THE RELEVANCE OF LANGUAGE USE

As shown in Figure 4.1 at the beginning of the preceding chapter, the wage premiums that accrue to persons with certain skills may be seen as an indicator (some linguists might call it “the trace”) of a deeper process through which these skills contribute to value creation. We have assumed this process to be characterised by language use as opposed to mere competence. One reason why economists working on language, however, have so far stressed competence more than use, as shown by the literature review in Chapters 3 and 4, is that the structural relationships between competence and use are still not very well known. Another reason for economists’ focus on competence is the lack of hard data on language use, particularly of data sets that could serve to connect language use to earnings. The absence of such data, in turn, can be explained in two different, though not mutually exclusive ways. The fi rst one is analytical and has already been mentioned in the introductory chapter: to the extent that skills are correlated with use, and if the analyst’s interest is not in understanding how language creates value but how much this value is, then Mincerian earnings equations are enough, and there is little analytical incentive to take a closer look at language use. The second reason is more mundane: it is very difficult to gather suitable data to estimate the link between language use and earnings. Quite apart from the fact that, in the absence of a proper theoretical model, it is hard to know exactly what data on language use are truly relevant and should be collected, actual collection implies taking a very close look at the production process inside fi rms, thus potentially uncovering procedures that they may not have documented and that they prefer to keep confidential, while at the same time gathering detailed facts about how their profits are generated—there again, information that fi rms may not have, and would not necessarily be willing to share if they did. What is more, for such information to be statistically valuable, similar data have to be gathered following comparable procedures and applying standard defi nitions in a large number of heterogeneous fi rms—all in all, a rather daunting task. All things considered, it is not surprising that the

76 The Economics of the Multilingual Workplace economic implications of language use are little known, and we have seen in Chapter 2 that information coming from other disciplines tends to be of limited help: although there is a considerable body of applied linguistics research on language use at work, those data remain unconnected to any indicator of economic performance. Let us therefore take a closer look at language use in production. To do so, we begin in Section 5.2 by briefly reviewing some results of economic analyses of the determinants of language use, in order to generate some assumptions about the linkages between language use on the one hand, and key economic variables such as productivity, costs and profits on the other hand. This will lead us to the conclusion that the only thing left to do is to roll up our sleeves and revisit the basic economic theory of production. This requires adding linguistic markers to some variables in a formal algebraic model, thereby generating what might be called a “language-augmented theory of production”, which is presented in Section 5.3. The algebraic apparatus is relegated to the appendix, so that we can keep this chapter almost entirely math-free. In Section 5.4, we derive a set of testable propositions from the model. Let us fi rst, however, pause to question our overarching assumption, already announced in Section 4.1, that foreign language skills create value (and are consequently rewarded through wage premiums) because they are used. This apparently straightforward assumption is not as self-evident as it might seem, and it is possible to think of other reasons why skills would be rewarded without being used. The best known of these reasons is encapsulated in screening theory (Arrow, 1973; Stiglitz, 1975; Riley, 1979), whose antecedents can be found in the work of the sociologist Ivar Berg (1970) in his essay delightfully titled Education and Jobs: The Great Training Robbery. According to screening theory—which in some ways can be seen as the antithesis to human capital theory—education and training do not serve to teach knowledge and skills that are useful in a person’s future working life. The purpose of education and training, rather, is to provide a rank-ordering of individuals. This rank-ordering need not be based on the knowledge and skills acquired during training, but can instead be designed to reward other aptitudes, some of which may be revealed and encouraged by the education system, while others may be innate or inculcated by a person’s socioeconomic milieu. Some of these aptitudes may be sought by employers not because they are directly useful on the job and increase productivity, but because they meet the needs of the employers in other ways: typical examples are obedience, punctuality or adherence to a set of social norms. Whatever skills are necessary to perform certain tasks would then be taught on the job. If, then, a certain mastery of foreign languages is seen as a marker of desired aptitudes but does not, as such, constitute one of them, a person may be rewarded for having them by being offered more attractive jobs with higher pay, even if these language skills are quite useless in the job for which she is being considered.

Language Use and the Production Process 77 A closely related interpretation is that of signalling (Spence, 1974), which may be interpreted as a weak form of the screening hypothesis and comes down to what is known as statistical discrimination: since it is difficult for an employer to know precisely how productive a job applicant will prove after being hired, she needs to fall back, when vetting applicants for a job, on indicators of likely productivity, even if productivity is not directly related to the skills expressed by those indicators. Foreign language skills may well constitute examples of just such indicators. For example, an employer recruiting staff for foreign postings may be looking for aptitudes such as adaptability, openness to other cultures and ease in dealing with foreign colleagues. Such aptitudes can be tested through psychological questionnaires1, but administering questionnaires may be cumbersome and add to the cost of a recruitment process, and even a high score on a questionnaire of this type may not always provide a reliable predictor of future performance in a posting in some distant land. A possible shortcut, then, is to assess a candidate’s foreign language skills, even if those skills are not actually needed for the job but are assumed to be correlated with the aptitudes desired. Evidence from Swiss data suggests that an element of signalling may be present on the labour market for foreign language skills, particularly in the case of women, since the net earnings differentials accruing to women are generally lower, and more likely not to be statistically significant, when they actually use their competence in English as a foreign language than when they do not (Grin, 1999a: 173). It is therefore quite possible that language skills are not directly useful on the job but serve, for some employers, as proxies for other desirable characteristics. Is this enough to reject the assumption that language skills are economically valuable because they are actually used and hence contribute to productivity? No, because there are several reasons for retaining this assumption, in addition to its commonsense appeal. Firstly, the econometric result just mentioned concerns only the female part of the sample, and then, not even all of it. Men, by contrast, are rewarded for having foreign language skills and using them. Secondly, returns on foreign language competence are statistically robust: they hold under numerous different specifications, and the fact (reported in Table 4.8) that they increase with competence level strongly hints at something more than mere signalling. It would be unsatisfactory to suppose that employers consistently pay more for a very strong signal (that is, “excellent” language skills) than for a merely strong one, such as “good” language skills; it is much more plausible to interpret the figures as an indication that higher skills do make a worker more productive on average, and that this applies to foreign language skills too. 2 For this reason, we will retain the assumption that the labour market value of foreign language skills is connected to their use, in line with the assumption that a language repertoire, in addition to being part of a person’s cultural identity, is also part of her human capital. The standard view is that human capital is rewarded, at least in part, because it is put to productive use.3

78

The Economics of the Multilingual Workplace

5.2

THE DETERMINANTS OF LANGUAGE USE

In order to move closer to the process pointed at in the bottom tier of Figure 4.1, let us take stock of some existing results of economic analyses of what determines language use at work. What matters to us is not so much these results themselves, but whether they may be interpreted as evidence that some linguistic practices (or, more specifically, the use of one or another language, or a certain way of combining languages) have a positive impact on productivity and profits, or may serve to reduce production costs. There is some literature on this question, some of which has been mentioned, albeit briefly, in the preceding chapters. Several empirical results presented in this literature hint at causal links that could easily fit into our diagrammatic presentation of the issue at hand, namely, Figure 1.1 in Chapter 1. Breton and Mieszkowski (1977), Hočevar (1975) and Vaillancourt (1980) have each identified various factors that in theory influence the language used in the workplace. These factors are the language contents of goods and services produced, the language of the various markets served, the language of production factors, particularly technology and the workforce, and the language of the owners or senior management. Morrison (1973) also points out the importance of the location of activities. And if we were to revisit the “culturalist” assumptions tested by Gómez-Mejia and Palich (1997), which we have discussed in Section 3.3, and transpose them to language, we might suggest a range of possible explanations for particular patterns of language use. As far as we know, the largest set of empirical studies of the use of languages by firms was carried out in the early 1970s for the Commission d’enquête sur la situation de la langue française et sur les droits linguistiques au Québec (Committee of inquiry on the situation of the French language and language rights in Québec), known as the “Gendron Commission” after the name of its chairman. One set of studies was carried out by the Institut international d’économie quantitative (IIEQ; see Inagaki et al., 1973). Of interest to us is the use of a survey of 19 economic sectors, with answers from 54 respondents out of 377 firms (ibid.: 24). Unfortunately, results by economic sector are not reported. Using a sample of 1,549 managers, Inagaki et al. (ibid.: 34) find that unilingual francophone managers have a smaller number of interactions with others than bilingual Francophones, while unilingual Anglophones have the same number of interactions as bilingual Anglophones (ibid.: 68). Bilingual Francophones speak French to bilingual Anglophones 40% of the time, and bilingual Anglophones speak English 60% of the time (ibid.: 87). In another major study with a sample of 4,914 individuals, Carlos (1973) investigates the use of French and English across various economic sectors, showing that the use of French can be as low as 55% for professionals in the manufacturing sector and as high as 97% for low-skill workers in the agricultural sector (ibid.: 54). Finally, Morrison (1973) notes that there was “excess capability” with respect to both English and French in manufacturing firms in Québec in the late 1960s, with greater knowledge than use of these languages. None

Language Use and the Production Process 79 of these studies, however, examine the impact of language on profits or other economic variables; at best, they allude to it in general terms. They also do not use a multivariate framework to study the determinants of language use on the workplace. In Table 5.1, we report fi ndings from a selection of more recent studies on the use of French as a language of work in various economic sectors in Québec, using data from three surveys of workers. The results are organised around three variables: • an ownership index, which varies from 0 to 100 and measures the share of employment in the sector where a given respondent works that is under the control of francophone owners. We expect this variable to have a positive impact on the use of French in the workplace; • a capital index or a technology index, where a distinction is made between the 1971, 1979 and 1989 sets of results. The fi rst two use a capital index, namely, an index obtained by dividing each sectoral capital/labour ratio by the smallest one. It thus varies by construction from 1 to 21 (where the highest value applies to the utilities sector). The third set of results uses a technology index, namely, information from the survey on the use of information technology. If information technology is used, the value of the technology index, serving here as an explanatory variable, is equal to 1; otherwise it is equal to 0; • the external market ratio, which varies from 0 to 100, and represents, for each sector, the share of total sales taking place outside Québec.

Table 5.1 Net Impact on the Use of French in the Workplace, Bilingual Francophones, Québec, 1971, 1979 and 1989, Percentage of Working Time Expected Sign

1971

1979

1989

Ownership index

+

10.0**

ns

0.13*

Capital-Technology index

-

ns

0.33*

-3.2*

External market index

-

-12.1**

-15.0*

ns

Occupation Experience Education

Occupation Experience Education

Occupation Experience Education Sex

2904

2716

4548

Control variables used N

Sources: Lefebvre (1981: Table 3.3, 62); Leblanc (1992: Table 3.4, 59) *Significant at the 5% level. **Significant at the 10% level. ns = not statistically significant at the 10% level.

80 The Economics of the Multilingual Workplace Table 5.1 shows that when significant, an increase in the share of francophone ownership increases the use of French, and an increase in the importance of external markets reduces the use of French. These results are obtained by examining the relationship between the use of French on the workplace by individuals and the characteristics of the industrial sector each individual works in. However, the use of a language is at least as workplace-determined as it is individual-determined. It is therefore more relevant to re-examine the issue with workplace-related information, which means, in practice, data at the level of the employer or the fi rm. This is done in Table 5.2, which reports results from Vaillancourt, Champagne and Lefebvre (1994) obtained using administrative data on the language of work collected at fi rm level. The authors examine the use in the late 1970s (1977–1979) of both oral and written French by high-, medium- and low-level francophone managers in 290 large companies numbering 500 employees or more. They relate the use of French to both the ownership of the fi rm and the sector it operates in. The main findings of their multivariate analysis are summarised in Table 5.2.

Table 5.2

Net Impact on the Use of French at Work, Large Employers, Québec, 1977–1979, in Percentage of Working Time Oral communication, three levels of management

Written communication, three levels of management

High

Medium

Low

High

Medium

Low

ns

ns

ns

0.13

ns

ns

Quebec Anglophone

-0.17

ns

ns

ns

ns

ns

Quebec Francophone

0.24

0.19

0.13

0.47

0.35

0.24

Manufacturing

ns

ns

-0.12

ns

ns

ns

Finance

ns

ns

-0.16

ns

ns

ns

0.19

0.12

0.11

0.30

0.23

0.16

Ownership: English Canadian

Economic sector:

R2

Source: Vaillancourt, Champagne and Lefebvre (1994: Table 5, p. 492). All reported values are significant at the 5% level. ns = not statistically significant at the 5% level. Reference categories: foreign-owned with respect to variable “ownership”; mining with respect to variable “economic sector”. Sectors not mentioned are “construction”, “transportation, communications and public utilities” and “personal services”, for which coefficients are not significant at the 5% level.

Language Use and the Production Process 81 The results in Table 5.2 show that ownership generally matters, since this is where most of the statistically signifi cant parameters are found. They appear to have greater impact on the use of written than oral French, presumably because this is a more formal decision by the organisation. They also show that the impact of ownership diminishes with a drop in the level of management considered; this refl ects the degree of interaction with owners by various levels of management. These results on the language of work may be interpreted as expressing a linguistic dimension of the production choices made within the fi rm, namely, the language tendentially used for interactions between various productive factors.4 Let us now turn to economic measures not usually associated with language. The results previously mentioned show that the use of French on the Québec labour market is determined, in part, by economic variables. But this does not demonstrate that the use of French has an impact on economic variables such as productivity or profits. What can be shown, however, is that one variable that has an impact on language use, the ownership of fi rms, has an impact on three major economic variables. Raynauld and Vaillancourt (1984) examine, for the manufacturing sector in Québec, the impact of ownership on the productivity of labour, labour costs and exports. They use 1978 data at the manufacturing establishment level for Québec (1979 for exports) and apply multivariate analysis to estimate the three following relationships: 1. Productivity (defi ned as value added per employee) is analysed as a function of capital5, fi rm size (whether measured in terms of number of employees or total value added), and ownership (where “Francophone” is used as a reference category, the other categories, expressed as dichotomous variables, being “Anglophone” and “Foreign”). Productivity is expressed in dollars per worker, and higher productivity is assumed to be preferable. 2. Labour costs (defi ned as the ratio of wages to value added) are analysed as a function of size and ownership (both defi ned as above). Labour costs are expressed as the ratio of wages to value added in percentage; lower labour costs are assumed to be preferable. 3. The importance of the domestic market for the establishment considered (where this importance is defi ned as the ratio between shipments to Québec and total shipments) is analysed as a function of economic sector (represented by 19 dichotomous variables where the reference category is the food and beverage sector), capital, size and ownership (where the latter three variables are again defi ned as above). A lower share of the domestic market in total shipments would be considered positive, since it denotes a workplace that is more capable of competing on larger markets.

82 The Economics of the Multilingual Workplace The results, reported in Table 5.3, simultaneously take account of several important processes: the impact of ownership on productivity is examined in relation with the amount of capital available to each place of work, approximated by the share of profits and depreciation in value added, as well as with the size of the place of work measured either by total employment (that is, the number of employees) or by net output (that is, sales minus inputs) or by the value added of the workplace. In the analysis of the impact on labour costs or exports, account is taken of the economic sector. Table 5.3

Net Impact of Ownership by Language Groups on Productivity, Unit Costs and Exports, Manufacturing Establishments, Québec, 1978

Control Variables

Anglophoneowned

N = 3968

foreignowned

Impact as % of mean differences AnglophoneFrancophone

Impact as % of mean differences ForeignFrancophone

Labour Productivity Impact in Current Dollars

Percentages

Capital, size (employment)

+1457*

+4680

36.6

43.9

Capital, size (value added)

+1361*

+4199

34.1

393

N = 3968

Labour Costs % Wages/Value Added

Percentages

Capital, size (employment)

-2.0

-8.0

23.0

53.7

Capital, size (value added)

-2.0

-7.0

23.0

47.0

N = 3958**

Domestic Market Share % of Québec Market

Percentages

Capital, size (employment), sector

-7.2

-18.4

36.7

70.8

Capital, size (value added), sector

-7.2

-18.7

36.7

71.9

Source: Raynauld and Vaillancourt (1984, Tables III.4, III.8 and III.11). All coefficients significant at the 5% level except *. *Significant at the 10% level. **Includes data for 1978 and 1979.

Language Use and the Production Process 83 The results of Table 5.3 show that ownership, and thus (because the former co-determines the latter), presumably, language used at work, matter to economic outcomes at the fi rm level. This, however, remains a conjecture that cannot be tested directly with the preceding approaches: we are progressively nearing the point where we need to take a jump and embark on a fundamental re-examination of production theory—as we shall do, in fact, in the following section. Before doing so, however, let us (as we did in Chapter 4) use Swiss data to complement the discussion illustrated so far with Québec data. For the purposes of this book, the chief interest of these results, presented in Tables 5.4 and 5.5, is that they provide an internal consistency check of our assumptions and confi rm the need to revisit the basics of production theory by taking explicit account of language. Although the results for Switzerland cover three languages used as foreign languages (French, German and English), limitations of space will lead us, as we did in the preceding chapter, to focus on the case of English as a foreign language. Using fi rst a set of individual data from a representative sample collected in 1994–1995 (see Section 4.4 of this book), we can analyse the likelihood that English is used at work as a function of respondents’ type of job and economic sector of activity. The likelihood that English is used at work daily or almost daily (as opposed to “not used at work daily or almost daily”) is expressed with the odds ratio (which is the standard output of logistic regressions), that is, the probability of the event occurring (that is, frequent use of English) divided by the probability of the event not occurring. Thus, the odds ratio may be viewed as a measure of association between a dependent variable (frequent use of English) and various independent variables. If the ratio is greater than one, it suggests that the independent variable considered increases the likelihood of using English at work daily or almost daily (and, conversely, reduces it if the ratio is smaller than one).6 The results in Table 5.4 indicate that the use of English at work is more closely associated with particular jobs in the French-speaking than in the German-speaking part of Switzerland. However, we can easily detect sensible association patterns between the reported use of English and the type of job performed. It is quite logical for the odds ratio to be higher for white-collar workers (e.g. “liberal professions”) and for professionals who are more likely to be involved in decision-making (e.g. “owners/ managers”, or “senior civil servant” v. “civil servant”). In the same way, a “mainly international orientation” has a strong positive effect (in both language regions) on the likelihood of using English. These figures give further credence to the assumption that foreign language skills are used in productive activity; however, they do not necessarily tell us what they are used for. We can edge closer to this question by resorting to data collected in 2007 at fi rm level, distinguishing

84

The Economics of the Multilingual Workplace

Table 5.4 Net Impact on the Use of English at Work, French- and German-speaking Switzerland, 1994–1995, Odds Ratios French-speaking Switzerland Constant

German-speaking Switzerland

0.084***

0.225***

Professionals

6.684***

0.993

Entrepreneurs

8.164***

3.049

Crafts/Small business

1.508

1.039

Farmers

1.302

0.306

Middle managers

7.069***

1.716**

Senior civil servants

6.115***

2.618*

Civil servants

3.068***

1.40

Unskilled and semi-skilled

0.149*

0.135**

2.658***

1.496**

Mainly internationally-oriented

5.117***

4.537***

Proximity to language border

0.517**

0.802

Gender: male

0.793

1.331

637

767

0.358

0.194

Type of joba

Economic sectorb Services Other independent variables

N Pseudo-R2

Source: Calculations by authors a Omitted category: employees. b Omitted category: manufacturing. c At firm level, as opposed to ‘mainly national’ and ‘mainly local’ orientation. *Statistically significant at the 10% level. **Statistically significant at the 5% level. ***Statistically significant at the 1% level.

between four types of function within the fi rm (management, production, purchasing and sales); the corresponding results are reported in Table 5.5. These results indicate that the importance of sales in English increase the use of English much more in the sales and management divisions of fi rms, but have little impact on production and purchasing. Swiss (as opposed to

Language Use and the Production Process 85 Table 5.5

Net Impact on the Use of English in Percentage of Working Time, Manufacturing Firms in French- and German-speaking Switzerland, 2007 (N = 191), Percentages Management Production Purchasing

Share of purchases in English Share of sales in English

ns

ns

Sales

0.35

ns

0.35

0.04

0.12

0.55

-10.08

ns

-5.62

ns

Share of firm employment in Switzerland

ns

-0.06

ns

ns

20 < FTE < 49.9

ns

ns

ns

ns

50 < FTE < 99.9

ns

ns

ns

4.61

100 < FTE < 199.9

ns

ns

ns

ns

Swiss-owned

200 < FTE

8.66

ns

ns

ns

Adjusted R2

0.487

0.118

0.360

0.634

Source: Calculations by authors with LEAP data base (Grin, Sfreddo and Vaillancourt, 2009). All reported values are significant at the 5% level. FTE = full-time equivalents; ns = not statistically significant at the 5% level. Employment measured in full-time equivalents; range boundaries may be non-integers. Omitted categories are foreign-owned and small companies (less than 20 full-time equivalents). Non-integer values reflect averages applying to groups of respondents.

foreign) ownership has a tendentially negative impact on the use of English, particularly for management. As to the share of employment in Switzerland (as opposed to abroad), its effect is non-significant or, in the case of production, very small, which suggests that aspects other than location are primary determinants of language use. A similar analysis using the same data base was conducted of the determinants of the use of French and German by Swiss employers. While the results are not reported here, one may want to note that as in the case of English, the size of the fi rm rarely has an impact on the use of either French or German, while the share of sales or purchases in either French or German has some impact on the use of these languages. The foregoing results tend to confirm our set of core assumptions, namely, that foreign language skills are valuable because they are used, that they are used for performing some tasks rather than other tasks, and that there are sound business reasons for doing so. However, the evidence presented so far remains circumstantial, and the implicit narrative that goes with it remains somewhat indirect. All this calls for a rigorous framework to establish a more explicit link between language use and economic outcomes. This is the purpose of the next section of this chapter, where we take things, as it were, “from the top”, by revisiting the basics of production theory, and add language to this core model of economic analysis.

86

The Economics of the Multilingual Workplace

5.3

THE PRODUCTION MODEL REVISITED

In order to assess the economic value of language skills and multilingualism, and since received production theory, as we have pointed out before, is “language-less”, we need to develop a “language-augmented” theory of production.7 This core theoretical approach enables us to identify the economic and linguistic variables that can be connected through the production process, and to formulate explicit relationships between them. We can then do two things: fi rstly, by specifying the relationships at hand, we can go beyond the rather vague idea that X and Y “are related”, and suggest how they are related; secondly, by working with variables that symbolise actual cases, we can propose falsifiable statements with broad relevance, that is, not confi ned to a particular case but applicable in general. The full algebraic model is presented in Appendix I, but the general principle of the approach is as follows: • fi rstly, if {E} symbolises a set of “standard” economic variables and {L} a set of variables incorporating a linguistic dimension, we can think in terms of a meta-level relationship of the form E = f (L), whose general idea is that these two sets of variables are related in systematic ways through some function f. This general link will be formally expressed through a set of more specific relationships, usually in the form of a system of structural equations; • secondly, these specific relationships need to be given a more precise algebraic expression, a necessary step for deriving the comparative statics of the model, that is, to compute how changes in linguisticallymarked variables affect economic variables—for example, whether an increase in the average level of foreign language skills in the population will tend to increase or decrease production costs; • thirdly, we can work out the comparative statics in the opposite direction, and use the model to look at the reciprocal set of relationships, which are of the form L = f –1 (E), indicating how changes in economic variables affect linguistically-marked variables—for example, if a rise in labour costs tends to increase or decrease the demand for language skills; • fourthly, once the set of relationships is formulated, they can be measured and quantified—but how one moves from a theoretical to a “measurable” model is a separate question that we address in Chapter 6; this amounts to testing the model, in order to check whether the relationships featured in it are likely to provide a sensible account of real-world processes. Two things need to be pointed out here. Firstly, quantification is not possible before theoretical model-building has reached a certain stage. In fact, it is only on the basis of theory that we have consistent reasons for deciding

Language Use and the Production Process 87 what data to collect, or what observations to make. Otherwise, the choice of data to collect may be based on a superficial, impressionistic and possibly misleading reading of the processes at hand, resulting in a somewhat haphazard approach to data collection (we have seen some examples of this problem in Chapters 2 and 3). Secondly, the relationships between {E} and {L} are not merely correlational. They are intended to capture some of the logical reasons why variables are interconnected. There is a narrative behind the equations, which contains an account of why some relationships obtain. Consider for example the case of a rise in labour costs, which will generally lead employers to reduce employment, either through direct layoffs or through the non-replacement of retiring employees. Suppose that we observe a stable relationship indicating that this reduction, all other things being equal, affects unilingual more than bilingual employees. Economic analysis (and, more specifically, production theory), offers an explanation: an increase in wages will eat away at the difference between the productivity of labour and the wages paid out to the workers. But since bilingual employees tend to have higher productivity (also because of their bilingualism), it is more likely that for a given increase in wages, it remains profitable for the employer to keep them on the payroll. By contrast, unilingual employees are more likely, all other things being equal, to become too expensive in relation to their contribution to value creation. Consequently, employment will contract for unilinguals more than for bilinguals. Once relevant data are collected, either from ad hoc surveys or through the appropriate combination of existing data bases, the statistical work involves moving from theoretical to estimated relationships. Data on {E} and {L} make it possible to assess the magnitude of the f relationship between them. Keeping to the meta-level, we could say that the E = f (L) relationship ^ can then be replaced by an E = f (L) relationship, where the circumflex over the “f” indicates that the link between the variables in sets {E} and {L} is captured through statistically estimated parameters.8 This opens the door to a whole new range of analyses. We can for example predict the expected average effect of changes in the level of linguisti^ cally-marked variables on economic ones, since Ê = f (L); reciprocally, we can predict the expected average effect of changes in the level of economic variables on linguistically-marked ones, since data on {E} and {L} allow us to come up with estimates not only of the function f, but also of the reciprocal function f –1. Thus the model enables us to calculate: ^

^

(1) dE = f ' dL ≡ (∂E / ∂L) ⋅ dL and: (2) dLˆ = ( f ^

^ −1

)' dE ≡ (∂L / ∂E ) ⋅ dE

where the primes denote fi rst-order derivatives.

88

The Economics of the Multilingual Workplace

Let us now take a few more steps inside the formal model. The basic idea of production theory is that the output, whether a good or a service, is produced using various inputs, also called “production factors”. The fundamental production factors are capital and labour, but this can be made more specific by distinguishing between various types of capital and labour. For example, labour may include, as we have seen in Chapter 4, skilled and unskilled labour, or labour with or without particular language skills. In the same way, “capital”, which is sometimes treated as a residual concept (that is, anything that is not labour), may be broken down to highlight the contribution not just of capital in the strict sense (such as buildings, machinery or other heavy equipment), but also of consumables like office supplies, raw materials or semi-finished goods.9 Just like labour may be broken down according to language characteristics (that is, workers with or without certain skills), so can other production factors, wherever this helps to address analytical questions. In line with the discussion opened in Chapter 1, we focus on the linguistic characteristics of workers and intermediate (or semifinished) goods and services. This is not to imply that these intermediate goods themselves necessarily have intrinsic linguistic characteristics, but services usually do, and even in the case of material goods, communication with the suppliers of those intermediate goods is linguistically marked. Any introductory textbook in microeconomics uses a production function where output (y) is a function of capital (k) and labour, usually measured in hours worked (h), such as: (3) y = y (h, k), Instead, we adopt a slightly more complex function where the output is a function of capital, but also of hours worked by two types of workers (unilinguals who speak only language R and bilinguals who speak languages R and S), and intermediate goods and services that are also linguistically marked: some will be linked to language R, others to language S, possibly owing to their intrinsic linguistic aspects but, more commonly, to the language generally used when negotiating and purchasing them. Our language-augmented production function thus reads: (4) y = y (cR , c S , hR , hRS , k), where cR and c S stand for the linguistically-marked intermediate goods and services, while hR and hRS stand for the unilingual and bilingual segments of the employed workforce respectively. This production function captures technical constraints: given a certain amount of labour, intermediate goods and services, and capital, there is only so much that a producer can produce, whatever the good or service considered—cars, ball bearings, restaurant meals, overcoats, medical care or translations. The producer also confronts a set of constraints

Language Use and the Production Process 89 over which he may have little or even no control. As announced in Chapter 1, we shall focus on the benchmark case of perfect competition, in which an individual producer has no control on the wage level (whether wR for unilingual or wRS for bilingual workers), the prices (respectively gR and gS) of intermediate goods and services organised in two “language categories”, the cost of capital (usually proxied by the interest rate i), and the unit price p of the output y. What the producer can choose, however, is the quantity of output to be produced and the number of production factors he will use. In a nutshell, let us say that the producer will choose output and input levels in order to maximise profits. Profit π is defi ned as the value of sales minus total cost. Sales, of course, are the product of output by its price, that is, p × y. Total cost is given by the sum of expenditures on the various inputs, where the expenditure on each input is the quantity of input used multiplied by its respective unit price. Thus, the producer’s problem is to choose the levels of the “choice variables”, namely, y, cR , c S , hR , hRS and k that maximise, for any given time period t, the profit function: (5) π = p ⋅ y(cR , c S , hR , hRS , k)–(gR cR + gS c S + wRhR + wRShRS + ik) In essence, the procedure requires deriving this profit function with respect to the choice variables, setting the derivatives equal to zero, and solving the system. This exercise generates mathematical solutions which represent the economically optimal amounts for each of the choice variables. These optimal amounts, which will be noted y*, cR*, c S*, hR*, hRS* and k* (an asterisk being a conventional way to denote the optimum level of a variable) are, in turn, functions of all the exogenous variables in the model, namely wR , wRS , gR , gS and p. By computing the fi rst-order derivatives of the optimal levels of each choice variable with respect to the exogenous variables, we can estimate the sensitivity of the choice variables to changes affecting the exogenous variables (for example, an increase in the wage rate of bilingual workers in the labour market); this, as explained earlier, is the exercise known as “comparative statics”. The full model is presented in Appendix I. For the purposes of this chapter, however, let us simply provide a bird’s-eye view in the form of a diagram (Figure 5.1). This model, fi rst introduced in Grin, Sfreddo and Vaillancourt (2009), provides a set of theoretical results predicting how the optimal levels of the choice variables will respond to changes in the value of any of the exogenous variables in the model. For example, we obtain analytical demand functions for the work of unilingual and bilingual employees, as well as for intermediate goods and services “in” languages R and S respectively. However, the actual level of demand predicted depends on the production function chosen, that is, on the algebraic formulation of the relationship between the inputs and the output.

90 The Economics of the Multilingual Workplace Wage rate of unilingual (R) and bilingual (RS) workforce Price of intermediate goods and services: - in language R - in language S

Output

Profit maximisation

Output price

Amount of capital

Figure 5.1

Input of unilingual (R) and bilingual (RS) workforce

Technology

Input of intermediate goods and services: - in language R - in language S

Language-augmented production model.

In analytical terms, expressing this actual level of demand matters less than the capacity to make predictions regarding the direction, positive or negative, in which demand changes following changes in the level of the exogenous variables. Typically, therefore, much attention is devoted, in theoretical economics, to “signing the effects”, that is, identifying under which formal conditions an increase in such-and-such an exogenous variable, all other things being equal, will cause the optimal level of such-and-such a choice variable to increase or to decrease. As shown in Appendix I, the effects predicted by the language-augmented model are perfectly sensible; this paves the way for moving on to the next stage, that is, quantifying the relationships in order to provide estimates of the parameters and, from there, to predict changes as shown in equations (1) and (2). A formal model constitutes both an analytical framework and a stepping stone for moving on to the following stages of the examination. The model reveals the range of data that are needed; this can be compared with the data actually available. However, there is, almost unavoidably, a more or less yawning gap between the two, and the existing data must often be subjected to some extensive work before they can be used to test the model and quantify the relationships between variables. This type of work is presented in Chapter 6, which is intended to introduce readers to some of the hands-on issues that arise in quantitative language economics. This interaction between a formal model and the data used to test it, however, is not a one-way process. It is not only a matter of processing and combining figures gathered through various surveys or derived from national accounts until they are transmogrified as sets of numbers that perfectly match the needs of theoretical analysis; this would, in fact, be an

Language Use and the Production Process 91 unusually ideal situation. If the context is less favourable, some adaptation also needs to go the other way, which means that the relationships to be tested must often be re-expressed differently. For example, instead of estimating the absolute value, in working hours, of an (expected) increase in the demand for labour with S-language skills resulting from a given absolute increase in the price of goods sold on the S-language market, we may have to content ourselves with less straightforward results, such as the relationship between the relative changes (that is, in percentage terms) of both variables. As we shall see in Chapter 7, however, combining the model with available data already generates a range of entirely new results. In Chapter 9, we return to the question of data and sketch out what an ideal data set would look like.

