# G Ö T T I N G E R S T U D I E N Z U R E N T W I C K LU N G S Ö KO N O M I K / GÖTTINGEN STUDIES IN DEVELOPMENT ECONOMICS

Stephan Klasen Felicitas Nowak-Lehmann (eds.)

# **Poverty, Inequality and Migration in Latin America**

Stephan Klasen Felicitas Nowak-Lehmann (eds.)

# **Poverty, Inequality and Migration in Latin America**

The causes and consequences of high inequality in incomes, assets, and many aspects of well-being in Latin America have recently (re-)emerged as a central research and policy issue. However, many open questions remain that will be dealt with in the contributions to this volume. First, the linkages between growth, inequality, and poverty in Latin America need further clarification. More analyses at the country and even sub-national level are required to understand these complex relationships and their most important determinants. Of particular relevance is to examine these relationships in the Latin American context of high economic instability with recurrent economic and financial crises, particularly in the 1990s. Secondly, measuring and addressing poverty remains a critical research area, in particular non-monetary including subjective indicators of well-being often tell a different story that needs to be considered when analyzing poverty trends and determinants. Lastly, the poverty/inequality issues need to be considered in an economic environment, where trade, migration, and economic integration are of particular importance. Thus the role of trade and migration in generating, sustaining, or reducing inequalities between and within countries is an area that requires further analysis.

Stephan Klasen is Professor of Economics at the University of Göttingen and director of the Ibero-America Institute for Economic Research. He has held positions at the World Bank, the University of Cambridge, UK, and the University of Munich. His research focuses on issues of poverty and inequality in developing countries.

Felicitas Nowak-Lehmann is a researcher at the Ibero-America Institute for Economic Research, and research associate at the Center for European, Governance and Economic Development Research (CEGE). She has held positions at the University of Göttingen and the Freie Universität Berlin. Her research focuses on issues of trade integration and trade policy in developing countries.

Poverty, Inequality and Migration in Latin **Amerika**

#### **Gottinger Studien zur Entwicklungsokonomik Gottingen Studies in Development Economics**

Herausgegeben von/Edited by Hermann Sautter und/and Stephan Klasen

> Bd./Vol. 20

Frankfurt am Main · Berlin · Bern • Bruxelles • New York • Oxford • Wien

Stephan Klasen/Felicitas Nowak-Lehmann (eds.)

> **Poverty, Inequality and Migration in Latin America**

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Cover illustration by Rolf Schinke

ISBN 978-3-631-75368-2 (eBook) ISSN 1439-3395 **ISBN** 978-3-631-57327-3

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# **Contents**

9. South-South Trade Agreements, Location of Production and Inequality in Latin America ........................................................ 269 *Alessia Lo Turco*

# **Introduction**

# *Stephan Klasen and Felicitas Nowak-Lehmann D.*

The causes and consequences of high inequality in incomes, assets, and many aspects of well-being in Latin America has recently (re-)emerged as a central research and policy issue. While in previous decades, concern about high inequality in Latin America was, following Kuznets' seminal work in the 1950s, largely focused on the impact of the development process on inequality, the new emerging literature is considering the reverse causality, i.e. the impact of inequality on the development process. Prominent examples of this renewed emphasis are a number of reports produced recently by the World Bank, including the recent World Development Reports on Poverty (World Bank, 2000) and Equity (World Bank, 2005), as well as reports focusing on Latin America, including the 2004 report 'Inequality in Latin America: Breaking with History' (World Bank, 2003) and the recent 2006 report 'Poverty Reduction and Growth: Virtuous and Vicious Circles' (World Bank, 2006). Similarly, academic research has taken on this issue with renewed vigor, as shown, for example, by works of Eicher and Tumovsky (2003), Deininger and Squire ( 1998), Forbes (2000), among many others.

