The International Structure of Income: Its Implications for Economic Growth

The International Structure of Income Its Implications for Economic Growth

Salvatore J. Babones

for my mother

Contents Preface ............................................................................................................xiii 1. Introduction.................................................................................................... 1 2. Trends in International Income Inequality...................................................... 7 3. From International Inequality to Global Inequality....................................... 35 4. The Structure of National Income ................................................................ 63 5. Neoclassical Economic Growth in a Structured World-Economy............... 103 6. Investment and Growth: Disentangling Causal Paths ................................. 143 7. Conclusion ................................................................................................. 163 References...................................................................................................... 169 Index .............................................................................................................. 177 About the Author ........................................................................................... 181

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List of Tables Table 1. Three methodologies for international income inequality .................. 19 Table 2. National inequality by decade and region (SD of Logs).................... 48 Table 3. Summary of the origins of cases for national inequality samples....... 50 Table 4. Modes of the global income distribution (logged) ............................. 58 Table 5. Modes of the global income distribution (1995 dollars)..................... 59 Table 6. Annual levels of PP and PC boundaries............................................. 87 Table 7. Position and mobility in the world-economy, 1960-1999................... 89 Table 8. Observed mobility across boundaries of the world-economy............. 96 Table 9. Organic zone membership (benchmark countries only) ..................... 97 Table 10. Regression of log(GNP) on neoclassical inputs, 1995 data ............ 128 Table 11. Long-run neoclassical growth model (1960-1999)......................... 129 Table 12. Long-run neoclassical model, LABOR excluded (1960-1999)....... 131 Table 13. Stratified long-run model (1960-1999) .......................................... 133 Table 14. Stratified medium-run model (1975-1995) .................................... 135 Table 15. Medium-run models (1975-1995).................................................. 138 Table 16. Stratified, differentiated medium-run model (1975-1995).............. 139 Table 17. Full model for growth and investment, 1975-1995 all avail. .......... 156 Table 18. Full model for growth and investment, 1975-1995 periphery ........ 157 Table 19. Panel model for growth and investment, 1975-1995...................... 159 ix

x

List of Figures Figure 1. FX versus PPP methodology (schematic)......................................... 14 Figure 2. Real Local Currency (RLC) methodology (schematic)..................... 16 Figure 3. International income inequality -- weighted VarLogs....................... 20 Figure 4. International income inequality, weighted Ginis .............................. 21 Figure 5. International inequality -- weighted VarLogs (China excluded) ....... 23 Figure 6. International inequality -- weighted Ginis (China excluded) ............ 24 Figure 7. Trajectory of Chinese growth since 1960 (FX, LCR, PPP)............... 26 Figure 8. Detail of Chinese growth since 1990 (FX, LCR, PPP) ..................... 29 Figure 9. Illustration of a Lorenz curve ........................................................... 38 Figure 10. Quintile-based (dashed) versus actual (solid) Lorenz curves .......... 40 Figure 11. Observed Ginis versus SDs of logs, "high quality" data ................. 42 Figure 12. Illustration of a lognormal distribution of income .......................... 44 Figure 13. Gini and SD of the logarithms for a lognormal distribution............ 45 Figure 14. Fit of national inequality to GNP per capita ................................... 48 Figure 15. Illustration of a gaussian kernel (United States) ............................. 52 Figure 16. Global distribution of income, 1960s (Tier II sample)................... 54 Figure 17. Global distribution of income, 1970s (Tier II sample) ................... 54 Figure 18. Global distribution of income, 1980s (Tier II sample)................... 55 Figure 19. Global distribution of income, 1990s (Tier II sample) .................... 55 xi

Figure 20. Two-population model of the global income distribution ............... 57 Figure 21. Distribution of countries in the world-economy (1965) .................. 83 Figure 22. Distribution of countries in the world-economy (1975) .................. 83 Figure 23. Distribution of countries in the world-economy (1985) .................. 84 Figure 24. Distribution of countries in the world-economy (1995) .................. 84 Figure 25. Average internal income inequality by zone, 1960s-1990s........... 100 Figure 26. Investment differentiation growth model...................................... 147 Figure 27. Investment differentiation model with reciprocal effects .............. 148 Figure 28. Structural model of the investment-growth nexus......................... 150 Figure 29. Investment-growth nexus, all countries, 1975-1995 ..................... 152 Figure 30. Investment-growth nexus, peripheral countries, 1975-1995 ......... 154

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Preface This book is a new and extensively edited version of my dissertation of the same title, which was deposited at The Johns Hopkins University in September, 2002. I have expanded and updated the introduction and conclusion, reorganized the substantive chapters, edited the whole text for readability, and added a detailed index. I have also eliminated the extensive technical appendices that, in the original, ran longer than the main text itself. Those seeking highly technical details like lists of the particular countries included in each analysis, tests of the robustness of the numerical results, and multiple alternate specifications of the statistical models are directed to the dissertation, available electronically in facsimile form from ProQuest. Most readers, however, will find the current volume much more useful and accessible. The material is better organized, the chapters are better laid out, and the index is invaluable. That said, most of the actual text of the main body of this volume is drawn directly from the 2002 original. Of course, much has changed in the world since the composition of that original text, but strangely little that would affect the interpretation of the results presented in this volume. It is, after a fashion, a striking confirmation of the ideas expressed here that they require little revision upon publication six years later. The simple fact is that the structure of world income has changed very

The International Structure of Income little in decades (or, for that matter, centuries) and is changing very little today despite the massive dislocations associated with the current global financial crisis. Once one looks beyond the topical ripples to anything approaching the longue durée, the structure of the world-economy has been remarkably stable over time. In terms of income, the countries of the world today rank pretty much where they did 100 years ago, a few historical oddities notwithstanding. The ultimate implication of this for economic growth may simply be to imply that growth is not very relevant for understanding our world. Structure clearly dominates. It was the problem of growth, however, that brought me to the topic. I first became interested in issues of economic development as a senior sociology major at the University of Montevallo. My advisor, guide, and confidant there was Jeri Bullard, now of the University of North Alabama. I did not go into sociology in order to study development, but when I "discovered" development I immediately saw the importance of studying it from a sociological perspective. There is no pure economics of growth and development; economics can help elucidate the relationship between economic allocation and economic growth, but it cannot, in the end, solve the root problems of allocation. The global allocation of resources, both within and among nations, is fundamentally a sociological phenomenon. This book is concerned not so much with the causes of the international structure of the allocation of economic resources as with its implications for growth.

Neoclassical and "new" growth theory in economics have largely

ignored the implications of sociological theory for understanding economic growth. Economists working in these traditions have not largely, but completely ignored the sociological literature on growth.

The result has been the

promulgation and proliferation of formal growth models that do not correspond xiv

Preface in any meaningful way to empirical reality. Contrary to established theory, capital investment does not flow to the poorest countries; international immigration is dominated by the relatively rich, rather than the relatively poor, and flows in the "wrong" directions; trade liberalization is not turning out to be the panacea it promised to be; convergence between rich and poor countries, if it is occurring at all, is occurring very, very slowly. To the credit of the economics profession, a vibrant tradition of criticism and debate among economists has led to a general awareness of these empirical shortcomings in their dominant theoretical perspectives.

This awareness,

however, has not led them to look farther afield to ideas emanating from other disciplines. However, economists are not the only students of economic growth who insist on wearing disciplinary blinders. A general aversion to the political and policy stances of the mainstream of the economics profession has led sociologists to devalue and deride the important orienting influence of formal modeling in economic theories of growth. Sociologists also rarely reference the current empirical literature in economics, preferring instead to get their economics from policy books and popular media such as magazines and newspapers.

As a result, although

sociologists do not operate in complete ignorance of economics, their understanding of economists' work on growth is generally topical and often a decade out-of-date. This is unacceptable. One need not accept the failed policy prescriptions of neoclassical economists in order to adopt and adapt the genuine theoretical accomplishments of neoclassical economics. I take neoclassical economics, and specifically the neoclassical growth model, as an orienting framework, which I then proceed to fill in with sociological data and insight. In my opinion, such an approach best leverages the strengths of the two fields: economics for formal mathematical modeling and xv

The International Structure of Income sociology for applied empirical analysis. Were I a sociological purist, I could have run the same models without locating their origins in economic theory, and were I an economist, I could have experimented with the non-traditional specifications found below without referring to their sociological inspirations. By taking an explicitly interdisciplinary approach, I have gained enormous insights from work in both fields. I hope that my readers, be they economists, sociologists, other social scientists or laypersons, will similarly benefit from this cross-fertilizing approach. As I said at the opening, this book has grown out of my dissertation research. Accordingly, most of the required acknowledgements are related to that period of my life. First and foremost, while I certainly owe my thanks to all of the members of my dissertation committee at Johns Hopkins, I owe a much greater measure of thanks to one scholar in particular. This is my academic advisor and personal friend, Christopher Chase-Dunn. Although this book does not flow directly from his work, it does flow directly from his unwavering support and encouragement, from my first week as a new graduate student at Johns Hopkins clear through to today. Quite simply, without his input, this book, and the dissertation it is based on, would not exist. Thank you, Chris. I also owe a great debt, both intellectual and personal, to my second reader (and de facto second advisor), Giovanni Arrighi. Much of Chapters 3, and 4 of this book is a replication, confirmation, and extension of his work. After joining the faculty at Hopkins, he graciously took on responsibility for helping oversee my dissertation work, going well beyond the call of duty to add a late-stage dissertation student to his docket of finishing students at Binghamton and new students at Hopkins. My thanks to him and to my third departmental reader at Johns Hopkins, Robert Gordon, for cheerfully pitching in when called upon to add this substantial burden to their already busy schedules. xvi

Preface One other sociologist deserves special mention with regard to the successful completion of this project.

Without the constant support and

encouragement of Binnie Bailey, my dissertation likely never would have been started, and certainly would not have been completed, and thus this book would not exist I would like to take the opportunity to thank her for all of her help over those challenging years. I would also like to thank the members of the Johns Hopkins sociology office staff, including Vonnie Wild, Pam Stichion, Mary Ann Zeller, and especially Amy Cline for her help in the home stretch. If Binnie has been the godmother to this project, they have been the midwives. Of course, in a much deeper sense, I "owe it all" to my mother. This book is dedicated to her, as was my dissertation. I hope she likes it.

Salvatore Babones Sydney, Australia November 1, 2008

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The International Structure of Income

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1. Introduction A decade ago in 1998 an interdisciplinary team of four economists and sociologists published a general review of the state of the literature on economic growth. Reviewing articles in both the sociology and the economics literatures, they found an almost complete disjunction between the two camps. The authors concluded: For more than two decades, economists and sociologists have pursued parallel cross-national quantitative investigations of the determinants of economic development. These investigations have proceeded in mutual ignorance despite the often large overlap in statistical methods and data employed. Apparently contradictory findings have resulted . . . . What the studies we have reviewed herein highlight, perhaps more than anything else, are the need to avoid simplistic theoretical formulations and the virtue of drawing upon the existing and related work of various disciplines. (Crowly et al 1998, p. 45) I agree wholeheartedly with the sentiments expressed by Crowley and her colleagues, but must protest that if there is a tendency to ignore literatures outside one's own discipline, it is much more marked among economists than

The International Structure of Income among sociologists. The present volume is a case in point: though a sociologist, I take the economics literature very seriously. Throughout I review results from economics alongside those from sociology, and I go so far as to frame my theoretical models as extensions of models from the economics literature. Though many of the sociologists I cite also cite economists, in the course of researching this book I never once came across an economist who cited the sociology literature. Interdisciplinarity seems to be a one-way street. It is none the less important for that fact. World-systems sociology in particular could benefit from importing tools and perspectives from neoclassical economics.

The fact that the assumptions and conclusions of neoclassical

economists tend to conflict with those of world-systems sociologists does not in any way invalidate the efficacy of the tools that neoclassical economists have developed to get from assumptions to conclusions.

In particular, the

neoclassical growth model is a flexible, robust framework for understanding how national-level variables are related to national-level economic growth. The neoclassical growth model depicts growth as the result of having capital inputs (both physical and human) higher than is typical for countries of a given level of national income. This base model seems reasonable, and there is no reason why variables of sociological interest (like dependency or demographics) shouldn't be added to it. The model is sufficiently flexible to allow for this, and provides a convenient framework for comparing the performance of theories from different disciplines. The neoclassical growth model is especially useful for helping us understand the degree to which countries are or are not growing more alike over the years. A major point of contention between economists and sociologists throughout the 1960s, 1970s, and 1980s was that economists asserted that countries' levels of national income would converge over time, while 2

Introduction sociologists observed that levels of national income were in fact diverging. This should not be a theoretical debate: the data exist to test the proposition. Both sociologists and economists have tested it, but have come, each within their own literatures, to conflicting conclusions. In Chapter 2 below I attempt a definitive treatment of this conflict, explaining the reasons for the differences in results reported by different authors. The balance of evidence seems to suggest no change: no convergence, but no major divergence either. I argue, however, that the problem is not the answer but the question. The question presumes the existence of a single economy in which the countries of the world compete on equal terms in a single economic space. The reality is that the world-economy is highly structured, with individuals and countries playing highly circumscribed structural roles. This is why economics needs sociology. One key element of the sociological world-systems approach to the study of economic development is the realization that economic opportunities are not smoothly and seamlessly distributed across all the areas of the world, but are strongly patterned by location in the world-economy. Economic activity is organized in global commodity chains linking ultimate producers with ultimate consumers. As formally theorized by Arrighi and Drangel (1986), there are two general types of locations in the commodity chains that make up the worldeconomy: core nodes, in which economic actors are able to extract a monopoly rent through the reduction or elimination of competitive pressures, and peripheral nodes, in which near-perfect competition drives economic rents toward zero. As a result, theory predicts that the population of the world is roughly divided into two broad income levels, with a great trough separating them. This prediction is at odds with distribution theory in economics, which generally supports the presumption of a unimodal, lognormal distribution of income. 3

The International Structure of Income The bimodal distribution of income predicted by Arrighi and Drangel (1986) refers to the global distribution of individual incomes. At the national level, countries are hypothesized to fall into one of three broad income categories. Core countries are those for which most economic activity occurs within core nodes of global commodity chains; they are thus high-income countries. Peripheral countries are those for which most economic activity occurs within peripheral nodes of global commodity chains; they are thus lowincome countries. Some countries, however, display a mix of core-type and peripheral-type nodes; these countries find themselves in an uneasy tug-of-war between relative wealth and poverty. They do not possess the critical mass of core nodes that would allow them to reduce competitive pressures in all of their nodes of economic activity, but they do possess sufficient core nodes to prevent a complete descent into peripheral-type activity.