6

6.1

From Theory to Measurement

ON MODELLING, CALIBRATION AND DATA

In Chapter 1, we have identified the channels through which linguistic diversity influences, from the perspective of standard microeconomic theory, fi rms’ performance. And after reviewing theoretical and empirical work on the matter, this has led us, in Chapter 5 to develop a new model that formalises some core relationships. Our model provides an analytical structure for analysing the linguistic aspects of fi rms’ performance, by highlighting how linguistically-marked economic variables determine each other. The relationships described in Sections 5.2 and 5.3 were expressed in generic terms, and no attempt has yet been made to quantify them. For example, we made the general assumption that a change in the quantity of goods and services produced by a fi rm required the quantity of inputs to be adjusted accordingly, but nothing was said about the actual extent of the adjustment needed in real world. It is now time to bridge the gap between theory and measurement. This is done following two mutually complementary strategies: fi rst, the synthetic quantitative information required by the model must be extracted from a multi-faceted reality; secondly, the theoretical approach itself must be adjusted in the case of missing data or of data incompatible with the specification of the model. In practice, these two tasks are performed in an iterative process, until the transformed models can accommodate the quantitative information that can be collected. The goal of this chapter is precisely to show how such an iterative treatment can be carried out, and what are its constraints and difficulties. Particular constraints arise from the relentless quest for generality already alluded to in Chapter 1, which naturally leads to quantitative research: because the approach rests on the standard “hypothetico-deductive” approach (without necessarily, however, embracing a strict Popperian methodology; see Caldwell, 1982; Blaug, 1992; Mayer, 1993), data are needed for experimental verification, and to be considered relevant, the data have to pass demanding tests. Precisely because the emphasis is not on describing real human action and then uncovering the meaning

From Theory to Measurement

93

embedded in it, direct observation of a few individual cases is considered radically inadequate, whereas it is the repository of knowledge in, say, ethnomethodology. As pointed out in Section 2.4, we have no intention of passing judgement on distinct epistemological approaches, but we must be clear about the type of data needed to handle the questions investigated in this book. In the tart words of an epidemiologist known to the authors, “data is not the plural of anecdote”. The emphasis on relationships between variables and, as much as possible, on causal relationships, leads to the quest of data in the form of observations about which information in some predefi ned format (variables) is recorded in a stable fashion. The data base needs to be representative of the population studied, and the data base must be of adequate size to lend itself to statistical treatment; the more elaborate the treatment is, the larger the necessary data base must be.1 This is not to say, obviously, that quantitative research does not raise considerable methodological difficulties, even when deeper epistemological ones are left aside. A word needs to be said about the problem of representations. Clearly, many of the variables that structure data bases are populated with information derived not from direct observation, but from respondents’ declarations, which themselves reflect actors’ perceptions and representations. For example, when several hundred people in a representative sample are asked whether, on average, they use a given foreign language “every day”, “not every day, but at least once a week”, “seldom”, or “never”, the analyst uses answers that may not quite match actual frequencies. The problem can be mitigated, however, by the quality of the data gathering procedure. This means chasing ambiguities in the questions asked, crossing variables to detect and correct inconsistencies, but most of all relying on the law of large numbers: unless there is a bias in the sample, overestimations in the answers of some respondents will be offset by underestimations in the answers of others, with the result that mean sample values will be acceptable approximations of actual mean values. Quantitative approaches, in any event, offer decisive advantages at three levels: to describe reality in a synthetic fashion, to collect information on different elements of reality in a systematic way and to process this information. Let us therefore introduce the idea of calibration, that is, the process through which a general theoretical model is transformed into one applicable to economic reality as reflected in quantitative data. Calibration can in fact hardly proceed without making extensive use of data, which must capture the main features of multi-faceted processes: data construction implies the search for the right balance between accuracy and manageability. Section 6.2 provides an example of how to strike such a balance by showing a possible approach to deal with labour heterogeneity, while capturing in highly synthetic way all the relevant information needed for the calibration of a production function. Section 6.3

94 The Economics of the Multilingual Workplace moves one step further and shows how the problem of the heterogeneity of goods can be tackled when the model is based on a cost function or a profit function. Whereas Section 6.3 focuses on quantities, the construction of price series, including wages and the price of other labour-related inputs, is presented in Section 6.4. In Section 6.5, we discuss the reasons why an analysis at industry level might sometimes be preferred to fi rm-level analysis, and the drawbacks and advantages associated with this choice. One way to get closer to a simple real-world economic representation is to calibrate the model. Calibration means assigning values to the parameters in the model such that the behaviour described by the model is as close as possible to the actually observed value of the variables included in the model. This defi nition might sound somewhat technical. To make it clearer, let us take an example. First recall that, as shown in Chapter 5, the relationship between the quantity produced and the level of inputs can be captured by a production function. Assume we wish to model the behaviour of producers in the manufacturing industry of a given country by looking at the current production of a sample of three fi rms, whose output (the endogenous variable) depends on the quantity of two inputs (the exogenous variables), namely, the number of workers (labour quantity) and the number of machines (capital stock). Let us assume that the current values of inputs and output for the three fi rms as shown in Table 6.1. Calibrating the model on the basis of the values in Table 6.1 consists of assigning a value to the parameters in the production function so that, when the number of workers takes on the values 10, 8 and 15, and the quantity of capital is 20, 20 and 22, then the value yielded by the production function (output quantity) is as close as possible to 100, 90 and 120 respectively. Once the production function is calibrated, it can be used to run simulations aimed at studying fi rms’ behaviour by assessing the potential impact of possible shocks on the firms’ performance, without having to resort to in vivo experiments. Calibration is most often performed mathematically using econometric techniques. 2 Alternatively, it can be carried out based on the researcher’s perceptions: in this case, the parameters are assigned a value that the

Table 6.1

Example of Relationship between Inputs and Output Number of workers

Number of machines

Quantity produced

10

20

100 units

Firm 2

8

20

90 units

Firm 3

15

22

120 units

Firm 1

From Theory to Measurement

95

researcher, given previous observation and experience, as well as information reported in others’ work, considers plausible. Despite resting on the subjective perceptions of the researcher, the latter approach can sometimes be used, in a slightly modified way, to assess the likelihood of the occurrence of severe consequences following a given shock. This is done by considering that the parameters cannot reasonably take on values which lie outside a given range. If, for values lying within this range, simulations suggest that, for example, a given increase in labour cost yields only mild consequences for the fi rm’s profit, then this piece of information is per se of interest and therefore valuable, since it reveals that in most instances the shock under analysis is not likely to severely affect the fi rm’s profitability. Thus, lacking information on the true value of the parameters should not stop the researcher from using simulations to investigate economic reality. Although knowing the value of the parameters is not a sine qua non condition to get valuable information on producers’ microeconomic behaviour, it is when they are known (or derived econometrically) that the analysis generates the most interesting, most precise and probably most useful results. Thus, calibration should be performed through econometric estimation whenever possible, which is feasible, however, only when appropriate data on a sufficiently large sample of fi rms, or of any other production units or clusters one wishes to study, are available.

6.2 DATA COLLECTION: DEALING WITH MULTI-FACETED LABOUR Calibration through econometric estimation raises the crucial question of what data to collect and how, and what alternatives the researcher may consider, should constraints force him to do so. Let us consider the case of a basic production function. To calibrate its parameters, the researcher should have access to the value of all the variables of the function—the quantity of capital, the quantity of labour and the volume of output produced—ideally, for a representative sample of fi rms. These data can easily be accessed at a fi rm level by the fi rms themselves: in practice, quantities of capital and labour can be approximated by the amount of fi xed assets and by the number of workers (or the total number of hours of work supplied by the staff), respectively, and output is best proxied with value added, that is, the difference between the value of production and the value of goods and services (other than labour services) used. While collecting basic data on capital, labour and output at a fi rm level through a survey is technically feasible, this information is often seen as sensitive by the fi rms’ management and hence disclosed with some reluctance. 3 Collecting data becomes more difficult when the series needed include the quantity, at fi rm level, of language competences available to the producer.

96 The Economics of the Multilingual Workplace This “stock of skills” can be treated as an input in the production function and can thus be cast in the role of a core variable in language economics. The easiest and most natural approach to handle information on the quantity of language competences in order to analyse their role in production is to divide workers in two categories, unilinguals and multilinguals, the latter group embodying the foreign language skills entering the production process. This, of course, raises the question of the definition of a “multilingual”, a point mentioned only in passing in Chapter 2. The issue arises for any research, qualitative or quantitative, which refers to constructs such as unilinguals, bilinguals, and so forth. As noted before, we are not wedded to the notion that languages are sharply distinct, “discrete” entities. However, identifying speech or written text in one language or another, and recognising that the capacity to produce or to understand speech or text in language R does not automatically imply the capacity to do the same in language S, where R ≠ S, hardly seems to do violence to human experience.4 The problem then becomes one of appropriately assessing levels of competence. There again, overestimations and underestimations will offset each other, and there are techniques for increasing the comparability of data, for example, when collecting individual data, by using the Council of Europe’s Common European Framework of Reference for Languages.5 Problems arise when the corresponding data need to be collected at fi rm level, since in this case fi rms are invited to provide information on their employees. Respondents, often from the human resources department, do not necessarily have this information. Moreover, some employers explicitly refuse, for the sake of internal confidentiality, to record electronically information other than administrative data. In such cases, data on workers’ language skills cannot be extracted from a centralised system and it is most unlikely that a fi rm would be prepared to launch a time-consuming internal survey just to meet a researcher’s request. The researcher must then resort to the information that respondents are willing to provide based on their subjective appreciation. Because subjective assessment might produce a somewhat blurred representation of reality, it may be of interest to consider an additional distinction between types of linguistically-marked, labour-related inputs, namely, the breakdown of working time by language used. Gathering information on language skills, as well as on productive and receptive language use, makes it easier to detect inconsistencies and mistakes in the answers provided. For instance, reporting some use of a given foreign language but no corresponding skills is most likely an error on the respondent’s side, which prompts appropriate inquiries, corrections or both. In a recent survey on Switzerland’s manufacturing industry, it is, surprisingly, the question of workers’ skills, rather than that their language use, that suffered from the higher rate of inconsistency.6 This approach to capturing linguistically-marked, labour-related input also raises the fundamental question of whether this input should be treated

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as a flow (which has a time dimension) or as a stock (which does not). To make this point clear, consider a fi rm employing two perfectly bilingual workers: employee A uses both the local and the foreign languages on a daily basis, while employee B mostly uses the local language and rarely uses the foreign language. Clearly, the foreign language input supplied, during a certain period of time, by employee B, is less than that provided by employee A, although the amount of competences both workers are endowed with is the same. In other words, the stock of competences is the same, whereas the flow of competences “flowing” from the workers into the production process during a given period of time is different. How can a “flow” of language be measured at the fi rm level? The most direct way is to quantify, by language, all communication entering the fi rm’s production process, whether passive or active, written or oral. In practice, this value can be approximated by asking respondents to break down their total communication time by language. One might argue that the flow approach better captures the actual input of language competences. While this is probably true, available public or semi-public data bases do not leave the researcher with much choice but to resort to the stock approach. This constraint is, however, fairly common (and hence is hardly ever discussed in economic literature), as it concerns all types of capital inputs for which the utilisation rate is unknown (as is normally the case for buildings, computers or machinery): a laptop computer used by the fi rm’s staff only on rare occasions will be assigned, in estimating the capital stock of the fi rm, the same weight as that of a desktop computer used many hours every day. The volume of services supplied by the latter is larger than that provided by the former and yet, in empirical economic research, such differences are rarely taken into account, not the least because of missing or unreliable data.7,8 Clearly, the question of how to measure input quality should also be addressed. Accounting for heterogeneity of the quality of labour naturally leads to the distinction between skilled and unskilled workers. The more skilled a worker is, other things being equal, the higher his or her contribution will be to the fi rm’s profit or to any other variable approximating the fi rm’s performance. Despite the lack of consensus on the best way to defi ne levels of qualification, with the possible exception of the International Standard Classifi cation of Occupations,9 public data bases exist that provide statistics on the number of skilled and unskilled workers, but these statistics are published by economic sector. Firm-level data are difficult to collect, partly because the interpretation of the term “skilled” is likely to vary greatly from one respondent to another—recall that respondents are not the workers themselves but often members of the human resources department—and also because, there again, information about workers’ skill levels is not necessarily centralised. In practice, one way to approximate the availability of skilled labour at fi rm level is to use the number of managers. These data are easy to gather, whereas

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The Economics of the Multilingual Workplace

collecting detailed data on workers’ skills would require the launch of a time-consuming internal survey.10 The approach to estimate the amount of language skills and the approach to estimate the value of (non-language-related) professional skills are conceptually similar. The former is based on the distinction between monolinguals and multilinguals; the latter is based on the distinction between non-managers and managers. In principle, given that each of these qualifications has a specific impact on the fi rm’s performance, labour input should be disaggregated using both breakdown criteria, namely, professional skills or hierarchical position, and language skills, as shown in Figure. 6.1. As the preceding paragraphs suggest, it is possible to estimate, without too much difficulty, the size of (labour) groups when these are defi ned using just one breakdown criterion: replacing the content of columns 1 and 3 in Figure 6.1 by the corresponding quantities is relatively easy. The task becomes more complex when one tries to assess the input quantities in column 2. In other words, the higher the number of breakdown criteria (or dimensions) used to categorise labour, the harder the task will be. What is more, this difficulty grows exponentially with the number of dimensions: using a third criterion to categorise labour (distinguishing workers with or without a university degree, for instance) increases the number of groups at the highest level of disaggregation from 4 to 8 (i.e. 2 × 2 × 2), even though each criterion is constrained to take on only two values. Categorising labour

Breakdown criteria Language skills (1)

Language skills and Professional skills (2)

Professional skills (3)

Monolingual staff Monolinguals

Staff Monolingual managers

All workers

All workers Multilingual staff Multilinguals

Managers Multilingual managers

Figure 6.1

Breakdown of labour by skills levels.

From Theory to Measurement

99

through an increasingly larger number of dimensions, that is, adopting a sequential-breakdown approach to treat multi-faceted labour, makes any attempt to assess the size of each group of workers, for each fi rm under analysis, virtually impossible: only a minority of mid- to large-scale fi rms will be able to tell, say, how many male operative workers hold a university degree but speak only the local language, or how many female managers do not hold a university degree but master a foreign language. And yet, taking account of the heterogeneity of labour is necessary whenever we wish to separate the contribution of language skills to the fi rms’ performance from the influence of other factors. One way to overcome the complexity of the sequential-breakdown approach and therefore to facilitate data collection is to consider that fi rms use services and skills provided by their workers, rather than to see fi rms as entities employing workers endowed with specific skills. Slight as it may sound, this difference is not trivial: as opposed to the sequentialbreakdown method, this alternative approach treats language skills and managerial skills as production factors per se, which can be combined with work to produce a given good. Production is thus viewed as a process using, in our case, three inputs: pure labour, language skills and managerial skills. The corresponding input quantities can be approximated by the total number of workers (or the total number of hours worked), the total number of bilingual workers and the total number of managers respectively. A bilingual manager will therefore be recorded three times in the production function: fi rst as a provider of working effort, second as a provider of language skills and third as a provider of managerial skills. The fact that the same worker can appear up to three times in the production function should not be confused with the mistake of double- or triplecounting, that is, counting the same input two or three times. Counting bilingual workers and skilled workers in parallel with total workers is merely a way to assess the total amount of language and non-language skills available for production. What has been captured through eight types of labour-related inputs in the sequential-breakdown approach is here captured by only three inputs, each of them referring to one single labour characteristic. We call this treatment of multi-faceted labour the parallel-breakdown approach. Reducing the number of inputs inevitably comes at the cost of flexibility: the parallel-breakdown method implicitly assumes that the impact of using or mastering foreign languages on the fi rm’s output is the same whether the speaker is a manager or a member of staff. On the other hand, by using this approach, the number of types of labour qualifications can be increased without adding significant complexity to data collection, and with a considerably smaller increase in the number of inputs than would be required otherwise: the researcher will have to deal with only one additional input per additional labour qualification. As a result, the parallel-breakdown approach is preferable in empirical research

100

The Economics of the Multilingual Workplace

whenever numerous characteristics of highly multi-faceted labour must be taken into account.

6.3

DATA COLLECTION: GROUPING GOODS

So far we have discussed problems concerning the measurement of variables entering a production function, mainly the quantity of labour-related inputs. Recall that, as shown in Chapter 5, the production function can be used to describe the behaviour of fi rms when these produce a single good or when no distinction between goods is needed, in which case the output is simply measured as the value added produced. The cost and the profit functions provide an alternative approach to model the relationship between outputs and inputs, and they can be brought in whenever it is necessary to distinguish explicitly between the different types of goods produced (and utilised), or, put another way, to explicitly take the heterogeneity of goods into account. Though straightforward in theory, the empirical implementation of this approach is problematic because the number of types of goods produced in the whole economy is very large. At a reasonable level of disaggregation, the HS classification,11 used to categorise traded products, contains roughly 5,000 items, and up to 12,000 at the highest level of disaggregation. Complexity increases when the language of the buyer (or the seller, in the case of input goods) also needs to be considered to identify products, languages being precisely the key criterion used to categorise goods in the theoretical models presented in Chapter 5. Although this issue is reminiscent of that associated with the sequential-breakdown approach adopted to tackle labour heterogeneity, the solutions to the problem differ: while it is reasonable, for example, to calculate the total hours worked by adding hours worked in language X to those worked in language Y and to treat language skills as a separate input (as is proposed by the parallel-breakdown approach), one cannot as easily combine outputs (or inputs) whose nature differs significantly, such as hammers and saws. Moreover, even if one wished to do so, the question would arise of what unit to choose to measure the quantity of the aggregated output: should the quantity of the group of goods “hammers and saws” be measured in kilograms? In item units? In cubic meters? Also one should recall that grouping goods produced by merely summing the corresponding values—in dollars, euros or roubles—is not the correct procedure when working with a cost or a profit function: values expressed in monetary units are a combination of prices and quantities, and hence they are not suited to capturing quantities alone. This is why we need to work with quantities first, before combining them with prices. The disaggregation of the total quantity of output or, equivalently, the aggregation of quantities of individual goods, is best performed by calculating a quantity index. A quantity index is an indicator capturing the quantity

From Theory to Measurement

101

(volume) of a group of heterogeneous commodities. The level of a quantity index is meaningless per se: what matters is how it changes through time or, in our case, how it changes across firms and markets. To illustrate the concept, assume that two firms, A and B, sell their goods on three markets, each of which is identified through its locally dominant language, say, French, Spanish and English. A quantity index can be constructed for each of the six (2 × 3) sets of goods: if the quantity index associated with the group of goods sold by A on the French-speaking market is equal to 120 and the index for goods supplied by B to the English-speaking market is 60, then we can say that the quantity that B sells on the English-speaking market is, on average, half that sold by A on the French-speaking market. Once their values are computed, quantity indexes can be used like any other quantities in either the cost or the profit function, while keeping the size of the model within reasonable limits. Index theory provides the necessary conceptual tools to construct, among other things, the quantity indexes best suited to the model at hand.12 These tools will not be discussed here: let us simply note that the construction of a quantity index requires the unit price or, alternatively, the value for each type of good sold (or used), along with the corresponding quantities. These data are recorded by most fi rms for internal use and could, in principle, be collected in a survey. However, some fi rms may be unwilling to participate in such a survey, either because of the time required or because they consider the data to be confidential.

6.4

PRICES AT FIRM LEVEL

Unlike production functions, which require data on quantities alone, cost and profit functions describe the input-output links using both prices and quantities. The measurement of the quantity of goods (used or produced), and in particular the need for aggregation are discussed in the previous section. Once groups of goods have been formed to calculate the corresponding quantity indexes, the same groups of goods must be kept to compute price indexes. Prices and quantities are then used to perform the econometric estimation of the cost or profit function. In practice, price indexes can be derived more easily than through aggregation: a price index can be indirectly obtained by dividing the value of the goods sold by the corresponding quantity index, just as dividing the dollar value of a truckload of apples by the number of apples (quantity) yields the price of one apple. It is worth noting that an analogous approach can be adopted to compute an indirect quantity index, provided that the related price index has previously been constructed by aggregation. Also, as is the case for quantity indexes, the absolute value of a price index per se is meaningless: it is its variations that matter, whether across time, firms or goods. The task of calculating price indexes becomes more complex when dealing with labour-related inputs (language or managerial skills, etc.): how can

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the price of one “unit of language skills” (or of any other qualification) be measured? To answer this question, recall that the “stock” of language skills available to a firm can be approximated with the number of bilingual workers (see Section 6.2). Because foreign language proficiency increases labour productivity, bilingual workers enjoy, on average, higher wages than monolingual workers, as shown in Chapter 4. Therefore, the difference in wages that can be specifically attributed to language competences can be seen as an approximation of the price of one unit of language skills. However, wage differentials cannot be observed directly, and even human resources departments find it difficult to assess them, that is, to price language skills. Wage differentials, along with the price of pure labour, can nonetheless be obtained indirectly using appropriate econometric techniques applied to data on workers’ wages and skills, among others. This is done in three steps: (1) forming small homogeneous groups of firms, so that each group provides a sufficiently large number of observations to work with; (2) using the information collected on wages and skills to estimate econometrically the language-related wage differential for each group of firms; (3) assigning firms the language-related wage differential of the group they respectively belong to. The data needed for this procedure are the same as those required to disaggregate labour using the sequential-breakdown approach and are therefore difficult to gather, as shown in Section 6.2. In practice, however, each fi rm will need to provide data for only a sample of its workers (not for all of them), since what is needed is a sufficient number of observations for each group of fi rms and not for each individual fi rm. This significantly reduces the burden for the fi rm and increases the response rate as a consequence.13

6.5

AGGREGATION AT INDUSTRY LEVEL

The preceding sections suggest that ad hoc surveys are the main source of fi rm-level data. They also show that surveys are a somewhat delicate tool to work with: as long as they are not compulsory, their response rate partly depends on the effort required from respondents, as well as on the level of confidentiality of the information collected.14 In order to estimate a cost or a profit function econometrically, the range of data that should be gathered from fi rms taking part in a survey should ideally include: • price and quantity for each type of commodity produced, broken down by language used with the purchaser; • price and quantity for each type of commodity and service purchased, broken down by language used with the supplier; • wages, total hours worked, education, professional skills, position in the company, language competences and/or communication time by language for every worker (or, at least a sample of them); • accounting data: fixed assets, sales, purchase of goods and services, etc.

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103

The burden for fi rms of providing such data may be significant, penalising the response rate. It is worth noting, however, that a low response rate does not necessarily imply weak representativeness: it is only when certain types of fi rms are less likely than others to fi ll a questionnaire that representativeness is jeopardized. For instance, if we assume that fi rms operating in a highly multilingual environment are those most interested in languagerelated issues and thus more likely to participate in the survey, then results will tend to overestimate the average time spent communicating in foreign languages (fi rms not using foreign languages will be under-represented). On the other hand, it can also be claimed that fi rms not using foreign languages will have less information to provide: their burden is lighter, thus boosting the response rate. Although the question remains open whether the two effects offset each other, running a non-compulsory ad hoc survey to collect the entire data set would be a somewhat risky endeavour, unless the institutional or regulatory context strongly encourages participation. This motivates the search for alternative strategies. So far we have focused on the construction of a data set aiming at the calibration of production functions, cost functions or profit functions at the level of the fi rm: the goal was to build a model to describe the behaviour of a representative firm for any given industry.15 Let us now imagine that we wish to study the behaviour of an entire industry, rather than a representative fi rm. Shifting the analysis from fi rms to industry would not alter the type of data needed: price and quantity of goods (purchased or sold) by language, wages, language skills, hours worked and so forth, would still be required, but at the industry level only. Industries would simply be considered as “large fi rms”. National statistics often provide data by industry: aggregate accounting data and price growth rates of output can be computed from existing public data bases; information on skilled and unskilled work by industry may also be available. Since much information at industry level already exists, it can be used with other data, collected ad hoc, to produce the series of prices, quantities, wages and so forth required for model calibration. For instance, within a given industry, the growth rates of the price of goods sold to Spanish-speaking trade partners can be extracted by combining growth rates in the general price level of output sold (which is publicly available information) with the breakdown of sales by language used with the trade partner (data that can be collected through a survey). The procedure requires some assumptions to be made, but these are not unduly constraining. For example, the share of total sales of goods sold in Spanish may be assumed to be fairly stable over a limited number of years. In this case, fi rms would simply be required to provide information on the breakdown of total sales by the language of the purchaser: no detailed or confidential data on prices and quantities for any type of goods produced would be required. The foregoing suggests that some data, in particular data on language use, would still need to be collected through a survey, but by combining a

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survey with information from public data bases, the extent of information to be retrieved directly from fi rms, and hence the length of a questionnaire, can be substantially reduced. Working at the industry level also makes it possible to combine databases covering different areas and to construct series relevant for production theory from data bases originally designed for other goals. For instance, business surveys usually do not collect data on workers’ language skills. These data are generally collected (if they are collected at all) through a population survey, or even a census, along with information on the respondents’ sector of activity (industry). Individual responses in the population survey can therefore be aggregated (with appropriate weighting) by industry, thus yielding valuable information on the stock of language skills and other qualifications available to the producers within each industry. It goes without saying that this procedure is possible only if the classifications of economic activity are uniform across data bases or if any differences can be corrected with minor adjustments (grouping of two or more industries, for example). It should be noted here that working with industry-level statistics involves a major drawback. While, as pointed out in Section 6.3, a quantity index can be constructed to compare fi rms producing a heterogeneous bundle of goods such as hammers and saws, no quantity index can be constructed to compare a fi rm producing hammers with another producing saws. For inter-unit comparisons, each unit must produce (approximately) the same types of goods, even if it is in different quantities. When working at industry level, one should remember that all the units producing the same type of goods are recorded within the same industry: the type of goods produced is the very element distinguishing industries from one another. Consequently, it is not possible to construct an index aimed at capturing inter-industry differences in the quantities produced at a given point in time. In this case, the only changes in quantities that the researcher can compute are those observed through time. To conclude, no level of analysis goes without constraints. On the one hand, one should ideally collect data at the fi rm level in order to have a more detailed picture of the relationship between inputs and outputs and on the link between language diversity and fi rms’ performance. On the other hand, the difficulty in obtaining information from individual fi rms may justify resorting to industry-level analysis, which can be performed by adequately combining data from various sources. Shifting the focus from fi rms to industries certainly involves the loss of some information, but that is probably the price to pay if, as is usually the case, the research cannot be carried out without model calibration.

7

7.1

The Contribution of Multilingualism to Value Creation

VALUE ADDED AND THE PRODUCTION FUNCTION

In the preceding chapters, we have explained what we wish to measure, for what reasons, and what the corresponding statistical requirements are. In essence, we are concerned with value creation, which is why a few words are necessary to recall the concept of value added. Value added is a centrally important notion in economic analysis. Roughly defi ned, it amounts to the contribution to a product’s value that originates in a particular stage of a production process. Consider a particular business, which buys various goods and services from other businesses (raw materials, semi-fi nished goods, consumables such as offi ce supplies, and all kinds of services ranging from cleaning to translation), up to a total amount which we shall call IGS (for “intermediate goods and services”). The fi rm uses these goods and services in its own production process, which gives rise to the production of Q units of a given good, sold at the unit price P. Total sales are given by the product P × Q. Value added (VA) is therefore given by VA = P × Q – IGS. This amount VA will cover the cost of capital, the cost of labour and what is sometimes called “extraordinary profit”, that is, profit exceeding the normal cost of capital. Summing value added across all the businesses in the economy yields the economy’s gross domestic product, or GDP. We are interested in estimating the part of value added that stems from multilingualism, which requires identifying where multilingualism appears in the process through which value is created, or added to what existed before. Figure 1.1 is a tool for the identification of the ways in which languages intervene in this process. Moving from a general, conceptual approach to a formal model, and then on to its calibration and testing, raises difficult methodological challenges; ultimately, the key instrument for connecting linguistic and economic processes is the production function, as we have shown in Chapters 5, where the theoretical model is developed, and Chapter 6, where the principles of our empirical work are presented.

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In this chapter, we begin with an overview of the data used to generate the estimations, focusing on language-related inputs and outputs, before presenting results from the estimation of a production function. In so doing, we get as close as possible to the heart of the matter, namely, how language affects value creation. In a closing section, however, we consider additional perspectives, which could be pursued with the help of more extensive data, and we present exploratory results on the estimation of cost and profit functions. A word must be said regarding the reference made, at various points in the analysis, either to pure value added or to gross production value. When language-specific intermediate goods explicitly enter the analysis, “output” is understood in a technical more than strictly economic sense, in that it is approached as the consequence of using various inputs, including some that are produced by other fi rms. This is necessary when investigating the interplay between standard economic variables and linguistically-marked variables. This approach, which reflects the logic of multi-input, multioutput or MIMO models, is embedded in the model presented in Chapter 5. However, technical relationships, which are necessary for some stages of model-building, are only a stepping stone for economic analysis, which emphasises the end result in the form of value creation. This leads to a focus on “output” as pure value added, and the most important results in this chapter, presented in Tables 7.5 and 7.6, are presented in terms of output defi ned as value added.

7.2

THE DATA

This section presents data on language-related inputs and outputs that are necessary to estimate a production function, a cost function and a profit function. For reasons of space, all discussion of non-language-related inputs will be omitted. The analysis will be developed using Swiss data, Switzerland being the only case where the data available make it possible to connect multilingualism with the production process; since the analytical model is, however, very general, it is applicable to any other country where appropriate data can be gathered. Let us begin with a look at the stock of language skills available to Swiss fi rms. Data are drawn from the CLES data base,1 which provides reliable statistics. (The often quoted Swiss federal censuses, by contrast, contain a question on language use but none on language skills.) For the purposes of investigating the language-economy link, we have divided the Swiss economy in nine branches, leaving out agriculture, as well as the production and distribution of electricity.2 Table 7.1 shows, for these nine branches, the stock of language skills available to Swiss fi rms, applying the principles presented in Section 6.2. The figures are expressed in units per 100 fulltime equivalent workers.

The Contribution of Multilingualism to Value Creation Table 7.1

107

Estimates of Stock of Language Competences Available to Swiss Firms, in Units per 100 Workers, 1995 data

Competences in → Sector ↓

Other main national language(s)*

English

Manufacturing industry

38

33

Construction

21

29

Wholesale and retail trade; repair of motor vehicles

36

24

Hotels and restaurants

36

45

Transport; communication

35

21

Financial intermediation

49

66

Services to businesses; real estate activities

44

47

Public administration; education; health

14

12

Other services

61

60

Whole economy

40

38

Source: Calculations by authors using the CLES data base.

The total stock of language competences can amount to more than 100 units per 100 workers, as is the case in fi nancial intermediation. This would be the case if, for example, all workers had mastered at least one foreign language and some more than one, thus providing the economy with more than one unit of language competence each. Despite the high density of language competences among Swiss workers, payments for language skills represent a relatively small share of total cost. According to our calculations, skills in a non-local major national language were rewarded with 2,900 Swiss francs per year on average, out of an annual average labour cost of 76,400 Swiss francs per worker in 1995.3 Competences in English were compensated at an annual 2,400 Swiss francs per worker. All in all, only 3.7% of total labour cost can be attributed to workers’ language skills, the highest share (5%) being recorded in the fi nancial and services-to-business sectors. Let us now turn to movements in the price and quantities of language skills over time. Data on the quantity of language skills are necessary for the cross-sectional estimation of language-augmented production functions in a given year. Prices series, however, are required for the estimation, which is run separately, of cost and profit functions. The data presented in Table 7.2 cover the secondary sector—that is, manufacturing and construction (with mining left out; in any case, it is hardly significant in Switzerland), the service sector, and the whole economy. For lack of direct observations, these

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Table 7.2

Changes in Price and Quantity of Language Skills and Labour from 1995 to 2004*, Switzerland

Type of input ↓

Sector ↓

Price

Quantity

Skills in other main national language

Secondary sector Service sector Whole economy

-52% -49% -50%

+9% +11% +11%

Skills in English

Secondary sector Service sector Whole economy

+71% +96% +88%

-24% +12% +14%

For reference: labour

Secondary sector Service sector Whole economy

+15% +21% +18%

-12% +7% +0%

Source: Calculations by authors (see Appendix II). *Data for 2004: average of 2003, 2004 and 2005.

price and quantity movements have to be inferred from the combination of various data bases (see Appendix II for details on data construction).4 Labour costs and labour quantity in full-time equivalents are given for reference purposes. Over the period considered, the price for skills in the main non-local national language (focusing here on French in German-speaking Switzerland and German in French-speaking Switzerland) has decreased, while the price of English-language skills has risen faster than the average labour cost. Labour-related quantities and prices must be combined with data on sales and purchases of goods by Switzerland’s manufacturing industry to estimate cost and profit functions. For this purpose, we have merged 2006 data on sales and purchases from a representative survey carried out with a sample of 200 fi rms in 2007–2008 (Grin, Sfreddo and Vaillancourt, 2009) with existing official sources on production and on the consumption of intermediate goods. This makes it possible to extract the movements in the prices of goods sold by the manufacturing sector, while distinguishing (as we need to for this investigation) between groups of goods according to the language used with the purchaser. As explained in Chapter 5, groups of goods are broken up in two categories: those sold in the fi rm’s locally dominant language (L1) and those sold in other languages (L2)—which means, in the main, other major national languages or English. Limited data availability prevents us from using a fi ner distinction. A similar rule is applied to the intermediate goods and services purchased. The foregoing enables us to distinguish two groups of four industries within the manufacturing sector: • low-density multilingualism group (group A): manufacturing of mineral and metallic products (MET), manufacturing of paper and wood

The Contribution of Multilingualism to Value Creation

109

products (PAP), manufacturing of textile (TEX), manufacturing of food and beverages (FOO); • high-density multilingualism group (group B): manufacturing of machinery, equipment and watches (MAC), publishing and printing (PUB), chemical industry (CHE), other manufacturing activities (OTH). This generates the following set of language-related price indexes (Table 7.3):

Table 7.3

Price Indexes for Sales and Purchases, by Language Used with External Trade Partners, Manufacturing Industries, Switzerland, Selected Years Price index* of goods sold in

Language →

L1

Price index* of goods and services purchased in language→

L2

L1

L2

Industries → Group Group Year ↓ A B

Group Group A B

Group Group A B

Group Group A B

1998

1.010

0.962

0.938

1.075

0.978

0.992

0.985

1.052

2002

1.021

1.019

0.998

0.978

0.991

1.013

1.054

0.975

2006

0.896

1.033

1.331

1.029

1.107

1.046

1.027

0.988

Source: Calculations by authors (see Appendix II). * These price indexes are normalized to 1 in year 2000 L1 = locally dominant language; L2 = other languages.