There are a number of reasons that have led to this re-emergence of inequality as a central research and policy issue in Latin America as well as the new emphasis on its development impacts. First, inequality was and is extremely high in Latin America. As shown in Table I, Latin America continues to have the dubious distinction of having the highest income inequality in the world, as measured by the Gini coefficient. Using other measures, or other dimensions, of inequality (e.g. assets) would yield similar results. More disconcerting is the persistence of inequality in Latin America across time. In contrast to the hope held out by the Kuznets Hypothesis that inequality will eventually decline with development, it has remained extremely high through the past 30 years and changed little even during episodes of fast economic growth. In contrast to the long secular and large decline in inequality in rich countries that took place between about 1930 and 1970, we have not experienced a similar secular trend in any Latin American country ( or, for that matter, in other developing regions, see Table I). In fact, most evidence points to a small but significant rise in inequality in most developing countries since the early to mid-1980s (Griln and Klasen, 2003; Cornia and Court, 2001). Latin America is no exception although the extent of increases in inequality since the 1980s vary by country and time period and there is some evidence that inequality has declined again in some countries by a small amount in the last few years (particularly in Brazil, see World Bank, 2006). The resilience of high inequality in Latin America to vastly different policy regimes and policy Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

interventions is remarkable and somewhat disconcerting. Neither the statist development strategies of the 1950s and 1960s nor the liberal market reforms of the 1980s and 1990s have greatly affected inequality, nor have great swings from populism to orthodox market fundamentalism.

Second, inequality is not only persistent in the aggregate, but it is also persistent across generations of individuals as mounting research on the intergenerational transmission of inequality is pointing out (World Bank, 2003, 2005).

Third, the detrimental effects of inequality for economic and human development have become more apparent recently. As has been shown theoretically as well empirically, high inequality not only translates into higher absolute income poverty at any given level of mean incomes, but it also reduces the poverty reducing impact of economic growth (e.g. Bourguignon, 2003; World Bank, 2000; Klasen, 2003). Latin America's very poor progress in reducing absolute poverty in recent decades is thus not only a consequence of its poor growth performance, but also its high inequality (World Bank, 2006).

Fourth, there is growing evidence that high inequality, particularly asset inequality, is detrimental to economic growth itself. While the empirical evidence is still under some dispute (see, e.g. Deininger and Squire, 1998, Forbes, 2000; Klasen, 2003; Banerjee and Duflo, 2003), the evidence is mounting that such a negative effect exists particularly in countries displaying particularly high inequality, with Latin America often being cited as the most prominent example of a high inequality region with a relatively poor growth performance, particularly in the past 20 years. The channels through which this effect is transmitted range from capital market failures that prevent the poor in high inequality countries to invest in human and physical capital or to insure against risk, social and political instability that deter investment, to social conflict leading to inefficiencies, economic and political uncertainties and growth collapses, to name the ones most prominently discussed in the literature ( e.g Alesina and Rodrik, 1995; Rodrik, 1998; Deininger and Squire, 1998; World Bank, 2003, 2006).

Fifth, there is growing debate and awareness about the well-being costs of high inequality in Latin America. Given inequality aversion for which there is convincing evidence from Latin America (e.g. World Bank, 2003), high inequality carries a welfare costs which can be sizable (see Griin and Klasen, 2006). Investigations of subjective well-being also point to the welfare costs of high inequality in Latin America. Moreover, since the 1980s, inequality has increasingly been associated with economic insecurity not only of the poor, but increasing sections of the middle class which has strongly undermined social cohesion and increased social and political conflict (Rodrik, 200 I).

Sixth, while there was a time where inequality was seen as a necessary evil to promote incentives and efficiency, survey evidence from Latin America clearly points to the fact that inequality is now seen as too high and as unfair (World Bank, 2005, 2006). Of particular concern is here that much of existing Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

inequality in Latin America is in fact inequality of opportunities, i.e. inequality related to one's origin, race, sex, or parental background, which is seen as particularly reprehensible ( e.g. Roemer, 1998; World Bank, 2005).

Seventh, the recent rise of populism in many Latin American countries (including Venezuela, Peru, Bolivia, and Argentina) has partly emerged as a result of the general dissatisfaction with high inequality, high poverty, poor growth, and social exclusion of minorities and marginalized groups. Clearly, inequality and persistent poverty is having a serious impact on political developments.