These countries form a

permanent "semiperiphery" within the world-economy. The world-systems theory of the structure of the world-economy thus predict a bimodal global distribution of income at the level of the individual, but a trimodal global distribution of levels of national income per capita at the country level. The next two chapters of this book confirm these two predictions in turn. In Chapter 3, I use newly available data to show for the first time that the global distribution of income at the individual level is, in fact, strikingly bimodal. In Chapter 4, I update and improve on earlier work by world-systems sociologists showing that the distribution of the national incomes per capita of the countries of the world is, in fact, trimodal. These two confirmational studies lay the groundwork for the construction of sociologically-informed neoclassical models of economic growth. Returning to the neoclassical growth model, the original formulation of the model is based on the input-output model of Solow (1956), in which 4

Introduction aggregate economic output is modeled as a multiplicative function of the level of technology and levels of inputs of labor, physical capital, and, in recent specifications, human capital. Since inputs of labor, physical capital, and human capital are subject to diminishing marginal returns on investment, permanent, steady-state growth can only be attributed to improvements in technology. In this sense, the neoclassical model can be seen as a formalization of Schumpter's (1951) theory of economic growth. Since technological change is exogenous to the model, the neoclassical model is sometimes referred to as an exogenous growth model. Over the very long term, potential rates of growth in national income are limited by the rate of technological change. Over shorter time frames, however, economic growth (or decline) from one equilibrium level of output to another through changes in the levels of inputs is possible. This kind of growth is termed "disequilibrium growth" because it represents the movement of an economy in disequilibrium towards a new equilibrium level of output. There is every reason to believe that the specific mechanisms of disequilibrium growth differ systematically across the three zones of the worldeconomy. Levels of human capital may be more important for growth outcomes in the core of the world-economy, where technological utilization is most intense; demographic factors may be more important in the periphery, where production is more labor-intensive; the effects of physical capital may differ according to source (domestic or foreign) in the differing political environments prevalent in the core, semipheriphery, and periphery. In short, the coefficients of the neoclassical inputs probably differ by zone. In Chapter 5, I estimate a series of neoclassical models for the world as a whole and for each zone of the world-economy, uncovering important differences in the growth process across the zones of the world-economy. In Chapter 6, I concentrate on an important theoretical debate in the dependency literature: the relationship between foreign 5

The International Structure of Income investment, domestic investment, and growth. Operating within the context of a neoclassical growth model, I am able to show that the previously reported beneficial short-term impact of foreign direct investment is, in fact, spurious. Investment as a whole, in fact, seems to have no causal impact on observed rates of economic growth. This very non-neoclassical result is a striking confirmation, using neoclassical tools, of sociological research in the dependency theory tradition. That this result is inspired by and based on models developed in the economics literature of the past five years demonstrates that powerful new insights can be generated through the cross-fertilization of sociological and economic thought. How much more can be accomplished pursuing this strategy is an open question, though we can hardly do worse than our current understanding of the growth process. In the Conclusion, I propose some immediate next steps that can be taken to apply sociological insights to further improve the specification of formal models of economic growth. In addition, although the focus of this book is on the patterning of growth processes by income zone of the world-economy, in the Conclusion I briefly discuss the implications of patterns of economic growth for our understanding of the global income distribution.

6

2. Trends in International Income Inequality One of the most striking features of the global social geography is the degree to which the world's income is concentrated in just a few countries, while the bulk of the world's population subsists on incomes that do not even exist, as conceivable incomes, in the developed world.

The 15% of the world's

population living in the "developed" countries of the world account for over 78% of global economic output. (World Bank 2001b) While we possess a plethora of theories that attempt to account for this pattern, no one theory or theoretical tradition has emerged to gain wide acceptance as an explanation. Moreover, though the specter of European colonialism may be invoked to explain the geography of global income at the end of the second world war, it implies the paradox that the global income distribution has remained equally wide, and by some accounts has widened much further, in the subsequent era of independent states.

Even dependency theory must admit that political

independence should have yielded (slightly) better economic performance than colonial rule. In this chapter, I present new results on the international distribution of income, working within the summary statistic tradition of existing international income inequality studies; in Chapter 3 I lay out a new course new course of inequality measurement based on the full distribution of income among the

The International Structure of Income whole population of the world. I begin this chapter with a review of the running debate on the international distribution of income. Since the main sources of dispute in this debate center on the choice of data series to be analyzed, I go into some detail on the origins of the relevant data. Then, I replicate and update the trends in inequality as reported in the literature, displaying inequality as computed from each of the three major data sources in parallel. This exercise clarifies the reasons for the wide variety of results reported in the literature, as well as pointing the way forward for a deeper understanding of inequality.

I then

examine the impact of China on international inequality measures. I conclude this chapter with a critique of the practice of using summary measures of international inequality to encapsulate the complexity of the global income distribution.

THE INCOME CONVERGENCE DEBATE Recent years have witnessed a renaissance in comparative empirical research on the international distribution of income. This research has taken a variety of forms, ranging from regression-based panel studies to comparative studies of national income distributions to longitudinal descriptions of between-country inequality.

It also straddles several literatures, including the economics,

development, and sociology literatures. The theoretical fount of much of this recent interest is the debate over neoclassical convergence. Writing after the second world war, neoclassical economists predicted the convergence in incomes of poorer with richer economies, as a result of capital movements from areas of relatively high capital utilization to those where capital intensity is relatively lower. This body of 8

Trends in International Income Inequality theory, based on the work of Samuelson (1948) and Solow (1957), was complemented by the work of heterodox theorists like Gerschenkron (1962), who emphasized the importance of technology transfer in contrast and complement to that of capital movement. Whatever the mechanisms, the new countries of the south were expected to grow rapidly in the decades following formal political independence. Empirically, however, this growth did not materialize, and a sociological critique emerged that capital transfers, far from promoting convergence in income between rich and poor countries, might actual promote and perpetuate divergence.

Building on the theoretical work of Frank (1969), Emmanuel

(1972), and Amin (1974), Chase-Dunn (1975) demonstrated empirically that international economic dependence -- the flip-side of capital movement -actually retarded economic growth. The ensuing empirical debate on foreign capital dependence continues to this day, with recent contributions from Kentor (1998), Dixon and Boswell (1996a, 1996b), and Firebaugh (1992, 1996). Only a hiatus in the production of new data seems to have slowed down the controversy. Although rarely citing the sociology literature on dependence, economists have now largely abandoned the concept of convergence driven by exogenous capital flows. The "new growth theory" of Romer (1986) and Lucas (1988), essentially an elaboration on Gerschenkron, implies that convergence between low and high income countries will only occur where sufficient human capital exists in the poorer countries to take advantage of the technological advances made in the richer countries.

Empirical studies of "conditional

convergence" -- convergence conditional on levels of educational attainment -have generally supported this view. Barro (1991), Barro and Lee (1993a) and Barro and Sala-I-Martin (1995) find that educational levels are significantly 9

The International Structure of Income related to growth rates, and that once education is controlled statistically, low income countries do grow faster than high income countries. More recently, economists have begun to question even the limited finding of conditional convergence, especially over the long-term. (Easterly 2001) More importantly for the present purpose, when no controls are made, Barro and his colleagues find no evidence of absolute convergence, but instead find a measurable (but statistically non-significant) divergence between high and low income countries over the past few decades. This is consistent with Romer's (1987) seminal finding of unconditional divergence, and has since been confirmed by Pritchett (1997). The long-term divergence between rich and poor countries over centuries is even more certain -- and much more striking. (Maddison 1995) In short, the rich are getting richer, and while the poor can't get much poorer, they are not keeping up.

Measures of International Income Inequality Taking a more direct approach to measuring changes in the global distribution of income, a different tradition has emerged in the sociology and development literatures. In a duo of roughly parallel articles, Korzeniewicz and Moran (1997) and Schultz (1998) both demonstrated that by far the largest contributor to overall global inequality is international, or between-country, inequality. By contrast, within-country inequality contributes a much smaller share of total global inequality, although estimates range widely depending on methodology. Essentially, this means that, in the aggregate, the income gaps between rich and poor countries far outweigh the income gaps between rich and poor people within each country. At issue, however, is the trend in between-country inequality itself in the years since 1960. While Korzeniewicz and Moran reported a rising trend in 10

Trends in International Income Inequality inequality from 1960-1992, Schultz reported a relatively stable level of inequality. The Korzeniewicz and Moran findings were based on levels of gross national product (GNP) evaluated in dollars at current contemporary exchange rates (in shorthand, a GNP/FX methodology), while Schultz's conclusions were based on levels of gross domestic product (GDP) evaluated in dollars at purchasing power parities (a GDP/PPP methodology).

That the two

methodologies yield conflicting results was pointed out by Firebaugh (1999), and has been confirmed by Babones (2002). The quantitative difference between GNP and GDP for a given country is usually minor (less than 10%), and since the ratio of GNP to GDP for a given country tends to be stable over time, the choice of one series over the other is not of critical importance for shaping the resulting trend in international income inequality. The choice of methodology for conversion to a common currency is, however, critical. Stepping into the debate, Firebaugh (1999) came down firmly on the side of the GDP/PPP methodology, asserting that: Though early studies in economics used FX estimates because PPP estimates were unavailable, PPP-based income is now the industry standard . . . FX rates are highly flawed calibrators of currencies for two reasons. First, many goods and services are not traded on the international market, so exchange rates are based on a restricted bundle of goods and services . . . Second, FX measures are not totally "free" but are routinely distorted by government policy and speculative capital movement. (pp. 1604, 1609) Of these two objections to the use of the GNP/FX methodology, the first is rather dubious.

Rather than seeing it as an objection, the thrust of

Korzeniewicz and Moran is to view it as a positive virtue. The whole point of comparisons of incomes across countries, according to Korzeniewicz and 11

The International Structure of Income Moran, is to compare purchasing power on the world market. Goods not traded on the world market are irrelevant to this purpose, and the emphasis in the GDP/PPP methodology on domestic consumption is thus misplaced for this purpose.

If the debate between the two methodologies depended only on

Firebaugh's first objection, there would be no debate: the GNP/FX methodology could be used for some purposes, the GDP/PPP methodology for others. This is the compromise solution offered by Korzeniewicz and Moran (2000) in a later exchange with Firebaugh on the issue -- a solution, however, scorned by Firebaugh (2000) in his portion of the exchange. The second objection raised by Firebaugh -- distortions in exchange rates -- is more serious, especially for the years before the early 1990s, when most exchange rates were fixed by governments rather than determined in open markets. Balancing this objection, however, are Korzeniewicz and Moran's objections to the methods underlying the computation of PPP conversion factors, in particular, the paucity of benchmark countries and years and the lack of accounting for the quality of goods and services. Moreover, the compilers of the main GDP/PPP data source, the Penn World Table (PWT), themselves assign "quality ratings" of "C" or better to only 55 of 139 countries in the PWT Mark 5, and of 47 African countries only Kenya even reaches the "C" level. (Summers and Heston 1991) Unfortunately, the choice of data series makes all the difference in determining the trend in between-country inequality in the four decades since 1960. Babones (2002) demonstrates one solution to this dilemma: the exclusion of the non-market economies of China and the Soviet bloc from the analysis. When analysis is restricted to market economies only, Babones reports rising trends in inequality using both the GNP/FX and GDP/PPP methodologies. The inclusion of China in particular is hotly debated on all sides: in population12

Trends in International Income Inequality weighted analyses, the values assigned to China are, for obvious reasons, of critical importance.

Both Firebaugh (1999) and Korzeniewicz and Moran

(2000) make a particular example of China in criticizing the other's methods.

An Alternative Currency Conversion Methodology Although they differ in the conversion factors used, the FX and PPP methodologies are relatively parallel in general outline. In each case, national income in local currencies (LCs) is converted to U.S. dollars by applying a conversion factor of some kind for that year, a conversion factor based either on exchange rates or on purchasing power. The reality is a little more complicated, in that the valuation of outputs into dollars occurs at different stages of the process for the FX and PPP methodologies, but the basic pattern is the same: income for a given (benchmark) year is converted to dollars for that year according to some conversion formula. Once this has been accomplished, the resulting dollar-denominated data can be used for comparisons across all countries for the given year. Both the FX and the PPP methodologies are particularly designed for comparing incomes across countries in a given year. They are not intended for comparing incomes over time. It is possible, however, to make the resulting income measures comparable from year to year. Once national income for each country in each year is expressed in the U.S. dollars of that year, it can be deflated to a common benchmark year using a U.S. dollar price series, such as the U.S. Consumer Price Index or the U.S. GDP deflator. This procedure is outlined schematically in Figure 1. The resulting "real" national income figures (expressed in constant U.S. dollars) can then be used for inter-temporal comparisons of incomes as well as international comparisons. There is, however, one major drawback to this methodology.

The 13

The International Structure of Income computation of inflation rates is plagued by the problem of shifting consumption patters.

This problem is particularly insidious due to the endogeneity of

consumption patters to the inflation rate: people reduce consumption of goods that rise in price relative to other goods, substituting goods that decline in relative price.

Such shifting consumption patterns are not parallel across

countries. In short, inflation rates for one country cannot simply be applied to another country with any kind of validity. Practically speaking, even assuming perfect foreign exchange markets or an impeccable PPP implementation, a country's GNP or GDP growth rate when computed using a constant U.S. dollar income series will not equal its growth rate when computed using a constant LC income series. Adjustment for inflation is currency-specific.

Figure 1. FX versus PPP methodology (schematic) Year

pounds

1995 (base year)

real real GNP$ GNP$ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ nominal --------> nominal nominal GNP£ ^ ^ ^ ^ ^ ^ nominal GNP£

real GNP$

real GNP$

1990 1985 1980 1975

yen M M->H Total

61 26 87

1.1% 0.5% 1.6%

H->M M->L Total

23 57 80

0.4% 1.0% 1.5%

Stability

5284

96.9%

Total all repeated observations of countries' zone assignments

5451

100%

Upward mobility

Downward mobility

Considering the existence of such a long-run stability of the structure of the world-economy, it is meaningful to speak of sets of countries that are "organically" core, semiperipheral, or peripheral countries. These are countries that, in terms of income, typify each of the three zones (respectively). Using the set of 100 benchmark countries for which data are available for all 40 study years, I have compiled a list of the countries that have been consistently classified into a single income zone (high, medium, or low) over the entire 40 year study period. There were 72 countries that fit this definition of "organic" zone membership. These are recorded in Table 9.

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The Structure of National Income

Table 9. Organic zone membership (benchmark countries only) CORE Australia Austria Belgium Denmark Finland France French Polynesia Iceland Ireland Israel Italy Japan Luxembourg Netherlands New Caledonia New Zealand Norway Spain Sweden Switzerland United Kingdom United States

SEMIPERIPHER Y Brazil Costa Rica Hungary Mexico Panama Seychelles South Africa Trinidad & Tob

PERIPHERY Bangladesh Benin Bolivia Burkina Faso Burundi Cameroon Central Afr. Rep. Chad China Congo, Rep. Cote d'Ivoire Ecuador Egypt, Arab Rep. Ghana Guyana Haiti Honduras India Indonesia Kenya Lesotho

Madagascar Malawi Mauritania Morocco Nepal Nicaragua Niger Nigeria Pakistan Pap. New Guinea Philippines Rwanda Senegal Sierra Leone Sri Lanka Suriname Swaziland Syrian Arab Rep. Togo Zambia Zimbabwe

Clearly, the striking feature of Table 9 is the shortage of "organically" semiperipheral countries.

This is, however, not so surprising.

Since the

semiperiphery is an intermediate category, it is possible for countries to move through it in both directions, whereas both the core and the periphery have "hard" boundaries on one side. Countries never rise above the core, nor do they fall below the periphery. In any case, the three big classically semiperipheral countries -- Mexico, Brazil, and South Africa -- do fall into the organic semiperiphery as operationalized here. In an additional confirmation of face 97

The International Structure of Income validity, the one historically socialist country in the benchmark data set, Hungary, also falls into the organic semiperiphery, in accordance with most theoretical categorizations. Similarly, the organic core reported here, although large by comparison to some other studies, passes face validity (with the exception of two low-population island groups, for which national income data are problematic). Thus, in general outline the zones of the world-economy uncovered in the forgoing analyses are consistent with accepted notions of the meanings of the terms "core," "semiperiphery," and "periphery."

DISCUSSION AND INTERPRETATION Whereas Chapter 3 was concerned with mapping the distribution of the world's people by income level, Chapter 4 has been concerned with mapping the distribution of the world's countries by average income level. The results are in some ways similar, but are analytically distinct. Taken together, the results of Chapters 3 and 4 clearly confirm Arrighi and Drangel's (1986) model of a world-economy in which there are two basic economic positions, but three distinct proportions in which those activities are likely to be mixed at the country level: core- and peripheral-type activities (or at least income levels) do in fact seem to be organized in countries that can be characterized as core, peripheral, and semiperipheral in average income. Moreover, the semiperipheral average income level is not merely the middle of a single distribution of countries; it is a distinct zone with a clearly definable and empirically estimable boundaries. This is a somewhat problematic conclusion. If, as researchers such as Korzeniewicz and Moran (1997) and Schultz (1998) suggest, global income inequality primarily reflects the between-country component of inequality, how 98

The Structure of National Income is it possible that an entire zone of the world economy, the semiperiphery, disappears in the graphs of global inequality presented in Chapter 3? This disappearing act is only possible if levels of within-country inequality in the semiperiphery are extremely high, in fact so high as to be comparable in magnitude to the observed differences in between-country inequality. Is this really the case? A long research tradition suggests that it is. Arguing from 19th and early 20th century data from the U.S., U.K., and Germany, Kuznets (1955) postulated an "inverted U" shaped relationship between income and inequality. In his model, countries began their modern developmental trajectories at relatively low levels of inequality, then passed through a high-inequality phase of economic expansion, after which inequality declined as the economies matured. This Kuznets Hypothesis has informed most of the inter-temporal and cross-national research on within-country inequality in the past half-century.