Table 7.4

Quantity Indexes for Sales and Purchases, by Language Used with the External Trade Partners, Sectors ‘MET’ and ‘MAC’, Switzerland, Selected Years Quantity index* of goods sold in

Language→

L1

Quantity index* of goods and services purchased in

L2

L1

L2

Industry→ Year↓

MET

MAC

MET

MAC

MET

MAC

MET

MAC

1998

1.040

1.016

0.930

0.908

1.027

0.990

0.958

0.929

2002

0.962

0.914

0.987

0.925

0.943

0.846

0.953

0.929

2006

1.143

1.078

1.113

1.085

1.105

1.059

1.105

1.128

Source: Calculations by authors. *These quantity indexes are normalized to 1 in year 2000 L1 = locally dominant language; L2 = other languages; MAC = manufacturing of machinery, equipment and watches; MET = manufacturing of mineral and metallic products.

110 The Economics of the Multilingual Workplace A price index is a synthetic measure whose variation reproduces the average changes in the price of a set of goods or services. When price indices are used to capture the changes through time in the average price of a bundle of goods or services, it is usually set to one (or 100) for a given year. If, for example, a price index moves from 1.010 to 1.021, it means that on average, the price of the goods and services in the bundle concerned has risen by 1.089%. For the purposes of illustration, 5 let us in Table 7.4 consider some similarly constructed quantity indexes (here, for the MET and MAC sectors), where index values can be interpreted as in Table 7.3. The data in Tables 7.3 and 7.4 are combined with price and quantity indexes of non-language-related inputs and goods and services sold in order to obtain the full set of variables needed to estimate not only a production function but also a cost function and a profit function.

7.3 APPLICATION: LANGUAGE SKILLS AND PRODUCTION FUNCTIONS Although a production function could be estimated for the economy as a whole, such an approach would be too blunt, because it would not do justice to the fact that real production processes are extremely different from one economic sector to the next. Conversely, it is not possible, owing to limitations in the availability of data, to go too far into detail. A compromise must be struck, which will depend on the purposes of the analysis and on the range of and detail of data available. In our case, we have broken up the economy in nine sectors as in Table 7.1. Value creation, as captured by a production function, is examined for the year 1995 for each of these nine industries. Let us recall that value added is a commonly accepted measure of output, which does reflect value creation because it represents the difference between value that existed before and value that exists after a production process. The amount produced also depends on labour-related inputs and capital. Labour-related inputs are given by the number of full-time equivalent workers and a stock of labour-related skills made available by workers to their employer: language skills, managerial skills, and other skills acquired either through work experience or through education. Additional variables are included in the estimation of a production function in order to account for differences in value added across fi rms and across industries, namely, the number of women in the workforce, fi rm location (in our case: the language region in which they are located) and the type of sector.6 Our interest here is not to assess how or how much such factors affect value creation but rather, by including them in the equation, to ensure that our estimations of the influence of the factors that do interest us here (the language-related ones) are not unduly biased upwards or

The Contribution of Multilingualism to Value Creation

111

downwards because of a possible (even if accidental) correlation to one or the other of these additional variables. Given the range of data available, we have retained the assumption made in the foregoing discussion, namely, treated “foreign language skills” as pertaining to the other main national language (French in German-speaking Switzerland, German in French-speaking Switzerland) and English. As to the algebraic shape of the production function, several options are available. The problem is that although each good or service is produced differently, it would be analytically impossible to operate with n different production functions—even if the production function for each good or service were actually known, which is not the case. Hence, we have to settle for one production function that will provide a reasonable approximation of the ways in which inputs (capital, labour, intermediate goods) on the one hand, and output (production) on the other hand, are related. The challenge is not unlike that of imagining a “meta-recipe” that could account for the production, with a given set of ingredients (fruit, vegetables, meat, spices, etc.), of “food” of all types, from starters to main courses and desserts. This is a challenge that any analysis of production across economic sectors is confronted with.7 Some production functions are simple, but they fail to do justice to the subtlety of the interaction between factors, which usually takes the form of more or less pronounced substitutability (factors can to a greater or lesser extent replace each other in production) or complementarity (factors are to a greater or lesser extent necessary to each other in production). Conversely, other production functions are more complex, but they quickly become completely unwieldy as soon as elaborate analytical operations need to be performed on them. After trying out several forms, we have settled for the well-established “translog” form. One way to use the translog function is to assume that production functions across industries differ only by a scaling parameter. Putting it differently, the production function has the same shape across all industries; only its scale changes.8 The CLES data base on language skills in Switzerland is used in the estimation of the production function. Let us recall that this data base served to produce the fi rst estimates of the rates of return on foreign language skills (see Chapter 4). The approach used here aims to ensure that the parameters of the production function, such as the wages associated with the corresponding production processes, are as close as possible to those recorded in the CLES database (more detail in Appendix II, which also contains the technical detail on the estimation procedure).9 The results of the estimation are presented in the form of elasticities. The concept of elasticity, which is familiar to all economics students, may need to be presented briefly to readers who have not yet been confronted with it. An elasticity is a ratio of ratios, where the numerator is the variation, expressed in percentage terms, of a dependent variable, while the denominator is the variation, also expressed in percentage terms, of an independent variable.10

112

The Economics of the Multilingual Workplace

An elasticity can be conceptualised as the relative variation of a dependent variable resulting from the relative variation of an independent variable; as such, it is an indicator of the sensitivity of a dependent variable to changes in the level of the independent variables. In addition to the example used in note 10 (the sensitivity of demand to changes in income), frequently used elasticities include own-price elasticity of demand (how the demand for a good changes, in percentage terms, as a result of a change in its price, also expressed in percentage terms); cross-price elasticity of demand (how the demand for a good changes, in percentage terms, as a result of a change, also expressed in percentage terms, in the price of another good); (own-)price elasticity of supply (how the supply of a good changes, in percentage terms, as a result of a change, in percentage terms, of the market price for this good); and so on. One great advantage of elasticities is that they are expressed as simple numerical values, completely dispensing with units of measurement (see example in footnote 10). Since any elasticity is a ratio of ratios, reported elasticities can be interpreted as the amount by which a dependent variable changes as a result of a change of 1% in the value of the independent variable. In this way, all kinds of relationships involving variables that would normally be expressed with very different units of measurement can be rigorously compared with one another using a number that does not need a unit of measurement. Table 7.5

Elasticity of Value Added with Respect to the Stock of Language Skills, Switzerland* Other main national language(s)**

English

Other main national language and English

Manufacturing industry

0.06

0.04

0.10

Construction

0.07

0.06

0.13

Wholesale and retail trade; repair of motor vehicles

0.06

0.02

0.09

Hotels and restaurants

0.03

0.10

0.14

Transport; communication

0.07

neg.

0.08

Financial intermediation

0.04

0.10

0.14

Services to businesses; real estate activities

0.03

0.10

0.13

Public administration; education; health

0.04

0.08

0.12

Other services

0.10

neg.

0.09

Whole economy

0.05

0.06

0.11

Change in → Branch ↓

Source: Calculations by authors. *Percent change in output following a 1% increase in the stock of language skills. **French in German-speaking Switzerland; German in French-speaking region; French or German in Italian-speaking Switzerland. neg = negligible value (close to zero).

The Contribution of Multilingualism to Value Creation

113

The relatively low values in Table 7.5 reflect the fact that although foreign language skills can be handsomely rewarded (as we have seen in Chapter 4), these language premiums make up only a small part of total labour cost (approximately 3.6%). Within reason, elasticities lend themselves to extrapolations which can make the results easier to interpret (indeed, we would generally not expect a variation of 1% in the level of a particular input to have any major impact on the level of output). Let us therefore interpret the figures in Table 7.5 using changes of 10% in the value of the independent variables. Thus, a 10% increase in the stock of skills in other main national languages will induce a 0.5% increase in the output of the economy as a whole—a figure that can be compared with the evolution of the hourly productivity of labour across the economy (which has oscillated, from year to year, from -0.8% to 2.9% in the 1995–2006 period).11 A comparable increase in the stock of English-language skills is associated with a slightly higher effect on output (0.6%). It is important to note that the magnitude of these effects varies across economic sectors and by language. An increase in the stock of national language skills would have stronger effects in construction (with an elasticity of 0.07) and in “other services” (0.10); by contrast, the effects of an increase in English-language skills would be particularly noticeable in the hospitality industry (hotels and restaurants), financial intermediation, and “services to businesses”, where the elasticities stand at 0.10. The same set of data can be used to assess the total contribution of foreign language skills to value creation in the economy as a whole. This contribution can be estimated by simulating the decrease in value added that would occur if foreign language skills were to evaporate, holding other skills constant: imagine for example that all bilinguals in the country are affected overnight by a mysterious linguistic amnesia that deprives them of their language skills in all languages other than the one they declare as their mother tongue, while all their other, non-linguistic competencies remain unaffected. The production function, however, is not an instrument that lends itself to such a brutal change as the complete disappearance of all foreign language skills, as it is based on calculus which is an appropriate tool for the study of small changes. To get around this problem, let us assume a 1% drop in foreign language skills, and then multiply the effect by one hundred. The resulting losses in value creation are reported in Table 7.6. Table 7.6 yields many novel insights in the role and importance of foreign languages in the economy. Firstly, we can observe that the disappearance of all foreign language skills would cause a drop of 10.8% in GDP. Chapter 4 (more precisely, Section 4.4 in the case of Switzerland) had provided proof that foreign language skills are highly valuable for individuals; Table 7.6 can be seen as the macroeconomic counterpart of this proof, showing that they make a major contribution to the performance of a national economy. Let us stress that 10.8% of GDP represents a considerable share, indicating that foreign language skills are assets of foremost economic importance.

114 The Economics of the Multilingual Workplace Table 7.6

Percentage Change in Value Added as a Result of a 100% Drop in the Stock of Foreign Language Skills, Switzerland Other main national language(s)*

English

Other main national language and English

Manufacturing industry

-6.1%

-4.2%

-10.3%

Construction

-6.8%

-6.0%

-12.8%

Wholesale and retail trade; repair of motor vehicles

-6.4%

-2.3%

-8.7%

Hotels and restaurants

-3.5%

-10.4%

-13.9%

Transport; communication

-7.2%

-0.4%

-7.6%

Drop in stock of → Branch ↓

Financial intermediation

-4.3%

-9.7%

-14.0%

Services to businesses; real estate activities

-3.3%

-9.7%

-13.0%

Public administration; education; health

-3.9%

-8.3%

-12.2%

Other services

-9.5%

neg.

-8.8%

Whole economy [GDP]

-5.2%

-5.6%

-10.8%

Source: Calculations by authors. *French in German-speaking Switzerland; German in French-speaking region; French or German in Italian-speaking Switzerland. GDP = gross domestic product; neg. = negligible amount.

Those skills might be expected to be particularly important in the case of Switzerland, because of the fact that the country is multilingual, and any non strictly local economic activity involves contacts with suppliers and clients across the language border within the country. Let us however remember that the languages concerned (German, French and Italian—leaving aside the Romanche-speaking community that makes up less than half a percent of the resident population) all are major languages spoken in G8 member countries, that is, fi rst-order economic players; Switzerland’s national languages are also used for trade contacts with those countries, suggesting that the economic value of its national languages cannot be wholly explained by internal diversity. According to Table 7.6, skills in other national languages explain 5.2% of GDP; this figure would be unlikely to be much lower in officially non-multilingual countries like Germany, France or Italy. What is more, Table 7.6 indicates that competence in English explains 5.6% of GDP. Considering that the Swiss, owing to widespread skills in German, French or Italian, can dispense with the use of English for contacts with some trading partners, their use of English may be slightly less than would otherwise be the case. Hence, the contribution of English-language skills to value creation is liable to be higher, if anything, in other countries. On

The Contribution of Multilingualism to Value Creation

115

balance, therefore, the considerable contribution of foreign language skills to GDP that we can observe in Switzerland is unlikely to be much lower in other countries. Nor should this effect be seen as exclusively a result of a strong export orientation, as we shall see in Section 7.5, suggesting that foreign language skills are relevant for most economies. Here again, we fi nd that the contribution of language skills differs by language and by economic sector. National languages make a more important contribution in transport, communications, construction, retail trade, manufacturing, and in the residual category called “other services”. English, by contrast, contributes more to value creation in the hospitality industry, fi nancial intermediation, various “services to businesses”, and a somewhat heterogeneous category combining public administration, education and health; if the data allowed for a fi ner degree of disaggregation, it is likely that the respective contributions of national languages and English would be quite different. Similar work was carried out for Québec using the 2001 Census. Twenty industries were regrouped in three sectors: the low bilingualism sector (six industries, where 42% of workers are bilingual in English and French); the medium bilingualism sector (seven industries; 59% of bilingual workers); and the high bilingualism sector (seven industries; 71% of bilingual workers). If all bilinguals were victims of sudden linguistic amnesia, the sectoral drops in value added are respectively 1.9%, 3.3% and 8.6%. These results indicate that our method can be generalised to countries other than Switzerland or to data other than those used here.

7.4

EXTENSION TO COST AND PROFIT FUNCTIONS

The preceding section focuses on production, because it is through the production process that value is created, but the approach can be expanded in order to connect language skills with costs and profits. The theoretical steps involved were described in Section 5.3, in which the core “languageaugmented” algebraic model was presented. A similar extension in statistical analysis, however, would require additional data that are not available: while production functions only use the quantities of a set of inputs and a set of outputs, costs and profit functions require, in addition, the prices of all the inputs and outputs considered. This makes working with cost and profit functions an even more complex exercise than dealing with production functions only. The difficulty is exacerbated by the fact that cost and profit functions are frequently used, in empirical research, to study the behaviour of multi-output firms, that is, in situations where the distinction between the types of goods produced are needed (a task of which a production function-based estimation procedure is, by its very nature, incapable). Therefore, while cost and profit functions are appropriate instruments for investigating the complexity of interactions between outputs and inputs, their empirical application does not

116

The Economics of the Multilingual Workplace

go without difficulties. Nevertheless, it is possible, using a set of simplifying assumptions with which desirable data are replaced by acceptable approximations derived from other data sources, to present experimental results regarding the effects of languages on costs and profits. Let us for both functions consider the following outputs and inputs. The outputs include goods sold in the locally dominant language (L1) and goods sold in other languages (L2). The inputs comprise goods and services purchased in the locally dominant language (L1), the goods and services purchased in other languages (L2) and all the inputs considered in the production function.

Cost Function Recall that the cost function tells us how much of each input an industry, or a firm, facing a given set of input prices, needs in order to produce a given quantity of output while keeping production cost at the lowest level possible. This lends itself to “multi-input, multi-output” (MIMO) analyses, where the prices of inputs and the quantity of outputs are treated as exogenous variables (King, 1997; Färe and Grosskopf, 2003); here again, “output” is understood in a technical more than fundamentally economic sense, because its value does not exclude the cost of intermediate goods and services. From a cost function, we can derive the corresponding output supply functions and input demand functions, and then calculate a set of elasticities on this basis (see Appendix II for technical detail). A selection of language-related elasticities is presented in Table 7.7. They have been calculated for the economy as a whole. These elasticities are value added-weighted averages of the corresponding figures for each of the eight industries in the manufacturing sector. At a general level, elasticities are of course defined exactly as in the preceding section. However, what we are interested in here is the sensitivity of some key economic variables (that is, how much they change in percentage terms) as a result of a change in the value of linguistically-marked variables (whose changes are also expressed in percentage terms). For example, we could ask ourselves what effect a 1% increase in the average price level of goods purchased in a foreign language will have on the demand for these goods (a case akin to that of “own-price elasticity” referred to by way of example in the preceding section) or, moving on to more complex linkages, on the demand for foreign language skills. Before discussing these results, let us recall that “pure labour” merely reflects the number of hours worked: its price is the theoretical price paid to a worker with no education, no work experience, no language competences and no managerial skills. Also remember that the elasticity of foreign language skills with respect to their price is not equal to the elasticity of the demand for bilingual workers with respect to their cost (that is, the wage paid by employers). This is because bilinguals provide at least two types of services to their employer: pure labour services and language skills. Their wage thus reflects not only the cost of language skills but also, and principally, the cost of pure labour.

The Contribution of Multilingualism to Value Creation Table 7.7

117

Elasticities Derived from a Cost Function, Selected Results . . . with respect to the price of . . . ↓

Elasticity of the Demand for ↓ Capital

Capital

Pure Skills in labour L2

-0.26

0.04

Pure labour

0.03

-0.31

Skills in L2

-0.07

0.36

Goods purchased in L2

0.05

0.06

Goods purchased in L1

0.13

0.15

-0.02

Goods Goods Goods Goods purpursold in sold in chased chased L2 L1 in L2 in L1 0.12

0.71

0.40

0.59

0.08

0.11

0.58

0.42

0.61

-0.11

-0.08

0.60

0.49

0.68

-0.01

-0.48

0.49

0.29

0.49

0.04

0.22

-0.84

0.46

0.65

Source: Calculations by authors.

The elasticities presented in Table 7.7 are what economists would call “well-behaved”, that is, they are consistent with general experience and expectations. For example, the more expensive an input is, the lower the demand is for it, as shown by the diagonal of negative figures in bold type in Table 7.7. It is interesting to note that the demand for foreign language skills is particularly insensitive to their own price (the corresponding elasticity is a mere -0.11) and fairly sensitive to the price of goods purchased in L1 (with an elasticity of 0.60): in other words, when the price of goods purchased in the locally dominant language goes up, so does the demand for foreign language skills. This may be explained by the fact that in such cases, producers turn to alternative suppliers, including in particular foreign ones with whom interaction using foreign language skills is particularly necessary, with corresponding implications for recruitment. More surprising is the fact that the demand for foreign language skills seems to be strongly (and positively) influenced by the price of goods sold in the locally dominant language, as shown by an elasticity of 0.68. One possible explanation is that an increase in the price of those goods reflects a larger volume of economic activity, which in turn causes demand for all kinds of inputs to rise.

Profit Function Unlike the cost function, the profit function treats the quantity of output as a choice variable. The fi rm can simultaneously choose the quantity of input and the quantity of output, and it does so in a way to maximise its profits, given of course existing constraints on input and output prices, technology and the information at its disposal at the time where decisions are made. This profit function serves to capture this idea of a joint choice of

118

The Economics of the Multilingual Workplace

levels of inputs and outputs. Putting it differently, profit functions assume fewer constraints than cost functions do in their analysis of fi rms’ decision making. Using a profit function, we can in principle get closer to the actual operations of a fi rm and analyse its responses to exogenous changes not in terms of the comparatively narrow concern of minimising cost but in terms of its ultimate, and broader, concern of maximising profit. Increasing analytical feasibility means rising technical difficulty. The corresponding multi-input, multi-output (MIMO) analyses therefore assume the prices of both inputs and outputs to be the exogenous variables, but the only fi xed input is capital. As was the case for the cost function, the output supply functions and the input demand functions can be derived from the profit function, from which, in turn, a set of elasticities can be calculated. These estimations are presented in Table 7.8. Note that capital is a fixed input in a profit-function framework. Hence, it is not a choice variable, and no elasticity with capital stock as an explanatory variable is presented. As before, the figures appearing in the table are weighted averages of the figures obtained for each of the eight industries. The meaning of each figure, however, remains the same as in Table 7.7.

Table 7.8

Elasticities Derived from a Profit Function, Selected Results . . . with respect to the price of . . . ↓ Pure labour

Goods Goods purSkills purchased chased in L2 in L2 in L1

pure labour

-1.43

-0.18

-2.30

-4.42

3.71

6.01

skills in L2

-0.61

-0.11

-2.56

-3.19

3.89

1.74

goods purchased in L2

-1.35

-0.38

-2.70

-3.05

3.20

5.72

goods purchased in L1

-1.23

-0.25

-1.61

-4.82

4.79

6.98

goods sold in L2

-1.14

-0.31

-1.72

-5.37

4.05

-1.58

goods sold in L1

-1.20

-0.14

-1.98

-5.05

0.02

3.17

Elasticity of . . . ↓ Demand for:

Supply of:

Source: Calculations by authors.

Goods Goods sold in sold L2 in L1

The Contribution of Multilingualism to Value Creation

119

Let us note that these elasticities tend to be larger, in absolute value, than the ones derived from a cost function: this reflects the fact that fi rms enjoy, as it were, “more freedom” with a profit function than with a cost function: they are not constrained by a fi xed output level, since this quantity can now be chosen freely. This leaves producers with more flexibility, resulting in larger elasticities. Let us note, however, that despite higher flexibility, the elasticity of the demand for foreign language skills with respect to their price displays the same value (-0.11) as in the framework of the cost function. Moreover, using either approach, this elasticity is just about the lowest (in absolute value) of all direct elasticities (this also holds for elasticities not reported in this section). Such a consistently modest own-price elasticity suggests that foreign language skills are a highly needed commodity, whose demand is mostly determined by the type and level of activity, rather than by its price.

7.5

PEEKING INTO THE BLACK BOX AGAIN

One of the main goals of this book is to open, if only a little, the “black box” of production, in order to see in particular how multilingualism (or, more specifically, multilingualism embodied in actors’ foreign language skills) influences economic processes and outcomes. In the fi rst part of this book, we saw how research work from various perspectives has been groping at this question—to some extent, groping in the dark, for want of a targeted analytical model that could be used as a starting point. In Chapter 4, which opens the second part of the book, we noted that the existence of handsome labour market rewards for foreign language skills strongly suggest that these skills do contribute to value creation when they are actually used in production. Just how language skills add value, however, is not immediately apparent from observable language-based earnings differentials, which are merely the trace of this beneficial effect. In order to understand what is actually going on, we need to delve a little deeper; this was the idea that we tried to convey with the three tiers of Figure 4.1. Having now, in the main, completed our investigation, what have we learned about the role of language in the creation of value added? In essence, our fi ndings fall under five main categories: 1. A formal, explicit and sensible link between foreign language skills and economic outcomes has been established through a robust theoretical model. Instead of assuming—as is generally done in most of the literature and surveys—that these variables are connected, the logical connections between them are now more explicit. Let us once again observe that economic analysis is not designed to pinpoint the ways in which particular factors (including foreign language skills) contribute to value creation in particular processes. This would be a task for industrial engineering

120

2.

3.

4.

5.

The Economics of the Multilingual Workplace or operations management, always with respect to the production of a specific good or service. Economic analysis is not meant to enter that degree of detail, since, as we saw in Chapter 1, it emphasises general relationships. This, however, is precisely what enables us to estimate orders of magnitude for the relationship between linguistic and economic variables, and the resulting figures are sometimes considerable. The estimates resulting from empirical investigation jointly indicate that linguistically-marked variables do affect key economic decisions. Moreover, these linkages make real economic sense; this is shown by the existence of well-behaved elasticities that dovetail with actors’ cost-minimising and profit-maximising behaviour; it is important to point out that foreign language skills (which are certainly relevant in the production of services that are inseparable from communication, as well as in the production of goods that have a strong built-in linguistic dimension, like books) are economically relevant even in the case of goods that have no such linguistic dimension. This investigation also provides orders of magnitude for the strength of these links, as indicated by the higher or lower level (in absolute value) of the elasticities presented in the preceding section; we can, in particular, show how much an identical exogenous shock in the value of a linguistically-marked variable differently affects key economic variables, as well as how much a given economic variable is differently responsive to shocks affecting different linguistically-marked variables. These elasticities reveal the importance of foreign language skills as a particularly indispensable asset, further confirming observations made in Chapter 4 to the effect that language skills are valuable because they are used, not only because they exist (as alternative approaches like screening or signalling would have it). The results obtained are particularly relevant with respect to the demand for foreign language skills, a fact that carries implications for the understanding of language dynamics. These results are built on data which suggest that the value of foreign language skills is pervasive and is not, contrary to what is often assumed, confi ned to international sales or (in the case of multinational or other high-diversity companies) communication among staff with different language profiles. For example, our survey of 200 fi rms in the manufacturing sector in French- and German-speaking Switzerland has shown that foreign language skills, far from being concentrated in fi rms’ sales departments, are also very prevalent in purchasing divisions.

Before closing this chapter, let us take a closer look at this last point, because it illustrates in a very straightforward fashion the type of questions that lie at the heart of the language-economy relationship. In Table 7.9 we present, on the basis of responses from the 200 fi rms participating in the survey, the percentage of communication time taking place in a non-local language.

The Contribution of Multilingualism to Value Creation Table 7.9

121

Share of Communication Time in a Non-local Language, by Language Region and Department within the Firm, Switzerland* French-speaking region

German-speaking region

0.35

0.28

0.13

0.14

Sales

0.61

0.41

Purchasing

0.42

0.31

Other

0.24

0.19

Total

0.19

0.20

Management Production

**

Source: Calculations by authors. * Including reported receptive and productive communication. ** Without clerical staff when provided.

Communication in a non-local language is more common in French-speaking that in German-speaking Switzerland for all but one department. One might thus expect the average value to be higher in the French-speaking region. As shown by Table 7.9, however, (line Total), this is not the case: on average, the French- and German-speaking parts of the country display virtually the same communication time in L2. The reason is that departments which use L2 extensively form a larger part of employment in firms of the Germanspeaking region, where 20% of employees work in the management, sales or purchases divisions, against 14% in firms in French-speaking Switzerland. In both language regions, foreign language skills turn out to be important in purchases divisions, not far behind sales and ahead of management. The implication is that these skills (which, as we have seen in Section 7.3, are likely to be relevant not only for officially multilingual countries but for any non-autarkic country12) are valuable for all companies purchasing goods and services from suppliers who do not speak the same language. To get a more precise idea of how language skills contribute to value creation, more abundant data are needed, both in terms of number of observations and in terms of detail. In principle, the type of information to be gathered should extend to a complete monitoring of language use in productive activity (with an equally extensive coverage of different departments within a fi rm), in a representative number of fi rms within each of the economic branches where we seek to understand the role of language in value creation. At this point, we progressively leave the field of economics and enter the realm of operations management or industrial engineering. Nevertheless, economics still provides the overarching analytical frame within which such questions can be organised. Qualitative studies in this direction have been undertaken, aiming to generate a set of indicators that can serve to assess the relative efficiency of different (that is, more or less multilingual) ways to handle communication in a multilingual setting (Gazzola and

122 The Economics of the Multilingual Workplace Grin, 2007). Such data then need to be matched with quality information about productivity, costs and profits. Much works remains to be done in order to bridge the gap between qualitative, highly case-dependent case studies on the one side, and general, representative, quantitative methodologies on the other side. Bridging this gap, however, remains a necessity if we are going to gain a finer-grained understanding of the ways in which language skills affect economic outcomes.

8

8.1

Foreign Language Skills and Hiring Strategies

A NEIGHBOURING, YET CRUCIAL ISSUE

Although the model presented in the preceding chapters is quite flexible and lends itself to the study of many different questions, it cannot explain the entirety of the economic decisions made by fi rms. In line with core microeconomic theory, we have focused on the determination of the optimal levels of inputs and output and changes in these optimal levels as a result of exogenous shocks. Nevertheless, various other choices that surround the production process are also interesting. They usually fall under the broad heading of “industrial organisation” (Tirole, 1988; Scherer and Ross, 1990). Many of these choices become necessary when we do not have perfect competition. They have to do with issues such as the exploitation of monopoly power (for example through price discrimination) or strategic interaction with competitors in situations like oligopoly or monopolistic competition. Other additional decisions, however, must be made irrespective of market structure: some have to do, for example, with advertising and marketing, and some with staff recruitment. In this chapter, we shall take a closer look at the latter question. There are three main reasons to do so. Firstly, the “language needs of business” is a topic that often moves to the forefront of discussions of multilingualism at work or in business—to wit, its prominence in the studies reviewed in Chapter 3; it is therefore useful to examine this question, if only to clarify its analytical status with respect to other aspects of multilingualism at work.1 Secondly, this question has major policy implications at various levels: as we have seen when discussing Figure 1.1 in Chapter 1, the issue of multilingualism at work is intimately connected to matters of language dynamics, and this link is also mediated, as it were, through fi rms’ hiring strategies: even when language policies do not directly affect the language practices of the private sector, they influence the context within which fi rms operate. Therefore, language policies that seek to influence the relative position of different languages (for example, when trying to protect and promote a

124 The Economics of the Multilingual Workplace threatened language) should be informed by knowledge about the role of employers in language dynamics. This, in turn, may affect language education policies: are there economic reasons to teach foreign languages, to whom, and aiming at what level of competence? Thirdly, existing data reveal intriguing patterns: when we compare foreign language use at work with employers’ requirements in terms of foreign language skills, considerable discrepancies appear—as if employers themselves did not quite know what they actually need. This alone would justify closer examination. We shall begin, in the following section, by looking at those discrepancies. Few relevant data are available on this matter, but the 1994–1995 Swiss survey already used in preceding chapters will again stand us in good stead. In Section 8.3, we model the “optimal recruitment” decision. We have noted earlier that the core analytical model developed in Chapters 5 and 7, flexible as it is, cannot provide an account of every single decision made by the fi rm. As shown in Appendix I, the analysis of choices regarding inputs and outputs alone already entails a certain degree of complexity. The model would quickly become intractable if it were saddled with the task of generating optimal solutions for additional variables. This is why, instead of trying to expand the core model, we have opted for an alternative strategy, that is, to develop a separate yet compatible model specifi cally designed to investigate optimal recruitment. In Section 8.4, we discuss implications for the linguistic management of the fi rm.

8.2

FOREIGN LANGUAGE REQUIREMENTS AND USE

A widely-held assumption is that when businesses do something, they probably do it for a good reason. Taking the long view, this is probably a sound belief, because a fi rm that consistently made bad decisions would ultimately be forced out of business, either by its competitors or even, in the absence of competition, under the sheer weight of the negative consequences of its own decisions. Yet, the wisdom of managerial decisions is not always plain to see. Let us again consider language data of the 1994– 1995 Swiss survey, in which a random sample of 2,400 respondents was asked about their foreign language skills, use of foreign languages, earnings and a number of additional questions on the sources of their linguistics skills. One questionnaire item asked respondents who were in gainful employment at the time of the survey whether foreign language skills were required at the time of hiring and if so, in what language or languages; “foreign”, in this case, referred to a language other than the main or official language of the respondent’s region of residence. 2 Such skills were indeed required from a significant proportion of respondents, as shown in Table 8.1.

Foreign Language Skills and Hiring Strategies 125 Table 8.1 Foreign Language Skills Requirements at Hiring, Switzerland, 1994–1995, by Foreign Language and Language Region, Percentages Language → Language region ↓

German

French

Italian

English

German-speaking (N = 688)

-

17.7

3.2

16.2

French-speaking (N = 564)

15.0

-

0.9

15.2

Italian-speaking (N = 446)

22.7

15.2

-

5.3

Source: Calculations by the authors using the CLES data base (Grin, 1999a).

For example, 17.7% of respondents living in German-speaking Switzerland mention that when they were hired, French-language skills were explicitly requested. Let us now compare these figures to those on foreign language use, setting the bar high, namely, using the language “daily or almost daily” (instead of a lower threshold, such as “at least once a week”), as shown in Table 8.2. Even if these figures do not tell us anything about the level of proficiency at which foreign languages are used, 3 they confi rm our earlier observations about the considerable importance of foreign languages on the Swiss labour market. But equally striking is the fact that these fi gures are systematically higher than the percentage of respondents who report foreign language requirements at the time of hiring (Table 8.1), even when only frequent users of the language (“daily or almost daily”) are taken into account. The under-requirement index, however, is not uniform, as shown in Table 8.3, where we subtract the numbers appearing in Table 8.1 from those in Table 8.2. Even if respondents’ representations may be at variance with actual facts (with respect to requirements at the time they were hired and to the current use of their skills), thus leading us to treat these figures with caution, let us recall that they are obtained through a large representative

Table 8.2

Use of Foreign Language Skills at Work, Switzerland, 1994–1995, by Foreign Language and Language Region, Percentages

Language → Language region ↓

German

French

Italian

English

German-speaking (N = 767)

-

35.9

17.9

33.8

French-speaking (N = 637)

29.9

-

11.8

27.5

Italian-speaking (N = 510)

51.6

51.8

-

18.6

Source: Calculations by the authors using the CLES data base (Grin, 1999a). Note: The number of respondents is higher since there were fewer “no reply”/”not applicable” returns on the “use” than on the “requirement” questionnaire item.