Lastly, the data and methods to analyze inequality and poverty and its linkages to growth and policy interventions have dramatically improved in recent years. Regarding data, the implementation of standardized regular representative household income surveys in nearly all Latin American countries in the past 10-15 years has enabled researchers to study poverty and inequality levels, trends, and determinants that was impossible in prior years where all that was available were occasional snapshots from a single household survey. This has also enabled an analysis of regional poverty and inequality dynamics which showed that regional inequality is an important driver of national inequality (World Bank, 2006). In addition to regular household surveys, specialized surveys, some using randomized designs, have allowed researchers to study the impact of particular government programs (e.g. Todd and Wolpin, 2005; World Bank, 2004). Regarding methods, there have been great improvements in combining micro and macro data to analyze and simulate the impact of policies on poverty and inequality as well as significant improvements in studying the spatial dimension of poverty and inequality, including the question of spatial poverty traps.

Table 1 Gini Coefficients by Region

Source: Griin and Klasen, 2006. The data refer to population-weighted averages for a consistent sample of countries (37 countries in total comprising some 75% of the world's population). The data have been adjusted to account for differences in survey design and income concept used.

Clearly, the high and persistent inequality in Latin America is one of the central, if not the central, economic policy challenge for Latin American policy-makers and we have new tools and data at hand to study this issue. So it is not surprising that there has been such a resurgence of interest in analyzing

dynamics and determinants of poverty, inequality, and the relationship of policy affecting them in Latin America.

While this renewed research interest has already generated many new insights, analyses, and policy recommendations, many open questions remain. Among them I will highlight a few that appear to be of particular relevance for understanding inequality and poverty dynamics as well as their policy drivers. First, the linkages between growth, inequality, and poverty in Latin America need further clarification. While the cross-country literature has generated important insights in this area, more analyses at the country and even subnational level are required to understand these complex relationships and their most important determinants. Of particular relevance is to examine these relationships in the Latin American context of high economic instability with recurrent economic and financial crises, particularly in the 1990s. Second, measuring and addressing poverty remains a critical research area. While there is now good data and analysis on monetary poverty measures, non-monetary including subjective indicators of well-being often tell a different story that needs to be considered when analyzing poverty trends and determinants. Also, developing context-specific poverty reduction policies in Latin America remains a challenge for research and policy analysis alike and more work in this area is certainly still needed. Lastly, these issues need to be considered in an economic environment, where trade, migration, and economic integration are of particular importance. Thus the role of trade and migration in generating, sustaining, or reducing inequalities between and within countries is an area that requires further analysis. In particular, given the increasing migration from poorer Latin American countries to richer ones and to the United States, both the determinants of that migration as well as the consequences for poverty and inequality require further analysis. Similarly, Latin America opened up its economies in the 1980s and 1990s while pursuing attempts to further regional integration. The impact of these trends on inequality and poverty within and between countries remains an area of active investigation.

The papers in this volume were selected from a conference entitled 'Poverty, Inequality and Policy in Latin America' that took place at the Ibero-America Institute for Economic Research in Gottingen, Germany in July 2005 which was funded by the German Science Foundation and co-sponsored by the CESifo research network. In the remainder of this introduction, we briefly summarize the contributions, their relationship to the open questions raised above, and close by identifying open research and policy questions.