For a brief

review of this literature see Deininger and Squire (1998: 275:276). Although the Kuznets Hypothesis is inter-temporal in focus, much the evidence mustered in its support has been cross-national, substituting several countries at different levels of development for single countries over time. Whether or not these studies do in fact lend support to the Kuznets Hypothesis, they certainly have confirmed the fact that middle-income countries tend to have higher levels of income inequality than both high- and low-income countries. Combining the results from Chapter 3 with those reported above, it is possible to measure the (population-weighted) average level of inequality for the "organic" members of the core, semiperiphery, and periphery. I do this in Figure 25. I graph the average levels of within-country inequality by zone of the world-economy and decade. Using only organic zone members gives 72 cases. Inequality levels are taken from the Tier III sample of Chapter 3 (to ensure that 99

The International Structure of Income observations are available for all countries). The results graphed in Figure 25 are weighted by population.

Figure 25. Average internal income inequality by zone, 1960s-1990s

Average levels of income inequality are roughly twice as high, on average, in the semiperiphery as they are in the other two zones. As a result, it turns out that income inequality across the semiperiphery are similar in magnitude to the overall level of international income inequality for the world as a whole, as reported in Chapter 3. Average levels of the standard deviation of income on the order of .45 (on the log scale) imply that the plus or minus two standard deviation range of the incomes within semiperiphery countries (the range within which roughly 95% of households are likely to fall) is enormous: 1.8 points on the log scale. To put this in perspective, this is roughly the difference between the high and the low income modes reported in Chapter 3. 100

The Structure of National Income Or, in other words, the richest 2.5% of the population in semiperipheral countries can afford to live in typical core-like conditions, while the poorest 2.5% of the population in semiperipheral countries must live in typical periphery-like conditions.

Semiperipheral countries truly do straddle two

different worlds.

101

The International Structure of Income

102

5. Neoclassical Economic Growth in a Structured World-Economy The descriptive results of Chapters 3 and 4 established that the world-economy is structured in discrete levels of output per capita. Taking the individual person as the unit of analysis, the population of the world is divided into two broad, but distinct, groups based on income level, a low-income group centered around $500/year annual income and a high-income group centered around $20,000/year annual income.

However, taking the country as the unit of

analysis yields three broad national income levels, introducing a middle level that has very high internal income inequality compared to low- and high-income countries. Both of these global patterns in the world-economy -- the bimodal distribution of individual income and the trimodal distribution of national income -- have been remarkably stable over the four decades 1960-2000, even as many countries experienced substantial mobility within and between zones. The existence and, especially, the persistence of both of these patterns are inconsistent with accepted theories of income distribution in a competitive, open world-economy.

This suggests that, at a minimum, models of economic

development should be adapted to take into account the general structural traits of the world-economy.

The International Structure of Income Since Solow's (1957) seminal article on the role of technological innovation in determining the long-run growth potential of an economy, empirical development economics has evolved a common theoretical language and methodological toolkit, built on a neoclassical framework that models output as a function of capital, labor, and technology. In contrast to the classical framework in which land, labor, and capital were the only inputs, the neoclassical model emphasizes the long-run dominance of technology in determining the level of economic output. In the first section of this chapter, I review the neoclassical model, highlighting its departures from the classical model and examining the nature and measurement of each of its components in detail. The

neoclassical

model,

however,

nowhere

accounts

for

the

differentiation of the countries of the world on the basis of their positions in the world economy. On the contrary, it presumes that all countries have access to the same level of technology and that capital is equally mobile across all borders.

Thus, in the neoclassical model all countries possess identical

production possibilities. This is almost certainly not true in the world as it exists today, or ever has existed.

To address these issues, I propose two major

improvements to the standard neoclassical growth model in the second section of this chapter. First, I argue that the model should be estimated separately for each zone of the world-economy, allowing for different coefficients on labor and capital in each of the three zones. Second, I adopt the key insight from the world-systems and dependency literature that in addition to responding to levels of capital investment, rates of economic growth depend on the source of the capital invested in an economy as well. I further suggest that the effect of the source of capital should itself be contingent on a country's position in the worldeconomy. 104

Neoclassical Economic Growth in a Structured World-Economy In the third section of this chapter I estimate a series of growth models. I start with a base neoclassical model of long-run growth. I then differentiate this model by zone of the world-economy. Using a similar methodology, but a shorter time frame (due to data limitations), I estimate a series of models that examine the effect of investment source by zone. I conclude this chapter with a section discussing and interpreting the results of these regression models.

THE NEOCLASSICAL FRAMEWORK FOR GROWTH In the classical economics of the late eighteenth and the nineteenth centuries, all economic output was hypothesized to result from the interaction of just three scarce inputs: labor, land, and capital. Labor was conceived as including both the number of workers and their skills -- early on, a distinction was made between skilled and unskilled labor. Capital generally was held to include all forms of physical capital (fixed and moveable), but not financial capital. The elevation of land (as distinct from human-made capital) as a special category of input reflected the importance of agriculture in the economies of the day. Neoclassical growth theory still builds on this basic framework, with some modifications. Human capital has become such an important input in modern economies that it is now typically operationalized as a separate input, distinct from both labor and from physical capital. Land, on the other hand, has been subsumed within the more general category of physical capital. Two key twentieth century innovations in modeling the determinants of economic output, however, completely changed how we account for the relationship between economic inputs and economic output.

In the neoclassical framework, the

classical inputs of labor and capital are modeled as being subject to diminishing returns on each marginal unit of input, with the resulting level of output 105

The International Structure of Income depending, in addition, on the level of technology available. Though both of these "new" concepts (diminishing returns and the importance of technology) have pedigrees reaching back to the nineteenth century, their specific application in a model of aggregate output can be traced to the work of Solow (1956, 1957). Solow's synthesis of the neoclassical output model is the foundation on which most subsequent development economics has been built. In the fullest development of the model originally proposed by Solow, Mankiw et al (1992) model aggregate output as a product of the levels of labor, physical capital, and human capital inputs, scaled according to the level of technology in use in an economy. The use of each input factor, though, is subject to diminishing returns -- at the margin, each additional unit of a given input results in less increase in output than did the previous unit of input. Under the simplest model, in which the marginal utility of each input factor decays exponentially, the contribution of each input factor is raised to a (positive) fractional power.

Using T to stand for the (exogenously given) level of

technology and LABOR, CAPITAL, and HUMAN to stand for (respectively) the labor supply, the aggregate level of physical capital, and the aggregate level of human capital in the economy, output in the neoclassical model is given by: OUTPUT = T * LABORα1 * CAPITALα2 * HUMANα3 where α1 + α2 + α3 = 1 The sum of the coefficients of labor, physical capital, and human capital (α1 + α2 + α3) is specifically constrained to equal one, since multiplying all three inputs by a constant must result in output rising by that same constant (e.g., if all three inputs are doubled, output must also double).

Mankiw et al (1992)

estimate that a good approximation for α1, α2, and α3 in this model is α1 = α2 = α3 = 1/3. In other words, all three input factors have about the same rate of 106

Neoclassical Economic Growth in a Structured World-Economy diminishing returns to additional inputs. The neoclassical model implies the existence of two kinds of economic growth, equilibrium growth and disequilibrium growth. Growth in output per unit of labor, or in output per capita more generally, can only result from increases in the relative intensity of use of other factors of production: more technology, physical capital, or human capital per person means more output per person. Of three potential substitutes for labor, however, two -- physical capital and human capital -- are subject to diminishing returns. Consequently, capital intensity per capita would have to increase exponentially in order to maintain a linear increase in output per capita. Clearly, in an economy that maintains a static equilibrium in the ratios of the factors of production, continuous annual increases in output per capita must be attributed to increases in the level of technology used. Since output is a linear, multiplicative factor of technology, there is no limit (in the model) to the level of output per person, so long as technological innovation continues at a steady pace. Technological innovation leads to a steady rate of "equilibrium growth." If the ratios of the factors of production are not constrained to be constant, however, increases in capital intensity (physical and human) can lead to economic growth. This type of growth is sometimes termed "disequilibrium growth," since it results when the level of capital intensity changes from its equilibrium level. The economy grows (or shrinks) until it reaches the level of output per capita commensurate with the new level of capital intensity. For example, the neoclassical convergence debate (see Chapter 2) has been predicated on the principle that capital should continually be moving from highincome countries to low-income countries, balancing levels of capital intensity around the world. In order to test this hypothesis, it is possible to construct a model of so-called "unconditional convergence," in which growth in income per 107

The International Structure of Income capital over a long time frame is regressed on initial income per capita. A negative coefficient on initial income per capita indicates slower (faster) growth in initially high (low) capital intensity economies.

The best-known

representative of this type of model, estimated over the very long term of over a century, is Baumol (1986), famously debunked by De Long (1988). Better specified disequilibrium growth models also take into account existing domestic levels of capital intensity, both physical and human. Human capital, in particular, may not be geographically mobile.

In these models,

growth in output per capita is regressed on measures of capital intensity, while controlling for the initial level of output.

Models that include both initial

income per capita and measures of human and physical capital intensity are thus known as "conditional convergence" models, since the predicted convergence effect is conditional on domestic circumstances as well as on . The models found in Barro (1991) and the subsequent empirical growth literature are of this type. Disequilibrium growth can be modeled in a linear regression framework, with the constant forming part of the equilibrium, the coefficient on initial output per capita representing the convergence effect, and the levels of the neoclassical inputs included as controls. Such a generic neoclassical growth model can be written as: GROWTH = A + B0*INITIAL + B1*LABOR + B2*CAPITAL + B3*HUMAN This model can be estimated using standard linear least squares techniques, though some care must be shown due to the strong colinearities among the predictors and the systematic relationship between initial output and the error variance. Measuring the initial level of output per capita and its growth rate are 108

Neoclassical Economic Growth in a Structured World-Economy problematic enough, as evinced in Chapter 2. Initial output is generally logged, both to compress the scale and to eliminate period effects due to the fact that growth in output is exponential. I follow this convention. For estimating all of the models in this volume, I adopt the GNP/RLC series, described in Chapter 2, to measure output per capita. For growth, this is equivalent to using real local currency denominated figures, since the same currency conversion factor is used for all years. I operationalize growth as the geometric mean growth rate in output per capita. Arriving at measures for the three neoclassical inputs is considerably more difficult. Below, I discuss how labor, physical capital, and human capital have been operationalized in the literature to date, and detail my own chosen measures.

For an alternative review of the use of these variables in the

sociology and economics literatures, see Crowly et al (1998). Note that the star of the neoclassical model, the level of technology, drops out of the growth regression entirely. Accounting for differences in the level of "technology," broadly construed, across zones of the world-economy is the subject of the next section of this chapter.

Labor and the Age Structure of the Population Incredibly, no major contribution to the empirical growth literature in economics in the last twenty years includes demographic factors as predictors of growth.

On the contrary, in a benchmarking sensitivity analysis of some 50

variables mentioned in the empirical growth literature, Levine and Renelt (1992) include only one demographic variable, population growth. Occasionally output growth is alternatively operationalized as growth in output per worker (as opposed to per capita), but nowhere is the age structure of the population considered as a fundamental control variable in growth regressions. This is all 109

The International Structure of Income the more surprising in context of the fact that reasonable demographic estimates are available for virtually all countries for all periods. One partial exception to this general trend in economics is a paper by Williamson (1998) on the growth-distorting effects of demographic transitions. A "demographic transition" from a state of high fertility and mortality to a state of low fertility and mortality has occurred in all countries that have grown from low- to middle-income status. The transition in all cases follows a similar pattern. First, the mortality rate drops, as improved medical care, immunization, and most of all better nutrition lower mortality at all age levels. This means that people live longer, often a generation longer than their parents did. As a result, the population begins to grow rapidly. Second, after a lag of several decades, the birth rate drops to developed-country levels. This returns population growth rates back to normal levels. At the beginning of a demographic transition, per capita growth rates are depressed by the shift of the age structure toward older, unproductive ages. This effect is short-lived, however, as the lower mortality rates at all ages allow more children live to adulthood, meaning that in the population as a whole there is a greater percentage more working-age adults than there was before the transition. This favorable demographic climate is improved further when, later in the transition, the birth rate drops. This leads to a bulge generation, the last large generation of children, maturing through the age structure, with smaller numbers of dependents behind them. This is the period of highest economic output. Finally, there is a demographic bust, as the bulge generation matures into old age.

With the passing of the bulge generation, the country returns to a

demographic equilibrium, at a higher proportion of working adults in the population than there were before the transition, but a lower level than during the "golden years" of the bulge generation's middle age. 110

Neoclassical Economic Growth in a Structured World-Economy Bloom and Williamson (1998) document the effects of the demographic transition on the east Asian "miracle" of the past thirty years, finding that demographic factors accounted for as much as one-third of total east Asian growth, and full half of "excess" east Asian growth (growth in excess of the world-wide mean of 2%). Williamson (1998) further argues that demographic factors -- mainly migration -- accounted for substantially all of the difference in economic growth rates between North America and western Europe in the period 1870-1913. The massive migration of working-age adults from western Europe to North America over this period kept the American population profile continuously over-weight in the productive range, and the European population correspondingly under-weight. The current paucity of demographic variables in growth regressions belies a long history of demographic theories of growth, beginning with Malthus and the classical economists and extending through the mid-century work of Leibenstein (1954, 1957).

Leibenstein (1954) argued that under conditions

where the fertility rate (and thus the population growth rate) was positively related to income over a range of incomes, any absolute economic expansion would simply feed back into population growth, potentially precluding any increase in per capita incomes. This is a realistic picture of the first stage of the demographic transition, and Leibenstein's model warned that countries may be permanently trapped in the first stage of an economic transition if their economies were not shocked over the hump to a level of income commensurate with lower fertility rates. Liebenstein (1957) formalized and generalized this observation into the theory of low-level equilibrium trap (LLET). China's one-child policy can be interpreted as an attempt to escape a LLET by shifting the age structure of the population upwards through direct action on population, rather than indirectly and automatically through 111

The International Structure of Income experiencing a natural demographic transition. In this interpretation, economic growth in China may, over time, make the one-child policy superfluous, as Chinese fertility rates begin to decline naturally as a result of higher incomes. Assuming that China's strategy for escaping a low-level equilibrium is ultimately successful (and assuming that the Leibenstein's theory is itself correct), it highlights the exceptional level of policy effort that may be required to break through the LLET. The existence of a LLET is, however, by no means proved. More careful research on the interaction between demographic factors and economic development is necessary before we will have even a basic understanding of the dynamics of demography and growth. As a preliminary step in this direction, I account for the labor market impact of the age structure of the population on growth in my implementation of the neoclassical growth model. To account for the level of labor available for the production of economic output, I use the percent of population that is of working age. I do not adjust for labor force participation, male or female, since this is a poorly defined and poorly measured concept. Data for the percent of population that is of working age is derived from World Bank (2001b) series SP.POP.DPND, the age dependency ratio (ratio of dependents to working-age population). The percent of population that is of working age is extracted from the dependency ratio by adding one and then taking the reciprocal: LABOR = 1 / [DEPENDENCY_RATIO + 1] = 1 / [(dependent/working) + 1] = 1 / [(dependent/working) + (working/working)] = 1 / [(dependent + working) / working] = 1 / [total / working] = working / total 112

Neoclassical Economic Growth in a Structured World-Economy I use the period average of the percent of population that is of working age to represent LABOR in all of the regression models below and in Chapter 6.