126

The Economics of the Multilingual Workplace

Table 8.3

Linguistic Skills Under-requirements (Differences between Foreign Language Use and Requirements), Switzerland, 1994–1995, by Foreign Language and Language Region, Percentages

Language → Language region ↓

German

French

Italian

English

German-speaking

-

18.2

14.7

17.6

French-speaking

14.9

-

10.9

12.3

Italian-speaking

28.2

36.3

-

13.3

Source: Calculations by the authors using the CLES data base (Grin, 1999a).

survey; their general direction is clear enough to warrant interpretation. One possible explanation is that employers are quite short-sighted and systematically underestimate the actual need for foreign language skills, since employees seem to use these skills very frequently, and certainly out of all proportion with the requirements they were asked to meet at the time of hiring. Another interpretation is that there are implicit language requirements not stated explicitly at hiring, a case exemplified later in this chapter. Deriving average values by language or by region would raise delicate weighting questions; however, some general trends emerge. Firstly, the discrepancy tends to be less for English than for German or French. It may be that employers are more keenly aware of their needs in this respect, insist on them more explicitly, and therefore are led to require English skills more systematically than they would in the case of other languages, thus reducing the discrepancy between requirements and use.4 Secondly, the need for foreign language skills seems to be more strongly underestimated in Italian-speaking Switzerland; this may refl ect the decidedly minority or even dominated status of Italian (also reflected in earnings effects; see Grin and Sfreddo, 1998), the result being that in Italian-speaking Switzerland, knowing other languages is taken for granted to a greater extent than in other language regions, with the result that foreign language skills are not even explicitly requested during recruitment:—having them and using them is simply seen as a matter of course. Despite such variations by language and region, the unambiguous character of the difference between requirements and use naturally leads to another question: is this discrepancy the same across jobs? To assess this point, we can return to the odds ratios presented in Section 5.2, focusing again on the case of English, as shown in Table 8.4. Columns 1 and 3 of the table report the odds ratios for English language skills having been required at the time of hiring, and columns 2 and 4, which reproduce the figures in Table 5.4, report the odds ratios for such skills to be used “daily or almost daily”. 5 Let us recall that the odds ratios represent the ratio of

Foreign Language Skills and Hiring Strategies 127 Table 8.4

English Language Skills Requirements and Use, by Type of Employment, Switzerland, 1994–1995, Odds Ratios

Language region

German-speaking

French-speaking

Requirement

Use

Requirement

Use

Professional

ns

ns

ns

6.684**

Entrepreneur

21.416*

ns

15.92**

8.164**

Crafts/small business

ns

ns

ns

ns

Farmer

ns

ns

ns

ns

2.223**

1.716*

2.419**

7.069**

Senior civil servant

ns

ns

3.747**

6.115**

Civil servant

ns

ns

ns

3.068**

Unskilled and semi-skilled

ns

0.135*

ns

ns

688

767

564

637

0.251

0.194

0.384

0.358

Middle manager

N 2

Pseudo R

Source: Calculations by the authors using the CLES data base (Grin, 1999a). Omitted category: “employee”. Coefficients for control variables not reported (“service sector”; “mainly internationally-oriented”, “proximity to language border”, “male”; see Table 5.4). Constant (significant at the 1% level in all four columns) not reported. *Statistically significant at the 5% level. **Statistically significant at the 1% level. ns = not statistically significant at the 5% level.

the probability of an event occurring divided by the probability of the reciprocal event. The use of bold type in some cells will be useful at the end of Section 8.3, where we shall refer back to this table; for now, let us simply note the high odds ratios for “requirement”, indicating that English was often required (or, in the case of self-employed workers, was a necessity from the start) for entrepreneurs (with odds ratios of 21.4 and 15.9 depending on language region), and middle managers (2.2 and 2.4 respectively). Correspondingly, the odds ratios for English to be used in these two types of employment are higher than for other jobs (even though there is a considerable gap between the odds ratios for requirements and use, a fi nding that dovetails with those in Table 8.3). By contrast, the likelihood for civil servants and senior civil servants to be required to know English is relatively lower, while the odds ratios for their use of the language are relatively high in French-speaking Switzerland. Thus, explicit language requirements by the employer seem to be an unreliable predictor of actual language use—again, as if employers themselves did not quite know what language skills are actually necessary, and if they ended up

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The Economics of the Multilingual Workplace

hiring and using skills well above what they explicitly seek. This returns us to the question raised earlier: are employers simply unaware of the language needs of their own business, or is something more complex going on? Let us examine this point in the following section.

8.3

MODELLING RECRUITMENT

Number of speakers

Recruitment is a costly process, and fi rms have a strong incentive to control the corresponding costs. How should they proceed? The problem is analysed in detail in the formal model presented in Appendix III, but its main ideas are developed in this section. Let us assume for simplicity that we are dealing with two languages only, respectively L (the locally dominant language) and F (the foreign language), and that applicants who do not possess the non-language skills necessary for a particular position have already been screened out, so that only their linguistic attributes (a notion introduced in Section 4.2) allows recruiters to differentiate between them. Let us now assume that the level of F-language skills required for a given position is α*, which can fall anywhere in the 0 to 1 range. The language skills of any applicant j can also be expressed on the same scale: for a monolingual who speaks L but has no competence in F, αj = 0, while for a fully fluent bilingual, αj = 1. The pool of potential applicants in the recruitment basin may be represented as a certain distribution of α , as shown in Figure 8.1. The total cost to the fi rm looking for the ideal employee includes three elements:

0 Monolinguals

0.5

Bilinguals

1

Competence levels Figure 8.1 Distribution of speakers by competence level in language F.

Foreign Language Skills and Hiring Strategies 129 • the recruitment effort cost, which is directly linked to the scarcity (or, conversely, to the oversupply) of workers with the necessary level of competence in F; • the inefficiency cost, that is, the expenditure on translation or the value of potential losses that would result from inadequate F-language skills (if the new employee’s αj < α*); • the salary cost, which rises along with a person’s foreign language skills—in line with the results presented in Chapter 4. The fi rm is therefore confronted with a decision-making problem that can be formulated as a “constraints-action-goals” triptych which we summarise as follows: given the three types of costs, the level α* of F-language skills needed for the job and the distribution of language skills in the potential pool of applicants, the fi rm will choose the level α’ of language skills that will be explicitly targeted by a recruitment campaign in order to minimize the total recruitment and employment cost. This suggests that the employer may decide, in some cases, to settle for an applicant j whose αj < α* in order to reduce recruitment cost (since finding someone whose αj is equal to, or even higher than α*, is difficult and costly) and salary costs (since persons with higher competence in F tend to cost more). However, if the firm hires a candidate with inadequate skills, it will have to face inefficiency costs as defined earlier. Depending on the relative value of the three types of costs, it is therefore quite possible that a rational, cost-minimising employer will gear the recruitment campaign towards potential applicants whose competence in F is below the desirable level α*. Let us simply highlight some key results and then move on to their implications. In essence, the fi rm will have to choose the level of two variables: αmin , which is the lowest level of F-language skills it is ready to settle for; and αmax, which is the highest level of F-language skills that a price-taking fi rm (that is, a fi rm who has no choice but to accept market prices as they are) is willing to compensate given the market-defi ned premium on F-language skills. All the other variables in the model are exogenous6 or proceed from the model; this is in particular the case for V, which is the amount to be spent on advertising the position, and which depends on the total number of potential applicants and the number of applications the fi rm wishes to receive in order to select one of them. Testing such a model would require data that are, in the main, not available. However, the implications of the model can be explored through simulations, which enable us to compute, for various scenarios, the average distance between α* (the level ideally desired) and employees’ actual F-language skills. Let us consider four scenarios: • • • •

Scenario (1): bilinguals are many, and the inefficiency cost is high; Scenario (2): bilinguals are many and the inefficiency cost is low; Scenario (3): bilinguals are few and the inefficiency cost is high; Scenario (4): bilinguals are few and the inefficiency cost is low.

130 The Economics of the Multilingual Workplace

1 Average targeted competence level

(1) (2) (3)

(4)

0 0

Figure 8.2

Competence level necessary for the position

1

Targeted v. necessary foreign language skills.

Note: the diagonal represents equality between the competence level necessary for the position and the competence level actually targeted during hiring.

The results of these simulations are represented as lines in Figure 8.2. If all applicants had the level of F-language skills necessary for the position, all the curves in Figure 8.2 would be superimposed with the diagonal. However, simulations with reasonable assumptions show that this practically never occurs. The gap between ideal and actual skills level, however, can vary in a number of ways. In particular: • it will be larger if the ideal level α* moves away from the most frequent linguistic profi le in the recruitment basin; • when the actual skills level αj is below the optimum level α*, the gap will be smaller if the inefficiency cost is higher. Thus, the results reported in the preceding section should not automatically be interpreted as proof that employers do not understand what goes on in their offices and factories; rather, the model provides an explanation for these empirical results. Firstly, it shows that there are many cases where the fi rm will deliberately target profi les with lower-than-needed F-language skills. Once hired, employees will somehow have to cope with situations with which they are confronted, and many workers who were not formally requested at hiring to have foreign language skills will have to use foreign languages anyway (as suggested by Table 8.3). Whether they actually possess the skills to adequately handle the tasks at hand is another matter.

Foreign Language Skills and Hiring Strategies 131 Secondly, the model predicts that this discrepancy is more likely to occur when language needs are at variance with the distribution of profiles in the recruitment basin. We would expect this to be reflected by a systematic bias in the differences between odds ratios for language requirements and language use, with the former systematically falling below the latter. This pattern may in fact be detected in the cells in bold type in the right-hand panel of Table 8.4 which reports results on French-speaking Switzerland; as it turns out, the average competence level in English, estimated using the same data base, turns out to be lower in this part of the country than in Germanspeaking Switzerland.7 Thirdly, the simulations indicate that all other things being equal, the gap will be smaller if the inefficiency cost is high; conversely, we may expect the gap to be larger when the inefficiency cost is small or perceived as such. This would once again be reflected in lower odds ratios for language requirements than language use, a case which occurs in the three sets of cells in bold type in Table 8.4. Two of them concern the civil service, where the fi nancial consequences of inappropriate decisions tend to borne less directly by the persons who have made those decisions.

8.4

TOWARDS LINGUISTIC AUDITS

The preceding discussion suggests that apparently straightforward data should not always be taken at face value: the apparent ignorance, by employers, of the extent to which their staff uses foreign languages may in fact not be ignorance at all; their restraint in requiring foreign language skills may simply reflect a fully rational optimisation behaviour. It does not follow, however, that all employers always have a clear notion of how languages are used for office work or on the shop floor. Findings from applied linguistics, though not necessarily representative in statistical terms, regularly underscore the complexity of linguistic practices, and in particular the high frequency of code-switching and code-mixing in multilingual settings, even if participants could have been expected to converge towards the use of a single language (Mondada, 2004, 2007). Referring to interview data, Reeves and Wright (1996: 38) note that “senior and middle management sometimes had differing or even erroneous ideas of foreign language use when it came to the everyday realities dealt with by individuals”8. Employers may be aware of this complexity; they may either try to suppress it by imposing a corporate language, or embrace it as a source of competitiveness, as many well-meaning reports (reviewed in Chapter 3) exhort them to. However, the ways in which more or less well-documented language practices affect economic outcomes remain little known; even if management knows how things are done, they are not necessarily aware of how they could be done—possibly delivering higher productivity. This is why extensive research into linguistic practices at work remains necessary. In addition

132

The Economics of the Multilingual Workplace

to its core scientific goal to pry open the “black box” of value creation and to move closer (as suggested by Figure 4.1 in Chapter 4) to the relationship between language skills-related value creation and monetary rewards, the data from such investigations may significantly enhance a firm’s knowledge of its own production processes and open the door to efficiency gains. Some of the corresponding public policy implications are explored in the following chapter, but this carries consequences at the level of individual fi rms, which could lead the latter to undertake in-depth linguistic audits. The notion of linguistic audit is certainly not new and has been developed in considerable detail by Reeves and Wright (1996), who define its goal as that of “helping the management of a fi rm identify the strengths and weaknesses of their organisation in terms of communications in foreign languages (ibid.: 5). Their concept of linguistic auditing rests on the mapping of “capability [ . . . ], functions and people against the identified need” (ibid.: 5). The reference to need, of course, directly meshes with the concerns of this book: what are foreign language skills needed for? Reeves and Wright approach this question by systematically listing types of foreign language use, using five levels of information: activity, language, topic, lexical field and frequency. On the basis of a typology of activities, each activity is paired with as many languages as may be used in the firm to perform it. One such activity is “purchasing and ordering from foreign suppliers”; each activity–foreign language pair is then analysed with a two-way table called the Foreign Language Needs Assessment Grid (ibid.: 42). The activity is further broken down in topic areas arranged in columns (for example: “discussing purchases with suppliers” or “calling for progress reports on orders”), but then performing the activity with respect to any given topic area may require the activation of different lexical fields (such as “dates and times” or “materials specifications”, for example). Crossing n topic areas with m lexical fields generates an n × m matrix, where not every single cell is necessarily relevant. In each relevant cell, however, some indication of frequency is requested. The Foreign Language Needs Assessment Grid, if performed systematically, certainly generates highly detailed information. However, it is obviously a very time-consuming endeavour, particularly if it is to be extended to all the “activities” identified in the fi rm. Three further problems arise. The first one has to do with reliability. Contrary to a widely-held view in applied linguistics (for example in conversation analysis; see Section 2.4), there is simply no systematic evidence that any information gathered through means other than direct observation is fatally flawed; in our view, questionnaires (also taking the form of grids) are legitimate sources of information, provided they are carefully designed and administered, and the sample is representative and of adequate size. There is no doubt that some informants will tend to overestimate, and others to underestimate, the frequency with which they use a foreign language, for example; however, we expect errors to compensate each other on balance. In the case of the Foreign Language Needs

Foreign Language Skills and Hiring Strategies 133 Assessment Grid, nevertheless, considerable effort needs to be expended in order to ensure that respondents have a similar understanding of both of the questions asked and of the terms with which they answer them. This, in turns, requires a massive, possibly excessive monitoring effort. Secondly, the information focuses on current practices. It is therefore well-suited to providing a descriptive account of who uses what language with whom, how often and for what purposes. But this does not quite amount to an identification of unmet needs. What is in fact needed is a two-level grid, where informants are invited to indicate, with respect to each cell, whether the language skills available are adequate or not.9 Thirdly, even fully reliable information would fall short of an estimation of the monetary implications of the use of foreign language skills or of the lack of such skills. A linguistic audit can give its full measure when the languagerelated information it collects can be systematically related to productivity, costs and profits—in short, to value creation in the economic sense. The language economics approach developed in this book may therefore usefully complement various approaches to linguistic auditing by providing a strategy for identifying economically relevant information to collect, as well as a framework for subsequently organising this information. Although audits tend, in general, to retrieve and process descriptive information, it is possible to go further. In our case, audits can take stock of the range of foreign language skills available among staff members at a given point in time, get a synthetic picture of the frequency of use of different languages in different divisions of a company, and help to develop a procedure for identifying which part of the structure is experiencing more pressing language needs. It is important to note that this type of exercise fully comes into its own if a company agrees to pool information with others, in order for comparisons to become possible. For example, all the information just mentioned can be set against indicators in the form of average values at industry or regional level, for example. Discrepancies between fi rm-specific and average values can then be correlated with other indicators of the fi rm’s operations and feed into analyses of the role of language in economic performance. However, an audit backed up by an explanatory model of the role of language in economic performance makes it possible to move beyond description and correlation. As we have seen in Chapter 7, a theoretically consistent and empirically tested model provides an instrument for predicting responses to exogenous shocks. No link-up of this type could be made in the absence of an analytical model. Banking on the latter, a fi rm can choose to engage in a linguistic audit aimed not only at descriptive but also at diagnostic work, which will serve to critically assess (including by comparison with average trends) its response to various shocks and potentially to improve it. This enhanced management capacity is particularly crucial for human resource management, where in-depth knowledge of the linguistic dimensions of a fi rm’s operations, together with a robust analytical

134

The Economics of the Multilingual Workplace

model, is the key to effective anticipation of needs—also in the area of foreign language skills. The preceding paragraphs propose a shift in emphasis from analysis to action. Equipped with our theoretical and empirical results, we can now transpose our results to broader language policy issues: what do our results tell us with respect to questions such as: is multilingualism valuable? Should society (through the state apparatus or its surrogates) encourage it, and how much? What type of information should be collected for sound decision-making in language policy? What more specific implications can be derived for various policies, fi rst and foremost—but not exclusively—in language education policy? These are the questions that we shall take with us as we move on to the third and last part of this book.

Part III

Policy Implications and Future Prospects

In this third and last part of the book, we step back to consider the broader implications of our investigation. We fi rst re-examine, with the benefit of the fi ndings of Parts I and II, some major language policy issues: is multilingualism really valuable? If so, how far, and in which ways, should it be supported? How should the effort to support it, but also the benefits that multilingualism, be shared? What more do we need to know, what information do we need to collect in order to better answer these questions? In our concluding chapter, after a brief recapitulation of our main fi ndings, we identify the ideal data set and suggest some priorities for future research at the frontiers of language economics and language policy.

9

9.1

Policy Implications

BASES FOR POLICY CHOICE

We are now in a position to move beyond the mere statement that foreign language skills are economically valuable. First, we have seen that investigating this issue raises questions that are sharply distinct from those that are usually addressed in applied linguistics, including in the research about multilingualism at work. We have then noted that the widely held view that foreign language skills are valuable is often based on non-generalisable, sometimes even anecdotal observations or indirect inference, with not infrequent mix-ups between normative and positive considerations. The only robust empirical basis for quantitative evaluations was to be found in the language-based earnings differentials reviewed in Chapter 4; apart from that, existing research offered little basis for any sort of estimates of the value of language skills. Language-based earnings differentials, however, are still at one remove from the production processes from which value emerges. By revisiting basic production theory, a set of direct, explicit causal relationships can be established between linguistic and economic variables. Some of these relationships are embedded in the production process and may be captured through the production function, which serves as a stepping stone towards an analysis in terms of costs and profits. This analysis does not tell us if foreign language skills contribute to value creation when such and such an action is performed—investigating this latter point would be the province of operations management or industrial engineering rather than economics. However, it is this economic approach that has allowed us not just to formulate these relationships, but to estimate orders of magnitude for them, yielding, among other results, the fi rst estimates of the contribution of foreign language skills to value creation at the macroeconomic level, as well as more specific information regarding the economic sectors in which it appears and, though indirectly, some information about the departments, in a company, where foreign language skills are most needed. We have insisted that our estimations concern contingent multilingualism: they show that given the fact that the world is multilingual, foreign

138

The Economics of the Multilingual Workplace

language skills generate net value in the economic sense. We shall turn later to the analytically distinct question of absolute multilingualism, and assess what our fi ndings tell us regarding the value of multilingualism versus unilingualism—or, more precisely, linguistic uniformity. But for now, let us simply note that to the extent that all societies are non-monolingual (that is: the world is linguistically diverse), the theoretical and empirical results presented in Part II can be expected to hold widely. What would change, of course, are the orders of magnitude of the effects estimated, and the languages for which these effects appear. Estimating these effects would, however, require replicating in other settings the type of econometric work used in this book, and ideally doing so with much larger data sets, provided they offer adequate guarantees of representativeness. Let us also point out that the results round off a series of estimations of the value of foreign language skills comprising four levels. The fi rst level, discussed in Chapter 4, is that of “private value”, in the form of language-based earnings differentials; its focus is the typical individual agent. These results tell us what languages are valuable on the labour market, how valuable they are, and how premiums on language skills change as the level of these skills increases. Generally, foreign language learning is a fi nancially attractive proposition for individuals. The second level, not discussed in this book, is that of “social value”, and its focus is the state, which can be seen as the emanation of society— hence the adjective “social”. Estimations of social value build upon results regarding private value, and take account of the investment that society makes in foreign language education, essentially in the form of expenditure for foreign language instruction in the education system. The mere estimation, from educational statistics not designed for this purpose, of the share of foreign language teaching in total educational expenditure can prove to be quite a daunting task in itself (Grin and Sfreddo, 1997). The estimation of social rates of return also includes consideration of the passage of time, in order to compute actual rates of return on foreign language learning expressed in units that make them directly comparable to fi nancial investments. In the few cases where data are available (Grin, 1999a, Chap. 9), they indicate that social rates of return on foreign language education are higher than the average returns on low-risk fi nancial assets. In other words (and still in the context of contingent multilingualism), the teaching of foreign languages is, from the standpoint of society as a whole, a highly profitable investment. In a third level, the focus is on the typical fi rm, whose concerns are productivity, costs and profits. We have fi rst noted that the data available can serve to show (see Section 5.2) that language use and productivity are correlated, before spelling out (Section 5.3) a set of formal relationships between core economic variables and variables reflecting language use within fi rms, a necessary step on the way to a more macroeconomic perspective (see following paragraph).1

Policy Implications

139

The fourth level considers the economy as a whole; it is therefore concerned with macroeconomic effects, which are estimated in this book for the fi rst time. From the standpoint of value creation as it arises from the activity of fi rms in a market economy, we also find that multilingualism generates value, amounting to about 10% of GDP in the case of Switzerland. It is important to note that this method has been shown, in Section 7.3, to be also applicable to the case of Québec, yielding converging results; the approach can be replicated to any country as soon as the necessary data become available. Summing up, with such a robust and consistent set of results, there can be little doubt left that the teaching and learning of foreign languages is economically advantageous. These results also provide stepping stones for the examination of further questions, which cannot be addressed in this book but deserve to be mentioned because of their considerable implications for language policy choices. Let us fi rst observe that throughout the examination, we have only considered hard-nosed market values, which are reflected in prices, costs, wages, and other such variables observed on a market. Yet although they are dismissed by some commentators as fuzzy, non-market values are equally important, and they pervade all aspects of social life. They are in fact frequently invoked in policy debates, albeit under various other labels, often referring to culture, identity or heritage, or human rights. Yet their non-market character does not make them any less relevant economically; the problem is only how to measure them, given their highly complex nature, and the absence of a market price that could serve as an appropriate indicator of value. Evaluation methods from environmental economics, however, could be applied (Grin, 1994) in order to generate estimates of the non-market value of multilingualism at the individual or social level—still taking care, however, to distinguish the cases of contingent and absolute multilingualism.2 Another point deliberately left aside in this book is that of distributive fairness. We have focused on allocative efficiency, because this is what most commentators spontaneously associate with the core of economic analysis (as does Becker, 1976, in his discussion of what economics is about). This is why we have investigated the connections between language skills and the allocation of resources, where different allocations can generate more or less value. But once we know how much value is being created by multilingualism, we can ask ourselves who benefits from it, at which point we enter the realm of resource distribution. The distributional implications of language policies have been analysed through approaches anchored in rational choice theory (Pool, 1991b) or normative political theory (de Briey and van Parijs, 2002; van Parijs, 2004a, 2004b); they belong to the most pressing issues in language policy evaluation, because they are indispensable for comparing alternative linguistic environments and hence alternative policy scenarios (Grin, 2004; Gazzola and Grin, 2007). These questions are of

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course closely connected to issues of language rights (Kymlicka and Patten, 2003; Ruiz Vieytez and Dunbar, 2007), possibly approached through the concept of linguistic human rights (Skutnabb-Kangas and Phillipson, 1994; Phillipson, 2000). In this chapter, however, we shall limit ourselves to the discussion of the policy implications at the allocative level. In Section 9.2, we formulate a more explicit set of connections between language economics and language policy, discussing in particular the responsibilities of the state. Section 9.3 uses the foregoing allocative results to derive some language policy recommendations applying to the context of contingent multilingualism—that is, if the world is multilingual, what is the appropriate response to this state of affairs? Along the way, we shall occasionally consider some implications for policies vis-à-vis the issue of absolute multilingualism, that is, diversity as opposed to uniformity. Let us insist, however, that this chapter is not intended as a primer on the economics of language policy—an endeavour that would deserve a full-length book. Our goal here is simply to spell out the connections between language economics questions and major language policy issues.

9.2 LINK-UP WITH LANGUAGE POLICY: THE ROLE OF THE STATE This book has a dual focus, in the sense that although its primary objective is to examine economic questions, by investigating how and how much economic processes or economic variables are influenced by linguistically-marked variables, it also addresses some language-related processes— not the fi ne-grained detail of multilingual conversations, for example, but more macro-level questions, such as how language policy operates and can be made more effective. We have pointed out, already in Chapter 1, that understanding the interplay between economic and linguistic variables is relevant to language policy, since this understanding sheds light on questions such as why fi rms require foreign language skills, how sensitive their demand for language skills is to various changes, and how much they are willing to reward them. This amounts to a deeper understanding of labour market demand for foreign language skills, which most certainly has an influence on the supply of such skills: if there is excess demand, the wage premiums that reward them will go up, encouraging individual language acquisition, as well as social demand for the education system to teach them. Broad patterns of language learning can therefore be traced back explicitly to economic processes, and the latter can sometimes even help predict evolutions in language learning. When a state wishes to engage in a proactive language policy, the emphasis is traditionally placed on policy measures affecting directly regulated areas like education, the language of public administration, courts of law

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or the military, before turning to relatively less regulated domains such as the media. Typically, economic life, being comparatively less regulated, is least concerned by language policy and its legal expressions (Pupier and Woehrling, 1989; Spolsky, 2009). However, an understanding of the way in which language dynamics respond to evolutions taking place in the economic sphere provides the state with a new range of policy instruments, which may call for regulation but can also be incentive-based. Consider for example the case of minority language promotion. Microeconomic research predicts that the efficiency of subsidies to different types of minority-language goods can be increased by targeting certain types of goods (essentially those that are used for pursuing activities taking place through the medium of the minority language, but where the relative input of time over market goods is relatively low, lest the effect of a subsidy on the unit cost of the activity be too small; see e.g. Grin, 1990: 166). In the same way, the fi ndings presented in Chapter 7 indicate that if the state wishes to encourage demand for X-language skills by businesses, it could for example decide to lower tariffs on a particular range of imported intermediate goods, namely, those whose purchase typically require commercial contacts in language X. In the following section, a range of policy recommendations of this kind is presented. For now, however, a preliminary question should be addressed, namely, do we have reasons for wanting the state to step in and engage in language policy at all? Could we not expect the spontaneous interplay of market forces to generate the optimum level of multilingualism? There is, after all, general agreement to the effect that when it comes to the production of most goods and services, the free market is better at allocating resources than alternative systems like central planning (which performed dismally in the so-called “Eastern block” economies). Here again, a distinction must be made between contingent and absolute multilingualism. In the case of the latter, a strong case can be made that linguistic diversity presents many of the characteristics of a “public good”. A public good, in economic analysis, is significantly different from a usual “private good”, and as a consequence, it is highly unlikely (in fact, next to impossible) that the interplay of free market forces would spontaneously deliver the optimal level of the public good. This situation is known as market failure (the corresponding demonstration is presented in detail in Grin, 2003b: 30–36). By contrast, the free market can be expected, at least in theory, to move towards optimality in the case of most other commodities; this is where, incidentally, the fundamental economic justification for the free market resides. Unless linguistic diversity is regarded as downright pernicious, state intervention in favour of absolute multilingualism, that is, as opposed to uniformity, is analytically justified.3 The issue is a little different for contingent multilingualism, which is the main concern of this book. In the case of contingent multilingualism, where the linguistic environment is assumed to be diverse anyway, the problem is

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no longer whether it is justified for the state to intervene in order to preserve linguistic diversity. The question is whether the state should intervene to ensure an appropriate response by economic agents to a situation already characterised by a given degree of diversity. The answer to this question is not as simple as it may seem, since we could expect the market to send the appropriate signals, for example in the form of substantial wage premiums for speakers with relevant foreign language skills. This should induce foreign language learning, and society and business will be able to draw on a pool of skills without particular need for state intervention. In many ways, the problem is akin to that of the public funding of education in general: should education be considered a public or a private good? If the benefits from education can be privatised, why should the state get involved at all? In our view, the state has a defi nite role to play with respect to contingent multilingualism, because it is generally recognised that it is incumbent upon the state to ensure favourable framework conditions for human activity, including economic activity (though of course not limited to it). In other words, the state may at least nudge agents in the desired direction. Let us fi rst observe that the state does get involved in educational policy, although the benefits of education can largely be privatised (Lévy-Garboua, 1995). The state’s action can be seen as one that reduces the overall cost of access to education by coordinating and pooling resources and know-how, and provides a modicum of coercion without which private actors’ investment in education, possibly daunted by the demanding character of learning goals, might be too low. What applies to education in general applies to the components of education, from mathematics to foreign languages. As we have seen in Chapter 3, many commentators deplore the fact that state authorities in Anglophone countries are failing their citizens by not insisting enough that they acquire skills in languages other than English, thus placing, for example, British fi rms at a disadvantage vis-à-vis more linguistically nimble competitors from the continent. It follows that even in the context of contingent multilingualism, there is cause for the state to step in by mandating foreign language learning in the education system. Does the state also have a role to play, in a context of contingent multilingualism, with respect to language issues in fi rms themselves, whether in terms of language learning or other aspects of language management? The case for state intervention tends to be weaker, because it may be assumed that fi rms usually know what is best for themselves, and act accordingly. It is only when fi rms do not know (for lack of factual information or lack of analytical instruments) that it makes sense to tell them. In this perspective, our results can be of use to businesses in order to better understand their environment, and what the economically efficient responses are. State involvement may take the form of tax breaks for fi rms offering language training to their staff, or of offering, through a specialist state agency, a language use diagnostic tool linked to fi rms’ participation in a regular, centrally-administered survey on language on the workplace. The language

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parameters of the fi rm could then be fitted into a simulation model calibrated on survey results. This may also help to draw managers’ attention to the often overlooked fact that foreign languages matter not only for selling, but for buying. However, we are now entering the sphere of actual policy measures, which brings us to the following section.

9.3 LANGUAGE POLICY PRIORITIES AND PROPOSALS: CONTINGENT MULTILINGUALISM The five sets of policy implications discussed in this section are based on the fi ndings presented in Chapters 4, 5, 7 and 8. While the empirical results presented in Chapter 4 and in the fi rst part of Chapter 5 have been available in the literature for some time, all the rest are novel—and so is, by necessity, their joint consideration. Therefore, some of these policy implications are given an empirical basis for the fi rst time. Let us again point out that they are located in a context of contingent multilingualism—that is, their goal is to help define the best policy response when bilingualism exists. However, given the highly complex interplay of variables involved, the range of policy proposals that can be suggested is constrained by the data available for the estimation of the magnitude of relationships between economic and linguistic variables. In what follows, we have therefore chosen to emphasise the recommendations for which the fi ndings provided substantial empirical backing, specifying in each case the type of information on which they rest, or the nature of the data that would ideally be needed in order to move on to more targeted recommendations. This latter point is taken up again in Section 10.2 on “the ideal data set”.

Should Foreign Languages Be Taught in General? The fi rst empirical fi nding of relevance is that there are economy-wide private returns to language skills. Whether in Québec (Table 4.2) or Switzerland (Table 4.8), bilinguals earn more than unilinguals, even after the influence of education and experience, which are the core determinants of earnings, is taken into account. Therefore, it is proper public policy to facilitate the acquisition of the relevant second language skills by the residents in the jurisdictions responsible for primary, secondary and tertiary education. Putting it more simply, teaching foreign languages is generally a well-advised policy choice. Let us recall, however, that this general recommendation, just like any recommendation in the field of language economics regarding what to teach, does not have an infi nite shelf-life. The reason is that the earnings differentials that may reward any type of competence depend on conditions of supply and demand for this competence at a given point in time. If the

144 The Economics of the Multilingual Workplace education system greatly increases the supply of a certain type of skills, this will ultimately drive down the market value of these skills, with a lag that would generally lie between five and fifteen years, depending on the number of years between the age of learners and the average age of entry on the labour market. At the same time, however, the demand for such skills may vary, positively or negatively, and in the case of language, given the network effects involved, an increase in supply may contribute to stimulate demand by heightening the perception of the relevance of such skills (De Swaan, 2001). The new set of results presented in Chapter 7 provides us with tools to detect future trends on the demand side, therefore placing language education policy on a surer footing.4 At the same time, the elasticities presented in Table 7.8 can serve to alert us to the unintended effects of proactive language policies. Suppose for example that the state wishes to promote language X, and does so very successfully. If the elasticity of the demand for X-language skills is very low (as is the case for “L2” in the table, where it is shown to be a modest -0.11), this implies that the demand for X-language skills is relatively insensitive to price; this would be represented, in a standard market diagram with a supply and a demand curve, by a very steep, almost vertical demand curve. Hence, if as a result of successful language policies, the number of persons fluent in language X increases significantly, the rate of return on X-language skills will drop—potentially sharply. This may, in turn, discourage language learning efforts among the public, because monetary rewards would no longer provide an incentive. This does not necessarily mean that the state should abstain from promotional policies in favour of language X. It means, however, that high rates of return on language X at a given point in time may not remain a valid argument indefi nitely, and that the promotional policy should include accompanying measures to compensate or mitigate this effect.5

At Which Level of Skills? Language education policy obviously involves the education system (in the main, the public education system, although state-fi nanced and even state-regulated private education can be a public policy tool, particularly for adult and continuing education). However, the ways in which languages are taught is, of course, a different matter. We have mentioned in passing, in Chapter 2, the need to keep them separate: public policy evaluation focuses mainly on “external effectiveness” (that is, “what are the skills taught actually useful for—and how much are they rewarded as a consequence?”), a question rather different from “internal effectiveness” (“how are those skills best taught?”).6 Much of the mainstream discourse on language policy tends to blur this difference: it begins by raising external effectiveness questions, before veering off towards internal effectiveness ones, such as the relative virtues of full or partial immersion, often

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approached, moreover, in terms of educational inputs rather than outputs (that is, levels of proficiency achieved by learners). While these are of course eminently relevant questions, they are also much more selfcontained, because they are not subjected to the daunting complexity of the “valuation” of skills in social and economic life. And showing that some pedagogical approaches are better than others at developing a given set of skills does not, per se, prove that these skills are necessary, useful, or valuable—let alone say at how much they should be valued. Returning to the rates of return on foreign language skills, an important aspect of the fi ndings is that the level of wage premiums accruing to people who possess such skills is clearly correlated to the degree of mastery of the languages concerned (as shown for example in Tables 4.5 and 4.7). The important implication is that imparting only a basic knowledge of foreign languages may be wasteful: the return on the investment becomes substantial only if the investment takes learners far enough. From a language economics perspective, there is a certain threshold to be reached. Whether and how this threshold should be differentiated (depending on groups of learners, for example) is a question we address below. At a general level, let us simply observe that this constitutes an argument in favour of relatively ambitious foreign language education, rather than the somewhat tokenistic (or perhaps fatalistic) approach still practised in many education systems, whose effectiveness, in terms of the levels of competence achieved by learners, is widely considered as disappointing. At this point, internal effectiveness considerations do come into their own: it is possible, for example, that the cost of ensuring a generalised increase in the average level of foreign language skills could be mitigated by resorting to underexploited forms of language education, particularly immersion schooling or “contents and language integrated learning”, often called by its acronym of CLIL (Baetens Beardsmore, 1993; Baker, 2001). One aspect of the issue which nevertheless falls within the purview of language policy is how to reach this policy goal if the supply of language learning opportunities is restricted. Suppose for example that in a predominantly Y-speaking society, there is consensus around a policy to encourage the learning of language X. The reasons for this policy choice can be very diverse; they may be inspired by the fact that the rates of return on competence in language X are high (as is the case for German and French in French- and German-speaking parts of Switzerland respectively; see figures in Chapter 4) or proceed from other, possibly political and cultural considerations.7 One problem may arise from the geographical discrepancy between the distribution of the potential teaching workforce within the jurisdiction, and the locations where this workforce is needed. For example, if the supply of fluent and fully confident X-speaking teachers is mostly concentrated in a particular region, residents of other regions will tend to be exposed to lower-quality teaching. Even if there may be technological solutions to correct this situation, this will drive

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up the average cost of teaching; such dimensions sit astride language and educational planning. As regards data needs for the formulation of language education policy proposals, the information needed includes, at the least, language skills, other determinants of earnings (particularly sex, education, age, ethnicity) and earnings in either a census or large-scale survey taken on a regular basis, such as every five to ten years.