The present volume encompasses three main themes. In the first, linkages between growth, inequality and poverty are examined at the regional, national, and sub-national level. In Chapter 1.1, Veronica Amarante gives a comprehensive overview of the literature on the relationship between growth and inequality, and presents her own findings on Latin America. She approaches the problem from two different perspectives: first, by examining the effect of income growth on inequality (testing the Kuznets curve), and second, by Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

investigating the link between inequality and income growth (analyzing the determinants of growth), building on previous research in the empirical growth literature. Amarante uses panel data for 22 Latin American countries and reestimates the Kuznets equation under alternative panel specifications. While a simple cross-section analysis reveals no relationship between growth and inequality, in the fixed effects specification, the existence of a Kuznets curve is confirmed for Latin America and Amarante finds a threshold value of \$3,526, above which growth decreases inequality and below which growth increases inequality. Turning to the inverse relationship-her second approach, the relationship between inequality and growth-the empirical evidence from panel studies is mixed and not robust. Pooled OLS and random effects specifications suggest that inequality does not have a significant impact on subsequent growth. When utilizing a fixed effects specification, however, the finding is very different: inequality now has a positive and significant effect on subsequent growth. Amarante additionally finds that in fixed effects models the effect of inequality on growth depends on the level of per capita GDP. For the GMM (Generalized Method of Moments) estimation, inequality and the interaction between inequality and GDP levels lose significance. Amarante concludes that although there is abundant empirical evidence on the link between inequality and economic performance, the results are not at all conclusive and the debate in the empirical literature remains open. A major finding of her study is that the results of cross-sectional analyses cannot be confirmed by panel analyses and vice versa.

The following four papers examine the relationship between growth, inequality, and poverty at a more disaggregated level, either focusing on particular historical episodes in a country or using sub-national data. In Chapter 1.2, "Growth, Inequality and Poverty: Some Empirical Evidence from Minas Gerais State, Brazil," Rosa Fontes, Elydia Silva, Luiz F. Alves, and Geraldo E. S. Junior study empirically the linkage between economic growth and income inequality in towns and microregions of the Brazilian state of Minas Gerais from 1970 to 2000. In order to test the income convergence hypothesis, they perform convergence tests (absolute 8-convergence, conditional 8-convergence, and convergence) and analyze the role of human capital in growth for the 66 microregions of Minas Gerais. Furthermore, they compare the very rich regions and very poor regions of the state to study the relationship between regional inequality and poverty. Using the -convergence test, Fontes et al. find little evidence of income convergence and inequality reduction over the past 30 years. Most of their estimations reveal a negative and highly significant relation between initial income and the rate of income growth during the period, suggesting that in general, the poorer regions and towns grew more than the richer ones (absolute 8-convergence). Further analyses also confirm that conditional 8-convergence took place, since proxies of human capital played an important role in Minas Gerais income convergence and growth. Despite absolute and conditional 8-convergence the authors also identify "convergence

clubs" among the poor and very poor regions, as well as among the richer ones, leading to different long-term steady states. This tendency shows an inability of the poor microregions to escape from the poverty trap, and highlights the need for public policies designed to overcome this obstacle and permit greater income equalization within Minas Gerais.

In Chapter 1.3, "Pro Poor Growth in Colombia 1996-2004" Adriana Cardozo investigates whether economic growth in Colombia has benefited the poor. Based on data from the Colombian household surveys, her study aims at evaluating whether growth in Colombia was pro-poor from 1996 to 2005 by deriving growth incidence curves (GIC) and calculating the pro-poor growth rate (PPGR) developed by Ravalli on and Chen (2003 ). After reviewing the concepts underlying the idea of pro-poor growth as well as of two of the most widely used techniques for calculating it, Cardozo analyzes GICs from 1996 to 1999, 1999 to 2002, and 2002 to 2005 to capture the effects of the economic slowdown, crisis, and recovery periods separately. Her results show that when applying a strict interpretation of the aforementioned methodology, growth in Colombia was indeed pro-poor between 1996 and 2005 given that the PPGR was higher than the mean growth rate (0.94 vs. 0.43). Nevertheless the difference is very small and the methodology does not indicate anything about how great the difference needs to be to conclude that growth was truly beneficial to the poor relative to the non-poor. A more balanced conclusion would be that growth was very low and was not averse to the poor (relative to the non-poor), yielding an almost unchanged incidence of poverty after 1996. Separating the results into smaller periods reveals an even clearer picture. From 1996 to 1999, the economic slowdown affected the poor much more severely than the nonpoor. In both urban and rural areas, income fell more dramatically for the extremely poor. Between 1999 and 2002, all GICs have a positive slope, and growth was pro-poor as shown by the PPGR results, which are heavily influenced by a statistical effect of income moving back to the levels observed before the economic crisis. In the period 2002-2005, gains from economic recovery were stronger, and income growth was pro-poor in urban and rural areas as well as for Colombia as a whole. Regional comparisons show that it was only in the Pacific region that growth was clearly anti-poor, while in the others, the results for the country as a whole hold: low income growth rates and small differences between the mean growth rate and the PPGR.