Investment as a Proxy for Capital Intensity Much more prominent in empirical growth models is the inclusion of a measure of capital intensity.

In principle, capital intensity should be

operationalized through some direct measure of a country's total capital stock. In practice, however, the level of capital stock in a country is unknown. On the argument that, at equilibrium, a constant rate of new investment is required to maintain a constant level of capital (due to depreciation), investment rates (investment as a proportion of output) are generally used as proxies for capital intensity. Almost all empirical growth regressions in the economics literature adopt the use of investment as a proxy for capital intensity. To address the robustness of investment and other variables as predictors of growth, Levine and Renelt (1992) conducted a sensitivity analysis of a wide variety of predictors of economic growth. They find that there is "a positive and robust correlation between average growth rates and the average share of investment in GDP." (P. 959) This is in sharp contrast to the large number of variables -- population growth, government expenditure, political stability, civil liberty, defense spending, etc. -- for which the relationship to growth was sensitive to the presence or absence of specific controls. Investment is not only widespread in the growth literature, but consistently a powerful predictor of growth as well. A major refinement to the investment -- growth argument is to differentiate between types of investment. De Long and Summers (1991) take up this challenge: We believe that many previous studies have been carried 113

The International Structure of Income out at an inappropriate level of aggregation. We see no reason to expect that investments in structures should carry with them the same external effects as plausibly attach to investments in equipment. We are not aware of previous work that has separated the components of aggregate investment and studied their differential impacts on growth in a cross section of nations. Given the clear differences in the composition of investment . . . it is not surprising that studies that have focused on total capital accumulation have understated the potential contribution of investment to growth. (p. 480) De Long and Summers find that equipment investment is the key component of investment for the purpose of promoting economic growth. In their words: Equipment investment has far more explanatory power for national rates of productivity growth than other components of investment, and outperforms many other variables included in cross-country equations accounting for growth. (p. 446) Unfortunately, De Long and Summers base their arguments on the study of a limited number of countries. Although their full sample contains 61 cases, they find that their results in this sample are highly sensitive to outliers. They thus focus their efforts on a sub-sample of 25 high-income countries. The generality of their results is thus unknown, and, unfortunately, later work on investment and growth has not followed up on their arguments. In any case, a reliable break-down of investment by type is not available for a sufficient number of countries, especially poorer countries, to make the general use of equipment in growth regressions investment possible. Two short studies by economists, however, call into question the direction of causality between investment and growth. Using similar data and 114

Neoclassical Economic Growth in a Structured World-Economy methods, Blomstrom, Lipsey, and Zejan (1996) and Barro (1997, pp. 32-35) both find that rates of economic growth strongly predict rates of investment. Further, they agree in detecting no reciprocal causality: they find that the direction of causation is one way only, from growth to investment. The empirical evidence for this view seems strong, although it is at odds with most theoretical arguments. Offering an alternative to orthodox accounts, Carroll, Overland, and Weil (2000) present a model that can explain growth's effect to promoting savings (which is construed to equal investment), but casts no light on investment's corresponding lack of effect on growth.

One

explanation may be De Long and Summers' (1991) argument that total investment is irrelevant for growth, and that machinery investment (the critical component) is not adequately captured in the investment figures used by Blomstrom et al and Barro. Whether or not the heterodox view that levels of investment have no causal effect on growth is correct, it is a fact that investment still figures prominently in most growth theories and empirical growth studies as a proxy for capital intensity.

It is thus essential to include it in a replication of the

neoclassical growth model, however questionable its pedigree. Annual investment is, of course, a flow variable, while the neoclassical model formally incorporates a stock variable, the level of physical capital. Over a period of several years, however, any initial stock of capital, foreign or domestic, must depreciate to zero. As Scott (1989: 171-179) argues, the stock of capital in an economy is reduced to the total of new or recent investment, since the high depreciation rate on capital is constantly making the existing capital stock obsolete. Thus, long-term growth can be modeled based solely on rates of new investment, without accounting for the initial capital stock. See Lal and Myint (1996: 76-82) for a review and endorsement of this view. While is it 115

The International Structure of Income feasible to separate stocks from flows in a short-term growth study, it is not feasible to do so in a long-term study, since today's flows become tomorrow's stocks. In my implementation of the neoclassical growth model, I adopt the general practice of the economics literature of using investment flows as a growth predictor, making no effort to estimate directly levels of capital stock. For the regression models estimated in this chapter, I operationalize the variable CAPITAL using period average levels of investment as a percentage of output. Specifically, for total investment I use World Bank (2001b) series NE.GDI.TOTL.ZS (gross capital formation as a percentage of GDP), and for foreign investment I use World Bank (2001b) series BX.KLT.DINV.DT.GD.ZS (net inflows of foreign direct investment as a percentage of GDP). Domestic investment is simply the difference between these two series, where both are available.

Education as a Proxy for Human Capital Under the influence of emerging endogenous growth theories, measures of human capital have routinely been included in growth regression models since the early 1990s. However, as Mankiw et al (1992) clearly demonstrate, human capital is most appropriately incorporated in traditional neoclassical growth models, since human capital is presumably subject to diminishing returns in the same fashion as physical capital. In almost all implementations, human capital is operationalized through some measure of average education levels, with some interesting exceptions noted in Crowly et al. Human capital has been variously operationalized as educational attainment (Barro 1991; Barro and Lee 1993; Barro and Sala-I-Martin 1995), fertility (Barro 1997: 22-25), and life expectancy (Sachs and Warner 1997). 116

Neoclassical Economic Growth in a Structured World-Economy Barro and Lee (1996) have compiled a composite database of cross-national measures of education, including years of schooling, attainment rates, teacherstudent ratios, teacher salaries, etc., all broken down by level into primary, secondary, and higher education. In his own work focusing on education and growth, Barro (1997) uses average years of attainment for males in secondary and higher education. An alternative to Barro's operationalization is to use the secondary school enrollment rate (the percentage of the age-appropriate population that is enrolled in secondary school) as a proxy for a society's annual level of investment in human capital. A major advantage of this measure is that it is parallel in construction to the use of investment flows to represent physical capital intensity at equilibrium. An additional advantage of the enrollment rate operationalization is that Levine and Renelt (1992) use this variable to represent human capital in sensitivity analyses, so it is known to be robust with respect to a large number of sample configurations and conditioning variables.

The

enrollment rate operationalization also has a longer pedigree in the literature, having been used, seminally, by Barro (1991) himself. It is also the measure adopted by Mankiw et al (1992). A potential confounding factor in estimating the relationship between education and growth is the close correlation between educational attainment and other psychometric variables.

Both personal value sets and individual

intelligence are highly correlated with educational attainment within and between groups. Other omitted variables that are uncorrelated with education may also be at work. Provocatively, McClelland (1961) correlated motifs in children's stories with subsequent economic growth rates, convincingly demonstrating that the direction of causality ran from what he termed "need for achievement" to subsequent development, and not the other way around. These 117

The International Structure of Income observations should counsel caution in the interpretation of education coefficients in growth regressions.

The general lack of data on other

psychometric quantities, however, precludes their inclusion in the conditioning set. Following the mainstream of the empirical growth literature, I operationalize the variable HUMAN using period average levels of the secondary school enrollment rate in all of the growth regressions estimated below. Data are taken from World Bank (2001b) series SE.SEC.ENRR, gross secondary school enrollment as a percent of secondary school age population.

Omitted Variables Appearing in the Growth Literature The neoclassical model variables of initial income per capita, investment, and education are standard predictors that now appear in virtually all empirical growth studies. Demographic predictors occur only sporadically, but are clearly have a strong theoretical mandate in terms of the model. Many other variables, however, have at one time or another appeared in empirical growth regressions, with or without theoretical justification. Levine and Renelt's (1992) sensitivity analyses, however, suggests that none of the additional variables that were in use at the time are robustly correlated with economic growth. Specifically, they conclude that: We found that a large variety of trade policy measures [including the black market premium] were not robustly correlated with growth when the equation included the investment share. We found that none of the broad array of fiscal indicators that we studied is robustly correlated with growth. We found that a large assortment of other economic and 118

Neoclassical Economic Growth in a Structured World-Economy political indicators [including inflation and political stability] are not robustly correlated with growth. (Levine and Renelt 1992: 959) One potentially important variable not included in Levine and Renelt's study, geography, has been analyzed in detail by De Long and Summers (1991). To their surprise, they found no spatial correlation in residual growth rates in their sample (pp. 487-490).

THE NEOCLASSICAL GROWTH MODEL IN A STRATIFIED WORLD-ECONOMY A major limitation embedded in the standard neoclassical framework is that it differentiates among countries only through their levels of inputs.

This

limitation is reflected in models of both equilibrium and disequilibrium growth. Regarding equilibrium growth, in the neoclassical framework all countries benefit equally from a constant accrual of technological progress. In reality, however, it seems likely that new technologies disproportionately benefit those countries already that are already close to the technological frontier. Regarding disequilibrium growth, in the neoclassical framework the relative prominence of excess levels of the various neoclassical inputs are equal for all countries (i.e., there are no interaction effects).

Without making any a priori arguments

regarding differential impacts of individual inputs, it would be interesting from an exploratory point of view to uncover any major departures from the implicit neoclassical assumption of an undifferentiated world. A second implicit difficulty with the neoclassical model is its assumption international capital flows are a major force for equalizing levels of income per capita across countries. As discussed above, the sociology literature on capital 119

The International Structure of Income and investment dependence and economic growth casts serious doubts on this optimistic view of the role of international flows. Amazingly, capital flows have never been explicitly accounted for in a neoclassical growth model in the economics literature, despite the centrality of capital flows in the neoclassical framework for growth. In sociology, Dixon and Boswell (1996a) and Firebaugh (1992) incorporate capital flows into their growth models, but these models are not fully-specified neoclassical models, and are estimated over a very limited time period (1965-1973).

Clearly, a more comprehensive treatment is

warranted. In this chapter, I lay out some first steps in this direction. In this section, I specify the details of introducing a structural component to the neoclassical growth model, stratifying countries by position in the worldeconomy. I then discuss my operationalization of foreign capital flows in the context of this stratified model.

I close this section with some notes on

estimation issues.

Stratifying the Neoclassical Model The clearest way to integrate the observed structure of the worldeconomy into a neoclassical growth model is simply to estimate the neoclassical model independently for each zone of the world-economy. Trichotomizations of the countries of the world by national income level for all years 1960-1999 were derived in Chapter 4, based on a benchmark sample of 100 countries. All other countries for which income data are available for any year were assigned a zone based on where their incomes fell relative to the observed perimeter of the periphery (PP) and perimeter of the core (PC) for the 100 benchmark countries. A cross-validation was made on the reliability of this mapping using data for 1995, the year for which data are available for the largest sample of countries. The 1995 histogram of the income distributions of the 179 countries for which 120

Neoclassical Economic Growth in a Structured World-Economy data are available in that year yielded PP and PC figures that were identical to those derived from the benchmark series. In the stratified models below, I divide the countries of the world into three sets according to their initial zones in the world-economy, and estimate separate neoclassical growth models for each zone.

A comparison of the

resulting estimates for the three different zones highlights differences in the growth process between zones. It also serves to restrict the regression models to comparisons of somewhat similar countries. This is important from a policy development perspective, since the countries within each zone are likely to face more or less the same universe of possible policy actions. This is patently not the case for countries in different zones of the world-economy.

Differentiating Investment by Source A second structural dimension that is missing from the standard neoclassical model is the differentiation of domestic and foreign sources of capital. International capital flows are the lynchpin of the neoclassical model of growth, in which income convergence between economies is driven by flows of investment from high-income countries to low-income countries. This leads to a potential confounding of investment as a proxy for capital intensity within countries and investment as a mechanism for capital equalization across countries. Domestic and foreign investment are rarely if ever differentiated in the economics literature. Instead, foreign investment flows have mainly been studied by sociologists within the broader context of the effects of foreign capital penetration and foreign economic control. A long tradition of empirical work in sociology, dating from ChaseDunn (1975), has studied the growth-retarding impact of economic structures that are based on a high level of dependence on foreign capital. Researchers 121

The International Structure of Income working in this tradition claim that "foreign capital dependence diminishes economic growth, enhances income inequality, and very probably impairs domestic capital formation" (Dixon and Boswell 1996a: 544). The most recent evidence for the negative effects of foreign capital penetration and a review of the literature on investment source and growth comes from a long-term longitudinal study by Kentor (1998): The results of this study confirm that peripheral countries with relatively high dependence on foreign capital exhibit slower economic growth rates than those that are less dependent. These findings have been replicated using different measures of foreign investment dependence, GDP data, countries, time periods, and statistical methods. This is a significant and persistent negative effect, lasting for decades. Further, a structure of dependency is created that perpetuates these effects. (p. 1042) In his models, Kentor shows that a small, short-term positive effect of foreign investment (flows) on economic growth is counterbalanced by a large, long-term negative effect of foreign capital penetration (stocks). Kentor's results are consistent with interpretations by Dixon and Boswell (1996a, 1996b) and Firebaugh (1992, 1996), who conclude that domestic investment has a stronger positive effect on growth than does foreign investment. If this is the case, then those economies that rely more heavily on foreign investment will display lower overall growth rates than those that do not. In the short run, however, any new (non-equilibrium or non-replacement) investment causes a growth shock, since investment is itself a major component of national income. Unlike the economics literature, the sociology literature makes a theoretical and empirical distinction between foreign investment flows and 122

Neoclassical Economic Growth in a Structured World-Economy foreign capital stocks. However, the major sociological results differentiating the effects of foreign and domestic stocks and flows are based on a short sevenyear time frame for studying growth (Dixon and Boswell 1996a). As argued above for investment generally, the distinction between stocks and flows of foreign investment disappears as the study period lengthens, due to the rapid depreciation of capital stocks.

Thus, in the models below I consider only

period-average flows of foreign and direct investment. With respect to the dependent variable, the impact of this compromise solution is to average out the effects of the levels of initial stocks and subsequent flows. For the longer (40year) study period, the coefficient for foreign investment can be interpreted as being primarily a stock effect, while for the shortest (10-year) study periods, the effect can be interpreted as a pure flow effect. For the 20-year periods it represents some combination of the two. Because my operationalization of investment -- both foreign and domestic -- does not distinguish between stocks and flows, my models do not reflect the traditional theoretical meaning of "foreign investment dependence" as implying foreign control of the domestic economy. In my opinion the issue of foreign control is a red herring. In the traditional dependency theory view, foreign investment gives a country's economy an immediate boost, only to come back to haunt the economy later with slower growth due to the extraction of surplus value from the domestic economy by foreign firms. Foreign investment is thus cast in the role of forbidden fruit -- tasty now, but poisonous in the long run. What I intend to show in this volume, however, is that the perceived immediate boost to be gained from foreign investment is not in fact grounded in empirical reality of the past forty years. If there is no immediate gain from foreign investment, there is no fruit to be forbidden. Thus, I am not here concerned with the long-term effects of dependency as such. Instead, taking 123

The International Structure of Income them to be negative, I show that the expected short-term boost does not appear in the available data. The specific operationalization that I adopt for investment source is based on control over the management of investments, rather then the ultimate source of the funds. I operationalize domestic investment rates with the variable INV-DOM and foreign investment rates with the variable INV-FOR, respectively the annual levels of domestic and foreign investment as a percentage of GNP. By construction, CAPITAL = INV-DOM + INV-FOR. In my differentiated growth models, I simply substitute INV-DOM and INV-FOR in the place occupied by CAPITAL in the neoclassical model. This has the effect of decomposing the effect of CAPITAL into its two components. Data for INV-FOR are taken directly from the World Bank (2001b) series BX.KLT.DINV.DT.GD.ZS, net inflows of foreign direct investment as a percentage of GDP. Since this series only includes foreign direct investment (as opposed to portfolio investment), it includes only investments that are associated with foreign managerial control. The variable INV-DOM is calculated as the residual level of CAPITAL after INV-FOR is subtracted out.