Language Skills for Use in What Economic Sectors? Table 7.5 shows that the importance of language as an input in the production process varies substantially between sectors and, for a given sector, between languages. Thus, public policy must take into account the structure of an economy before allocating resources to the learning of languages. For example (using the case of the Swiss economy that generates the estimations presented in the table), for an economy where the hospitality industry (that is, hotels and restaurants) plays an important role, an increase in foreign language skills will generate a greater increase in output than if the dominant sector is, say, transport. This result is generalisable to the extent that the elasticities estimated for other economies fall in the same range as those calculated for the Swiss economy. Although some degree of convergence may safely be assumed (because production functions are unlikely to be markedly different between countries, and in an increasingly globalised economy, economic actors are confronted with increasingly comparable prices), proper language policy requires that a similar set of calculations be carried out for each economic area in which language policy is to be implemented. We have also seen that the use of a specific language in the workplace is linked to economic factors such as ownership (Tables 5.1 and 5.2), the importance of external markets (Tables 5.1, 5.4 and 5.5) and the size of a business (Table 5.5). Since a language is likely to have a higher value on the labour market if it is used in productive activities, public policies may want to target businesses owned by specific language groups if there is evidence that these businesses are disadvantaged due to overall societal constraints. For example, this might be the case for businesses run by members of a minority language community with little access to the financial system; such policies will indirectly target the language of such minorities. The evidence at the manufacturing fi rm level reported in Table 5.3 indicates that the language group of the owner impacts on economic results such as productivity or labour costs at the establishment level. The range of data necessary to set a proper policy is described in Section 10.2, but it is important to note that the number of observations must be sufficiently large to allow disaggregation by sector of employment of the information collected. As to the use of one or another language in the workplace, data can be collected by a survey of individuals. However,

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linking economic outcomes at the fi rm or establishment level with indicators of language use requires collecting detailed data on the use of different languages by employees performing different types of tasks in the fi rm, as described in Sections 6.1 and 6.2.

Which Languages Should Be Taught? A frequent concern is that of the choice of languages to be taught, not only in the compulsory education system (which typically runs, roughly, from ages 5/6 to 16/17 in most countries and where social, political and cultural considerations often play a determining role in the defi nition of the range of languages offered as school subjects), but at later stages of education (“postsecondary”, “post-obligatory”, etc.) and, of course, in adult and continuing education. In the latter contexts, the relative influence of economic considerations tends to be greater. However, it would be rash to make simple, unconditional policy recommendations in this regard, since such recommendations obviously depend on changing market conditions. This is also why the results presented in Section 7.3 are expressed as elasticities, which capture the sensitivity of dependent variables to changes in the value of independent variables. Hence, policy recommendations are conditional. All elasticities do not carry the same policy implications. Cost elasticities (Table 7.7) capture the sensitivity of fi rms to exogenous changes in seven different types of prices, but holding the quantity of output constant. They reflect the adjustments that fi rms will make to minimise costs—for example by substituting one input for another, if the price of the latter has gone up. This may be interpreted as a relatively short-run reaction. However, language policies can hardly be expected to operate in the short run, and it would not be reasonable to design language policies in response to short-run reactions. While some language policies can be rapidly implemented (for example, the decision to switch from unilingual to bilingual road signs), others take more time. This is in particular the case for all policies targeting language skills which take time to acquire. Thus, it is more relevant to consider another set of elasticities, namely, those that are derived from a profit function (shown in Table 7.8). They incorporate the effects of decisions that fi rms can make with respect to the level of output, which can be increased or decreased depending on which maximises profit. Let us take the example of the Brazilian economy, which at the time of writing is poised to become an important global player. Further assume that survey data have confi rmed that the language used in trade with Brazilian clients is, in the main, Portuguese. Suppose that the growth of the Brazilian economy translates into an increase in the demand, by Brazilian buyers, of goods imported from non-Portuguese-speaking countries, and therefore in an increase into the price of such goods. Table 7.8 tells us that (in the case of the Swiss economy), a 5% increase in the average price of

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goods and services sold to Brazil will translate into a 19.45% increase in the demand for Portuguese language skills (since 5% × 3.89 = 19.45%). Let us now consider a very different type of change, such as a drop in the prices of goods bought from Brazil, whose purchase entails trade contacts in Portuguese. This drop may result from the lowering of tariffs or nontariff barriers to trade, from exchange rate fluctuations, etc.; it makes Brazilian suppliers generally more competitive. Assume that prices go down by, say, 5%. This will also result in an increase in the demand for Portugueselanguage skills, and the increase can be estimated at 12.8% (since [-5%] × [-2.56] = +12.8%). Thus, if certain changes in market conditions regarding the Brazilian economy can be anticipated, and if survey data with fi rms in the exporting country are available to provide the necessary information regarding the importance of Portuguese in trade relations with Brazilian clients and suppliers, the impact on the need for Portuguese-language skills can be estimated, and policies developed accordingly. With this type of approach, informed decisions can be made in order to develop rapid reaction programmes for the teaching of languages like Portuguese, Chinese, Russian or Arabic. It is important to note that these estimates are not the result of a simple, technical relationship between inputs and outputs. They incorporate several layers of information, precisely because they are based on elasticities derived from a profit function. In other words, they take account of fi rms’ profit-maximising behaviour, thus capturing the role of market forces in the estimation of the effects of changes in linguistically-marked variables. Obviously, policy-makers may wish to have at their disposal a set of simpler, unconditional recommendations. However, precisely because of the multiplicity of variables at play, it is would not be realistic to expect policy implications to apply independently of outside events. As explained in Chapter 7, elasticities are designed to deal with this type of complexity and to provide conditional predictions, depending on the nature and magnitude of the changes occurring in reality. Table 7.3 shows that in Switzerland, the price index of linguistically different goods, both sold and purchased, has varied from 1998 to 2006. The evolution of these indices is not the same by language; it is also different for commodities bought or sold. Thus, a fi rm selling most of its goods in its “own” (local) language has seen the unit price of its sales decline. If its purchases were also mainly in the local language, for which the price index has risen over the same period, the fi rm would have found itself squeezed on both sides. It could not mitigate the impact of this joint evolution as easily as if its purchases had been in a foreign language. But it is precisely the possibility to turn to another market if necessary that can serve to minimise costs; and better foreign language skills can help to turn to other markets. Hence, it is not only the language(s) used for exports, but also the language(s) used in contacts with suppliers of imports that should be the target of language policies. Put differently, targeting the language of

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exports is akin to a mercantilist policy such as Colbertism, while targeting the language of imports is akin to a free trade policy such as the repeal of the Corn laws in Britain in 1846.8 It may be preferable, for various social, political or cultural reasons, to diversify the languages in which one can import inputs than the languages in which one exports; this however will vary from country to country and over time. The elasticities presented in Chapter 7 and used in the examples above have been estimated with Swiss survey data. For similar conditional predictions to be made in other economic contexts, they need to be recalculated with corresponding country data, following the approach outlined in Chapters 6 and 7, and in Appendix II. The nature of the ideal data needed for this purpose is detailed in Section 10.2. In order to strengthen the corresponding policies, data on price movements for linguistically-marked goods and services (by which we do not mean goods and services with explicit language content, but goods and services whose trade implies the use of one or another language with trading partners) should ideally be monitored through regular surveys.

How Should the Burden Be Shared? Returns to language skills have been shown to vary between sectors of economic activity (Table 4.4). This implies that language skills are of greater value to some employees and some employers than others, a fact which carries consequences for the organisation of teaching and for the sharing of the corresponding costs. More specifically, some employers may be willing to contribute directly to the acquisition of those skills by potential employees, while learners may alter some of their decisions to take this factor into account. For example, employers in the financial sector (which tend to benefit more from language skills than others sectors do, as shown in the third column of Table 7.6) could be induced to endow programs that facilitate the acquisition of foreign language skills, while students in management schools may prefer attending those that allow them to acquire language skills if bilingualism is better rewarded in that sector than, say, in construction. Generally, it makes sense that language training beyond initial instruction be differentially funded by sectors. For example, if there are apprentice programs in place in various sectors, then the linguistic training to be provided or financed by employers should vary according to a set of results similar to those discussed above. Yet the sharing of the burden should be seen in connection with other dimensions of the problems discussed in the preceding paragraphs. In order to illustrate the interaction between these dimensions, let us take one more example, and assume that projections on the movements of prices and on various economic variables point in the direction of an increase in the demand for skills in, say, Arabic in a non Arabic-speaking country, leading the authorities to plan for an increase in the supply of Arabic language skills in the economy.

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However, the extent of the need may be low, medium or high (an aspect that can be evaluated using the set of elasticities presented in Chapter 7). In the same way, the economic sectors affected may be few or concentrated (for example, in export-oriented goods); or, at the other end of the spectrum of possible scenarios, the entire economy may be affected by this likely increase in demand for Arabic-language skills; this dimension may be approached through sectorspecific data on language use. Combining these two dimensions generates a rationale for calibrating language teaching policy and the sharing of its cost among actors (Table 9.1):

Table 9.1

Calibrating and Sharing the Cost of Language Education Policy

Sectors affected →

Few / Demand for skills ↓ Isolated

Several (e.g. in exportoriented sectors)

The economy as a whole

Low

Hiring by firms of Arabic-speaking employees (immigrants, staff recruited abroad or locals with L2 skills in Arabic); no state involvement

Hiring by firms of Arabic courses offered as part of Arabic-speaking adult or continuing employees, along education, financed with Arabic courses as part of mostly by firms employer-financed with limited financial contribution by adult or continuing education; no the state state involvement

Medium

Hiring by firms of Arabic-speaking employees, along with Arabic courses as part of employer-financed adult or continuing education; no state involvement

Arabic courses Arabic courses offered as part of offered as part of adult or continu- adult or continuing education, financed ing education, mostly by firms financed mostly with significant by firms with state support; limlimited financial ited offer of Arabic contribution by as an elective in the state the public education system

High

Hiring by firms of Arabic courses Introduction of Arabic-speaking offered as part of Arabic as a widely employees, along adult or continu- accessible elecwith Arabic ing education, tive subject in the courses as part of financed mostly public education employer-financed by firms with system. adult or continuing significant state education; easing support of state regulations regarding the hiring of foreign staff

Policy Implications

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These strategies are not mutually exclusive; Table 9.1 merely provides a basis for organising a policy response, taking account of general principles regarding the optimal extent of state intervention (Boadway and Wildasin, 1984).

What Do the Results Tell Us for Economic Policy? Finally, let us observe that although our focus in this section is on language policy—yet in the sense of a language policy that takes due account of market forces and even, to the extent possible, exploits them—we can also use some of our results with other concerns in mind, as would be the case for economic policy. Thus, the logic of the investigation would be reversed, and economic policy would then attempt to “exploit” language by using the language-economy relationships as conduits for successful economic policy, one of the main concerns of which is, of course, employment. The empirical fi ndings in Tables 7.7 and 7.8 show that fi rms behave in an economically sensible way, reducing their use of inputs in their production activities when their price goes up. Of relevance here is the fact that the demand for linguistic inputs is the least elastic one; this indicates that these inputs are less easily replaced than other inputs in the production process. Thus, language policies that produce language skills for which demand exists are likely to be a wise use of public funds since the outputs thus produced will be employed.

10 Multilingualism at Work A Prospective Glance

10.1

TAKING STOCK

This closing chapter is devoted to a prospective look in two parts. The discussion is kept deliberately brief, since the questions raised would best constitute material for another book. In Section 10.2, we shall list the type of statistical data that it would ideally be necessary to have in order to provide more detailed estimations of the contribution of different language skills to value creation, both in order to evaluate the amount of this contribution and to explain how it comes about. Such data are necessary, in particular, to generate more specific and disaggregated predictions regarding the sensitivity of productivity, costs and profits to changes affecting language-related variables. Again, our interest is not in explaining what happens in specific production lines, and even less what happens between given persons in a particular company, but to understand the processes at play in the economy as a whole or in given economic sectors with respect to multilingualism in general or with respect to specific languages. Then, in Section 10.3, we shall try to identify questions which we consider to have particular relevance for a deeper understanding of how linguistic variables affect economic processes, particularly value creation; this will lead us to discuss the links between multilingualism and creativity. An issue we shall leave open, however, is that of the further studies that should be undertaken in order to improve our knowledge of the ways in which economic processes affect various facets of language use and language spread: this, again, would constitute material for another book, whose fi rst task would be to clarify what counts as an “economic variable”. Let us remember the distinction introduced at the beginning of this book between “economic” and “the economy”: not everything that happens in contexts generally assumed to be part of “the economy” (say, a work meeting) is economically meaningful; conversely, economic questions arise well beyond the confi nes of standard expressions of economic activity (say, bartering on a marketplace) and turn up as soon as choice between competing alternatives is a crucial feature of human action.

A Prospective Glance 153 Let us, however, begin by reviewing the key fi ndings of the preceding nine chapters. In Chapter 1, we have observed that economic and linguistic processes are related in a number of ways. In particular, the sphere of what is generally described as “economic” is influenced by what is generally described as “linguistic”. This also concerns the use of language skills, including multilingual skills, at work. However, moving beyond this very general statement raises some difficulties and fi rst requires clarifying what is exactly being put in relation with what, and for what reasons. This forces the researcher to explain what type of analytical instruments are used to establish the notion that relations do exist—which in turn implies adopting a certain epistemological and methodological stance. The next step is to specify the nature of the relationships considered, which is necessary before they are spelled out more formally and, in a subsequent step, empirically tested. In Chapter 2, our review of the work carried out in the language disciplines—particularly various branches of applied linguistics—on language use and multilingualism at work has enabled us to show that in the main, these lines of research are not chiefly interested in the influence of language on economic variables at all. The latter are generally not even mentioned in this literature, which therefore provides a richly textured ethnography of language at work but is of limited help in the investigation of the economics of the multilingual workplace. In Chapter 3, we have reviewed a range of more economically-oriented surveys about multilingualism at work. This has led us to conclude that these surveys, informative as they are, generally lack the theoretical grounding that would make it possible to derive a general economic interpretation of the data gathered. Of the relatively few examples of empirical work that are based on a more formal analysis of the underlying processes, some concern “language” (rather than languages or multilingualism), while others raise serious methodological problems. Therefore, there was a need to take things, as it were, “from the top” and to build a theoretical perspective on multilingualism at work. Given the considerable range of processes involved, the task is a potentially daunting one, making some stepping stones useful. In Chapter 4, we have presented the most important of these stepping stones, namely, language-based earnings differentials, that is, the wage premiums accruing (all other things being equal) to persons with more foreign language skills. The interest of these well-established results which consistently show, through statistically robust estimations, that foreign language skills are often highly rewarding, rests with the fact that such differentials reveal the existence of a link between multilingualism (at least in the form of foreign language skills that workers possess) and value creation. Yet these earnings differentials are only a reflection, or an indicator, of the economic contribution of multilingualism, hence the need to look beyond them in order to get a better understanding of the issues at hand.

154 The Economics of the Multilingual Workplace In Chapter 5, therefore, we have embarked upon the task of revisiting basic microeconomic theory, and more precisely the economic theory of production, in order to include in it several linguistically marked variables. The resulting formal model proposes an explicit and formal connection between the economic and the linguistic sphere as they intersect in the context of productive activity. This amounts to a “language-augmented theory of production”, whose interest, apart from theoretical development per se, is to provide a basis for econometric estimation, starting with the very mundane (but hitherto often neglected) question of justifying which data it is relevant to collect. Chapter 6 has provided an opportunity to review the complex steps leading from a theoretical model, with its challenging standards of precision and consistency, to a no less demanding “calculable” model, that is, a system of equations that lends itself to empirical testing with quantitative data. The procedures described in this chapter serve to highlight the nature of the data needed to move beyond illustrative anecdotes. Without quantitative data, no statement of general validity on the value of multilingualism, particularly its order of magnitude, can be ventured. Chapter 7 puts figures on the relationship between language and production. We have seen that the key economic variables determining the levels of inputs and output are systematically affected by language-related variables. In particular, confronting changes in the value of the latter with the economic rationale of cost-effective production, cost minimisation and profit maximisation allows us to calculate, for example, by how much employers’ demand for multilingual skills will vary, following a given percentage change in, say, the average selling price of goods “sold” in a foreign language. This range of results has general validity: they provide a template into which other exogenous changes can be fitted, generating predictions about the resulting change in the level of various economic variables. Here again, the emphasis is on general causal links, some of which have considerable policy import. For example, figures show that following a given rise in the average cost of labour, which generally causes labour demand to go down, unilingual workers are twice as likely as multilingual workers to lose their job; this suggests that multilingualism should receive much more attention in employment policy. The approach developed in Chapter 7 also enables us to assess, for the first time, the share of a country’s gross domestic product (GDP) that can be traced back to multilingualism. Data for Québec and Switzerland show this share to be considerable, at around 4% and 10% respectively for the economy as a whole. Chapter 8 proposes a slight detour from our main line of argumentation and focuses on the issue of recruitment: how can firms go about hiring staff with the appropriate level of foreign language skills, knowing that such skills are relatively expensive (bilingual workers generally command a higher wage than unilinguals), that the recruitment process itself is not free, but that inefficiencies may result from hiring staff with inadequate skills? The model helps to explain an apparent paradox found in the data, namely,

A Prospective Glance 155 that foreign language skills are systematically used by a percentage of workers higher than the percentage of people from whom such skills were explicitly required at the time of hiring. Behind an appearance of systematic error, firms may in fact be acting quite rationally: in order to minimise total cost (made up of the components just mentioned), it is often better to aim for language skills levels lower than what is actually needed for the job. In Chapter 9, we have begun by recalling that a proper understanding of the role of economic considerations in the determination of language dynamics is useful to language policy, because it can help language planners harness market forces, rather than try to work against them. We have then reviewed the economic reasons for state intervention in favour of multilingualism, not with respect to absolute multilingualism (a theme which would deserve a separate volume), but with respect to contingent multilingualism. The results presented in this book largely converge towards one conclusion: given that, at this point, the world is multilingual, it is in the interest of individuals and organisations, including firms, to acquire and use foreign language skills. From an economic standpoint, it is justified for the state to nudge social actors in this direction. In addition, a state may have legitimate linguistic objectives that have emerged from a democratic political process. Finally, the state may use language for the purposes of economic policy. Thus, the analysis developed in this book suggests different types of policy measures that can usefully build on the language-economics linkages: they concern the relevance of foreign language teaching in principle (generally an economically sound proposition), the levels to be reached (skills should go beyond basics if they are to pay off), the importance of the structure of the economy (since the share of value creation due to multilingualism varies across economic sector and according to language, language teaching should take account of the estimations regarding the absorption capacity of skills in different languages by different economic sectors), the role of market forces like profit-maximising behaviour (given information about exogenous shocks, the consequences on a range of variables, such as the magnitude of changes in demand for language skills in given foreign languages, can be estimated), the sharing of the cost of foreign language teaching (the role of the state varies from nil to near-exclusive depending on estimations regarding changes in the amount of language skills demanded and the sectors affected by these changes), and the leverage that language can offer to traditional economic policies (language skills are a good safeguard against layoffs). Many of the results mentioned in the previous paragraph, and explained in detail in the preceding chapters of this book, are novel, and so is their combination into an integrated approach to multilingualism at work. Yet of course it would have been desirable to go further. For example, we might expect the analysis to provide more specific pointers regarding which languages to learn, which language regimes to implement in order to boost company profits, and so forth. Some of these questions, because they are highly dependent on the specific nature of the productive activity concerned,

156 The Economics of the Multilingual Workplace are beyond the concern of this book and would be in the province of industrial engineering or operations management. Others, however, are squarely within the confi nes of economic analysis and could be investigated if more data were available. The issue, therefore, is which data should be collected for this purpose; this is the question addressed in the following section.

10.2

THE IDEAL DATA SET

Answering the type of questions raised in this book requires, apart from targeted theoretical model-building, sustained econometric work. The latter becomes more complicated when data sets are patchy, because missing data have to be replaced by estimations derived from existing information. Moreover, lack of data, whether in terms of detail or amount, restricts the range of analyses that can be carried out and of results that can be obtained. This is why more effort should be expended towards the systematic collection of adequate data sets. Collecting data is demanding work at the best of times; it is particularly true when the data has to be collected from businesses. There are two main reasons for this. The first is that firms are notoriously disinclined to devote time and effort to answering researchers’ queries and filling out questionnaires. This is often the case when questionnaire items ask questions that can only be answered with some effort, such as assessing what proportion of their time staff members in purchasing or sales spend, on average, speaking language X or Y with clients, suppliers or colleagues in subsidiaries of the same firm. The second is that in order for the data to be scientifically relevant, they need to be representative. This raises sampling problems which are well-known in principle but hard to solve in practice, unless considerable financial resources can be invested to generate a very large data set, or if firms are legally obligated to take part in surveys—typically administered by state statistical agencies. At this point, different groups of data users (national ministries, local administrations, the research community) often find themselves in competition with each other, with the result that few ever obtain the ideal data set. What research can offer, however, are means to identify the “best” data set, in the sense of the minimal amount of data required to generate a particular set of key results, in indirect application, as it were, of the time-honoured (if possibly apocryphal) principle attributed to William of Ockham and known as Ockham’s razor. Table 10.1 provides such a list, focusing on the data needed to estimate the three core elements of the analysis, namely, production, costs and profit functions. The table features two columns, reflecting the processing work to which both theoretical and empirical inputs need to be submitted in order to arrive at robust estimations; the left-hand side column lists the raw data to be collected, mostly through surveys, while the right-hand side column indicates in what form these data can be used in the econometric work.

A Prospective Glance 157 Table 10.1 The Ideal Data Set RAW DATA to collect by survey*

FINAL DATA to use in econometric estimation Production Function

• Complete list of workers showing: posi- • Labour-related inputs: number of worktion in firm, years of education, working ers (in FTE), number of managers, numexperience (duration), language skills ber of bilinguals (with breakdown by level (by language), average number of language), average duration of working hours worked per week, gender experience, average length of education • Value of fixed capital used (machinery, • Capital stock buildings and intangible capital such as software) • Value of total production (sales plus increase in stock of finished goods) • Value of intermediate consumption (purchases plus increase in stock of intermediate goods; including operational expenses and rent)

• Value added

• Locally dominant language(s)**

• Locally dominant language(s)

Above data for a given year

Above data for a given year

Cost Function and Profit Function • Complete list of workers showing: position in firm, years of education, working experience (duration), language skills level (by language), average number of hours worked per week, gender, salary received (plus social contributions paid by the employer)

• Quantity of labour-related inputs: number of workers (in FTE), stock of managing skills, stock of language skills (with breakdown by language), quantity of female work, stock of working experience, stock of skills acquired through education • Price of labour-related inputs: pure wage (cost of pure working time), price of managing skills, price of language skills (by language), wage differential associated with female work, price of skills acquired through work experience, price of skills acquired through education

• Value of fixed capital used (equipment, • Quantity of capital buildings, intangible capital such as software, . . .)§ • User cost of capital (price index of • Profit, interest, capital depreciation capital use) (continued)

158 The Economics of the Multilingual Workplace Table 10.1 (continued) RAW DATA to collect by survey*

FINAL DATA to use in econometric estimation

Cost Function and Profit Function (continued) • Value of total production (sales plus increase in stock of finished goods), by language used with purchaser

• Quantity of goods sold, by language

• Year-on-year percent change in price of goods sold, by language used with purchaser†

• Price (index) of goods sold, by language

• Value of intermediate consumption • Quantity of goods and services (purchases plus increase in stock of purchased, by language intermediate goods; including operational expenses and rent), by language used with supplier • Price(index) of goods and services • Year-on-year percent change in price purchased, by language of intermediate goods and services purchased, by language used with supplier† Locally dominant language(s)**

Locally dominant language(s)

Above data for a sample period (5–10 years)

Above data for a sample period (5–10 years)

*

For large multi-plant firms, some of the information provided might refer to the whole firm, while other information might refer to only a subset of plants, assumed to be representative of the firm’s activity. In this case, the researcher would need to know what proportion of the firm’s activity takes place in these plants. ** May also be collected through censuses. § In principle, capital stock should be measured in constant-price terms, that is, after correcting for changes in its price (inflation). † Changes in prices must be corrected for changes that can be attributed to improvements in goods sold or in goods and services purchased.

Let us note that these data can be gathered for a representative sample of fi rms. Therefore, estimations can be run for the typical firm rather than for an industry as a whole (as we had to in Chapter 7 for lack of data). Hence, the production, cost and profit functions, featuring language and languagerelated variables, would be estimated at the level of the representative firm. This procedure yields fi ner results, since it allows variables to vary across fi rms within the same industry, while the industry-level approach implicitly (and probably unrealistically) assumes that all fi rms face the same environment. It also involves simpler statistical treatment by eliminating the need for data “robustification” or rescaling, which is sometimes necessary when industry-level variables are not directly observable and therefore need to be quantified through indirect measurement techniques.

A Prospective Glance 159 Several signals suggest that at the time of writing, language planning bodies are sharpening their awareness of the range of data needed to get a better grasp of the language-economy relationship, particularly with respect to planning needs for the training of language teachers, translators and interpreters. One of the goals of this book is to put future endeavours in this direction on a fi rmer footing.

10.3

NEW AVENUES

As we have seen, a considerable amount of work lies ahead. We hope, however, that this book contributes to show what needs to be done and how, in order to better understand the economics of language at work and the corresponding policy implications. In this closing section, however, we would like to mention three research themes which, though slightly further away from the specific questions we have tried to address, hold in store some particularly promising perspectives. The following is not intended as an in-depth discussion resting on a new literature review, but merely as a set of speculative considerations on exciting avenues waiting to be explored.

Multilingualism and Creativity The first of these main themes is rooted in the cognitive sciences which have been devoting, in recent years, more attention to the multilingual brain. Much of this research, helped by developments in neuro-imagery, has focused on the parts of the brain activated by the performance of multilingual tasks. However, the practical implications of neurolinguistic research regarding the value of multilingualism—even when broadening the notion of value well beyond its economic meaning—are not always plain to see. For example, the greater accuracy or speed achieved by bilinguals when performing some non-linguistic tasks (such as pressing the proper computer keys in response to visual stimuli appearing on screen; see Bialystok, 2009) may not make much of a difference to the usual activities of everyday life or to economic efficiency. For example, demonstrating (“native”) bilinguals’ superior ability through experiments such as “flanker tests” is still a far cry from proving that their productivity is higher. This is why we believe that greater attention should be devoted to the investigation of possible links between multilingualism and creativity. This question, of course, is not entirely new, even though efforts to investigate it at closer range have only started recently.1 However, it provides a golden example of the very type of issues where the temptation to jump to conclusions is sometimes irresistible—making caution all the more advisable. Just like some conversationalists confidently claim that the very fact of code-switching among participants in a meeting “proves” that multilingualism can be financially profitable

160

The Economics of the Multilingual Workplace

(without explaining how this happy outcome could be measured, let alone making any attempt at backing up the claim with figures, and blithely ignoring the possibility that higher proficiency of all participants in one given language could turn out to be more efficient), some commentators seem happy to assert (also without any kind of proof) that multilinguals are individually more creative, and multilingual teams more productive. The confidence with which such pronouncements are made is perhaps unsurprising, considering that it dovetails with politically correct views and easy metaphors about diversity as a “treasure”. Although we consider this correlation to be very plausible, it bears repeating that it remains, at this time, unproven, and more focused research is necessary (Mitchell and Nicholas, 2006; Maddux and Galinsky, 2009). The line of investigation through which such a correlation may be established—or, at least, the specific conditions under which it can appear—is, in our view, less likely to be found in neurolinguistics than in psychology, where considerable effort has been expended in order to define what creativity actually is, how it can be measured and which variables are correlated with measurements of creativity (Simonton, 2008; Kaufman and Beghetto, 2009). Since the data used in this research usually include several individual sociocultural dimensions, a natural next step is to constitute data bases which, in addition to the variables that psychologists use to assess creativity, include information on respondents’ foreign language skills. Crucial questions, in this respect, will have to do with whether results differ, all other things being equal, depending on the level of foreign language skills, the age at which those skills have been acquired, the channels of foreign language acquisition, the language repertoires (L1, L2, L3, etc.) involved and, of course, the type of creativity considered. The array of potentially relevant interaction terms between these variables is such that the range of studies that can be considered, data availability permitting, is potentially endless. After measurements of creativity have been put in relation with measurements of language skills “upstream”, they need to be connected “downstream” with measurements of economically relevant performance. Let us once again stress that in our view, “economically relevant” is a notion that needs to be understood in a broad sense. In fact, a claim could be made that some creativity research has been waylaid by an overemphasis on a mercantile approach to creation. Hence, some researchers have started focusing on “ideation” or “ideational creation”, as distinct from a notion of creativity that was beginning to be excessively compromised by association with marketing. Let us be clear that what matters to us here is, indeed, the possible link between multilingualism and ideational creation, which may or may not be correlated, in turn, with economic value creation; it is important to keep these stages distinct.

Linguistic Intensity Indices Although indications of a link between foreign language use and economic value creation are piling up (and this book is intended as a contribution

A Prospective Glance 161 to the building up of an organised body of empirically robust evidence), many questions remain regarding how language skills contribute to value creation. On this count, the preceding chapters may have helped to crack open, just a little, the “black box” of value creation. But how can we go further in understanding how language creates value, without drowning in the idiosyncratic detail of case-specific experience, which stifles attempts at generality? The answer may lie in refi ning and justifying the linguistic intensity indexes briefly described in Section 3.3. These indexes, used in recent work by Spanish and Portuguese economists (see in particular García Delgado et al., 2007), are meant to reflect the relative importance of “language” (in general) in productive activity. A similar approach could be developed to capture the relative importance of foreign language skills (or even of skills in duly identified foreign languages) in productive activity possibly by banking on empirical experience on related questions developed in psychology (e.g. Dinsbach et al., 2007). Depending on the degree of disaggregation aimed at, this could amount to a colossal enterprise, where sampled professions within each economic sector should be scrutinised to assess, for example, the share of oral and written communication time taking place in respective languages. Results should then be weighted in order to account for the relative importance, in an economic sector, of each profession sampled, in order to arrive at sector-level average values. However, in order for such a task to be carried out properly, it is crucial to avoid oversimplifications or being misled by appearances. A solid, hands-on understanding of what it means to use different languages at work is necessary. For this purpose, various specialisations in applied linguistics provide indispensable know-how, and the development of linguistic intensity indices currently offers some of the most stimulating perspectives for interdisciplinary cooperation.

Complex Language Policies The question also arises of how to exploit the knowledge gained through language economics for language policy; this, in turn, should lead us to re-examine what is meant by language policy and what may be expected from it. Recent research suggests that a promising theme now deserving increased attention is that of new, complex language policies. Some of the seeds of this research have been planted by applied linguists, whose work in recent years stresses the subtlety of processes of multilingual communication and develops more complex notions of language skills. Instead of discrete competence in sharply distinct languages, they stress the relevance of multilingual repertoires in which knowledge of different languages is combined rather than fragmented (Shohamy, 2006). At the same time, current developments in language policy (for example Ricento, 2006; Spolsky, 2009) indicate that research is progressively freeing itself from the dictates of outward form, in which language policy and planning were defined as

162

The Economics of the Multilingual Workplace

explicit (though not necessarily overt) interventions on language. The focus is now shifting to more interdisciplinary and also deeper perspectives, where deliberate intervention on language is approached through its underlying social, political and economic rationales, including, in particular, the notion of choice. This is just the type of developments in which language economics can make a valuable contribution. However, such developments go further: they open the door to what can be considered the “next generation” in language policy research, whose focus is not on individual strategies but on balanced combinations of strategies (Grin, 2008a). Echoing the complexity of real-world communicational processes in multilingual contexts, language policies will be progressively led to accommodate the joint resort to different ways of handling communication: foreign language learning, of course, holds centre stage, but other strategies should be viewed in complementarity rather than in competition with individual multilingualism. These range from intercomprehension (also known as receptive competence in neighbouring languages; see ten Thije and Zeevaert, 2007; Conti and Grin, 2008) to the controlled use of lingue franche—a topic where current research (e.g. Arzoz, 2008) demonstrates the need to move beyond simplistic clichés—as well as to the targeted, decentralised use of translation and interpretation, seen as part of broader multilingual services (Grin, 2008b; Guidère, 2008b; Melitz, 2007). The next frontier in language policy may well be that of the design of complex language policy architectures doing justice to multilingual communication in the real world. This would, in turn, contribute fresh perspectives on each of these strategies for dealing with multilingualism, which would then be viewed as components of a partnership for a balanced, sustainable multilingualism. Many of the most exciting challenges still lie ahead.