The following chapter also deals with the poverty and inequality impact of an economic crisis. In Chapter I.4, "Crisis and Recovery in Argentina: Labor Market, Poverty, Inequality and Pro-poor Growth Dynamics" Melanie Khamis examines the development of employment, unemployment, poverty, and inequality in Argentina in the period 2001-2004. Using micro-level household data from the official Argentine household survey (EPH), she studies labor market dynamics, poverty, income changes, and pro-poor growth features of the Argentine economy during the crisis of 2001-2002, the early recovery period of 2002-2003, and the later recovery period of 2003-2004. As to the labor market Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

dynamics, Khamis finds that the economic crisis and the recovery were largely reflected in the general labor market trends: increased unemployment and inactivity alongside decreased employment during the crisis, and the reverse picture during the recovery period. With respect to economic sectors, four emerged as the most dynamic in terms of changes in employment, unemployment, and inactivity: the manufacturing sector; other services; the construction sector; and the trade/retail, restaurants and hotel sector. Poverty and extreme poverty increased substantially during and after the crisis of 2001-2002, and in the period 2002-2003, poverty rates and indigence rates still showed small increases despite the continuation of government programs providing subsidies to the poor (through the Plan Jefes workfare program). A trend toward decreasing poverty and indigence was seen in the period from October 2002 to the second half of 2004. All inequality measures agree that inequality increased in Argentina during the 1990s, whereas they disagree over what happened between 2001 and 2003. Indices that attach greater weight to the bottom of the income distribution find a fall in inequality (Atkinson with parameters 1 and 2, and entropy with parameter 0) since relative incomes of the very poor increased. When using equivalized household labor monetary income or another inequality indicator, an increase in inequality is found between 2001 and 2003. Answering the question of whether Argentine growth cycles have been pro-poor, she finds that the poor experienced a very strong decline (-36.70 percent) in their household income in the period 2001-2002. In the period 2002-2003, their income increased by 7.27 percent and in the period 2003 and 2004, pro-poor growth was even higher at 15.40 percent. It has to be kept in mind, however, that both the growth component and redistribution (through government transfers) are important in explaining poverty and indigence changes for Argentina during the period 2001-2004. The pro-poor features of the early recovery period (2002-2003) were mainly accounted by government transfers (through Plan Jefes), whereas in the later stages of recovery (2003-2004), income increases among the poor were less a result of government transfers than of the pro-poor pattern of growth itself.

Staying with Argentine but covering a longer time period, Maria Santos examines regional inequality in Chapter 1.5 in her paper "Factors Influencing Income Inequality across Urban Argentina". She uses a panel dataset of 38 cities (cross-sections) over the period 1998-2003. The urban agglomerations covered by the survey contain 71% of the urban population of Argentina, and 62% of the entire country's population in the following six statistical regions: Greater Buenos Aires, Northeast Argentina, Northwest Argentina, Cuyo, Pampeana and Patagonia. Santos assesses inequality using four different measures: the Gini coefficient, the Theil l and Theil 2 indices, and the coefficient of variation (CV). In the period 1998-2003, she finds a steady increase in inequality over time with a peak in 2002 after the December 2001 crisis and a decline after 2002. However, the overall increase in inequality between 1998 and 2003 is relatively small. Inequality across the six statistical regions (in other words, the inequality Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