It implicitly

includes net foreign portfolio investment, which is subject to domestic managerial control. As with CAPITAL, I use period average values of INVDOM and INV-FOR in all regression analyses. Not only is this parallel with the treatment of other variables in the neoclassical model, but it also dampens the high annual volatility of INV-FOR.

Study Periods and Estimation Issues There are two natural periods over which to estimate the base neoclassical growth model and its structured variants. The first is the maximum time period over which data are available. Around the year 1960 is the earliest 124

Neoclassical Economic Growth in a Structured World-Economy point in time for which models can be constructed that can fairly be said to represent the entire population of the world, since before 1960 much of the world was ruled as colonies by the European powers. Coincidentally, 1960 is the first year contained in the World Bank (2001b) database, which makes possible the sourcing of all data from a single, homogeneous source. The final year in the current World Bank (2001b) database is 1999. Long-run growth models will thus be estimated over the 40-year period 1960-1999. I also estimate growth regression models over a more restricted period, 1975-1995. Several factors point to 1975 as an appropriate start date. First, by 1975 the process of new nation-creation through decolonization was virtually complete. This means that from 1975 on data exist for a maximum number of countries. Second, in the emerging consensus view of economic historians, the reconstruction of Europe from the Second World War was not complete until around 1975. (Temin 1997) The same may also be true for the reconstruction of the former Japanese conquests, though the evidence is that Japan itself completed its reconstruction earlier. (Arrighi et al 1993) Taken together, all this implies that post-war growth rates for many major countries are substantially tainted by war-related factors until around 1975. Third, by 1975 the collapse of the Bretton Woods system had brought to a close the post-war period of fixed exchange rates tied to the dollar. In a development organically related to this event, the mid-1970s marked the beginning of a new phase of financial expansion, following the exhaustion of the post-war expansion in commodity trade. (Arrighi 1994: 298-299) In short, the overarching political economy in which the countries of the world operated changed dramatically in the years just before 1975. Similar, though less cataclysmic, factors suggest 1995 as a reasonable end date. Economic data series for many countries in the World Bank (2001b) 125

The International Structure of Income database thin out beginning in 1996. An end date of 1995 excludes both the emerging market crises and the massive U.S.-led asset bubble of the late 1990s. Last, and critically, due to data constraints the investment differentiation models can only easily be estimated over the period 1975-1995. For these reasons, all of the models in this chapter are estimated over the two periods, 1960-1999 and 1975-1995. The differing period lengths, start dates, and end dates also serve as a robustness check on the resulting estimates. Countries that experienced major changes in borders at the end of the Cold War (Germany, the USSR, and Yugoslavia) are not included in the dataset for either period. Note that China is present in the 1975-1995 regressions but absent for the 1960-1999 period due to missing data. Taiwan is absent from the database altogether, as the World Bank does not report data for Taiwan. All other major countries are in the regression datasets for both periods. All regression equations are estimated using both ordinary least squares (OLS) and weighted least squares (WLS). In the WLS analyses the initial populations of the countries are used as the regression weights.

Initial

population is taken from World Bank (2001b) series SP.POP.TOTL, total population. The variance of observed growth rates (like most other economic variables) is known to be systematically related to income per capita (variance declines as income rises). Whether this is due to increased economic stability or to better measurement at higher income levels, some adjustment for this heteroskedasticity should be made to ensure the reliability of the resulting regression estimates. The estimation of separate regression equations for the countries in each zone of the world-economy solves this problem, by associating countries in each sample only with other countries at a similar level of income per capita. Thus, the models for peripheral countries can be expected to have 126

Neoclassical Economic Growth in a Structured World-Economy higher error variances than the models for core countries, even though within each zone error variances are relatively constant. The larger number of cases in the periphery should help offset the larger error variances to be expected there.

RESULTS In this section I estimate the basic, stratified, and investment flow variants of the neoclassical growth model, using the specifications developed above. Before doing so, however, I think it would be worthwhile to verify some of the general assumptions of the neoclassical growth framework. First, equilibrium growth presumes that there is a general rate of positive economic growth, due to technological progress. In fact, geometric mean annual economic growth rates over the period 1960-1999 for the 100 countries for which we have data have averaged 1.90%, with a standard deviation of 1.68%.

The fastest average

growth rate was that recorded by Botswana (6.37%, beginning from a very low base), followed closely by Singapore and South Korea. The worst performance was turned in by Niger (-1.72%). In all, 88 out of 100 countries showed positive growth. Weighting growth rates by population in 1960 gives a higher global mean of 3.29% (SD 1.66%). This is an even stronger confirmation of the general assumption of positive growth. Disequilibrium growth is based on the assumption that each of the neoclassical inputs -- labor supply, physical capital, and human capital -- tend to increase with increasing levels of output per capita. When a country exhibits a level of inputs that is incommensurate with its current level of output per capita, the economy grows (shrinks) until a new, higher (lower) equilibrium is established. This model only makes sense if there is a positive correlation between levels of economic output and levels of the neoclassical inputs 127

The International Structure of Income (regardless of the potential for reverse causality). Regressions of the log of GNP per capita on the three neoclassical inputs are reported in Table 10 below. Data are from 1995; results for other years are similar.

Table 10. Regression of log(GNP) on neoclassical inputs, 1995 data dep. var.: LOG(GNP) (constant) LABOR CAPITAL HUMAN R2 (n)

Unweighted Coeff. (SE) .690 (.499) .040 (.010) -.007 (.005) .011 (.002) .680 (118)

t-stat 1.384 3.392 -1.365 6.178

Population weighted Coeff. (SE) t-stat .548 (.572) 0.958 .041 (.013) 3.199 -.037 (.006) -6.636 .016 (.002) 7.483 .787 (118)

In a positive sign, the three neoclassical inputs, taken together, account for 68% -- 78% of the variance in levels of output per capita, depending on whether the unweighted or the weighted results are adopted. The variables LABOR and HUMAN are strongly and positively related to output. But there is one major departure from the neoclassical assumptions: relationship between CAPITAL and output.

the negative

Even worse, in the weighted

analysis, this relationship is highly significant statistically.

The negative

relationship between CAPITAL and output shows up in the bivariate correlation as well. This is clearly at odds with the basic assumption of the disequilibrium model, that the (known positive) investment -- growth correlation is due the overall correlation between investment and output per capita. This results is, however, consistent with Blomstrom et al's (1996) and Barro's (1997, pp. 32-35) arguments that growth leads to investment, not the other way around. In any case, in the growth models estimated below, I will continue to include the 128

Neoclassical Economic Growth in a Structured World-Economy variable CAPITAL, but in light of its anomalous behavior here, the coefficients for CAPITAL below must be interpreted with care.

The Base Neoclassical Model A first step in building a stratified model of economic growth is to estimate a base neoclassical model, including all countries for which data is available. I have estimated base neoclassical models for geometric mean growth rates over both the long term (1960-1999) and the medium term (1975-1995). The results are reported in the tables below. In Table 11, I regress growth in GNP per capita over the period 19601999 on initial GNP per capita and the period averages of the three neoclassical inputs. Only 63 countries displayed full data on all required variables, for both the unweighted and the population weighted models.

The percent of the

variance in growth outcomes explained by the neoclassical predictors is well over 50% for both models. Similar coefficients are obtained when the model is estimated over the shorter period 1975-1995, the period that maximizes the available sample of countries (n=88; see Table 15).

Table 11. Long-run neoclassical growth model (1960-1999) dep. var.: GROWTH (constant) INITIAL LABOR CAPITAL HUMAN R2 (n)

Unweighted Coeff. (SE) -10.99 (2.89) -1.54 (0.54) .256 (.059) .147 (.037) -.001 (.014) .551 (63)

t-stat -3.799 -2.852 4.355 3.985 -0.068

Population weighted Coeff. (SE) t-stat -8.33 (2.45) -3.397 -1.02 (0.26) -3.952 .182 (.046) 3.943 .150 (.032) 4.726 -.002 (.012) -0.190 .626 (63)

129

The International Structure of Income The first major result from Table 11 is a strong confirmation of the conditional convergence hypothesis: after controlling for the neoclassical inputs, growth is negatively and significantly related to initial GNP per capita. This implies that poor countries would tend to grow faster than rich countries if they had equal levels of the three neoclassical inputs. This result is completely consistent with the economics literature on convergence, and with the current state of the convergence/divergence debate discussed in Chapter 2. Moving on to the three neoclassical inputs, LABOR displays a positive, significant relationship with growth, implying that demographic factors are, in fact, important for development. This is a novel, if not surprising, result. It is, moreover, a large result. Holding the level of GNP per capita constant, a move of just 4 or 5 percentage points, from a South Asian or Middle Eastern mean of around 55.5% of population that is of working age to a Latin American mean of 60%, would imply an increase in average growth rates of around 1%. To take a case where the cultural similarities are stronger, the six point difference between Latin America and Europe could account for substantially all of the growth differential between the two regions over the period 1960-1999. Results of this magnitude certainly argue for deeper analysis. CAPITAL also displays a positive relationship to growth, as expected. Both the size and the statistical significance of the coefficient are consistent with previous findings. As noted above, however, the lack of a correlation between CAPITAL and total output makes this result difficult to interpret in the context of the standard neoclassical growth framework. Moreover, CAPITAL does not vary systematically by geography as LABOR does. Nonetheless, the inclusion of LABOR as a control does nothing to diminish the predictive value of CAPITAL in the neoclassical growth model. For education, on the other hand, Table 11 reveals a strikingly 130

Neoclassical Economic Growth in a Structured World-Economy unconventional result: the coefficient for HUMAN is non-significant, and even negative! The explanation for the observed departure from the literature is the introduction of LABOR in the model reported in Table 11. The age structure of the population is not found as a variable in the empirical economics literature, and LABOR and HUMAN are highly correlated (r > .8). Removing LABOR from the equation gives the results reported in Table 12.

Table 12. Long-run neoclassical model, LABOR excluded (1960-1999) dep. var.: GROWTH (constant) INITIAL LABOR CAPITAL HUMAN R2 (n)

Unweighted Coeff. (SE) -.172 (1.694) -.853 (.590) -.160 (.042) .029 (.014) .405 (63)

t-stat -0.101 -1.445 -3.809 1.992

Population weighted Coeff. (SE) t-stat .906 (.809) 1.120 -1.245 (.287) -4.345 --.175 (.035) 5.014 .031 (.009) 3.342 .525 (63)

The results reported in Table 12 are consistent with well-confirmed results from the empirical economics literature. This confirms that it is, in fact, the inclusion of LABOR that partials out the marginal impact of HUMAN on growth. This suggests that the growth-enhancing effects of education touted in the growth literature of the past dozen years may have been due to nothing more than education's correlation with the excluded variable, percent of population of working age. Countries that have relatively fewer children in the population are better able to educate those children that they do have. The epiphenomenality of education's effect on growth is consistent with recent work by Easterly (2001) and Pritchett (2001). Easterly's (2001: Chapter 4) review of the effect of education on growth draws mainly on recent work on 131

The International Structure of Income changes over time in education levels and rates of economic growth, including Pritchett's study. Easterly concludes that: Despite all the lofty sentiments about education, the return to the educational explosion of the past four decades has been disappointing. ... Education is another magic formula that failed us on the quest for growth. (Easterly 2001: 84) Easterly's work was motivated by the observation that although education levels in the world's poorer countries have improved dramatically over the past four decades, growth in those countries has in general declined over the same period. If, growth regression results like those reported in Table 12 to the contrary, education itself actually has no effect on growth, education levels must be correlated to something that does. The evidence of this chapter points to the conclusion that that something is the age structure of the population. Taken in combination with the suspect status of investment in the neoclassical model (also highlighted by Easterly 2000: Chapters 2 and 3), the lack of any impact of education on growth vitiates the standard neoclassical growth model, as it is generally formulated. However, as I report below, fresh life may be blown into the neoclassical model when it is restructured to accommodate differences between zones of the world economy.

Stratification of the Neoclassical Model by Zone of the World-Economy In this section, I re-estimate the base neoclassical growth model of Table 11, this time dividing the sample into three groups based on countries' zones in the world-economy as of 1960. The 63 countries of Table 11 break down to 9 core countries, 20 semiperipheral countries, and 34 peripheral countries. Regression results for each zone are reported in Table 13.

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Neoclassical Economic Growth in a Structured World-Economy

Table 13. Stratified long-run model (1960-1999) dep. var.: GROWTH Core (constant) INITIAL LABOR CAPITAL HUMAN R2 (n) Semiperipher y (constant) INITIAL LABOR CAPITAL HUMAN R2 (n) Periphery (constant) INITIAL LABOR CAPITAL HUMAN R2 (n)

Unweighted Coeff. (SE)

t-stat

Population weighted Coeff. (SE) t-stat

9.23 (12.73) -3.44 (2.92) -.040 (.117) .118 (.056) .080 (.033) .895 (9)

0.725 -1.178 -0.344 2.119 2.442

-1.28 (11.13) -2.51 (2.69) .101 (.088) .109 (.029) .056 (.023) .984 (9)

-0.115 -0.932 1.150 3.816 2.220

-14.42 (4.52) -2.87 (1.41) .456 (.082) .071 (.051) -.039 (.025) .800 (20)

-3.190 -2.042 5.564 1.406 -1.568

-2.01 (5.50) -4.50 (1.74) .313 (.084) .005 (.060) .025 (.023) .746 (20)

-0.365 -2.580 3.731 0.076 1.092

-3.86 (6.24) -2.07 (1.10) .125 (.108) .196 (.059) -.001 (.021) .460 (34)

-0.620 -1.889 1.161 3.344 -0.069

-8.80 (4.90) -1.11 (0.79) .155 (.077) .270 (.064) -.014 (.015) .681 (34)

-1.799 -1.398 2.006 4.208 -0.918

As Table 13 demonstrates, the structure of neoclassical economic growth differs dramatically across zones of the world-economy. The pattern of the results is near-identical in the unweighted and population-weighted regressions. 133

The International Structure of Income Inputs of capital, both human (HUMAN) and physical (CAPITAL), are the main independent contributors to growth rates in the core.

CAPITAL is also

important in the periphery, but HUMAN is not. And in the semiperiphery, neither form of capital has a significant independent effect on growth. Instead, growth is driven almost exclusively by LABOR; demographic effects predominate. In addition, the neoclassical conditional convergence effect is significant only within the semiperiphery.

This suggests the increasing

consolidation, over time, of a semiperipheral level within the world-economy; in other words, it suggests that the PC and PP boundaries are both difficult to cross, at least from within. One shortcoming of the regression results reported in Table 13 is that they are estimated over a 40-year period, a period during which the values of each of the neoclassical inputs may change substantially. The LABOR input is particularly troublesome in this regard, since countries' demographic profiles do not just evolve over generations, but evolve in a somewhat deterministic fashion. Re-estimating the equations from Table 13 over a shorter time frame may capture more causal detail, especially considering that the model underlying the regression equations is a "disequilibrium" growth model. Using a shorter time frame also increases the number of countries for which data are available. Changing the time frame to the medium run of 1975-1995 yields the estimates reported in Table 14 below. Again, the unweighted population-weighted regressions yield parallel results.

Strikingly, in both the core and in the semiperiphery, the only

independent predictor of economic growth is LABOR.

In the periphery,

LABOR is joined by CAPITAL. Interestingly, in the periphery the coefficient on HUMAN is negative, in the weighted model significantly so. The results reported in Table 14 might be taken to suggest that the growth process is not 134

Neoclassical Economic Growth in a Structured World-Economy strongly differentiated by zone of the world-economy, after all.