Appendix I Language-Augmented Production Model

1

THE CORE MODEL

Consider a fi rm that provides product A in an economy where all goods, services and production factors are traded on perfectly competitive markets. The fi rm interacts with its environment using national language R and one foreign language S. The production of A requires intermediate goods and services, labour and capital. Intermediate goods and services are linguistically marked according to the language skills necessary for purchasing and using them, which reflects, in turn, the language of suppliers, instructions for use and after-sales services. Let us distinguish “R-language” and “S-language” intermediate goods and services. In the same way, let there be two profiles for workers, namely, unilinguals who know only language R and bilinguals who know R and S. The fi rm’s production function is: , where ci is the amount of i-language intermediate good used in the production of y; hj represents the working hours provided by a unilingual (j = R) or bilingual (j = RS) worker used in the production of y; and k is the amount of capital used for the production of y Let us assume constant returns to scale with a Cobb-Douglas production function: , with γ R + γ S + ϑ R + ϑ RS + β = 1 and 0 < γ R , γ S , ϑ R , ϑ RS , β < 1.

(1)

164 Appendix I In a competitive economy, factors are paid their marginal product:

p

∂y = gi ∂ci

(2)

p

∂y = wj ∂h j

(3)

where p is the unit price of output, gi is the unit price of the i-language intermediate good, and wj is the wage rate of a unilingual (j = R) or bilingual (j = RS) worker. Since

∂y y = γi , ∂ci ci

(i = R, S)

(2) can be rewritten as:

γi =

gi ci . py

Coefficient γi denotes the share of the i-language intermediate good in the value of the output. Likewise, (3) can be rewritten as:

ϑj =

wjhj py

(j = R, RS).

where coefficients γ et ϑ represent factor intensities. 2 DERIVED DEMAND FOR INPUTS AND DERIVED SUPPLY OF OUTPUT Let us focus on the variable inputs c and h and assume k to be fi xed, bearing in mind that on a perfectly competitive market, output and input prices are exogenous. The respective derived demand functions for inputs are:

ci = ci ( p, g R , g S , wR , wRS , k )

(i = R, S)

h j = h j ( p, g R , g S , wR , wRS , k )

(j = R, RS).

These two functions are the solutions of the following system, based on equations (2) and (3):

Appendix I 165

­ ∂y °° p ∂c = g i i . ® ∂y °p = wj °¯ ∂h j Solving cR , c S , hR et hRS , we get:

[ [ [ [

­ c = (γ R ° R ° cS = (γ R ® ° hR = (γ R °h = (γ R ¯ RS

] p] p] p]

g R ) R (γ S g S ) S (ϑR wR ) R (ϑRS wRS ) RS p γ

γ

ϑ

ϑ

g R ) (γ S g S ) (ϑR wR ) (ϑRS wRS ) γR

γS

ϑR

ϑRS

g R ) (γ S g S ) (ϑR wR ) (ϑRS wRS ) γR

γS

ϑR

ϑRS

g R ) R (γ S g S ) S (ϑR wR ) R (ϑRS wRS ) RS γ

γ

ϑ

ϑ

1/ β

(γ R (γ S 1/ β (ϑR 1/ β (ϑRS 1/ β

g R )k

g S )k

wR )k

(4)

wRS )k

We derive the supply function by substituting into the production function (1) the demand functions for factors cR , c S , hR and hRS by their respective functions as given by (4), to yield:

[

y = (γ R g R ) R (γ S g S ) S (ϑR wR ) R (ϑRS wRS ) RS γ

γ

ϑ

ϑ

]

1/ β

1− β

k⋅p

β

(5)

Since 1 − β = γ R + γ S + ϑR + ϑRS , (5) may be rewritten as: 1/ β

§ § γ p ·γ R § γ p ·γ S § ϑ p ·ϑR § ϑ p ·ϑRS · y = ¨ ¨¨ R ¸¸ ¨¨ S ¸¸ ¨¨ R ¸¸ ¨¨ RS ¸¸ ¸ ¸ ¨ g g w w © © R ¹ © S ¹ © R ¹ © RS ¹ ¹

k.

(6)

Equation (6) shows that supply is a function of the ratio between the price of variable inputs and the price of output; output will not be affected if all prices change in the same proportions. It is easily shown that under the assumptions made regarding the signs of the parameters, supply is a positive function of output price and a negative function of the price of production factors.

3

COMPARATIVE STATICS

As shown by (4) and (6), a change in the price gR of input R does not only modify the optimal level of input cR , but also the optimal levels of other inputs (c S , hR and hRS) as well as the optimal level of output y. The effects are given by:

166 Appendix I

∂ci ∂g k ∂h j ∂g k

­ γ i + β ci °°− β g k =® γ i ci ° − °¯ β gk γ hj =− k β gk

if i = k if i ≠ k

γ y ∂y =− k ∂g k β gk The preceding results show that a rise in the price of an intermediate good causes the level of all endogenous variables to go down, and that (in line with the specification of the production function), the factors are complements rather than substitutes. In the same way, the effect of a change in the hourly wage rate wk (k = R, RS) on the endogenous variables is given by:

ϑ c ∂ci =− k i β wk ∂wk ­ ϑj + β hj °− β wk ° =® ϑj hj ∂wk ° − °¯ β wk ϑ y ∂y =− j β wk ∂wk ∂h j

si j = k si j ≠ k

Finally, a variation in the price p of output affects the optimal level of endogenous variables as follows:

∂ci 1 ci = ∂p β p ∂h j 1 h j = ∂p β p ∂y 1 − β y = ∂p β p 4

(i = R, S ; j = R, RS ).

THE VARIABLE PROFIT FUNCTION

Any combination of prices and inputs in relation with a certain quantity of output gives rise to a certain profit obtained by subtracting total costs from total revenue:

Appendix I 167

π = p ⋅ y − ( g R cR + g S cS + wR hR + wRS hRS ).

(7)

Profit is defi ned as variable since the cost of the fi xed production factor (capital) is not subtracted from total revenue. Substituting cR , c S , hR , hRS and y by the corresponding functions (4) and (6), (7) becomes a variable profit function with exogenous variables gR , gS , wR , wRS , p and k. It may be rewritten as follows:

π = π ( g R , g S , wR , wRS , p, k ) ª§ γ ·γ R § γ = p «¨¨ R ¸¸ ¨¨ S «¬© g R ¹ © g S

· ¸¸ ¹

γS

(8) ϑR

§ ϑR · ¨¨ ¸¸ © wR ¹

ϑRS

§ ϑRS ¨¨ © wRS

· ¸¸ ¹

º » »¼

1/ β

1− β

β

k⋅p

1/ β

ª§ γ ·γ R § γ ·γ S § ϑ ·ϑR § ϑ ·ϑRS º − «¨¨ R ¸¸ ¨¨ S ¸¸ ¨¨ R ¸¸ ¨¨ RS ¸¸ » «¬© g R ¹ © g S ¹ © wR ¹ © wRS ¹ »¼ § γ γ ϑ ϑ × p1/ β ¨¨ g R R + g S S + wR R + wRS RS g g w wRS R R S ©

· ¸¸k . ¹

After simplification, and using the fact that 1 − β = γ R + γ S + ϑR + ϑRS , (8) may be rewritten as:

πi = β ⋅ p

1/ β

ª§ γ ·γ R § γ k «¨¨ R ¸¸ ¨¨ S «¬© g R ¹ © g S

ª§ γ ·γ R § γ = β ⋅ k «¨¨ R ¸¸ ¨¨ S «¬© g R ¹ © g S

· ¸¸ ¹

γS

· ¸¸ ¹

γS

ϑR

§ ϑR · ¨¨ ¸¸ © wR ¹ ϑR

§ ϑR · ¨¨ ¸¸ © wR ¹

§ ϑRS ¨¨ © wRS

§ ϑRS ¨¨ © wRS

ϑRS

· ¸¸ ¹

º » »¼

1/ β

.

ϑRS

· ¸¸ ¹

pi

ª§ γ p ·γ R § γ p ·γ S § ϑ p ·ϑR § ϑ = β ⋅ k «¨¨ R ¸¸ ¨¨ S ¸¸ ¨¨ R ¸¸ ¨¨ RS «¬© g R ¹ © g S ¹ © wR ¹ © wRS

· ¸¸ ¹

ª§ γ p ·γ R § γ p ·γ S § ϑ p ·ϑR § ϑ = β ⋅ k «¨¨ R ¸¸ ¨¨ S ¸¸ ¨¨ R ¸¸ ¨¨ RS «¬© g R ¹ © g S ¹ © wR ¹ © wRS

· ¸¸ ¹

γ R +γ S +ϑR +ϑRS + β

ϑRS

º pβ » »¼

ϑRS

º » »¼

º » »¼

(9)

1/ β

1/ β

1/ β

p

It is easily seen that (9) is fi rst-degree homogeneous in prices and in the quantity of the fi xed input. Consequently, (9) can be transformed as a variable profit function per unit of capital (π/k). Let us fi nally observe that by applying Hotelling’s lemma, the output supply function and the input demand functions may be obtained by derivation:

168

Appendix I

y=

5

∂π ∂π ∂π , hj = and ci = . ∂p ∂w j ∂g j

USING THE TRANSLOG FUNCTION

A Cobb-Douglas technology, though relevant for illustrating the derivation of output supply and factor demand functions, is less suited to a realistic representation of complementarity and substitution between inputs. Because all inputs are treated as complements, it preempts the analysis of cases where a fi rm would respond to a rise in the price of the S-language intermediate good by reducing c S and increasing cR , combined with unilingual workforce (speaking R only). Let us therefore revisit the preceding results, using a more flexible translog functional form, starting, however, from the profit function (9). Recall also that fi rst-degree homogeneity allows us to defi ne profit by unit of capital. Thus, the number of exogenous variables is reduced from six (the prices of the four variable inputs, the price of the output and the amount of capital) to five (the same, minus capital). Profit per unit of capital therefore becomes the endogenous variable (for a discussion of the properties of the translog function, see Diewert and Wales, 1987 and Christensen, Jorgenson and Lau, 1973). Let us focus here on the determination of profit per capital unit in the case of a producer selling output y at price p, buying intermediate goods at prices cR and c S and paying workers at the wage rates wR and wRS respectively. The translog variable profit function (π/k) is: ln(π / k ) = α1 ln p + α 2 ln g R + α 3 ln g S + α 4 ln wS + α 5 ln wS

(10)

1 + ϕ11 (ln p) 2 + ϕ12 ln p ln g R + ϕ13 ln p ln g R + ϕ14 ln p ln wR + ϕ15 ln p ln wRS 2 1 + ϕ 22 (ln g R ) 2 + ϕ 23 ln g R ln g S + ϕ 24 ln g R ln wR + ϕ 25 ln g R ln wRS 2 1 + ϕ 33 (ln g S ) 2 + ϕ 34 ln g S ln wR + ϕ 35 ln g S ln wRS 2 1 + ϕ 44 (ln wR ) 2 + ϕ 45 ln wR ln wRS 2 1 + ϕ 55 (ln wRS ) 2 2

In order to guarantee fi rst-degree homogeneity in prices, the following restrictions need to be imposed on the parameters:

Appendix I 169

α1 + α 2 + α 3 + α 4 + α 5 = 1 ϕ11 + ϕ12 + ϕ13 + ϕ14 + ϕ15 = 0 ϕ12 + ϕ 22 + ϕ 23 + ϕ 24 + ϕ 25 = 0 ϕ13 + ϕ 23 + ϕ 33 + ϕ 34 + ϕ 35 = 0 ϕ14 + ϕ 24 + ϕ 34 + ϕ 44 + ϕ 45 = 0 ϕ15 + ϕ 25 + ϕ 35 + ϕ 45 + ϕ 55 = 0

(11)

As shown in (11), the output supply function may be obtained by deriving the profit function with respect to output price:

y=

∂π . ∂p

(12)

However, we know that since k is independent of p:

p ∂ (π / k ) ∂ ln(π / k ) . = (π / k ) ∂p ∂ ln p p ∂π = π ∂p

(13)

Using (13), the supply of output (12) can be rewritten as:

y=

∂ ln(π / k ) π ∂ ln p p

y=

∂ ln(π / k ) (π / k ) k. ∂ ln p p

or (14)

(16) is easily calculated since k and p are known, and elements ∂ ln(π / k ) and ∂ ln p (p/k) can be derived from profit function (10). The input demand functions may be expressed in the same way, yielding:

ci = − and

∂ ln(π / k ) (π / k ) k gi ∂ ln g i

i = R, S

(15)

170 Appendix I

hj = −

∂ ln(π / k ) (π / k ) k wj ∂ ln w j

j = R, RS.

(16)

Profit functions take variable inputs as negative outputs; the negative sign in (15) and (16) are necessary for ci et hj to be positive.

Appendix II Estimation Procedure and Results

This appendix describes the steps undertaken to perform the econometric estimation of the production, profit and cost functions. It is intended for readers interested in the technical aspects of data construction and econometric estimation, and focuses on the main calculations. Data construction is described in steps 1 to 5, while the econometric estimation and data manipulation directly related to it are briefly described in steps 6 to 8.

1

DATABASES USED

While building series of prices and quantities of macroeconomic variables is straightforward given extensive access to electronic databases, the task becomes more complex when one wishes to include the linguistic dimension. Typically, data on language skills and language use ready for economic analysis are scattered across various databases, which are often only partially compatible with each other. Indeed, one of the major challenges when producing the data used for the econometric estimation leading to the results in Chapter 7 was to address compatibility issues by making the best use of all the information available. We thus combined some of the information provided by the following databases: a) CLES databases (presented in Chapter 4; see Grin, 1999a) from which we drew socio-economic data (including income) and information on language skills for approximately 1,500 workers; these data were collected in 1995 through a telephone survey; b) value added statistics (VAS1), covering total production, intermediate consumption and value added (VA), by economic sector for years 1997 to 2006; c) value added statistics (VAS2), covering total production, intermediate consumption and value added (VA), by economic sector for years 1996 to 1997;

172 Appendix II d) census-based labour statistics (CBLS) by economic sector, language region and gender for year 1995; e) survey-based labour statistics (SBLS) by economic sector for years 1996 to 2006; f) balance-sheet structure statistics (BSSS), providing information on balance sheet structure and profit-and-loss structure for large companies operating in Switzerland, for years 1998 to 2005; g) Swiss Household Panel (SHP), which provides, among other things, socio-economic data (including income) and information on language skills for approximately 1,500 workers for year 1999; the same sample was surveyed from 2000 to 2005, although information on language skills stopped being collected in 2000; h) data from the LEAP survey, mainly breakdown of total sales and total purchases by language used with the corresponding trade partner; the data were collected for year 2006. The classifications of economic activity differ across databases. Differences mainly concern the level of disaggregation: • Disaggregation is high in CBLS, SBLS, VAS1 and BSSS: the economic activity is classified using the NOGA-A60 classification.1 • Disaggregation is medium in CLES and LEAP: the classification used here will be referred to as CLES-sectors classification. • Disaggregation is low in SHP and VAS2: the corresponding classification is the NOGA-A17. Within the same level of disaggregation, some minor problems of group overlapping also arise. Group overlapping occurs when a given database X has information on groups A+B and C, and database Y has information on groups A and B+C. In this case, group overlapping was addressed mainly by either breaking down the groups in sub-groups using a disaggregation key (possibly derived from detailed labour statistics in CBLS) or by merging groups A, B and C into a larger group (A+B+C).

2 VALUE ADDED, LABOUR PAYMENT, CAPITAL PAYMENT, CAPITAL STOCK AND LABOUR QUANTITY FOR YEARS 1995–2006 For years 1995 and 1996, total production and intermediate consumption in VAS1 were completed by applying, to each sector of activity, the growth rates of the corresponding higher-level sector of activity (drawn from VAS2). BSSS plays an important role in the estimation of labour payment and capital stock by sector of activity. Missing data in BSSS were completed by interpolation, by extrapolation or by using information from specific databases (banks statistics, for instance).

Appendix II 173 Labour payment by sector of activity was estimated by applying the labourcost-to-sales ratio to the value of production (which we assumed to be equal to sales, with a few exceptions). The labour-cost-to-sales ratio was derived mostly from the BSSS. Capital payment was calculated as the difference between VA and labour payment. For years 1995 and 1996, capital and labour payments were estimated assuming that their share in the VA was the same as in 1997. Capital stock by sector of activity was estimated by applying the fi xedcapital-to-labour-cost ratio to labour payment. The fi xed-capital-to-labour-cost was derived from the BSSS. These calculations were made for both classifications NOGA-A60 and NOGA-A17. 3 USING THE CLES DATABASE: ADJUSTMENTS AND PRICE EXTRACTION To construct the original CLES database, data were collected so as to be representative of the working-age population according to criteria such as gender, place of residence and age. Weights were available to refine representativeness. It appears however that these weights can bias the sample with respect to a criterion of crucial importance from the perspective of production theory, namely, the distribution of employment among sectors. Therefore, to ensure compatibility between the CLES database and production theory, the weights needed to be recalculated: they were computed so as to reflect actual employment (in full-time equivalents, or FTE) by gender in each NOGA-A60 sector of each language region (German-, French- and Italian-speaking region) for 1995. Given the high quality of the CLES data, the CLES database was used as the foundation on which all input prices and quantities were constructed. The fi rst task was to identify labour-related inputs and extract their price: each of these prices contributes to the cost of one worker. Besides pure labour services, we considered eight such factors: • • • • •

having a managing position (factor mgmt) years of education (factor edu) years of professional experience (factor exp) gender (factor sex) skills in the non-local main national language (French in the Germanspeaking region, German in the French-speaking region, French or German in the Italian-speaking region) (factor Dl2) • skills in English (factor Den) • language region (factor reg) • sector of activity (as per either NOGA-A17 or CLES-sectors classification) (factor D)

For each worker, the labour cost was calculated as his or her full timeequivalent wage augmented by 15%, that is, including an estimate of the non-wage and social contributions paid by the employer.

174

Appendix II

We used the CLES data to extract the price in Swiss francs paid annually by producers for each of these attributes. To do so, we regressed the following Mincerian equation: ln(w) = c0 + c1·mgmt + c2·edu + c 3·exp + c4 ·sex + c5·Dl2 + c6 ·Den + c7·regG + c8·regI + d2·D2 + d 3·D3 +. . .+ dn·Dn where the (slightly modified) factor names must be interpreted as follows: • • • • • • • • •

mgmt = 1 if the worker is a manager (0 otherwise); edu = numbers of years of education; exp = numbers of years of professional experience; sex = 1 if the worker is female (0 otherwise); Dl2 = 1 if the worker has good skills in the “other” main national language (0 otherwise); Den = 1 if the worker has good skills in English (0 otherwise); regG = 1 if the worker lives in the German-speaking region (0 otherwise); regI = 1 if the worker lives in the Italian-speaking region (0 otherwise); D2 , D3 , . . . , Dn = 1 if the worker’s sector of activity is number 2, 3, . . . , n (0 otherwise).

The preceding equation was estimated through weighted least-square regression. Estimated coefficients c1 to c8 were applied to the estimated average labour cost2 to compute the mean price for each of the corresponding attributes. The base unit labour cost (pb) was computed as e(c0 + d2·D2 + d3·D3 +. . . . dn·Dn) : it represents the theoretical cost of a male worker with no education, no professional experience and no foreign language skills, working in the French-speaking region (one base unit labour cost per sector). While this regression enabled us to get a first estimate of the price of labourrelated inputs, the CLES data were appropriately combined with weights to estimate the quantity of the same inputs available to each sector. With price and quantity of each input at hand, we should make sure that the sum of the payments for each input equals total labour payment in each sector: Total labour payments = pb·qL + pmgmt·qmgmt + pedu·qedu + pexp·qexp + pedu· qedu+ psex· qsex + pDl2·qDl2 + pDen·qDen + pregG ·qregG + pregI·qregI where qL is the total employment (in FTE) of the sector under analysis, while pi and qi represent the price and the quantity of factor i (for instance, qregI represents the number of workers, in FTE, working in the Italian-speaking region). In order for the identity to hold, prices and quantities were scaled proportionally, with the exception of qL , known to be exact.

Appendix II 175 4 EXTENDING CLES-DRAWN PRICES AND QUANTITIES BEYOND 1995 The prices and quantities extracted from the CLES database were extended to 2005 using the VAS1, the SBLS and the SHP databases. CLES-derived price and quantity of each labour-related input were extended over the 1996–1999 period by applying the trend observed in average labour prices (that is, total labour payment/FTE employment) computed for each sector (as per NOGA-A17 classification). The same set of prices and quantities was extended over the 1999–2005 period using information from SHP. From the SHP database, we used only observations with data on language skills: questions on language competences were included only in the 1999 survey but the same sample was surveyed until 2005, although with a higher and higher number of drop-outs. By using 1999 to 2005 socio-economic data combined with 1999 information on language use, we implicitly assume that the language skills of the workers surveyed did not improve or deteriorate over the 1999–2005 period. For the 2000–2005 period, prices and quantities of labour-related inputs were estimated by applying, to the 1999 values, the trend extracted from the SHP data. Extraction was carried out in the same way as with the CLES database, with the following differences: • weights were computed based on FTE employment by sector (and not by sector and by gender and by region); • SHP uses the NOGA-A17 classification (and not the more disaggregated CLES-sectors classification); • due to insufficient number of observations, no dummy variable was included for the Italian-speaking region. Before scaling them using total labour payment as benchmark, prices and quantities were slightly smoothed using a Hodrick-Prescott fi lter, which was necessary to take account of the instability in the series, itself resulting from the decline in the number of surviving observations in the latest periods (a common problem with panel data).

5 EXTRACTING PRICES AND QUANTITIES OF GOODS AND SERVICES The last group of prices and quantities we needed to construct concerned the goods sold as well as the goods and services purchased, broken down by language used with the corresponding trade partners (suppliers and purchasers). The calculations were made only for the manufacturing industry by combining information from the LEAP database with VAS1 data.

176 Appendix II We considered two types of languages: the locally dominant language (L1) and the other languages (essentially the “other” important national language and English, or L2E). This yielded four index prices to extract: (1) price index for goods sold in L1; (2) price index for goods sold in L2E; (3) price index for goods and services purchased in L1; (4) price index for goods and services purchased in L2E. Data availability and technical constraints prevented us from considering a wider array of language groups. In an initial step, we formed two relatively homogeneous groups of sectors in terms of language. 3 Sectors were grouped based on information derived from the CLES data base, according to the “density” of their multilingualism, defi ned as the proportion of workers in a given sector with skills in at least a second language. The following groups were formed: • Low-density multilingualism group (group A): manufacturing of mineral and metallic products; manufacturing of paper and wood products; manufacturing of textile; manufacturing of food and beverages. • High-density multilingualism group (group B): manufacturing of machinery, equipment and watches; publishing and printing; chemical industry; other manufacturing activities. The LEAP database provides the breakdown of total sales and total purchases of goods and services by language used with the external trade partner for approximately two hundred Swiss fi rms in year 2006. For each sector, we thus computed the weighted-average breakdown of sales and purchases by language group (with weights based on the fi rms’ size). We assumed that from the perspective of a firm, production is equivalent to net-of-tax sales, and intermediate consumption is equivalent to purchases of goods and services. The price index associated with production can thus be seen as an average of (net-of-tax, ex-factory) sales price indexes. More precisely, we assumed that, at any given point in time: PPIi = sL1,i × PL1,i + sL2E,i × PL2E,i , where PPIi is the production price index of industry i, sj,i is the share of sales made in language j by industry i, and Pj,i is the price index of sales made in language j by sector i. If two or more sectors share the same price indexes, then, knowing PPIi and sj,i, we can easily derive their common price indexes Pj through a simple no-constant regression: regressing the equation PPI = µ1 × sL1,i + µ2 × sL2E using sales share from various sectors yields estimates for coefficients µ1 and µ2 , which can be interpreted as the unknown price indexes PL1,i and PL2E,i. Recall that sharing the same price indexes merely means that price growth is the same across sectors and not that the price level is the same across sectors. The same equation was regressed 18 times (2 groups of sectors × 9 years, that is, from 1998 to 2006). Although this procedure implicitly assumes that the

Appendix II 177 breakdown of sales by language is constant through time, simulations revealed that reasonable changes in the shares did not significantly alter the estimates. The same procedure was applied to extract the price indexes (broken down by language used) with respect to purchases. 6

ESTIMATION OF THE PRODUCTION FUNCTION

Let us now turn to the econometric estimation of production, cost and profit functions. Notice that some of the symbols or letters used here to refer to certain variables or coefficients might already have been used in the sections on data construction to refer to other variables or coefficients. The new defi nitions naturally apply. Also notice that, for readability, we will use subscripts sparingly and only to avoid ambiguity. To estimate a production function, we assume perfect competition on the labour market. In line with step 3, we assume that the cost for one worker (essentially his or her net wage plus social contributions) depends on a set of labour-related inputs. More precisely, we assume that, given competition in the labour market, each of such inputs is paid its marginal product. Consider that the value added (VA) generated by a given sector A depends on the quantity of labour-related inputs and the quantity of capital with which this sector is endowed. The VA produced by this sector can be represented by the following production function: VA=y(x1, x2 , . . . , xN-1 , xN), where x1, . . . , xN-1 are the quantities of the N-1 labour-related inputs and xN the amount of capital stock. Each worker i provides the sector she works for with a given quantity of labour-related inputs. Therefore her gross wage wi (that is, including social contributions) must be equal to the sum of the marginal products of the inputs she provides:

wi =

∂y (.) ∂y (.) ∂y (.) xi1 + xi 2 + ... xiN −1 , ∂x N −1 ∂x2 ∂x1

(1)

where xi1, . . . , xiN-1 are the quantities of the N-1 labour-related inputs provided by worker i. Once the functional form for y(.) has been selected and the x’s have been computed for each sector, equation (1) can be estimated using data on workers’ income, skills and other attributes, such as data available in the CLES or SHP databases. Notice that the estimation of equation (1) uses values computed at the sector level (x1, . . . xN) as well as values collected for each worker i (xi1, . . . xiN-1). We estimated (1) using data from the CLES database and we chose the translog form to represent the production function. The following variables were used:

178 Appendix II Table A-II.1 List of Empirical Variables Sector-level Variables VA: sector’s value added —

Worker-level Variables

Remarks

— wi: FTE wage for worker i

x1: FTE number of workers

xi1: 1 for each worker

Each FTE worker provides 1 unit of pure labour service

x2: number of managers

xi2 : 1 if worker i is a manager, 0 otherwise

These variables capture the quantity of managerial skills

x3: total years of education among workers

xi3 : years of education of worker i

These variables capture the quantity of skills gained through education

x4: total years of work experience

xi4 : years of work experience of worker i

These variables capture the quantity of skills gained through work experience

x5: total FTE number of female workers

xi5 = 1 if worker i is a women, 0 otherwise

These variables capture the gender-differentiated contribution to value added4

x6: total FTE number of xi6 = 1 if worker i works workers operating in in the German-speaking the German-speaking region, 0 otherwise region x7: total FTE number of xi7 = 1 if worker i works workers operating in the in the Italian-speaking Italian-speaking region region, 0 otherwise x8: total FTE number of workers mastering the other (non-local) main national language

xi8 = 1 if worker i speaks the other (non-local) main national language, 0 otherwise

x9: total FTE number of workers mastering English

xi8 = 1 if worker i speaks English, 0 otherwise

x10: total stock of capital available to the sector’s firms

These variables capture regional differences in marginal revenue product of labour

These variables capture the quantity of skills in the other (non-local) national language

These variables capture the quantity of skills in English

—

FTE = full-time equivalent; VA = value added.

We assume that the production functions differ between sectors only by a scaling factor. This yields the following translog production function:5

ln(VA) = α1 D1 + ... + α M DM + ¦iβ i ln xi +

1 ¦¦ γ ij ln xi ln x j , i, j 1,..., N 2 i j (2)

Appendix II 179 where γ ij = γ ji (∀i, j ) , ¦iβ i = 1 , ¦iγ ij = 0 and where Dh = 1 for sector h (h=1, . . . ,M) and 0 otherwise. Coefficient constraints ensure that the production function is homogeneous of degree 1. Dummy variables Dh ensure that the production functions differ between sectors only by a scaling factor (recall that the dependent variable is expressed in natural logarithm). To apply (1) to a translog production we fi rst compute the share functions sj(.), which capture the ratio between payments for input j and total value added (that is, the share of input j in total value added):

sj =

∂ ln(VA) = β j + ¦iγ ij ln xi . ∂ ln x j

(3)

Making use of (3), we can adapt (1) to the case of a translog production function as follows (with subscript i referring to worker i):

wi = s1 (.)

VA VA VA xi1 + s2 (.) xi 2 + ... + s N −1 (.) xiN −1 x1 x2 x N −1

ª x x º x = VA« s1 (.) i1 + s2 (.) i 2 + ... + s N −1 (.) iN −1 » x1 x2 x N −1 ¼ ¬

(4)

The econometric estimation was based on equation (4). When estimating a translog function using (4), the VA’s should, in principle, be represented explicitly by the antilog of the translog production function, that is, by the antilog of the right-hand side of equation (2). However, the resulting equation is long and non-linear in its parameters, which makes the econometric estimation cumbersome. Therefore, the VA’s in expression (4) were replaced with the actual values added. Expression (4) was then further simplified. The reason is that, once estimated, lengthy translog production functions often display irregular curvature (they are not strictly concave). A possible way to reduce the risk of translog functions not meeting regular curvature conditions is to “shorten” the production function by merging two or more inputs in one aggregate input. This can be done as follows. Assume that, for each of the M existing sectors, we wish to aggregate R inputs in one composite input. Then, defi ning QH as the quantity of the composite input of sector H, we can aggregate L inputs through the following Törnqvist index (sometimes called translog index):

ln(QB QA ) =

1 L ¦ (si, A + si,B )ln(x i, A xi,B ), 2 i=1

(5)

where si,H is the share of input i in the total value of the inputs to aggregate for sector H:

180

Appendix II

§ L · si ,H = ( pi xi ) ¨ ¦ pk xk ¸ , with pi being the price of input i. © k =1 ¹ Equation (5) shows that the composite quantities of any two sectors are expressed relative to each other: thus, taking sector A as reference and setting QA equal to 1, all the other composite quantities can be expressed as indexes. Aggregating inputs using a Törnqvist index and using the resulting composite input in a translog production function closely mimics the behaviour of a “well-behaved” translog function that uses non-aggregated inputs.6 All the non-language-related inputs were thus aggregated into one composite input by means of a Törnqvist index. This makes it possible to reduce the number of inputs in the translog production from ten to three, namely, the quantity of skills in the other main non-local national language, the quantity of skills in English and the quantity of composite input. The parameters of the (shortened) production function were estimated in two steps. First, equation (4) was estimated using all the (usable) observations in the CLES database.7 One of the explanatory variables in (4) is the amount of composite input provided by worker i, which was computed by differentiation:

xiC = ¦ j =1 L

∂Q xij , ∂x j

where subscript j indicates the jth component the composite input Q and L the number of inputs merged into the composite input Q. This yielded estimates of all the parameters of the production function (2) except the scaling factors a1, . . . , a M: Table A-II.2 Production Function: Estimation Results* (αh) Coefficient Value

β1 γ11 γ12 γ13 β2 γ21 γ22 γ23 β1 γ31 γ32 γ33

1.156884 0.021668 0.044677 -0.06634 0.616391 0.044677 -0.02999 -0.01469 -0.77327 -0.06634 -0.01469 0.081032

Standard Deviation

t-value

0.243595 0.019514 0.024375 0.018297 0.306546 0.024375 0.040429 0.024168 0.228579 0.018297 0.024168 0.021216

4.75 1.11 1.83 -3.63 2.01 1.83 -0.74 -0.61 -3.38 -3.63 -0.61 3.82

n = 1366. R2 : (N/A given constraints). In βi and γij, subscripts 1, 2 and 3 refer to the composite input, to skills in non-local national language and to skills in English, respectively.

*

Appendix II 181 The second step consisted in calculating the scaling factors. These were computed, one for each sector, as the difference between the logarithm of the actual value added and the estimated logarithm of the non-scaled value added. More explicitly, for each sector h, we computed the scaling factor αh as follows:

§ ©

α h = ln(VAh ) − ¨ ¦iβ i ln xi +

1 · γ ln xi ln x j ¸ ¦¦ i j ij 2 ¹

(Recall that Dk = 0, ∀ k≠ h.) Scaling factors are reported in Table A-II.3.

Table A-II.3 Production Function: Scaling Factors (αh) Sector

αh

Manufacturing industry

25.29084

Construction

25.14464

Wholesale and retail trade; repair of motor vehicles

25.18829

Hotels and restaurants

24.78128

Transport; communication

25.05755

Financial intermediation

25.76826

Services to businesses; real estate activities

25.58101

Public administration; education; health

25.29554

Other services

25.28624

Elasticities were then computed through simulation using the estimated function.