rankings) changed over the period under analysis: in 2003, all inequality measures ranked Greater Buenos Aires (GBA) as the most unequal region, followed by the Northeast and Northwest. In the panel analysis, inequality is explained by labor market characteristics (unemployment rate, returns to education, share of the employed in the secondary sector), human capital assets (proportion of people who completed primary school, secondary and higher education, demographic characteristics (proportion of indigenous households, age distribution of the population) and level of development characteristics (per capita electricity consumption, its square, percentage of population with unsatisfied basic needs). The study finds that unemployment and higher returns to education increase inequality, and a higher share of employed people in the secondary sector decreases inequality. The rate of primary education has a strong inequality-decreasing impact, whereas the rate of secondary education appears to have an inequality-increasing impact. The dependency index has the expected positive coefficient and is significant. The log of per capita electricity consumption, the proxy for GDP per capita, is significant and positive in all cases. And the percentage of people with unsatisfied basic needs is significant and positive for all inequality measures, except for CV. These results suggest that urban agglomerations are more unequal-not just because they are located in a particular region such as the North, for example, but because compared to other cities, they are likely to have a lower proportion of the population with primary education, less developed industrial sectors, and higher unemployment. These regions may also have a high level of structural poverty and dependency, and are affected by the presence of indigenous groups in the local population.

Chapter II moves to poverty analysis and poverty policy. In Chapter 11.6, "The Determinants of Subjective Poverty: A Comparative Analysis in Madagascar and Peru", Javier Herrera, Mireille Razafindrakoto, and Frarn;ois Roubaud concentrate on the subjective evaluation of poverty. Recent studies in developed countries have found that subjective well-being is based not only on monetary income and consumption but also on other factors such as employment and health. This paper examines the factors that determine households' subjective evaluation of their living standards through a comparative analysis of data from two developing countries, Peru and Madagascar. In their study, Herrera et al. use a database of first-hand observations to group objective individual variables (households' socioeconomic characteristics, environmental and individual trajectories provided by the two panel studies) together with corresponding questions on subjective wellbeing for both countries. The study confirms how important it is to study the non-monetary dimensions of poverty in developing countries: including these dimensions doubles the explanatory power of the econometric models of subjective well-being. It is interesting to note that overall, the results produced by Herrera et al. confirm the well established findings on determinants of subjective well-being from developed countries and speak in favor of applying a similar methodology in developing countries. Nonetheless, a few significant Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

differences emerge from this study. First, the data on Peru and Madagascar confirm that there is a positive, significant correlation between subjective wellbeing and monetary income, but that this correlation is significantly less than 1. In Madagascar, the poorer country, the strength of an association between subjective well-being **(SWB)** and income is higher than in the middle-income country, Peru. In both cases, other dimensions of well-being (such as health, education and job quality, but also family structures) play a non-negligible role. Furthermore, social interactions and trajectories also affect perceptions of wellbeing. With a fixed personal income, the average level of income in the neighborhood has a negative impact on SWB, confirming the rivalry hypothesis. Past income has a positive impact in both countries, partly capturing an effect of permanent income. Finally, social capital, social origins, and the ethnic factor (in Madagascar) have a significant impact on the perception of well-being. Apart from these commonalities, which prove the relative robustness of the results, interesting differences between Peru and Madagascar also emerge. Whereas local inequalities play a positive role in the perception of well-being in Peru, they play a negative role in Madagascar. The authors put forward the hypothesis of two different models of mobility and social norms: in the first model (Peru), inequalities are apparently seen as the result of strong social mobility, which is valued highly by the population; while in the second (Madagascar), social homogeneity is valued more highly, being viewed as the basis and/or the result of social relationships.