Table 14. Stratified medium-run model (1975-1995) dep. var.: GROWTH

Unweighted Coeff. (SE)

Core (constant) INITIAL LABOR CAPITAL HUMAN R2 (n)

5.08 (8.11) -6.21 (1.84) .307 (.104) -.023 (.076) .038 (.021) .540 (26)

Semiperipher y (constant)

-11.08 (14.34)

t-stat

Population weighted Coeff. (SE) t-stat

0.627 -3.368 2.938 -0.307 1.798

-10.20 (6.89) -1.42 (1.39) .263 (.086) .032 (.036) .002 (.020) .545 (26)

-1.481 -1.018 3.061 0.906 0.113

-0.773

-8.71 (10.63)

-0.820

INITIAL LABOR CAPITAL HUMAN R2 (n)

-3.78 (4.74) .362 (.175) .207 (.137) -.010 (.042) .543 (17)

-0.799 2.073 1.517 -0.230

-5.22 (3.78) .422 (.109) .120 (.148) .013 (.040) .754 (17)

-1.380 3.857 0.812 0.334

Periphery (constant) INITIAL LABOR CAPITAL HUMAN R2 (n)

-17.24 (5.67) -0.62 (1.10) .300 (.094) .201 (.057) -.034 (.022) .478 (45)

-3.039 -0.569 3.212 3.544 -1.513

-22.42 (5.29) -0.71 (0.72) .393 (.085) .268 (.038) -.056 (.020) .922 (45)

-4.241 -0.985 4.637 7.102 -2.777

But this would ignore the fact that, of the three neoclassical inputs, 135

The International Structure of Income LABOR is the one that differs most in its internal dynamics across zones of the world-economy.

As Williamson (1998) argues, the effect of demographic

variables on growth warrants greater attention. Contrasting the stratified results reported in Tables 13 and 14 with those found when all countries were included in a single sample suggests that the level of CAPITAL is, in fact, an important driver of convergence between zones of the world-economy. CAPITAL, after all, is a significant independent covariate of growth rates when all countries of the world are analyzed together, but not a stable and significant predictor of growth rates within each zone of the world-economy. Much of the power of CAPITAL as a predictor, therefore, must come from its correlation with growth rates across zones. This is consistent with the neoclassical convergence model in which capital is internationally mobile but labor, and thus implicitly human capital, is not. It is possible to test this conjecture explicitly by separating domestic from foreign capital flows in the growth regressions, both at the aggregate level and at the stratified level. I do this in the next section.

Investment Differentiation in a Medium-Run Growth Model Levels of domestic and foreign investment as proportions of national income are practically uncorrelated (r = .179; ns). This means that the effects of foreign and domestic investment are additive, making it possible to decompose the relationship between investment and growth into its foreign and domestic constituents.

Making this decomposition more appealing is the fact that

investment, domestic, foreign, or total, is not significantly correlated with any of the other variables in the neoclassical growth model. Colinearity is thus not a major issue in interpreting the regression coefficients for investment. In Table 15, I investigate effects of investment on growth, differentiating 136

Neoclassical Economic Growth in a Structured World-Economy total CAPITAL into its components INV-DOM and INV-FOR. In the top panel, I estimate the medium-run (1975-1995) base neoclassical growth model for the sample of all countries for which data are available. In the bottom panel, I estimate an equivalent model with CAPITAL broken into domestic and foreign components. The samples are slightly different, due to the absence of eight countries from the differentiated model for which data on foreign investment were not available, but the R2 statistics and regression coefficients for the two models are almost identical for all non-investment variables. In both the unweighted and the population-weighted models, the coefficient for INV-DOM is nearly identical to that for CAPITAL. This is not surprising in light of the fact that, on average, domestic investment accounts for well over 90% of all investment; the two variables are nearly synonymous. The coefficients on INV-DOM are, like those for CAPITAL, highly significant statistically. The results for INV-FOR reported in Table 15 are broadly consistent with the assumptions of neoclassical growth theory.

INV-FOR displays a

strong, positive relationship with growth rates, after controlling for the levels of other neoclassical inputs. This relationship is statistically significant in the population-weighted model, but not in the unweighted model. Dollar for dollar, the impact of foreign investment is more than twice as large as that of domestic investment in the weighted model. This directly contradicts Dixon and Boswell (1996a), who find that the effect of foreign investment is only half as large as that of domestic investment. Dixon and Boswell's estimates, however, are based on growth rates over the very restricted time period 1965-1973, a time period, moreover, which does not overlap that for which the results reported in Table 15 are estimated.

137

The International Structure of Income Table 15. Medium-run models (1975-1995) dep. var.: GROWTH Base (constant) INITIAL LABOR CAPITAL HUMAN R2 (n)

Unweighted Coeff. (SE)

t-stat

Population weighted Coeff. (SE) t-stat

-11.47 (2.83) -1.89 (0.55) .267 (.061) .124 (.038) .009 (.014) .420 (88)

-4.047 -3.469 4.400 3.286 0.622

-18.08 (2.38) -1.68 (0.30) .362 (.046) .199 (.026) -.012 (.012) .892 (88)

-7.586 -5.570 7.820 7.618 -0.991

-13.27 (3.05) -1.80 (0.60) .291 (.062) .141 (.039) .236 (.150) .004 (.015) .496 (80)

-4.346 -3.020 4.710 3.637 1.579 0.238

-17.42 (2.22) -1.79 (0.28) .349 (.044) .195 (.026) .472 (.160) -.005 (.012) .919 (80)

-7.833 -6.297 7.968 7.497 2.952 -0.418

Differentiated (constant) INITIAL LABOR INV-DOM INV-FOR HUMAN R2 (n)

Table 16 breaks out the lower panel of Table 15 into separate regression models for each of the three zones of the world-economy. In no zone is INVFOR significant for predicting differences in growth outcomes among the countries within the sample.

The coefficients for INV-FOR are, however,

positive in all three zones in both the unweighted and the population-weighted models. The coefficient is particularly strong in the semiperiphery, suggesting that mobility for semiperipheral countries may be closely tied to foreign investment. The results can only be characterized as "suggestive" on account of the large standard error of the estimate. 138

Neoclassical Economic Growth in a Structured World-Economy Table 16. Stratified, differentiated medium-run model (1975-1995) dep. var.: GROWTH Core (constant) INITIAL LABOR INV-DOM INV-FOR HUMAN R2 (n)

Unweighted Coeff. (SE)

-14.26 (13.14)

t-stat

-1.085

Population weighted Coeff. (SE) t-stat

-14.63 (7.77)

-1.883

-0.02 (3.31) .179 (.149) .045 (.081) .354 (.201) .033 (.041) .523 (20)

-0.007 1.202 0.560 1.759 0.810

2.17 (1.67) .146 (.096) .033 (.033) .150 (.152) -.032 (.022) .565 (20)

1.295 1.522 1.002 0.986 -1.444

Semiperipher y (constant) INITIAL LABOR INV-DOM INV-FOR HUMAN R2 (n)

-9.03 (14.19) -4.46 (4.70) .355 (.172) .232 (.135) .782 (.565) -.020 (.042) .593 (17)

-0.637 -0.948 2.063 1.712 1.385 -0.469

-2.82 (11.78) -8.08 (4.59) .450 (.112) .272 (.195) 1.223 (1.124) -.022 (.050) .780 (17)

-0.240 -1.761 4.007 1.395 1.088 -0.445

Periphery (constant) INITIAL LABOR INV-DOM INV-FOR HUMAN R2 (n)

-19.84 (5.67) -0.70 (1.09) .353 (.093) .185 (.057) .304 (.265) -.023 (.022) .544 (43)

-3.5000 -0.646 3.785 3.261 1.148 -1.033

-27.92 (4.51) -0.05 (0.61) .462 (.074) .230 (.039) .346 (.210) -.033 (.017) .951 (43)

-6.186 -0.088 6.227 5.953 1.643 -1.874

139

The International Structure of Income

DISCUSSION AND INTERPRETATION The single most important predictor of neoclassical disequilibrium growth seems to be the supply of labor. Amazingly, although the supply of labor is central to the neoclassical model, empirical growth regressions in the neoclassical tradition have not to date included LABOR as a predictor. The omission of LABOR in the literature has contributed to the consistent finding of a now seemingly spurious correlation between education and growth.

The

strong correlation between the age structure of the population (reflected in LABOR) and countries' abilities to educate their populations (reflected in HUMAN) has allowed HUMAN to usurp the explanatory power of LABOR in growth regressions from which LABOR was excluded. While more work is certainly warranted to more clearly differentiate the effects of LABOR and HUMAN, it is clear that LABOR or a similar variable should be included in neoclassical growth regressions in the future. The behavior of investment, the second major input in the neoclassical growth model as commonly estimated, is also problematic. Although associated with growth, especially in the periphery, CAPITAL is not associated with GNP per capita itself. This means that any impact that CAPITAL has on economic growth cannot operate through the disequilibrium model that forms the theoretical foundation for neoclassical growth regressions. More likely, as Blomstrom et al (1996) and Barro (1997) argue, the level of investment is itself determined by the rate of economic growth.

This

conclusion is even more likely in the case of foreign investment. If rapid economic growth creates incentives that encourage investment (such as higher returns on capital), those incentives will have a greater impact on the flow of international investment than on the flow of domestic investment.

This is

because the elasticity of domestic investment depends largely on its 140

Neoclassical Economic Growth in a Structured World-Economy attractiveness relative to domestic consumption, while the elasticity of foreign investment in a given country depends largely on the attractiveness of investment in that country relative to other countries. A higher growth rate than other countries directly implies a higher attractiveness for investment. Despite the poor theoretical showing of the neoclassical growth model, its empirical performance is quite strong. Consistent with the mainstream of the empirical growth literature, I find that the base neoclassical model accounts for around 42% of the variance in growth outcomes over the twenty years 19751995. This rises to 55% of the variance in growth over the longer term of 19601999. Weighting on population gives stronger results still: 63% for the long term and 89% for the medium term.

Thus, in my theoretically preferred

operationalization, the base neoclassical model accounts for almost nine-tenths of all of the weighted cross-national variation in growth outcomes. Similarly high percents of variance explained obtain for the stratified models. Specific coefficients, however, vary dramatically across zones of the world-economy. LABOR is the key input in the semiperiphery. This suggests that the successful navigation of the demographic transition from high birth and death rates to low birth and death rates is crucial for the long-term economic prospects of middle-income countries. London (1988) finds that high levels of dependence can impede the completion of a successful demographic transition; combined with London's result, this suggests a previously unexplored pathway for the effect of dependence on growth. LABOR is also an important indicator for the periphery, though not as strong and consistent an indicator as investment. Although Blomstrom et al (1996) and Barro (1997) find that investment is only a lagging indicator of growth for the countries of the world taken as a whole, it is possible that this would not be the case for the periphery taken in isolation. Given the prominence given to investment in international development policy, 141

The International Structure of Income this is an important topic for future research. In contrast to the results from the semiperiphery and periphery, regression results for the core are highly unstable. For the small sample of nine 1960 core countries in the long-run growth model, CAPITAL and HUMAN are the two significant independent predictors of economic growth.

For the

expanded core of 26 countries studied over the period 1975-1995, however, LABOR displayed the only significant coefficient. The latter result, based on better data and a larger sample of countries, is probably the more reliable. For core countries, however, the interpretation of LABOR is different than it is in the semiperiphery and periphery.

All core countries have already passed

through the demographic transition, and are facing problems arising from their high numbers of aged adults relative to workers, rather than a high number of children. The only major solution to this problem is immigration. This suggest that immigration rates should be studied in more detail as predictors, rather than consequences, of economic growth. To summarize, the major novel finding of this chapter is that demographic factors are much more important for predicting disequilibrium economic growth than previously thought. While this is especially true for the periphery and semiperiphery of the world-economy, it is even, in some models, true for the core. Capital -- physical and human -- is much less important than its prominence in the literature would suggest. I demonstrated in this chapter that the relationship between human capital and growth seems to be spurious. In Chapter 6 I demonstrate that the observed correlation between physical capital (investment) and growth also does not represent a causal relationship.

142

6. Investment and Growth: Disentangling Causal Paths The neoclassical growth model estimated in Chapter 5 clearly demonstrated that cross-national differences in levels of human capital, measured in terms of educational attainment, have little or no direct role in explaining cross-national differences in rates of economic growth. Levels of physical capital, on the other hand, seem to be more powerful in predicting disequilibrium growth, particularly when represented by domestic investment flows, and particularly in the periphery of the world-economy. Recent work in economics suggests that this observed positive relationship between investment and growth may also be spurious.

It is possible that the observed relationship is a case of reverse

causality, in which growing economies attract more investment, while increased investment contributes little or nothing to increased growth. In the case of human capital, it was possible to identify the confounding variable that accounted for the spurious correlation between education and growth.

Human capital is highly correlated with the percentage of the

population that is of working age. When this variable (LABOR) was omitted from the regression models for growth, human capital (HUMAN) had a strong and significant coefficient. When LABOR was included, the apparent effect of

The International Structure of Income HUMAN disappeared. In dealing with the relationship between physical capital and growth, the causal relationships are more difficult to resolve.

Here, the potential

confounding variable is not an alternative independent variable, but the dependent variable itself. Previous attempts to separate the effect of growth on investment from that of investment on growth have relied on time-series methods. (Blomstrom, Lipsey, and Zejan 1996; Barro 1997) Both of these articles use the Granger test of causality, finding that prior period levels of growth predict future investment (even when prior period investment is controlled for), while prior period levels of investment do not predict future growth (when prior period growth is controlled). The interpretation is that growth holds predictive value for future investment above and beyond its current relationship to investment, while investment holds no such additional predictive value for growth. There are at least three shortcomings to this approach. First, the Granger test is, statistically, extremely conservative. statistically significant results whatsoever.

Granger tests often return no The finding that growth holds

predictive power for future investment even after controlling for prior investment thus a strong result, which should be accepted at face value. On the other hand, the investment's lack of predictive power for future growth is less convincing, since the bar of statistical significance is, in this case, set very high. The result is thus suggestive, rather than conclusive. The second and more important limitation of the Granger test approach is that it does not consider potential contemporaneous effects of investment on growth (or vice versa). Instead, contemporaneous effects are controlled in an attempt to isolate lagged effects.

This approach is reasonable when the

"contemporaneous" period is short enough that it can be assumed that no 144

Investment and Growth: Disentangling Causal Paths important causal effect is occurring over the time period. In the present case, however, both Blomstrom et al and Barro use "contemporaneous" periods that are a decade in length, in order to smooth growth rates over the course of the business cycle. Substantial contemporaneous causality may occur over such a long period. Ironically, the decade-long period averages used by Blomstrom et al and Barro are also, from another perspective, too short. This leads to the third problem with their causality tests: they are fundamentally mis-specified. Investment, as such, does not appear in the neoclassical growth model. Investment over time is instead a stand-in for the level of physical capital, which is difficult to measure directly. Over twenty- or forty-year periods, like those studied in Chapter 5, the total or average level of new investment can be taken to be equivalent to the level of physical capital, since any initial stock of capital would depreciate to nothing over the course of the period of study. A ten-year period, on the other hand, may be too short for this purpose. In this chapter, I take a different approach to measuring the causal relationship between physical capital and growth. In place of the time series based techniques generally found in the economics literature, I specify two distinct kinds of structural equation models to elucidate the causal paths. First, I formulate a reciprocal effects model specifying domestic investment, foreign investment, and growth all as endogenous variables.