7

ESTIMATION OF THE COST FUNCTION

The cost function is an alternative representation of the optimum relationship between inputs and outputs. We assume that producers minimize the cost to produce a given quantity of goods sold in the local language and a given quantity of goods sold in other languages (mainly the main non-local national language and English) using various inputs. The inputs considered here are goods and services purchased in the local language (L1), goods and services purchased in other languages (L2E) and all the inputs considered for the production function presented in the previous step (except that no distinction is made now between non-local languages spoken by workers).

182

Appendix II

The level of total cost incurred by the fi rm (or the industry) can be represented by the following translog cost function:

ln(C ) = α 0 + ¦iβ i ln yi + ¦ j η j ln w j + +

1 ¦ ¦ γ ij ln yi ln y j 2 i j

(6)

1 1 δ ln yi ln w j , ¦¦ ϕij ln wi ln w j + 2 ¦¦ i j ij 2 i j

where wi and yj denote the unit cost of input i and the output quantity of good j, respectively. To ensure linear homogeneity, the following constraints must be imposed:

¦β ¦δ i

i

=1,

j

ij

=0

¦η

i i

= 1 , γ ij = γ ji , ϕ ij = ϕ ji ,

¦γ i

ij

= 0,

¦ϕ

i ij

= 0,

¦δ

i ij

= 0,

Applying Shepard’s lemma to (6) and knowing that the price of output is equal to the output marginal cost yields the following the share functions:8

sxi =

∂ ln(C ) = η i + ¦k ϕ ik ln wk + ¦ j δ ij ln y j ∂ ln wi

syi =

∂ ln(C ) = β i + ¦k δ ik ln wk + ¦ j γ ij ln y j , ∂ ln yi

(7)

where sxi and syi are the share of input i and the share of output i in total cost, respectively. Table A-II.4 shows how to compute elasticities, noted ε , of the demand for input i with respect to the price of input h and the quantity of output k: Table A-II.4 Elasticities from a Translog Cost Function Elasticity of the demand for input i with respect to . . . . . . the price of input h:

. . . the quantity of output k:

Appendix II 183 Unit costs for goods sold and for goods and services purchased are expressed in the form of indexes (see step 5). Cost functions must be convex in output quantities and concave in input prices, but the curvature of estimated translog cost functions rarely matches these conditions. This is why, once more, inputs were aggregated as much as possible, leaving relevant inputs out of the aggregation for each of the elasticities to be estimated. Aggregation was performed through the following Törnqvist price index:

ln(Wt Wt −1 ) =

1 L ¦ (si,t + si,t −1 )ln(wi,t wi ,t −1 ) , 2 i =1

where si,t is here the share of input i in the total cost of the inputs to aggregate at time t:

§ L · si ,t = (wi ,t xi ,t ) ¨ ¦ wk ,t xk ,t ¸ , © k =1 ¹ with xi,t and wi,t being the quantity of input i and the cost index of input i, respectively, at time t. Aggregation is hence performed not relative to a reference sector (as was the case in the previous step), but relative to a reference point in time. This is because the cost indexes themselves are not constructed to capture intersectoral relative prices, but only changes through time (see Chapter 6 in the text and step 5 of this appendix). When set equal to 1 for a given year, Wt becomes the cost index of the composite input. Equation (6) is estimated as follows. Suppose we wish to compute the impact of the change in the price of input h on the quantity demanded of input k (k ≠ h). We fi rst aggregate all non-k, non-h inputs into a composite input to compute the corresponding cost index. If needed, the corresponding quantity (index) can be obtained by dividing the value of the composite input by its cost index, keeping in mind that the value of the composite input is merely the sum of the values of its components. The cost function thus has five explanatory variables (plus industry-specific factors, which will be discussed shortly): the quantities of outputs sold in L1 and in L2E, the quantity of the composite input and the quantities of inputs h and k. In order to take advantage of information provided by economic theory and to avoid relying on inadequate degrees of freedom, it is preferable to estimate system (7) instead of the cost function (6) directly. System (7) is augmented by dummy variables to take industry specificities into account, and can be rewritten as follows:

sxi =

∂ ln(C ) M = ¦η ih Dh + ¦k ϕ ik ln wk + ¦ j δ ij ln y j ∂ ln wi h=1

∂ ln(C ) M syi = = ¦ β ih Dh + ¦k δ ik ln wk + ¦ j γ ij ln y j , ∂ ln yi h =1

(8)

184

Appendix II

where Dh = 1 for sector h (h = 1, . . . ,M) and 0 otherwise. System (8) differs from system (7) only by the fact that it allows the constant (that is, the coefficient associated with the respective industry dummy) to change between sectors.9 System (8) is thus estimated using 64 observations, that is, using eight annual data for each of the eight industries considered.10 Homogeneity of the cost function implies that one equation per sub-system must be dropped for (8) to be estimated: the estimation is thus performed on three equations with cross-equation equality constraints on coefficients δij.11 Finally, the elasticities of the demand for input k with respect to the price of input h (k ≠ h) are computed as shown in Table A-II.3. The procedure is similar when one wishes to focus either on the impact of a change in quantity of output yh on the quantity demanded of input xk, or on the impact of a change in price of input xk on the quantity demanded of the same input xk. In these cases, the number of equations to estimate is reduced further, since all except one input (instead of two) can be aggregated. Specific input aggregation and function estimation were performed for each of the 35 elasticities reported in Table 7.7 in the text. For each sector, we computed the average of elasticities over the 2000–2004 period. Finally, the elasticities for the whole manufacturing industry were calculated as value-added weighted averages of sectoral elasticities.

8

ESTIMATION OF THE PROFIT FUNCTION

The procedure to estimate a profit function is very similar to that for a cost function, and inputs and outputs considered here are the same as in the previous step.12 Recall that the profit function yields the profit made by a fi rm (or an industry) which chooses the quantity of output and the quantity of variable inputs in order to maximize its profit, given perfect competition in the goods and labour markets. We assume that fi rms have only one fi xed input—capital—and that the profit function is linearly homogenous in prices and in fi xed inputs. It follows that, when represented using the translog form, the profit function is:

ln(π ) = α 0 + ¦i β i ln pi +

1 ¦ ¦ γ ij ln pi ln p j , 2 i j

(9)

where π is the profit per unit per capital and pi denotes the unit price of input i. Let us note that variable inputs are treated as negative outputs. In our case, all inputs are variable, with the exception of capital. Constraints ¦i β i = 1 , γ ij = γ ji , ¦i γ ij = 0 must be imposed on coefficients to ensure linear homogeneity in prices. Applying Hotelling’s lemma to (9) and knowing that the price of inputs is equal to their marginal product yields the following share function:

Appendix II 185

si =

∂ ln π = β i + ¦ j γ ij ln p j , ∂ ln pi

(10)

where si is the share of output (or variable input) i in total profit. Elasticities of the demand for output (or input) i with respect to the price of output (or input) j are calculated as follows:

ε ij = ε ij =

γ ij si

γ ii si

+ sj,

i≠ j (11)

+ si − 1,

i= j

Input and output aggregation was performed following the same principles as for the cost function, with shares of outputs in total profit expressed as positive values and shares of inputs expressed as negative values. Again, share functions (10) were modified to take industry specificities into account.

si =

M ∂ ln π = ¦ β ih Dh + ¦ j γ ij ln p j . ∂ ln pi h=1

(12)

With equation (12) estimated, elasticities were obtained using (11). Elasticities for the whole manufacturing industries were computed in the same way as for the cost function.

Appendix III A Simple Recruitment Model

This appendix provides some technical details on the recruitment model described Chapter 8.

1

THE CONTEXT

A fi rm has a position to fi ll, for which the ideal level of language skills (LS) is A*. LS can be measured on a 0 to 1 scale, with 0 indicating pure monolingualism and 1 perfect bilingualism. We assume that there are only two languages: the local language (L1) and the non-local language (L2). By launching a recruitment campaign, the cost of which is P, the fi rm aims to attract n applications. The fi rm must then choose the upper (l0) and lower (l1) limits for the range of LS targeted by the recruitment campaign: these limits are chosen in order to minimize the expected total cost of employment of the successful applicant. We assume that workers are employed for a limited period (for example five years). This assumption is compatible with non-zero turnover rate. We defi ne the total cost of employment of the applicant as the sum of the following costs: • the recruitment cost, • the wages paid to the applicant during the working period, and • the cost of ineffectiveness, that is, the cost that arises when the applicant’s level of LS is lower than A*.

2

DISTRIBUTION OF SPEAKERS

For simplicity, we assume homogenous non-linguistic professional skills; only language skills are allowed to vary between workers. The distribution of speakers according their LS follows a doubly-truncated normal distribution, with lower and upper limits of 0 and 1, respectively (see the density function f below as an example).

188

Appendix III

Density f

m

0

1

LS

Figure A-III.1 Distribution of L2 speakers.

3

COSTS

Wage Cost We assume that wages demanded by workers can be broken down into: • a fi xed component, which can be interpreted as the payment of nonlinguistic professional skills; • a variable component, which depends positively and linearly on workers’ LS level. The wage of worker i, wi , is hence determined as follows: wi = α0 + α1·li

(α0 ,α1 > 0),

where li is the LS level of the worker i.13

Recruitment Cost The number of applicants (n) depends positively on: • the integral between l0 and l1 in the density function, that is, the proportion of workers that are in the LS target range; • the intensity of the call for applications (advertisements, research, . . . ), which is assumed to depend on the amount spent on the campaign.

Appendix III

189

Density f

S 0

Ɛ0 Ɛ1

1

LS

Figure A-III.2 Distribution of job applicants’ language skills.

Formally: n = S⋅N⋅g(P), where: • S is the area comprised between l 0 and l1; • N is the size of labour force (recall that all workers are assumed to be endowed with the same non-linguistic professional skills); • P is the amount spent in the call for applications; • g(P) is the intensity function (dg/dP > 0, d2g/(dP)2 < 0). Let us assume that the intensity function is: g(P) = γ0 P + γ1 P2

(γ0 > 0, γ1 < 0).

To get n applications, the theoretical amount P to invest is: P = −γ0 ± [γ02−4*⋅n/(S⋅N)]1/2/(2γ1). However, only the (positive) amount P = −γ0 + [γ02−4*⋅n/(S⋅N)]1/2/(2γ1) makes economic sense. The recruitment cost (r) is the sum of the cost of selection and the cost of call for applications: r = n⋅v + P, where v is the cost of examining one application.

190 Appendix III The element “n⋅v” is the cost of selection and P is, once again, the amount to invest to get n applications.

Ineffectiveness Cost Let us denote the LS level of the newly hired worker by A0 : • If A0 < A*, then the employer incurs a cost resulting from additional (formal and informal) translations needed, from lost productivity, from higher risk of error due to insufficient communication skills, etc. These costs make up the cost of ineffectiveness (z). • If A0 > A*, then the worker’s linguistic potential is not fully exploited, but the cost of ineffectiveness is zero. Thus we assume that the ineffectiveness cost z is given by: z = β1(A*—A 0)2 z=0

4

if A0 < A* if A0 ⱖ A*.

THE FIRM’S PROGRAMME

The cost associated with a worker is his or her employment cost C, which is given by: C = w + r + z. The fi rm chooses P, l 0 and l1, and receives n applications. It does not know a priori the LS level of each applicant, but it knows the distribution of LS in the labour force: this distribution results from the density function y. Let us assume that the n applicants are randomly selected from the sub-population in the shaded area S between l 0 and l1:

Density f

S 0

Ɛ0 Ɛ1

1

LS

Appendix III

191

From the n applicants, the fi rm chooses the applicant with the lowest cost of employment C, which will be denoted C min. C min depends on n (among other things): the higher n, the higher the selection cost (n*v) and hence the higher C min. But the distribution of C min also depends on n. Let us explain why with an example. If the fi rm aims to receive one application (n = 1), there will be only one applicant and the cost incurred by the form is the cost associated with this applicant: the distribution of C min will hence be equal to C, and E(C min) = E(C). On the other hand, if n = 2, the fi rm will choose the applicant with the lower employment cost: the distribution of C min will not be equal to the distribution of C (its standard-deviation will be lower), and E(C min) < E(C). At the time of planning the recruitment campaign, the fi rm obviously does not know C min yet, and therefore it cannot minimise it. However, the fi rm will try to minimise the expected value of C min , E(C min). Therefore fi rm’s program is the following:

choose Ɛ0 and Ɛ1 to minimize E(Cmin).

(1)

The amount P and the difference between l0 and l1 must be large enough for the number of applicants n to be reached. However, if l0 and l1 are too high, the wage cost would be unnecessarily high. Finally, while a rise in P increases the recruitment cost, it narrows the expected gap between the applicant’s LS level and the LS level of the position to be filled (A*), thus reducing the ineffectiveness cost (z). The solution to (1) can be found analytically or by simulation. The analytical solution is certainly more flexible, but it is mathematically complex to obtain.

5

ANALYSIS OF RESULTS

Once l0 and l1 are known, one can: • compute E(l), that is, the expected LS level of the applicant to be hired; • compute the gap between E(l) and the ideal LS level of the position to fill (A*); • assess the impact of a change in the distribution of language competences among workers (through a change in m, for instance) on E(l), and hence on the gap between E(l) and A*. The results of programme (1), obtained by simulation, are presented in Chapter 8.

Notes

NOTES TO THE INTRODUCTION 1. Fishman (1971), quoted by Edwards (1994: ix).

NOTES TO CHAPTER 1 1. This concern with linguistic diversity often fi nds expression in the very name of these journals—to wit, Multilingua, Plurilingua, Journal of Multilingual and Multicultural Development, Journal of Multilingualism, etc. By implication, the same holds true of journals concerned with language policy, such as Language Policy or Language Problems and Language Planning, in which the topics handled almost always have to do with questions that arise because several languages are present in a given social, political or economic space. 2. Source: International Monetary Fund, World Economic Outlook Database, April 2009; see www.imf.org. World economy is defi ned as GDP. 3. In downtown Geneva, there is a flower shop where the goods are displayed on a freely accessible shelf, and buyers are expected to leave the price of their purchases in a till nearby. The entire operation can take place wordlessly. 4. This raises the question of the goals of human action and the meaning of “rationality”. The economic meaning of “rationality” is often misunderstood and taken to be substantive, whereas it is only procedural. Therefore, it is not coterminous with Zweckrationalität, as cogently shown by Gellner (1991: 27); see also Elster (1989: 22 ff.). 5. Textbooks are so numerous that it is difficult to suggest any one in particular. The interested lay reader will fi nd an excellent introduction to microeconomics in Economics: Principles and Policy, by William Baumol, Alan Blinder and William Scarth, of which four editions have been published between 1985 and 1994 (Dryden Press). Another well-known reference book is Hal Varian’s Intermediate Microeconomics. A Modern Approach (Norton, 1999). 6. For example, we could treat skilled and unskilled labour as two different types of inputs, but the proportion of skilled and unskilled labour is likely to be roughly similar across fi rms in a given economic sector, precisely because they use similar or even identical technologies. 7. It is important to steer clear from the metaphor of the “linguistic market” popularised by Bourdieu (1982); more on this in Chapter 2. 8. For a fundamental discussion of choice as the key to all economic analysis, see Becker (1976). Recent work sitting astride sociolinguistics and the

194

Notes sociology of language (Spolsky, 2009) edges closer to economic reasoning by explicitly positioning “choice” as the pivotal notion of language policy.

NOTES TO CHAPTER 2 1. For a general treatment of the economic implications of language from a linguist’s perspective, see for example Coulmas (1992). 2. The discourse of linguists on economics (apart from specialists of economic discourse; see the following paragraph) would in itself deserve closer examination, if only because it sometimes suggests inadequate understanding of what economics is about; uncovering the origins of such misconceptions would constitute a worthwhile object of study. Examples of recurring mistakes (which we have attempted to discuss elsewhere; see e.g. Grin, 2003b, 2005a) include the (already mentioned) serious misrepresentation of the economic meaning of “rationality”; or the assumption that economists’ agents are either completely unaffected by political and cultural context; or, conversely, that their behaviour is entirely determined by structure; or the notion that economic agents are by defi nition “selfish”. 3. This is why Mark Blaug’s Methodology of Economics (1992) is subtitled “how economists explain”; on this question, see also Mayer (1993). 4. “Domain” is a well-established analytical concept in sociolinguistics. Fishman (1967: 70) defi nes it as “a group of social situations typically regulated by a given set of behavioural norms”. Further examples of sociolinguistic domains are “family”, “friendship” and “religion”. 5. This does not mean that we view classical sociolinguistics as any less relevant than the perspectives described later on in this section. In particular, we disagree with the often heard claim that a construct like “domain”, which is used not only in classical sociolinguistics but also in contemporary work (see for example Spolsky, 2009: 3), is necessarily static or is wedded to an inadequate interpretation of each language as a “bounded” system. 6. An augmented version of the same argument was later published in English under the title of Language and Symbolic Power (Cambridge: Polity Press, 1991). 7. Bourdieu was not the fi rst to mention a linguistic market. The expression, just like other concepts like “linguistic capital”, can be found in the earlier work of the Italian linguist Ferruccio Rossi-Landi (1968, particularly in Chapter 1), but it turns up in strikingly similar form (with nary a bibliographical reference or other form of acknowledgement) in Bourdieu’s Ce que parler veut dire. 8. Which are referred to as explanatory or independent, as opposed to dependent variables. 9. Supply and demand are located in an n-dimensional hyperspace from which two dimensions are singled out; apart from the price-quantity plane, incomequantity is a commonly used one, although either function could also be positioned on other planes defi ned by other dimensions, including political, psychological or cultural ones. 10. Similar reservations can be expressed regarding Bourdieu’s use of the term “profit”. 11. “Every individual [ . . . ] intends only his own gain, and he is in this, as in many other cases, led by an invisible hand to promote an end which was not part of his intention” (Smith, 1993 [1776], Book IV, Chap. 2). Note, however, that Adam Smith was not the fi rst to identify these dynamics, which are

Notes

12.

13.

14.

15.

195

also mentioned in Robert Mandeville’s Fable des Abeilles, fi rst published in 1714. This critical approach insists that languages (French, English, Catalan, Spanish, etc.) are viewed by other approaches as “discrete” entities as a result of sustained political work by modern states, whose elites have mobilised language as a tool for nation-building, thereby giving rise to an ideologically tainted view of bilingualism as the mere juxtaposition of two monolingualisms. This warning is well-taken. However, it is unclear if it makes any major analytical difference, considering that after taking great pains to explain that languages should not be seen as “whole” or “bounded” systems, the same authors generally have no qualms, in their subsequent arguments, about referring to “French” and “English” as identifiable and distinguishable codes (Da Silva, McLaughlin and Richards, 2007; Heller, 2007b). In the same vein, Shohamy (2006) insists that language is “personal, open, free, dynamic and constantly evolving” (p. xvi; an almost similar collection of adjectives, adding “energetic” but dropping “free”, appears on p. 5; “fluid” joins the list on p. 10), concluding that “it is therefore argued that fi xed boundaries that are imposed on languages in terms of categorization with titles and names are artificial, given the endless number of varieties, hybrids, mixes and fusions” (p. 11). We have nothing to object to this view, which indeed heightens researchers’ awareness of the profoundly situational and interactive nature of language (although we believe it would be interesting to spell out more precisely the semantic and analytical purpose of each of these terms). Let us however observe that Shohamy also refers to many languages (for example when talking about the “own languages” of some “linguistic communities”, mentioning “Irish, Welsh, Catalan, Maori, etc.” (p. xix), and later on Albanian, Arabic, Basque, Castilian, English, French, Gaelic, Galician, German, Hebrew, Latvian, Nynorsk, Spanish, Tagalog and Yiddish). Thus, despite the fluidity of language and the artificiality of boundaries between them, there seems to be little difficulty in identifying them. For the purposes of language economics, that’s all we need: identifiable codes generally perceived as distinct. Our approach does not need to rely, any more than these authors’ do, on the notion that languages are “whole” or “bounded”. This imprecision suggests that that the concept of “new economy” should be handled with caution (let us note that economists themselves use it sparingly, and when they do, they usually avoid assigning any major analytical meaning to it). Other confusions arise with respect to the very meaning of economic sectors (see e.g. Da Silva et al., 2007: 194), since the “new economy” is contrasted with the “old economy of primary resource extraction and industrial transformation”; these authors seem to be unaware of the fact that services, also called the tertiary sector, have been a major and growing part of economies around the world for decades, with a share well over 50% already before 1970 for countries like Australia, Denmark, France, the Netherlands, Sweden, the United Kingdom and the United States (Wölfl , 2005: 8). In order to describe this orientation, the term “functional pragmatics” was suggested to us by linguists taking part in the conference on “Langue, Économie, Gestion” at the University of Sorbonne-Nouvelle (Paris), 27–29 March 2008. We do not, however, presume to impose this label on the authors whose work is presented in this section. Interactionism is, of course, much broader, also encompassing perspectives such as Alfred Schutz’s phenomenological approach to sociology or Erving Goffman’s sociology of experience.

196

Notes

16. In this sense, economics is in agreement with mainstream anthropological perspectives, which Cancian (1966: 467) had summed up by writing that “the use of maximization as a scientific strategy involves seeking out the motives (or whatever the investigator sees as the impetus of behavior) and attempting to rank order them so as to see the behavior as the (conscious or unconscious) maximization of these things. They become the ends being maximized [ . . . ] It is in this sense that all people always maximize or economize. There can be no argument about it”. 17. For a critique of the underlying post-Wittgensteinian methodological dualism (which inspires ethnomethodology), see e.g. Blaug (1992: 42 ff.), who takes the gloves off and describes it as “fatuous”. 18. Another level at which ethnomethodology is at variance with the standard methods of the social sciences is that its insistence on the radical uniqueness of each observation preempts the very possibility of distinguishing variables from data, a strategy which is, by contrast, crucial for economic analysis (Coenen-Huther, 1989), not to mention quantitative approaches in sociology or psychology.

NOTES TO CHAPTER 3 1. This shift in emphasis from the core question of “is multilingualism worth it?” to language education and training issues mirrors a tendency that can be observed in much of the literature on multilingualism, which starts out by identifying its costs and benefits as a key question, but then quickly veers off into different, often meta-level issues; see for example the collection of essays edited by de Cilia, Krumm and Wodak (2003). 2. It is interesting to note that, perhaps under the influence of English, the expression “Moyen-Orient” (“Middle East”) is progressively being relexified to cover regions that would traditionally have been referred to as “ProcheOrient” (“Near East”), namely Israel, Palestine, Lebanon, Syria and Jordan, sometimes extending a little farther east. 3. Available on www.reflectproject.com/docs/summary.htm; last consulted 28 April 2009. The REFLECT study, along with two other studies going by the names of ELISE and ELUCIDATE, was supported by an EU programme. ELISE covers Denmark, the Netherlands, Northern Ireland, Scotland and Sweden, while ELUCIDATE covers central France, southern Germany and western Spain. These studies are reviewed in Hagen (2006: 12 ff.). 4. The respective roles of education systems and fi rms are not always clearly demarcated, and different approaches yield different implications regarding how language education should be organised. A particular case is that of socalled “dual” vocational training, where young people receive most of their training at work in a fi rm and take classes (provided by the vocational training sector of the public education system) for one to two days per week on average. This approach is widely used in Germany, Austria, Switzerland and Singapore, and it may concern more than half of any given cohort of young people. In principle, this approach to vocational training allows for a high degree of integration between the foreign language skills needs of businesses and language education policies. It also reflects the fact that in the countries concerned, foreign languages are not perceived as relevant only to elite, preacademic secondary education, but to vocational training as well (see Grin and Strobel, 2001). 5. Summary available on www.letitfly.it/esquare/default.asp?c=offerta&c2= politiche; last consulted 28 April 2009.

Notes

197

6. The literature also includes several quantitative studies on minority languages, particularly the Basque Country and Catalonia (see e.g. Mateo Aierza, 2004 or Riera Gil, 2004), but their focus is less on the notion of foreign languages and their economic value than on the extent to which Basque and Catalan respectively are used in business and commerce despite the dominance of Spanish, and whether this warrants targeted promotional measures. 7. More detail on the institutional arrangements regarding Swiss multilingualism can be found in Section 4.4. 8. The size and reliability of the ELAN sample is, in several ways, problematic. The report itself contains a diagram confusingly entitled “rate of response per country” (p. 64), giving the impression that the figures presented in it constitute what is usually understood as the “response rate”, that is, the ratio of actual sample size over the total number of informants (in this case: fi rms) initially targeted by a survey. Upon closer examination, however, this chart turns out not to indicate the response rate at all, but rather the percentage of the fi nal sample coming from each participating country. The figures can therefore be interpreted in terms of “coverage rate”, namely, the ratio of actual sample over the “population” studied. Unfortunately, this “coverage rate” is rather low. With only 28 questionnaires from Germany (whereas the estimated number of SMEs in Germany is 3.6 million, thus implying a coverage rate of less than 0.0008%) and 60 questionnaires from Iceland (whose GDP is roughly 178 times smaller, and its population 257 times smaller than Germany’s), the validity of the database is questionable. 9. Trade diversion would occur if the goods were obtained in Germany as opposed to Great Britain (less international trade). Of course, another possibility would be to purchase from a UK supplier capable of offering services in German, but at higher price than the fi rst supplier approached. 10. In addition, Gómez-Mejia and Palich mention the difficulties of responding to culturally diverse customer preferences, while “cross-sell[ing] products in culturally related markets [ . . . ] reduces the information gathering expense [ . . . ]” (Gómez-Mejia and Palich, 1997: 312). This point is well-taken, but it refers to a cost arising from diversity among customers, not diversity in the firm. 11. Cultural proximity and cultural distance are a popular topic in business studies; see for example frequently quoted work by Hofstede (1980) or the extensive review of the field in Schneider and Barsoux (2003). For a list of mishaps arising from inadequate linguistic or cultural skills, David Ricks’s hugely entertaining Blunders in International Business (1993) is a classic.

NOTES TO CHAPTER 4 1. However, it would indeed be rash to assume that foreign language skills are rewarded only if they are actually used. Although this remains the case in general, some exceptions certainly occur. We return to this question in Section 5.1. 2. Readers interested in a more extensive discussion of these developments may turn to Vaillancourt (1985), Grin (1996a, 1997) or Grin and Vaillancourt (1997). For an analysis of the decomposition of inter-group wage differentials, allowing to single out that part of those differentials that may be residually assigned to discrimination, the classic reference is Oaxaca (1973). For an alternative perspective on the development of language economics, see the introduction to the collection of papers edited by Lamberton (2002). 3. A little outside of the main categories of empirical research on linguistic rates of return, Patrinos and Hurst (2007) analyse the earnings of Bolivian

198

4. 5.

6. 7.

8.

9. 10.

11. 12. 13.

Notes residents, showing that even with a standard set of control variables, monolingual Spanish-speaking males earn about 23% more than bilinguals who speak “an indigenous language” (presumably Quechua or Aymara for most respondents). In other words, this is a case where bilingualism has a negative impact on earnings. It is highly likely, however (although this point is not investigated by Patrinos and Hurst), that racism or other unobserved variables, rather than the potentially damaging impact of having more skills than other people do, lie at the source of this effect. More detailed presentations are available in Vaillancourt (1985) and Grin (1999a, Chap. 3). For a primer on regression analysis, see Lewis-Beck (1993). The focus on men results from the fact that data on women are generally less reliable, particularly with respect to “work experience”. The latter variable is often approximated by “age minus the number of years of education”. The resulting term EXP, however, may be overestimated for women, who are more likely, for social and cultural reasons not discussed here, to have left the labour market temporarily in order to raise young children. The overestimation of the experience term may impact on the estimation of its effect on earnings, and if the overestimation is linked to language, it may bias the estimation of the net effect of language skills on earnings. Hence, unless precise data on women’s personal work history (including interruptions) are available, estimates of the value of language skills for women are likely to be less reliable than for men and, more specifically, to be underestimated. For the same reason, some of the results presented in this chapter focus on the case of men. The squared term provides a better statistical fit by allowing the estimation to take account of the progressive obsolescence of skills over a person’s career, which generates a concave earnings function. Let us symbolise the estimated coefficient for variable vj by the Greek letter βj, which by defi nition is equal to the fi rst derivative of the logarithm of earnings with respect to vj. However, logarithmic expressions are not always easy to intuit, which justifies transforming the estimated coefficient βj into a more readily interpretable notion. It can be shown that the contribution of vj to earnings can be expressed in percentage terms as b = e β—1, where “e” symbolizes Euler’s number, and the results presented in this chapter are expressed in percentages. However, this transformation only really matters if β is relatively large. If β is relatively small (for example if its value is below 0.1), β and b can be considered as roughly equivalent. While the fi rst three are fully official and formally enjoy identical legal status, Romanche is official, at the federal level, only for the purposes of communication between the government and Romanche-speaking citizens. For details on the Swiss arrangement, see for example Grin and Korth (2005) and for its historical dimensions, see Dardanelli (2008). Switzerland numbers 26 cantons of which six, for historical reasons, have half a cantonal vote in the upper house of Parliament. The impact on residents’ first language has also been investigated (Grin, 1997), revealing disturbing wage penalties on the Italian-speaking Swiss, even after controlling for education, experience, and competence in other languages (German, English or French); see Grin and Sfreddo (1998). See http://www.coe.int/T/DG4/Linguistic/CADRE_EN.asp. See the preceding section on net earnings differentials in Québec. The term “weeks worked” does not need to appear in the equation, since earnings have fi rst been converted to full-time equivalents. The coefficient for English-language skills in Italian-speaking Switzerland is 11.8%; however, it is only significant at the 10% level.

Notes

199

14. However, estimates of the rates of return on skills in English at the national level, replacing language region by L1 as an independent variable but including a set of eight occupational dummies (professional, entrepreneur, small business, farmer, middle manager, senior public employee, public employee, unskilled and semi-skilled worker), as well as skills in national languages, confi rm the robustness of the fi ndings reported in Table 4.8. 15. Interestingly, however, use of Turkish at work (as opposed to mere competence in the language) is associated with a stronger gradient in mean earnings, and although wage premiums on the use of Turkish remain non-significant, they are always positive.

NOTES TO CHAPTER 5 1. Such as the well-known Multicultural Personality Questionnaire; see Van der Zee and Van Oudenhoven (2000). 2. As to the “strong” version of screening theory, it is quite easily dismissed through a reductio ad absurdum: if the knowledge and skills provided through formal education were indeed irrelevant, and if all the skills needed to perform a job were acquired later on the job, then one would expect patients about to undergo surgery not to insist on being operated on by a fully trained surgeon. The fact that they usually do suggests that no-one seriously believes that education is truly useless. 3. Another reason for rewarding unused foreign language skills could appear in the case of a worker whose non-linguistic abilities are particularly necessary to a given company, say company Z. Since this worker could earn a wage premium by taking a job with a fi rm that needs his foreign language skills and would be ready to reward him accordingly, company Z will have to pay an equivalent wage premium to retain this worker. This explanation, however, rests on the assumption that in some companies at least, foreign language skills are rewarded, which takes us back to our general assumption. 4. We are well aware of fi ndings in applied linguistic research, already discussed in Chapter 2, which highlight the high occurrence of code-switching in multilingual work settings, even where there is an offi cial company policy to use only language X for internal communication. Let us also recall, however, that those fi ndings are hard to generalise, since they are derived from terrain observations gathered without particular concern for the representativeness of the data. It is also important to point out that the data used by Vaillancourt, Champagne and Lefebvre do not imply anything like monolingual practices; they simply reflect the relative frequency of use of different languages as reported by actors themselves; see Frederiksson, Barner-Rasmussen and Piekkari (2006). For a theoretical approach to the economic implications of using the services of bilingual foremen for communication between management and workers with different native languages, see Lang (1986). 5. In the absence of data on capital, the authors use the fact that total value added in the economy is equal to the sum of the remuneration of inputs—in the main, capital and labour. Thus, the share of value added accruing to capital (representing the cost of capital) can be proxied by: 1—(total wages/ total value added). 6. Let us note, however, that odds ratios do not indicate the probability for, say, a manager to use English by comparison with an employee who does not have a managerial position. Thus, it does not allow statements such as “all other things being equal, managers are twice as likely as employees to

200 Notes use English at work on a daily basis”. This type of proposition, however, can be derived from the odds ratios by computing the adjusted risk ratio (ARR), which is relatively little-used in the social sciences and is more common in other disciplines, such as epidemiology (see Beaudeau and Fourichon, 1998). If, however, the event captured by the dependent variable is rare, the corresponding odds ratio provides an acceptable approximation of probability. 7. Surprisingly, this had apparently not been done before. A paper by Crémer, Garicano and Prat (2007), though entitled “language and the theory of the fi rm”, turns out not to deal with what is usually understood as “languages”, but actually refers to “technical languages” or “codes” (2007: 378). Thus, their analysis is closer to information management than language economics. Their (strictly theoretical) modeling exercise leads them to conclude that the adoption of a common codes across divisions in an organisation (which may, in practice, mean things like terminological harmonisation) is likely to allow for effi ciency gains (for example through a lowering of diagnosis cost when handling a problem). This may, in turn, influence the structure of the organisation and encourage delayering and horizontal integration. 8. Let us illustrate this with an example from Chapter 4: the theoretical relationship between foreign language skills and labour income is replaced by an estimated relationship, in which “net impacts”, as presented for example in Table 4.8, represent the estimated parameters linking language skills to earnings. 9. In so doing, we are departing from the basic production function approach in which the output is treated as pure value added; the logic underpinning our model is closer to the “multi-input, multi-output” (MIMO) approach, which though more complex, offers greater flexibility in the treatment of variables. We return to the MIMO approach in Chapter 7.