The objective of Chapter 11.7, "Geography, Livelihoods, and Rural Poverty in Honduras: An Empirical Analysis Using an Asset-Based Approach", by Hans G.P. Jansen, Paul 8. Siegel, Jeffrey Alwang, and Francisco Pichon, is to develop a conceptual and analytical framework that can be used to simulate and better understand the prospects for growth and poverty reduction in rural Honduras. Jansen et al. employ complementary quantitative and qualitative methods of analysis driven by an asset-based approach. Justifying this emphasis by the high inequalities found in the distribution of productive assets among Honduran households and geographical areas, the authors focus on household assets (broadly defined to include natural, physical, human, financial, social, and locational assets) and the combinations thereof that would be needed to take advantage of economic opportunities. They examine the relative contributions of these assets and identify the combinations of productive, social, and locationspecific assets that matter most for raising incomes and taking advantage of the prospects for poverty-reducing growth. They use factor and cluster analysis techniques to identify and group different livelihood strategies, and econometric analysis to investigate the determinants of different livelihood strategies and the major factors affecting income. Spatial analysis, community livelihood studies, and project assessments are also brought in to complement the more quantitative household survey data.

Five key findings with important strategic implications emerge. First, the rural areas of Honduras show significant heterogeneity in terms of asset

endowments. Even areas with high economic potential often suffer from persistently high poverty because the poor simply lack the basic assets needed to capitalize on the existing potential. Second, poverty is deep and widespread in rural Honduras-particularly in the hillside areas, where most households have limited assets on which to base their livelihood strategies. The high poverty density of hillside areas and the fact that some 80 percent of all rural poor are located there should make these areas a main target of national rural poverty reduction strategies. Furthermore, the overlap between high poverty rates and high poverty densities there means that investments in the hillside areas have good chances ofreaching significant proportions of the country's rural poor, and a minimal risk of' leakages'. Third, although agriculture should form an integral part of the rural growth strategy for hillside areas, its potential is limited. Public policies and investments must focus on issues of food security, natural resource security, access to land and forests, infrastructure provision, improved natural resource management, non-agricultural rural employment, and migration in order to foster broad-based and sustainable agricultural growth and to reduce rural poverty. Fourth, there is a need to move from geographically untargeted investments in individual assets toward a more integrated and geographically based approach that promotes complementarities among different measures. A multi-sector investment program is needed to upgrade and improve access to household assets and to generate and foster complementarities. Finally, asset investment programs need to be adapted to the specific needs of regions and households. While some household asset programs should be national in nature, others require local adaptation and should run in tandem with the national measures, but should focus on the specific needs of regions and households. Investment strategies should be formulated on a broad regional basis, but options within regions should be tailored to local asset bases.

The last chapter addresses questions of trade and migration. In the first contribution, Chapter III.8, "Trade versus Migration, and the Role of Diversity: A Simple Analytical Framework", Leonardo Auemheimer develops a theoretical model to explain migration dynamics, which is particularly relevant to explain migration dynamics between Latin America and the US. In it, migration is determined not only by real wage differentials but also by a preference for 'cultural' homogeneity: thus, social interaction factors also play a role. Auemheimer distinguishes four cases of migration equilibria. In the first, no migration occurs, since the gains in real wages for migrants are dominated by the loss of cultural homogeneity. In the second, the migration equilibrium is extremely unstable, being determined by only one (very low) desirable level of migrants in the host country. Backward migration results if this (low) level is surpassed (for example, if migrants face unfriendly treatment) or if migration does not occur due to the host country's cultural unattractiveness to migrants. In the third case, migration is dominated by the wage differential. There is also a unique value for the ratio of migrants to total labor in the guest country, but the ratio is much greater than in the second case, and therefore, migration always Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

takes place. The most interesting case is the fourth, where the model obtains two equilibrium values for migration: if migration is below the lower equilibrium value, there is no incentive to migrate to the host country, and backward migration would even occur. If migration is above the upper equilibrium value, some migrants would return home because of a feeling of no longer being welcome in the host country. Only if migration remains between its upper and lower equilibrium values would migration take place. The particularly interesting implication of this scenario is that migrants and host country residents will have different preferences regarding trade versus migration. While the host country residents would favor migration over trade, the migrants would favor trade over more migration. It would be well worth considering whether this model can help understand the politics of immigration in the US, with respect to Latin American immigrants.