The model is

contemporaneous; all variables are measured over the same period (1975-1995). Both domestic and foreign investment are allowed to have reciprocal effects with growth -- that is to say, growth can affect them and they can affect growth, contemporaneously. I separate the effects of domestic and foreign investment in order to allow them to have different causes and different relationships to growth. The specifics of model specification, the estimation results, and a 145

The International Structure of Income discussion of those results appear in the first two sections below. Second, as a check on the results reciprocal effects model, I estimate a series of simple panel regression models of the relationship between investment and growth. In these models, I regress current period growth on both prior period investment and prior period growth; a significant coefficient for investment would indicate the presence of a causal path. Similarly, I regress current period investment on both prior period investment and prior period growth, to see whether growth affects investment. I perform these regressions for both domestic and foreign investment rates, using the periods 1975-1985 and 1985-1995 as the prior and current periods, respectively. These models are presented and discussed in the third section below. In a final concluding section, I summarize the implications of the empirical analyses conducted in this chapter for our understanding of the causal paths connecting physical capital and growth.

A RECIPROCAL EFFECTS MODEL FOR INVESTMENT AND GROWTH The first step in constructing a reciprocal effects model to elucidate the causal relationships between foreign and domestic investment and growth is specify models for each of the three dependent variables.

Below, I begin with the

investment differentiation growth model from Chapter 5, then add additional instrumental variables to supplement the models for foreign and domestic investment. The full structural model that results is sufficiently over-identified to permit the estimation of reciprocal effects.

146

Investment and Growth: Disentangling Causal Paths Implementation of the Neoclassical Model for Growth In the investment differentiation growth model of Chapter 5, GROWTH is regressed on the four inputs LABOR, HUMAN, INV-DOM, and INV-FOR, while also controlling for INITIAL (initial GNP per capita). In graphical terms, this model can be represented with the diagram below (Figure 26). The arrows in the model represent causal effects; the epsilon contributing to GROWTH represents the effect of all omitted variables, or, from the perspective of the model, error.

The variables LABOR, HUMAN, and INITIAL have been

grouped in a common box to simplify the display.

Figure 26 exactly

corresponds to the models estimated in Table 15 (lower panel) and Table 16.

Figure 26. Investment differentiation growth model INV-DOM

e LABOR GROWTH HUMAN INITIAL

INV-FOR

The reciprocal effects model endogenizes the two investment variables. In addition to being independent variables in the model for investment, they are also dependent variables in their own right. The resulting model is depicted in Figure 27. The dashed arrows represent new causal paths that have been added to the model. Since INV-DOM and INV-FOR are now dependent variables in 147

The International Structure of Income regression models of their own, they now have their own corresponding error components (epsilons). Figure 27 conceptually represents the causal paths to be estimated.

Figure 27. Investment differentiation model with reciprocal effects e

INV-DOM

e LABOR GROWTH HUMAN INITIAL

e

INV-FOR

Although this model could be estimated as depicted (using reiterative maximum likelihood techniques), the reciprocal effects between growth and the two investment variables are relatively poorly identified. The stability and reliability of estimates can be improved through the addition of instrumental variables that are correlated with INV-DOM and INV-FOR. Two such variables are proposed below.

Instrumental Variables Identification of the structural model for investment and growth will benefit most from the addition of variables to the models for investment. The 148

Investment and Growth: Disentangling Causal Paths economics literature suggests that a key determinant of domestic investment is the level of domestic financial development. (King and Levine 1993; Levine 1997) Conceptually, financial development represents the scale of domestic financial intermediation. The more national financial systems operate on the basis of commercial credit, the higher the level of financial development. Recent research has shown that the relationship between financial development and investment is causal and unidirectional. (Levine, Loayza, and Beck 2000) The correlation between financial development and domestic investment is on the order of .3 or more. An even stronger case can be made for the addition of merchandise trade to the model for foreign investment. Foreign investment is often organically linked to foreign trade, as when a manufacturer sets up an offshore assembly plant. Although the direction of causality may between investment and trade may be difficult to establish, it is not directly relevant to the use of trade as an instrumental variable, since trade will remain exogenous to the structural model. The important consideration is whether foreign investment and trade are sufficiently highly correlated.

The correlation between trade and foreign

investment is on the order of .7 or more. I operationalize financial development with the variable FIN_DEV, defined as the period average level of domestic credit provided by the banking sector as a percentage of GDP. Data are taken from World Bank (2001b) series FS.AST.DOMS.GD.ZS. This operationalization is consistent with the recent literature on financial development, investment, and economic growth. I operationalize the variable TRADE as the period average level of imports plus exports as a percentage of GDP. Data are taken from World Bank (2001b) series NE.TRD.GNFS.ZS.

This choice of operationalization is

supported by Frankel and Romer's (1999) finding that observed levels of trade 149

The International Structure of Income are unbiased for the purpose of estimating the effect of trade on growth.

The Full Structural Model The addition of FIN-DEV and TRADE completes the structural model for growth, domestic investment, and foreign investment. These two variables are added to the models for INV-DOM and INV-FOR, respectively, as well as to the model for growth.

Additional causal paths are possible, but are not

necessary for identifying the relationships of interest among the variables. The resulting structural model is depicted in Figure 28. The arrow connecting the error terms for INV-DOM and INV-FOR represents a correlation of residuals. Since a wide number of factors may affect both domestic and foreign investment, the residuals are allowed to correlate in order to account for these potentially omitted variables.

Figure 28. Structural model of the investment-growth nexus e

FIN-DEV

INV-DOM

e LABOR GROWTH HUMAN INITIAL

TRADE

150

e

INV-FOR

Investment and Growth: Disentangling Causal Paths The final structural model, with six degrees of freedom, is comfortably over-identified. Data exists to estimate this model on a sample of 78 countries for the period 1975-1995. Although there are too few core or semiperipheral countries in this sample to permit estimation for those zones separately, there are enough peripheral countries (49) to estimate a periphery-only version of the model.

Results on the all-country and periphery-only relationship between

investment and growth are detailed in the next section.

RECIPROCAL MODEL RESULTS In this section I estimate the structural model represented by Figure 28 for all countries for which data are available (n=78) and for all countries that are initially members of the periphery (n=49). For each sample, I estimate structural models using both the unweighed and the population-weighted correlation matrix. Results are detailed below. I begin by concentrating on the revealed relationship between investment and growth, after which I highlight the role of the instrumental variables in the model for growth. I have also estimated the equivalent structural model without instrumental variables (the model represented by Figure 27). The results (not reported) for investment and growth are similar in this model to those of the fully specified model. The introduction of the instrumental variables does not change the general direction or strength of the results.

Relationship between Investment and Growth The results of the structural models confirm that growth has a strong and generally significant causal impact on both domestic and foreign investment flows, while investment flows have virtually no effect on growth. In Figures 29 151

The International Structure of Income and 30 below, I detail the investment-growth nexus from the causal model of Figure 28.

Coefficients have been standardized for ease of comparison.

Statistically significant coefficients (∀ = .05) are marked (*).

Full results,

including causal paths not shown in these detail figures, will presented in full later on in Tables 17 and 18. In the full-sample estimation (Figure 29), the weighted results (right panel) clearly show the positive effect of the growth rate in attracting and promoting investment, both domestic and foreign.

The relatively lower

coefficient for foreign investment is surprising. In the unweighted model, the coefficient is small enough to lose statistical significance (though it is still positive and still five times the effect of foreign investment on growth). This suggests, counterintuitively, that domestic investors are more sensitive to rates of economic growth than are foreign investors.

Figure 29. Investment-growth nexus, all countries, 1975-1995 Unweighted

Weighted by Population e

e

Domestic Investment .104

.372*

Domestic Investment e

Growth rate

.128

0.25

Foreign Investment

152

-.121*

.083

.862*

e

Growth rate

e

.426*

.009

Foreign Investment

e

.059

Investment and Growth: Disentangling Causal Paths An alternative interpretation is that economic growth does not just attract domestic investment, but also generates it directly. It must be remembered that "investment" is measured on the consumption side, not on the income side. It represents outlays on additions to fixed assets (plus net changes in inventories). According to the U.N. system of national accounts, Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. (World Bank 2001b) Economic growth is likely to generate the need for such outlays organically, and as domestic firms expand their very expansion would be recorded as domestic investment. In contrast, foreign investment is not organically tied to economic performance, although it is apparently attracted to growing economies. This is especially true in the periphery of the world-economy, where the effect of growth on foreign investment is much stronger than in all-nation sample. (Figure 30) When the sample is restricted to peripheral countries only, the relationship of growth with foreign investment comes close to that of growth with domestic investment, in the weighted model. Also in the weighted model, there is a suggestion -- though well below the bar of statistical significance -- that, if foreign investment has an effect on growth, it is negative rather than positive. One surprising aspect of both the all-country and peripheral models is the significantly negative correlation of the residuals of domestic and foreign investment in the unweighted analyses. A positive correlation would have been expected, since any omitted variables that affect investment would be expected to affect domestic and foreign investment in the same direction. The fact that 153

The International Structure of Income this surprising result disappears in the weighted models indicates that it is primarily due to factors affecting (demographically) smaller countries. Further investigation would be necessary to establish the reasons for this anomalous result.

Figure 30. Investment-growth nexus, peripheral countries, 1975-1995 Unweighted

Weighted by Population e

e

Domestic Investment -.095

.612*

Domestic Investment e

Growth rate

.230

-.048

Foreign Investment

-.202*

.037

.765*

e

-.029

Growth rate

e

.680*

-.134

e

Foreign Investment

Full Model Results The results above focus narrowly on the investment-growth nexus. Here, I briefly review the remaining coefficients from the structural equation models. In general, the models for growth are similar to those reported in Chapter 5. In every model, the percent of population that is of working age (LABOR) is the only statistically significant neoclassical input. Of the two instrumental 154

Investment and Growth: Disentangling Causal Paths variables, trade has a significantly positive impact on growth in both of the weighted models, while the effect of financial development on growth is unstable and not significant. Models for domestic and foreign investment came out as anticipated, with FIN-DEV positively correlated with INV-DOM and TRADE positively correlated with INV-FOR. Full results for all models are reported in Table 17 (all countries) and Table 18 (peripheral countries) below. The results for trade are consistent with a growing body of literature in economics on the small but positive contemporaneous effect of trade on growth. One open question, however, is the long-term effect of greater trade openness. It is conceivable that, like foreign investment, foreign trade has a negative longterm effect on growth. This is all the more likely given the strong relationship between foreign trade and investment: if foreign trade contemporaneously promotes foreign investment, and foreign investment has a long-term negative impact, then trade may ultimately have a long-term negative net effect. Careful work is required to trace out these possibilities. The results for financial development are strikingly at odds with the larger economics literature on financial development. In this literature, financial development is consistently shown to have a significantly positive effect on growth. My own experiments show this result to be highly contingent on the choice of period and sensitive to the presence of control variables. While financial development as typically measured is clearly an important variable for predicting some economic outcomes (such as investment rates), it is not clear that it has important implications for growth.

155

The International Structure of Income

Table 17. Full model for growth and investment, 1975-1995 all avail. (1) dep. var.: GROWTH (constant) INITIAL LABOR HUMAN INV-DOM INV-FOR FIN-DEV TRADE R2

Unweighted Coeff. (SE) t-stat -12.96 (3.19) -4.068 -2.13 (0.63) -3.382 .345 (.068) 5.043 .012 (.016) 0.721 .046 (.068) 0.679 .044 (.407) 0.108 -.291 (.273) -1.069 .004 (.010) 0.380 .477

Population weighted Coeff. (SE) t-stat -7.96 (2.05) -3.877 -2.99 (0.41) -7.369 .308 (.041) 7.441 -.003 (.009) -0.346 .038 (.043) 0.880 .015 (.110) 0.139 .056 (.162) 0.346 .006 (.003) 2.112 .879

(2) dep. var.: INV-DOM (constant) FIN-DEV GROWTH R2

Unweighted Population weighted Coeff. (SE) t-stat Coeff. (SE) t-stat 19.49 (1.34) 14.593 18.637 (0.69) 26.995 .951 (.574) 1.657 .674 (.286) 2.361 .831 (.364) 2.286 1.926 (.126) 15.293 .249 .806

(3) dep. var.: INV-FOR (constant) TRADE GROWTH R2

Unweighted Coeff. (SE) t-stat -0.55 (0.18) -3.117 .023 (.002) 11.227 .072 (.064) 1.128 .662

Population weighted Coeff. (SE) t-stat -0.37 (0.24) -1.586 .017 (.002) 7.012 .241 (.054) 4.471 .457

Error cov. for (2) and (3) Error cov.

Unweighted Coeff. (SE)

Population weighted Coeff. (SE) t-stat

156

-.897 (.469)

t-stat -1.912

-.436 (.285)

1.531

Investment and Growth: Disentangling Causal Paths

Table 18. Full model for growth and investment, 1975-1995 periphery (1) dep. var.: GROWTH (constant) INITIAL LABOR HUMAN INV-DOM INV-FOR FIN-DEV TRADE R2 (2) dep. var.: INV-DOM (constant) FIN-DEV GROWTH R2

Unweighted Coeff. (SE) -5.54 (12.19) -0.51 (1.32) .472 (.144) .028 (.028) -.041 (.127) -.105 (.641) -8.359 (4.837) .008 (.021) .369

t-stat -0.447 -0.386 3.291 1.011 -0.320 -0.164 -1.728 0.382

Population weighted Coeff. (SE) t-stat -38.52 (8.58) -4.491 -1.76 (0.76) -2.305 .658 (.117) 5.647 -.008 (.010) -0.775 .016 (.068) 0.235 -.296 (.195) -1.519 3.327 (2.640) 1.260 .030 (.009) .914

3.197

Unweighted Population weighted Coeff. (SE) t-stat Coeff. (SE) t-stat -24.72 -1.443 -12.82 -1.092 (17.136) (11.74) 21.359 2.680 15.664 2.834 (7.971) (5.527) 1.422 (0.433) 3.282 1.776 (0.197) 9.023 .429 .894

(3) dep. var.: INV-FOR (constant) TRADE GROWTH R2

Unweighted Coeff. (SE) t-stat -0.82 (0.32) -2.532 .026 (.004) 6.440 .104 (.083) 1.260 .525

Population weighted Coeff. (SE) t-stat -1.01 (0.33) -3.068 .026 (.004) 6.434 .308 (.055) 5.636 .578

Error cov. for (2) and (3) Error cov.

Unweighted Coeff. (SE)

Population weighted Coeff. (SE) t-stat

-1.52 (0.74)

t-stat -2.061

-0.22 (0.27)

-0.806

157

The International Structure of Income

CONFIRMATION: A PANEL MODEL OF INVESTMENT AND GROWTH The reciprocal effects model seem to definitively preclude any contemporaneous causal path from investment to growth, but this leaves open the possibility of a lagged relationship.

The twenty-year period averages used in the

"contemporaneous" model should, to some extent, capture lagged effects as well, but it is possible as a confirmation to explicitly model the lagged relationships among the variables. In this section, I estimate such lagged effects between investment and growth using the two periods 1975-1985 and 19851995 as prior and current periods, respectively.

The method, as described

briefly in the introduction to this chapter, is to estimate three models of the forms: GROWTHt = A + B0*GROWTHt-1 + B1*INV-DOMt-1 + B2*INVFORt-1 INV-DOMt = A + B0*INV-DOMt-1 + B1*GROWTHt-1 INV-FORt = A + B0*INV-FORt-1 + B1*GROWTHt-1 The B0 coefficients are the stability coefficients for each model, and represent the stability of the dependent variable from its previous period value. The B1 (and B2) coefficients represent the effects of the independent variables, after controlling for the stability of the dependent variable over time. The effects of all other variables are presumed to be subsumed in the stability effect and in the model error. Statistically significant values for the B1 (and B2) coefficients indicate causal relationships. The models detailed above are estimated for the current (t) period 19851995 and prior (t-1) period 1975-1985 in Table 19 below. Results are reported for all available countries (n = 96). Similar results were found for a sample of peripheral countries only (n = 55; not shown). The coefficients for the model 158

Investment and Growth: Disentangling Causal Paths for GROWTH did not change substantially when the effects of INV-DOM and INV-FOR were estimated individually in separate models (not shown).