NOTES TO CHAPTER 6 1. There is, however, no necessary confl ict between qualitative and quantitative research, and sociologists like Boudon and Fillieule (2002) regard opposition between them as artificial; rather, they correspond to different stages in a research process, comfortably fitting into variants of the hypotheticodeductive model (di Ruzza, 1988). 2. Econometrics combines statistics and economic theory to analyse economic relationships and make predictions about the value of economic variables in future periods. 3. More issues related to fi rm-level data collection using surveys are discussed in the following section. 4. Interestingly, the professionals of multiple languages, translators, have no problem at all looking at, and working with “languages” as easily identifiable entities (Guidère, 2008a); this is not even mentioned as a problem in theoretical work in the field (Gentzler, 1993). It may be that the sociolinguistic critique of the very notion of “languages”, though fully apposite in theory, does not always have major bearing on practice. 5. The econometric treatment of data on foreign language skills collected through telephone interviews, but with an instrument based on the Common European Framework of Reference for Languages, reveals the statistical robustness of the data, itself a strong sign of the validity of the information collected.

Notes

201

6. For example, the use of various languages when performing given tasks was indicated by respondents who, however, failed to mention the existence of the corresponding language skills. 7. The distinction between flow and stock also applies to labour: the number of workers is a stock, the number of hours worked is a flow. 8. In the same way, the stock approach implicitly assumes that the stock of foreign language skills made available by a trilingual worker is twice as large as that of a strictly bilingual worker. 9. The International Standard Classification of Occupations (ISCO) is a tool for organizing jobs into a clearly defi ned set of groups according to the tasks and duties undertaken in the job. See www.ilo.org for details. 10. Early studies using statistics on workers by skill level focused on the distinction between manual and non-manual workers; see Freeman (1995). 11. The Harmonized Commodity Description and Coding System (HS) of tariff nomenclature is an internationally standardised system of names and numbers for classifying traded products. 12. The properties of a wide panoply of indexes and their link with economic theory are discussed extensively in Diewert (1976), Färe and Primont (1997) and Balk, Färe and Grosskopf (2004), for instance. 13. Interestingly, Switzerland’s business survey on earnings (http://www.bfs. admin.ch/bfs/portal/fr/index/infothek/erhebungen__quellen/blank/blank/ sle/01.html) requires fi rms to answer 21 questions for each of their workers. No information about language competences, however, is recorded. 14. A vast literature on the impact of questionnaire design on survey response rate has developed over the last 40 years. The link between the length of the questionnaire and the response rate is not systematically negative, but has been found to be so in most cases (Bogen, 1996; Edwards et al. 2002). 15. The term “industry” (rather than “sector”) is used here to refer to any of the two-digit branches of the International Standard Industrial Classifi cation of All Economic Activities (ISIC). ISIC is a UN classification of economic activities: at the two-digit level of disaggregation, the latest version of ISIC (ISIC Rev. 4) includes 100 branches, 34 of which belong to the manufacturing sector. Specific national or international classifications of economic activities are often derived from ISIC (NACE in the European Union, NAICS in North America, for instance). The systematic use of an internationally recognised classification of economic activities ensures compatibility among databases, a particularly crucial feature when databases must be compared, combined or merged; see the United Nations Statistics Division website, http://unstats. un.org/unsd (state as of June 2009).

NOTES TO CHAPTER 7 1. CLES is the acronym of “Compétences linguistiques en Suisse” (Language Skills in Switzerland), a survey on language skills, language acquisition and earnings carried out in 1994–1995 across Switzerland’s three main language regions with a representative sample of 2,400 respondents. 2. Agriculture, which represents a mere 1.2% of GDP, makes up a negligible part of the sample, while the operations of the electricity branch (also a marginal component of productive activity), being heavily regulated, only indirectly reflect the economic rationale investigated in this chapter. 3. Given exchange range fluctuations, equivalents in other currencies can only be indicative. In early August 2009, 1 American dollar (USD) and 1

202

4.

5.

6. 7.

8.

9.

10.

Notes Euro (EUR) were approximately equal to 1.09 and 1.53 Swiss francs (CHF) respectively. Consequently, the resulting series are subject to margins of error against which, given the current lack of data, no satisfactory immunising strategy can be proposed. Thus, the data extraction procedure applied here should be seen as a temporary solution for solving the technical challenges raised by estimation; the steep changes in the remuneration of language skills suggested by Table 7.2 should be seen as merely indicative of a general trend. To compute an “implicit quantity index”, the observed expenditures figure (that is, price × quantity) is divided by the relevant price index (as obtained in Table 7.3). Thus, since expenditure values differ across sectors, so do the corresponding quantity index values. Since we have retained eight economic sectors, each with two groups of price series (sales and purchases), which are further broken up by language (L1 and L2), a total of 32 index values per year must be computed; only a subset is therefore presented in Table 7.4. For example, since not all parts of the country benefit from the same capital stock, labour productivity may differ as a result. Such differences must therefore be taken into account prior to the estimation of the production function. This of course does not mean that all dishes, or, for that matter, products, are assumed to be identical. Suppose that all dishes are produced by adding all of the available ingredients numbered 1, 2, 3, . . ., n. The amount of each ingredient to be used, however, will depend on the dish being prepared. In the production of dish F, ingredient j, for example, may be needed in the amount “one teaspoon”, while ingredient k may be needed in the amount “zero”, while the amounts needed of these two ingredients in the preparation of dish G may, for example, be equal to “zero” and “two teaspoons” respectively. Thus, the terms “ingredient j” and “ingredient k” will appear in the meta-recipe applying to the preparation of all dishes, but the amounts needed for the preparation of different dishes will be symbolised by parameters sj, sk, etc. that will differ from one dish to the next. Similarly, if production functions are used to run separate estimations for different economic sectors, the value of the parameters will change even if the list of factors remains the same. “Scaling” is a technical notion that can be explained with reference to the relation between inputs and output. Consider two different industries where despite the identical shape of the production function, similar factor inputs yield different levels of output. Suppose that the quantity of one input goes down by, say, 10%. If the two industries differ only by a scaling parameter, then the relative drop in output will be the same in both industries. Ideally, an updated version of the CLES data base, which dates back to 1995, should be used. However, no such update is available (let us also recall that in most countries, with the notable exception of Canada, no comparable data are available at all). The series presented in the tables in Section 7.2 suggest, however, that the returns on English have probably risen, while the returns on a second national language have probably declined since then; such a trend, however, would need to be confi rmed with fresh survey data. Let us take a standard textbook example, namely, income elasticity of demand. Income (often symbolised by the letter Y) is treated as the independent variable which (together with other variables) explains the level of demand for a particular good or service; let us symbolise this level of demand by the letter Q. The percentage variation in demand (that is, the consequence) can therefore be written as dQ/Q. The percentage variation in income (that is, the cause) can be written as dY/Y. The income elasticity of demand, commonly symbolised by the Greek letter η, is therefore equal to:

Notes

203

(dQ/Q)/(dY/Y). Thus, if income rises by 2% and demand (for a particular good) rises by 3%, η = 3%/2% = 1.5, which is a very compact measure of the relationship between Q and Y; another way of expressing this relationship is to say that when income rises by 1%, demand rises by 1.5%. Typically, for example, the value of η is under 1 for foodstuffs, and a little above 1 for clothing. A value of 1.5 suggests that we are dealing with a highly desirable or prestigious good, which textbooks call a “superior good”. 11. Source: http://www.bfs.admin.ch/bfs/portal/fr/index/themen/04/03/blank/ key/02.html. 12. An exception to this rule would be a country whose main or dominant language is used for trading purposes (at least in commercial relationships with the country concerned) by foreign suppliers and clients too. The case of large, predominantly English-speaking economies like the United States immediately springs to mind (Carr, 1985). However, it is an economy’s sheer size (and associated dominance of domestic as opposed to foreign trade), rather than its Anglophone character, that appears to shield it from the need to develop foreign language skills, as shown by frequent expressions of concern over the United Kingdom’s lack of linguistic savvy (see Chapter 3). The share of foreign trade in GDP (exports + imports divided by GDP) exceeds 50% in the case of the United Kingdom, but is only a little above 25% in the United States.

NOTES TO CHAPTER 8 1. In fact, for lack of a clear analytical vision of multilingualism at work, logically distinct questions such as “the economic value of multilingualism”, “the returns on foreign language skills in the labour market”, “the contribution of language skills to competitiveness”, “language training practices in companies” or “companies’ needs for foreign language skills” often get mixed up; this confusion is much in evidence, for example, in the report of the Business Forum for Multilingualism (2008). 2. Recall that Switzerland is an officially quadrilingual country with fairly strict territoriality; see Section 4.4. 3. For example, the very high occurrence of the use of German in Italian-speaking Switzerland may in part be explained by the fact that Italian-speaking Switzerland is a very popular tourist destination among German speakers, whether from German-speaking Switzerland or from Germany. Germanspeaking visitors are routinely served in German in restaurants catering to tourists in predominantly Italian-speaking towns like Lugano and Locarno, even if staff members have Italian as a fi rst language (or as a second language, in the case of foreign migrants). 4. Alternatively, they may, as some commentators suggest (e.g. Phillipson, 2003a, 2003b; Durand, 2004), feel some fascination for the English language, also because of its dominant position in international commerce and culture. 5. Self-employed workers, who are not hired into their job, were asked instead whether they had to acquire foreign language skills quickly. 6. The interested reader is invited to turn to Appendix III for details. The exogenous variables of the model are the following: w: wage rate; N: number of potential applicants having the non-language skills required; n: number of applications targeted; v: unit cost of application processing; z > 0: inefficiency cost if αj < α*; otherwise z = 0. 7. See Grin (1999a: 102, Table 5.10). Though not by much, the distribution of English-language skills is located further to the right (that is,

204

Notes

skewed towards higher skills levels) in German-speaking by comparison with French-speaking Switzerland. Respondents’ mean scores for English, using a competence index given by the unweighted average of oral comprehension, speaking, reading and writing skills, stands at 34.2 and 42.0 points on a 0 to 100 scale in French- and German-speaking Switzerland respectively. 8. The authors, however, do not specify whether these “differing” views arose among managers or between management and workers. 9. This question was addressed in a survey of 200 fi rms in the manufacturing sector in German- and French-speaking Switzerland; see Grin, Sfreddo and Vaillancourt (2009).

NOTES TO CHAPTER 9 1. One often-heard argument in favour of multilingualism as opposed to uniformity can be sought in the frequently assumed—but, to our knowledge, as yet unproven—positive correlation between individual or societal multilingualism on the one hand, and creativity, flexibility, aptitude to divergent thinking and imaginative problem-solving on the other hand, the latter traits being generally regarded as conducive to value creation in the context of productive activity; we return to this issue in Section 10.3. 2. It is important not to confuse the argument made here with a very different one that makes a parallel between linguistic and cultural diversity on the one hand, and natural or ecological diversity on the other hand. While we consider the link between the two as intriguing, significant methodological problems in the measurement of the variables concerned suggests that at this time, a correlation between the two remains a working hypothesis; see www .terralingua.org. 3. If distributive aspects are taken into account, the legitimacy of state intervention is even more manifest. Let us recall that a market system has no claim to ensuring a fair distribution of resources. At best, staunch advocates of economic laissez-faire can argue that the market generates a “natural” distribution, inasmuch as the market tends, at least in theory, to reward a person’s usefulness to others. Thus, it is appropriate for the state to step in with redistributive measures matching social perceptions of what is fair redistribution. The most obvious measure of this kind is progressive taxation, whereby the marginal tax rate increases with earnings. 4. An important fi nding appearing in Table 7.2 is that the price of both French and German on the one hand and English on the other moved in opposite directions. The value of English went up while the value of the other two languages went down over the 1995–2004 period. This suggests that policies to improve the level of knowledge of English among Swiss workers who already speak some (in the whole economy and particularly in the service sector) over the period considered would have led to increased earnings of these workers, everything else being equal. 5. Recent econometric research by Borooah, Dineen and Lynch (2009) indicates that on the Irish labour market, competence in Irish yields a small but significant advantage for access to professional, managerial or technical jobs. Thus, if the demand for Irish-language skills is also inelastic (as is “L2” on the Swiss labour market), promotional efforts in favour of Irish should beware of “overselling” the associated advantages for access to jobs. 6. This question pertains less to language economics than education economics; see e.g. Hanushek (1987), Lemelin (1998) or Bank (2005); on the specific

Notes

205

connection between internal and external effectiveness with respect to language skills, see Grin (2001b). 7. This is for example the case for Irish in the Republic of Ireland, whose successive governments have regularly reasserted their support for Irish, even formulating the objective to have 250,000 active daily users of the language by the year 2028; see Nic Pháidín and Ó Cearnaigh (2008). 8. A mercantilist policy is one that aims at increasing the value of the trade surplus (exports minus imports) of a country by restricting imports through tariffs and quotas. The British Corn Laws forbade imports if their market price fell below a certain threshold. Their repeal amounted to the return of a free trade policy, which allows the increase of both exports and exports in order to let trading countries fully exploit their respective comparative advantages.

NOTES TO CHAPTER 10 1. The European Commission has entrusted a preliminary investigation into this matter through a project called “CREAM” (“Creativity and Multilingualism”), whose report was unavailable at the time of writing. See http://eacea.ec.europa. eu/llp/studies/documents/study_on_the_contribution_of_multilingualism_to_ creativity_final_report_en.pdf.

NOTES TO THE APPENDICES 1. NOGA (“Nomenclature Générale des Activités”) is the classification of economic activity commonly used in Switzerland. It is derived from the European Union NACE (“Nomenclature des Activités Économiques des Communautés Européennes”) classification. 2. In order to ensure compatibility with the specification of the regression equation, the average labour cost was computed as exp[(¦ ωi ln wi ) ¦ ω j ], with ωi representing the weights. 3. A preferable alternative would be to assume that sectors can be grouped in such a way that the sectors of a given group share a similar movement in each of the four price indexes. Unfortunately, lack of information prevented us from grouping sectors in this way. 4. The procedure of price extraction described in Section 3 reveals that women are paid lower wages than men, other things being equal. Therefore, for consistency with the assumption that workers are paid their marginal product, gender must be taken into account in the estimation of the production function. 5. For a review of functional forms used in empirical economic research and examples of application, see Kohli (1991). 6. For an analysis of the relationship between translog functions and Törnqvist indexes, see Diewert (1976). 7. The estimation was performed using weights reflecting the distribution of the labor force across sectors and across region, by gender. 8. For an interesting presentation of cost functions, their properties and their application in empirical economic research, see Kohli (1991). 9. The branches are the same as those listed in step 5. 10. The data available for estimation cover the 1998–2005 period; at the time of estimation, the capital stock for 2006 could not be computed due to unavailability of data.

206

Notes

11. No attempt was made to use panel estimation techniques or instrumentalvariable techniques. 12. See Kohli (1991) for a review of the properties of profit functions and examples of application. 13. Alternatively, we could choose the form w = α0 + α1 l + α2 l2 (α0 , α1 , α2>0), which better reflects the increasing marginal cost of learning L 2 incurred by the speakers.

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Author Index

A

C

Alarcón, Amado, 47, 49 Alesina, Alberto, 53 Alonso, José Antonio, 49 Andres, Markus, 46 Arrow, Kenneth, 76 Arzoz, Xabier, 162

Caldwell, Bruce J., 92 Cancian, Frank, 196n16 Carliner, Geoffrey, 59 Carlos, Serge, 78 Carr, Jack, 203n12 Chalmers, Douglas, 38 Champagne, René, 80, 199n4 Chiswick, Barry R., 69 Chittick, Gary, 41 Christensen, Laurits R., 168 Church, Jeffrey, 59 Cigada, Sara, 11 Coenen-Huther, Jacques, 2, 196n18 Connell, Tim J., 13, 38 Conti, Virginie, 162 Coste, Daniel, 11, 35 Couet, Jean-François, 30 Coulmas, Florian, 194n1 Coulon, Alain, 35, 36 Crémer, Jacques, 200n7 Cremer, Rolf D., 13, 35 Crystal, David, 12, 34

B Backhouse, Roger, 30 Baetens Beardsmore, Hugo, 145 Baker, Colin, 145 Baldi, Jean-François, 43 Balk, Bert, 201n12 Bank, Volker, 204n6 Barner-Rasmussen, Wilhelm, 26, 199n4 Barsoux, Jean-Louis, 197n11 Baumol, William, 193n5 Beaudeau, François, 200n6 Beaudin, Maurice, 34 Becker, Gary S., 15, 29, 58, 139, 193n8 Beghetto, Ronald A., 160 Behr, Irmtraud, 34 Béland, Robert, 58 Berg, Ivar, 76 Bialystok, Ellen, 159 Blaug, Mark, 15, 92, 194n3, 196n17 Blinder, Alan, 193n5 Boadway, Robin, 151 Bogen, Karen, 201n14 Borooah, Vani K., 204n5 Borzeix, Anni, 32 Bouchard, Pierre, 42 Boudon, Raymond, 3, 200n1 Bourdieu, Pierre, 31, 193n6 Boutet, Josiane, 33 Brémond, Janine, 30 Breton, Albert, 59, 78

D Dalmazzone, Silvana, 59 Dardanelli, Paolo. 198n8 Da Silva, Emanuel, 195n12, 195n13 de Briey, Laurent, 139 de Cilia, Rudolf, 196n1 de Melo, Jaime, 71 De Swaan, Abram, 12, 53, 144 Diewert, Erwin W., 168, 201n12, 205n6 Dineen, Donal A., 204n5 Dinsbach, Aloyse A., 161 di Ruzza, Renato, 200n1 Dubois, Lise, 34 Dudley-Evans, Tony 30 Dunbar, Robert, 140

222

Author Index

Durand, Charles, 203n4 Dustmann, Christian, 69

E Edwards, John, 11 Edwards, Phil, 201n14 Elster, Jon, 15, 193n4 Esperança, José Paulo, 50

F Faini, Riccardo, 71 Färe, Rolf, 116, 201n12 Feely, Alan J., 26, 50 Fidrmuc, Jan, 52 Fidrmuc, Jarko, 52 Filleule, Renaud, 200n1 Fixman, Carol S., 38, 42 Fogel, Walter, 58 Fourichon, Christine, 200n6 Fraenkel, Béatrice, 32 Frederiksson, Riika, 26, 199n4 Freeman, Richard B., 201n10

G Galinsky, Adam D., 160 García Delgado, José Luis, 49, 50, 161 Garfinkel, Harold, 36 Garicano, Luis, 200n7 Garzone, Giuliana, 13, 35 Gazzola, Michele, 121, 139 Gellner, Ernest, 193n4 Gentzler, Edwin, 200n4 Gilardoni, Silvia, 11 Ginsburgh, Victor, 53, 60 Gobard, Henri, 12 Goffman, Erving, 195n15 Gómez-Mejia, Luis R., 50, 78, 197n10 Gould, William, 69 Graddol, David, 13 Grenier, Gilles, 69 Grin, François, 12, 16, 28, 59, 67, 71, 77, 89, 108, 122, 126, 138, 139, 141, 162, 194n2, 197n2, 198n4, 198n8, 198n10, 203n7, 204–205n6 Grosskopf, Shawna, 116, 201n12 Guidère, Mathieu, 162, 200n4 Gumperz, John J., 35

H Hagen, Stephen, 48, 196n3 Hanushek, Eric, 204n6 Harris, Richard, 47 Harzing, Anne-Wil, 26, 50

Hauschildt, Jürgen, 37, 50 Heiniger, Monika, 13, 35 Heller, Monica, 11, 16, 33, 195n12 Hellinger, Marlis, 11 Helliwell, John F., 52 Henderson, Willie, 30 Hentschel, Dieter, 34 Hočevar, Toussaint, 59, 78 Hofstede, Geert, 197n11 Holmes, Janet, 30 House, Juliane, 11 Hurst, Michael E., 197n3 Hymes, Dell, 35

I Ilie, Cornelia, 13, 35 Inagaki, Morido, 78 Ingram, David, 41

J Jiménez, Juan Carlos, 49 Jones, Eric, 12, 52 Jorgensen, Dale W., 168

K Kauffmann, Michel, 34 Kaufman, James C., 160 Kaya, Bülent, 70, 71 Keen, Steve, 2, 15 Keller, Rudi, 32 Kern, Anja, 34 King, Ian, 59 King, William D., 116 Kohli, Ulrich, 205n5, 205n8, 206n12 Korth, Britta, 198n8 Kossoudji, Sherrie, 69 Krumm, Hans-Jürgen, 196n1 Ku, Hyejin, 52 Kymlicka, Will, 53, 140

L Lacoste, Michèle, 33 La Ferrara, Eliana, 53 Laitin, David, 53 Lamarre, Patricia, 34 Lamarre, Stéphanie, 34 Lambert, Richard D., 38 Lamberton, Donald M., 197n2 Lang, Kevin, 199n4 Lau, Lawrence J., 168 Laur, Elke, 43 Lavoie, Marc, 58 Leblanc, Michel, 79 LeBlanc, Mélanie, 34

Author Index Lefebvre, Lise, 79, 80, 199n4 Lee, J., 41 Leibenstein, Harvey, 2 Lemay, Dominique, 62, 63 Lemelin, Clément, 204n6 Lévy-Garboua, Louis, 142 Lewis-Beck, Michael, 198n3 Lo Bianco, Joe, 40, 41 Lüdi, Georges, 11, 13, 35 Lynch, Nicola, 204n5

M Maclure, Jocelyn, 53 Maddux, William W., 160 Marion, Gérald, 58 Marschan-Piekkari, Rebecca, 26, 199n4 Martí, Fèlix, 12 Martín Municio, Ángel, 50 Mateo Aierza, Miren, 197n6 Matthey, Marinette, 11 May, Stephen, 12, 53 Mayer, Thomas, 2, 15, 92, 194n3 McCloskey, Deirdre, 30 McGroarty, Mary E., 30 McLaughlin, Mireille, 195n12 McManus, Walter, 69 Melitz, Jacques, 52, 162 Mettewie, Laurence, 46, 47 Mieszkowski, Peter, 78 Migué, Jean-Luc, 58 Miller, Paul W., 69 Mincer, Jacob, 58, 61 Mitchell, Rebecca, 160 Mondada, Lorenza, 35, 36, 131 Morrison, Robert, 78

N Nicholas, Stephen, 160 Nic Pháidín Caoilfhionn, 205n7

O Oaxaca, Ronald, 197n2 Ó Cearnaigh, Seán, 205n7 Ottaviano, Gianmarco, 53

P Palich, Leslie E., 50, 78, 197n10 Patrinos, Harry A., 197n3 Patten, Alan, 140 Pauwels, Anne, 11 Peri, Giovanni, 53 Pes, Johanne, 64 Phillipson, Robert, 12, 140, 203n4

Piekkari. See Marschan-Piekkari Pogge, Thomas, 53 Pool, Jonathan, 15, 139 Prat, Andrea, 200n7 Prieto-Rodriguez, Juan, 60 Primont, Daniel, 201n12 Pupier, Paul, 141

R Ramallo, Fernando F., 38 Raynauld, André, 58, 81, 82 Reeves, Nigel B., 38, 131, 132 Rehbein, Jochen, 11 Reich, Rob, 53 Rei Doval, Gabriel, 38 Repetto, Gaston, 69 Ricento, Thomas, 161 Richards, Mary, 195n12 Ricks, David, 197n11 Riera Gil, Elvira, 197n6 Riley, John G., 76 Rivera-Batiz, Francisco, 69 Roberts, Celia, 30 Ross, David, 123 Rossiaud, Jean, 70, 71 Rossi-Landi, Ferruccio, 32, 194n7 Ruiz Vieytez, Eduardo, 140

S Sabourin, Conrad, 59 Salort, Marie-Martine, 30 Scarth, William, 193n5 Scherer, Frederic, 123 Schmäle, Günter, 35 Schneider, Susan, 197n11 Schutz, Alfred, 195n15 Sfreddo, Claudio, 85, 89, 108, 126, 138, 198n10, 204n9 Shohamy, Elena, 161, 195n12 Silverman, David, 35 Simonton, Dean K., 160 Skutnabb-Kangas, Tove, 12, 140 Smith, Adam, 32, 194n11 Spence, Michael, 77 Spolsky, Bernard, 141, 161, 193n8, 194n5 Stanley, John, 41 Stanton, Philip J., 41 Stiglitz, Joseph, 76

T Tainer, Evelina, 69 ten Thije, Jan D., 162 Theme, Anne, 11

223

224

Author Index

Tirole, Jean, 123

U Über Grosse, Christine, 42

V Vaillancourt, François, 21, 59, 62, 64, 78, 80, 81, 82, 85, 89, 108, 197n2, 198n4, 199n4, 204n9 van der Zee, Karen, 199n1 van Langevelde, Ab, 38 van Oudenhoven, Jan Pieter, 199n1 van Parijs, Philippe, 12, 53, 139 Varian, Hal, 193n5 Vollstedt, Marina, 37, 50

W Wales, Terence J., 168

Walsh, John, 38 Weber, Shlomo, 53 Welch, Dennis, 26 Welch, Finis, 69 Welch, Lawrence, 26 Wheelan, Charles, 21 Wildasin, David, 151 Willes, Mary J., 13, 35 Winch, Peter, 15 Wodak, Ruth, 196n1 Woehrling, José, 141 Wölfl, Anita, 195n13 Wright, Colin, 38, 131, 132

Z Zeevaert, Ludger, 162 Zimmermann, Klaus F., 71 Zussman, Asaf, 52

Subject Index

A advertising, 23, 24, 123 Albanian, 46 allocation (of resources). See efficiency Arabic, 149–150 Australia, 40–42, 60, 61, 69 Austria, 196n4

cost (and cost function), 14, 18, 38, 78, 81–82, 86, 87, 89, 100–104, 115–117, 133, 138, 147, 152 creativity, 43, 152, 159–160, 204n1, 205n1 Croatian, 46 culture. See diversity, cultural v. linguistic

B

D

Basque, 197n6 Belgium, 40, 42, 47, 56 Bolivia, 197n3 Brussels, 46–47

development (economic), 31 discourse analysis, 30 discrimination, 58, 197n3 diversity cultural v. linguistic, 11, 51, 197n10, 197n11 objective v. subjective, 11–12 value of (see multilingualism, absolute v. contingent) domain, 3, 31, 194n5 Dutch, 40, 46–47

C calibration (of models), 93–95, 103 Canada, 33, 56, 59, 61. See also Québec Catalan 33, 40, 47–48, 197n6 Catalonia, 47–48 capital, 18, 79, 88, 95, 97, 110, 118, 199n5. See also production factors and human capital Chinese, 46, 148 CLIL (content and language integrated learning), 145 code-switching, 34, 35, 37, 159, 199n4 commerce. See market Common European Framework of Reference for Languages, 66, 96, 200n5 communication. See multilingual communication competence receptive, 16, 162 competition, 18, 42, 123. See also market complementarity, 111 consumption, 30 conversation analysis, 33, 35–37, 132, 159

E earnings differentials (language-based), 26, 54, 57–71, 107–108, 137, 153, 197n3, 204n4 economics, 4, 17, 28–29. epistemology of, 4–5, 15–17, 20–27, 30, 36, 196n18 v. economy, 29–30, 113, 152 economics of language, 16, 17, 28, 133, 197n2 efficiency, 21, 37, 50, 139, 200n7 ELAN study, 48–49, 197n8 elasticity, 111–114, 116–119, 202n10 employment, 33 English, 12, 13, 33, 35, 40–42, 44, 46, 48, 51, 53, 58, 60–65, 67–69, 78–85, 107, 111, 114, 115, 125–127, 198n13, 199n14

226

Subject Index

epistemology (and methodology), 2–3, 36, 37, 92–93, 153, 200n1. See also economics, epistemology of ethnic business, 31, 70 ethnography, 14, 34, 36, 153 ethnomethodology, 35–37, 93, 196nn17–18 European Commission, 48 Union, 48–49, 60

F fairness, 21–22, 139, 204n3 firms, 15, 17–27, 43, 86–91, 117–118, 138, 140, 142 data collection in, 95–97, 102–104, 142, 156–158, 201n13 language needs of, 24, 25, 38, 39, 40–49, 123–134, 137. See also recruitment multinational, 26–27, 50–51, 120 ownership of, 78–81, 146 France, 42, 44, 114 Francophonie, 40, 42–45 French, 33, 40, 42–48, 58, 60–66, 68–69, 78–85, 111, 114, 125–126

G German, 40, 44, 45, 46, 65–66, 68–69, 83–85, 111, 114, 125–126, 203n3 Germany, 60, 61, 69, 114, 196n4 globalisation, 12, 33, 42 human capital, 59, 76, 77

I identity, 27, 43, 59, 139 indexes (price and quantity), 100–102, 109–110, 202n5 immigrants. See migration income. See earnings differentials indexicality, 36 industrial engineering, 17, 27, 58, 119, 137, 156 inequality, 32. See also language and power innovation. See creativity inputs. See production factors interactionism, 14, 33, 35–37, 195n15 Irish, 204n5, 205n7 Israel, 60, 61, 69 Italian, 40, 45, 46, 65, 68, 114, 125–126, 198n10 Italy, 45, 114

L labour, 18, 22, 87–88, 95–101, 108, 110, 154, 199n5. See also production factors labour market, 14, 26, 40, 47, 56, 58–64, 69, 77, 81, 138, 140 language and power, 31, 33 as a supercollective good, 59 at work, defining, 13–14 choice, 34 dynamics, 14, 25–26, 31, 37, 120 economics. See economics of language of economics, 31 learning, training, education, 37, 43, 45, 124, 140, 142, 143–150 minority, 141, 197n6 omission of, 19 policy, 43, 123, 134, 139–151, 155, 161 rights, 140 skills, 13–14, 16, 21, 22, 26, 35, 39, 41, 43–47, 55–71, 77, 86, 96–98, 107, 113–115, 137, 139, 160, 199n3 (see also competence, receptive, and firms, language needs of, and multilingual repertoires) sociology of, 1, 3, 193n8 territoriality, 56, 65 use, 35, 46, 65, 71, 75–85, 120 linguistic attributes, 21, 23, 59 audit, 38, 132–133 environment, 21, 22, 23, 25 hegemony, 12 human rights (see language rights) intensity, 16, 47–48, 49–50, 52, 160–161 market, 31–32, 193n6 uniformity (see multilingualism, absolute v. contingent) linguistics applied, 3, 14, 16, 131, 132, 137, 153, 161 LOTEs (languages other than English), 13, 41, 142 Luxembourg, 59

M management, 26–27, 38, 42, 50–51, 81, 131 market, 22–23, 31–32, 38, 43, 81–82, 139, 141–142. See also labour market

Subject Index v. non-market, 29–30, 139 material v. non-material. See market v. non-market methodology. See epistemology migration, 12, 60, 69–71 MIMO (multi-input, multi-output) models, 106, 116, 118, 200n9 monopoly, 18, 123 mother tongue, 60 multiculturalism, 40 multilingual communication, 15, 21–25, 26, 34, 37, 43, 50–51, 52, 121, 132, 161, 162 competence. See language skills repertoires, 21, 34, 38, 41, 77, 161 multilingualism absolute v. contingent, 40, 52–54, 137–138, 140–142, 155 increasing v. decreasing, 11–12

O oligopoly, 18, 123 operations management. See industrial engineering optimality, 19, 89, 131 output. See production

P Portuguese, 46, 50, 147, 148 pragmatics, 34–35, 195n14 production, 19, 22, 26, 30, 50, 94, 111, 119 factors, 18–19, 22, 78–81, 94–99, 110–111, 146, 193n6, 199n5, 201nn7–8 function, 23, 57, 95, 105, 110–115, 137, 146 theory, 17–19, 20, 23, 76, 86–91, 137, 154 productivity, 38, 57, 58, 69, 77, 78, 81–82, 87, 102, 113, 131, 133, 138, 152, 202n6 profit (and profit function), 14, 15, 18, 21, 31, 78, 89, 95, 101–104, 115, 117–119, 133, 138, 147, 148, 152

Q Québec, 42, 44, 55, 58, 59–64, 78–82, 115, 139

R racism. See discrimination

227

rates of return (on language skills). See earnings differentials rationality, 29, 129, 131, 193n4 recruitment, 25, 123, 128–131, 154 reflexivity, 36 representations, 93 Romanche, 65, 198n8 Russian, 46, 148

S scarcity, 29 screening, 76–77, 120, 199n2 Serbian, 46 signalling, 77, 120 SMEs, 44, 48–49 sociolinguistics, 16, 30–34, 194n5 critical, 33–34, 195n12, 200n4 Spanish, 33, 40, 44, 46, 48, 49–50, 197n6 state, 138, 140–151. See also language policy substitutability, 111 supply and demand. See market Switzerland, 40, 42, 44, 45–46, 55, 56, 59, 61, 65–69, 70–71, 83–85, 106–107, 112–114, 121, 125– 127, 139, 196n4, 198n3, 198n8

T technology, 18–19, 78, 79 terminology, 34 trade diversion, 49, 197n9 international, 12, 41, 43, 52, 114, 148–149, 205n8 translation, 34, 159, 162, 200n4 trilingualism, 47, 60 Turkish, 46, 70–71, 199n15

U United States, 58, 60, 61, 69, 203n12

V value added. See value creation value creation, 27, 33, 38, 52, 55, 57–58, 75, 87, 105, 110, 113–114, 119, 121, 132, 133, 137, 152, 160, 161

W wage premiums (for language skills). See earnings differentials work silent, 13, 193n3 sociology of, 32–33 See also language at work