Table 19. Panel model for growth and investment, 1975-1995 (1) dep. var.: GROWTHa (constant) INV-DOMb INV-FORb GROWTHb R2

Unweighted Coeff. (SE) 0.23 (1.08) .017 (.048) .120 (.220) .335 (.108) .145

(2) dep. var.: INV-DOMa (constant) INV-DOMb GROWTHb R2

Unweighted Coeff. (SE) 7.35 (2.00) .537 (.087) .561 (.194) .439

t-stat 0.213 0.347 0.546 3.093

Population weighted Coeff. (SE) t-stat 0.06 (0.88) 0.064 .059 (.042) 1.410 -.858 (.330) -2.601 .850 (.087) 9.794 .769

t-stat 3.681 6.166 2.891

Population weighted Coeff. (SE) t-stat 6.27 (1.46) 4.305 .592 (.069) 8.522 1.305 (.144) 9.040 .874

(3) dep. var.: Unweighted a INV-FOR Coeff. (SE) t-stat (constant) 0.52 (0.16) 3.180 b INV-FOR .954 (.105) 9.115 GROWTHb -.105 (.049) -2.121 R2 .476 a Current period (1985-1995) b Prior period (1975-1985)

Population weighted Coeff. (SE) t-stat 0.19 (0.14) 1.403 .942 (.162) 5.827 .190 (.028) 6.820 .426

The results correspond closely in direction and magnitude to those reported for the contemporaneous model. Growth in each case significantly predicts investment flows, though in the unweighted model for INV-FOR the coefficient is negative (this indicates that, controlling for 1975-1985 levels of INV-FOR, high growth is correlated with lower later investment). This anomaly 159

The International Structure of Income disappears in the weighted results. On the other hand, nowhere does investment have a significant impact on GROWTH (controlling for prior period GROWTH), except in the case of INV-FOR in the weighted model, where the effect is negative. This echoes the results of the peripheral contemporaneous model (Table 18), in which foreign investment was found to have a negative impact on growth among countries in the peripheral zone. In short, the crosslagged panel models, though less stable than the contemporaneous models, yield generally the same results. The poor stability of the panel model can be ascribed to massive colinearity between current and previous period values of the dependent variables. This problem also affects the Granger tests found in the economics literature. Only the contemporaneous, reciprocal effects model (with adequate instrumentation) fully eliminates this difficulty.

DISCUSSION AND INTERPRETATION The results of this chapter unequivocally confirm the thesis that the arrow of causality runs from growth to investment, with no measurable return causation. Development policy that aims to promote growth by promoting investment is probably just pushing on a string. Policies should aim to promote growth directly, and an appropriate level of new investment will follow. Despite its consistency with emerging results from the empirical economics literature, this finding is likely to be controversial, both because it is counterintuitive and because it flies in the face of several decades of received wisdom about the predictors of economic growth.

The present results

notwithstanding, it is still possible to argue that investment does indeed spur growth, but that the measures used here (and elsewhere) are not reliable enough 160

Investment and Growth: Disentangling Causal Paths to allow detection of the relationship.

The high raw correlation between

investment and growth makes this argument difficult, though not impossible, to sustain. One would have to argue that although exogenous investment shocks do cause economic growth, such shocks are relatively rare. Instead, most of the variation in investment (and relative capital intensity) actually observed among the countries of the world is due to endogenous factors. By this argument, although exogenously-originating changes in the level of physical capital do cause growth, they are swamped in the data by the much more common endogenously-originating changes in the level of physical capital. It is thus possible to square the circle of the investment-growth nexus, but only just. In any case, if exogenous shocks to investment flows are empirically so rare as to be undetectable in the 40 years of data under study here, it begs the policy question of whether or not to bother to pursue them. What the present results leave more obscure than ever is the nature and identity of the variables that do cause growth. Clearly, demographic effects are important. None of the other neoclassical inputs, now studied in a variety of periods on a variety of samples and with a variety of conditioning variables, have such a consistently positive measured effect on growth. Nonetheless, in the unweighted models, about half the variance in growth outcomes remains unexplained.

This optimistic note must be tempered, however, by the

observation that in the weighted models, virtually all of the variance in growth outcomes is accounted for. The structural models specified and estimated in this chapter are a first step toward building a full structural model of the growth process. An early attempt at a full causal model of the development process was made by Hout and Meijerink (1996), and Kentor (2000) uses causal models to study the term structure of the effects of foreign investment, but neither of these articles 161

The International Structure of Income attempts to model the effects of demographic variables.

A marriage of

sociology, economics, and demographics in this area is surely long overdue.

162

7. Conclusion In the Introduction, I laid out my ambition to take some first steps towards integrating the sociology and economics literatures on economic growth. In studying the form of the global income distribution, the differential performance of the neoclassical model by zone of the world-economy, and the (spurious) roles of domestic and foreign investment in fostering economic growth in all three zones of the world-economy, I have attempted to pursue an integrated approach that draws on the strengths of both fields. The new insights generated from the integration of world-systems theory into the neoclassical growth model suggests that similar success might result from adding a sociological dimension into economists' models. In addition, the surprising importance of the labor supply in explaining growth calls for increased attention to the interaction of economic variables with demographic variables such as fertility, migration, and mortality. In short, the general implication of this study is that social policy probably matters much more than economic policy in determining countries' growth rates. Growth may not, however, be very relevant in the broad scheme of things. For the past four decades, individual and national incomes across the world-economy have exhibited a stable pattern that is consistent with the

The International Structure of Income predictions of sociological world-systems theory. At the individual level, there exists a strikingly bimodal pattern in the logged income distribution. The world can truly be said to consist of "haves" and "have-nots," with relatively few people in between. At the national level, there are three discernable income levels, corresponding to the world-systems notions of "core," "semiperiphery," and "periphery." Extreme levels of internal inequality in the semiperiphery of the world-economy indicate that countries falling into this zone of the worldeconomy do, as anticipated, comprise both core- and peripheral-type activities within their borders. These overall patterns in the global distribution of income have been a constant feature of the post-WWII world-economy, even as individual countries have moved up and down the national income ladder. It seems fair to say that these patterns represent the general "structure" of the world-economy. It is at the same time both optimistic and a pessimistic to observe that most meaningful upward mobility in national income levels has come from reductions in birth rates expanding the proportions of populations in poor countries that are of working-age. It is pessimistic because so few countries have historically been able to achieve demographic shift of sufficient scale to bring about transformative economic growth.

The four Asian "Tiger"

economies of South Korea, Taiwan, Hong Kong, and Singapore are the main examples. Moreover, one might assert that these four aren't even "countries" in the statistical sense: all are city-states, or nearly so. Hong Kong and Singapore are clearly cities, Singapore only recently separated from its parent nation of Malaysia and Hong Kong more recently reunited with its parent nation of China. Taiwan and South Korea, however, are also problematic cases. Over half the population of Taiwan lives in the metropolitan area of the capital, Taipei, and almost half the population of South Korea lives in greater Seoul. Throwing in 164

Conclusion Pusan cements the South Korean case: well over half the national population lives in these two urban areas. Why does this matter? It matters because urban areas worldwide are areas of low fertility. Reducing fertility in a small fully-urbanized country is much easier than reducing fertility in a continental nation-state. Consider the case of China today. Shanghai exhibits income per capita levels not too far behind those of Seoul and Taipei. Unlike those latter two cities, however, Shanghai possesses an infinite (for all practical purposes) demographic hinterland.

Native Shanghai residents may adopt urban lifestyles with low

birthrates, but there will always be new immigrants from the hinterland to take up the demographic slack. So despite China's dramatic success in reducing birth rates (which predates the one-child policy by at least a decade), China still has a vast rural population (over 500 million people). Moreover, rural areas nearly everywhere are less productive (economically) than urban areas.

China is

growing rapidly, but does anyone really think that it will grow to developedcountry levels of national income per capita? This would make China four times as rich and powerful as the United States. Such a scenario seems unlikely for this century, or even for the next. The four Asian Tigers seem more an exception than a rule to be followed. Nonetheless, it is to some extent optimistic to observe that reducing the birth rate is the key to transformative growth because at least we know how to reduce the birth rate.

Governments can promote reductions in people's

preferred family sizes through education efforts, encouragement of female labor market participation, and enforcement of women's human rights. They can also help people achieve the smaller families they want through financial support for family planning. These steps are difficult, but at least they are well-understood. Attempting to promote growth through macroeconomic management, on the 165

The International Structure of Income other hand, has proved completely futile. No country under IMF tutelage has done any better than average in growth outcomes. We simply don't know what macroeconomic policies (if any) promote growth. Even the neoinstitutionalist arguments dominating the growth literature today do little more than explain how to avoid outright catastrophe. Neoinsitutionalism has emerged as a newly hegemonic approach in both the sociology and economics literatures on growth. Once again, the two literatures are developing in parallel, with little or no cross-referencing. In any case, neither sociologists nor economists have much to show for their efforts. Dysfunctional national institutions can be disastrous for national economies; that's little more than common sense. How does a country with poor institutions get better ones? We don't know. Do countries with "good" institutions grow remarkably faster than average?

No.

The promotion of healthy national

institutions and good governance is certainly a worthy aim, but even if successful it will not lead to economic growth on such a scale as to transform the peripheral countries of today into the core countries of tomorrow. Formally speaking, the growth models estimated in this volume are "disequilibrium" models, in which increases (decreases) in input levels are hypothesized to lead to growth (decline) towards a new equilibrium level of output. By achieving an ideal mix of labor, physical capital, and human capital countries can maximize their overall levels of national income. Ultimately, however, output is limited by the level of technology available. "Technology" here is to be construed in the broadest sense, as the sum total of all knowledge used in the production process. For some countries in the core zone of the world-economy, which already have access to the most advanced productive technologies available anywhere in the world, long-term growth prospects may indeed be limited by the rate of advance of technology. For most of the nations 166

Conclusion of the world, however, the hard output barrier represented by the level of available technology in the world at a given date in history is so far from present levels of output as to be irrelevant for assessing current growth potential. In these countries, substantial opportunities for equilibrium growth must exist. Equilibrium growth is driven by changes in technology, again, broadly construed. I suspect that the most important element in this "technology" of economic production may be something we rarely think of under that rubric: the social organization of society itself. After all, most of the physical technology of the world is available anywhere in the world for a market price. There is certainly some increased burden associated with peripheral locations, but it beggars belief that barriers to physical technology transfer could be so high as to account for the 100-fold national income differential between, say, western Europe and west Africa.

Such a massive gap in productivity can only be

accounted for socially: people in Europe are far better organized (from an economic standpoint) then people in Africa. In this sense, the argument harkens all the way back to Adam Smith. The main message of The Wealth of Nations is not the mysterious wisdom of the invisible hand, but the irrefutable logic of the division of labor. Adam Smith is invoked tirelessly by economists, but is much more often read today by sociologists. Whatever its determinants, truly transformative economic growth, growth on a scale to make poor countries rich, the kind of growth that is the only fair outcome for the 80% of the world's population living outside today's core of the world-economy, is extraordinarily rare. Today's economic fault lines were drawn in the nineteenth century and through two world wars, the Great Depression, and the rise and fall of Communism have changed very little since then.

Granted, any real income convergence bringing today's peripheral

countries up to core consumption levels will devastate the world's environment 167

The International Structure of Income through runaway global warming and natural resource depletion, but it is inconceivably selfish to use this as an excuse to keep 80% of the world in poverty. Given that we will not reduce our consumption levels to theirs, we must work to raise theirs to ours, whatever the ecological consequences. The only thing delaying (though in the long run certainly not preventing) complete ecological catastrophe is that we don't know how to do it. That is perhaps the most important lesson to be learned from this study: we know very little about any of the topics covered here. The dynamics of the global income distribution; how growth is patterned by income levels; how growth itself affects income levels, economic growth itself ... despite decades of intensive academic research, all remain mysterious. Existing theoretical models do not stand up to a careful examination of the empirical facts. Clearly, new models are needed. Where they will come from is anyone's guess, but it seems unlikely that they will come from research that continues in existing disciplinary traditions. The facts cry out for radically new interpretations. These may arise out of today's separate disciplinary debates, but both sociologists and economists have much to learn from each other about the mechanics of income and growth, if they are willing to listen. That said, the discourse between them will be most profitable if it operates in both directions. Hopefully, we will pay greater heed to each others' ideas in the future than we have in the past.

168

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176

Index Asian Tigers, 164 Capital, 2, 107

conditional, 9 Core, 4, 5, 61, 63, 86, 134

domestic, 121

Demographic transition, 111

flows, 120, 121

Dependency theory, 6, 7, 9, 104, 141

foreign, 121

Diminishing returns, 106, 107

human, 5, 9, 106, 116, 131, 134,

Disequilibrium growth, 107, 119,

140, 143 physical, 5, 106, 113, 130, 134,

127, 143, 166 Divergence, 3, 10, 59

140, 143

Division of labor, 167

stocks, 113

Education, 9, 116, 131

China, 126, 165 effect of, 22 one-child policy, 111

Equilibrium growth, 107, 119, 127, 167 Exchange rates

Colonialism, 7

market, 12, 22

Commodity chains, 3

purchasing power parity, 12, 22

core nodes, 3

Financial development, 149, 155

peripheral nodes, 3

Gaussian kernel, 51, 82

Convergence, 2, 8, 20, 59, 107 absolute, 10

Gini Definition, 38

The International Structure of Income Relationship to Lorenz curve, 37 relationship to VarLog, 42, 44

153, 155 Invisible hand, 167

Global warming, 168

Kuznets curve, 47, 99

Heteroskedasticity, 126

Labor, 5, 106, 109, 130, 134, 140,

Income

143, 154

bimodal distribution of, 4, 53, 103

Lorenz curve, 37

global distribution of, 10, 35, 53

Low-level equilibrium trap, 111

lognormal distribution of, 43, 51

Mobility, 86, 103

lognormal distribution of, 3

National income, 2

trimodal distribution of, 4, 64, 85, 103 Income quintiles, 38 Inequality between-country, 10, 12, 18

GDP/PPP, 11, 13 GNP/FX, 11, 13, 79 GNP/RLC, 15, 79, 109 Neoclassical economics, 2 Neoclassical growth model, 2, 4,

Gini, 19, 21, 41

104, 105, 119, 129, 141, 143, 145,

global, 10, 35

147

international, 10

Neoinstitutionalism, 166

national, 47

Network analysis, 71

VarLog, 19, 20, 40

blockmodeling, 72

within-country, 10

role equivalence, 72, 73

Instrumental variables, 146, 148, 151 Investment, 6, 113, 123, 143, 145, 151 domestic, 123, 136, 140, 143, 146, 153, 155 foreign, 6, 123, 136, 140, 146, 178

structural equivalence, 72 New growth theory, 9 Organic

zones

of

the

world-

economy, 96 Perimeter of the core, 85, 120, 134 Perimeter of the periphery, 85, 120, 134

Index Periphery, 4, 5, 61, 63, 86, 134, 151, 153 Reciprocal effects, 146 Reverse causality, 143, 160 Semiperiphery, 4, 61, 63, 68, 86, 98,

Technology, 5, 9, 106, 166 Trade, 149, 155 VarLog relationship to Gini, 42, 44 World-systems theory, 2, 3, 63, 104

134

179

The International Structure of Income

180

About the Author Salvatore Babones is a Lecturer in the Department of Sociology and Social Policy at The University of Sydney.

From 2003 - 2008 he was Assistant

Professor of Sociology, Public Health, and Public and International Affairs at the University of Pittsburgh. He is the editor of Social Inequality and Public Health (2009 The Policy Press) and co-editor with Christopher Chase-Dunn of Global Social Change: Historical and Comparative Perspectives (2006 The Johns Hopkins University Press). His work has been published in sociology, political science, public health, and interdisciplinary journals and he is currently the editor for global development studies of the International Studies Association's Compendium of International Studies. His research focuses on income inequality, its roots in our globalizing world, and its consequences for human health. He holds both the MSE in mathematical sciences (2002) and the Ph.D. in sociology (2003) from The Johns Hopkins University. He now resides in Sydney, Australia.