In Chapter IIl.9, "South-South Trade Agreements, Location of Production, and Inequality in Latin America", Alessia Lo Turco investigates the impact of the Latin American South-South Agreements (MERCOSUR, CAN (Andean Community) and the CACM (Central American Common Market) on the concentration of production and the divergence or convergence of income levels in Latin America. To examine how Regional Free Trade Agreements (RTAs) potentially lead to concentrations in production she uses panel analysis using a model that investigates the determinants of industry location. Lo Turco finds that production concentration increased in a number of sectors, especially in the Central American Common Market (Costa Rica, El Salvador, Guatemala) and the Andean Community (Colombia, Ecuador). To deal with the issue of inequality, she uses a model based on aggregated data to determine whether the three regional south-south trade agreements have contributed to convergence or a divergence in income levels among the countries involved. A difference-indifferences approach is used to separate the integration effect from the general path of development in Latin America. A central outcome of Lo Turco' s study is that MERCOSUR and CACM have led to a divergence in income levels, while the renegotiation of the Andean Pact (CAN) has fostered a convergence.

The chapters in this volume add considerably to our understanding of the dynamics of inequality and poverty in Latin America as well as the scope for policy to address this issue. Nevertheless, open questions remain. The role of migration and migration policies for Latin American economic development will be a subject that will require further scrutiny, both on the theoretical but particularly on the empirical front. The evidence of large differentials in regional development is similarly intriguing but calls for more research to solidify the results, examine the determinants of spatial poverty traps in more details, and study whether these traps have become more or less severe over time. The impact of economic crises on poverty and inequality, at the national and sub-national level, will remain an active area of research as long as Latin America's growth trajectories remain so unstable. Lastly, most research will be needed on the multi-dimensionality and subjectivity of poverty in Latin

America. The experience of the 1990s demonstrates that moderate growth with some income poverty reduction has done little to reduce the social unrest and instability which have given rise to the re-emergence of populism in an increasing number of countries in recent years. Unless the problems of high inequality and poverty, objectively measured and subjectively felt, are addressed, more instability in Latin America is bound to lie ahead.

# **References**

Acemoglu, D. S. Johnson and J. Robinson. 2001. The Colonioal Origins of Comparative Development: An Empirical Investigation. American Economic Review 91(5): 1369-401.

Alesina, A. and D. Rodrik. 1994. Distributive Policies and Economic Growth. Quarterly Journal of Economics I 09:465-490.

Banerjee, A. and E. Duflo. 2003. Inequality and Growth: What can the data say? Journal of Economic Growth 8, 267-299.

Bourguignon, F. (2003). The Growth Elasticity of Poverty Reduction: Explaining Heterogeneity across Countries and Time Periods. In T. Eichler and S. Tumovsky (eds.). Growth and Inequality. Cambridge. MIT Press.

Deininger, K. and L. Squire. 1998. New ways of Looking at Old Issues: Inequality and Growth. Journal of Development Economics 57: 259-287.

Eicher, T. and S. Tumovsky. 2003. Growth and Inequality. Cambridge: MIT Press.

Engermann, S. and K., Sokoloff. 2002. Factor Endowments, Inequality, and Paths of Development among New World Economies. Economia 3: 41-109.

Forbes, K. 2000. A Reassessment of the Relationship between Inequality and Growth. American Economic Review 90: 869-87.

Griin, C. and S. Klasen. 2003. Growth, Inequality, and Well-Being: lntertemporal and Global Comparisons". CESifo Economic Studies 49: 617-659.

Griin, C. and S. Klasen. 2006. Growth, Income Distribution, and Well-Being: Comparisons across Space and Time. Mimeographed, University of Gottingen.

Klasen, S. 2003. "In Search of the Holy Grail: How to Achieve Pro Poor Growth?" In Tungodden, B., N. Stem, and I. Kolstad (Hrsg.). Toward Pro Poor Policies-Aid, Institutions, and Globalization. New York: Oxford University Press, pp.63-94.

Rodrick, D. 1998. "Globalization, Social Conflict, and Economic Growth" (Prebisch Lecture), The World Economy 21(2).

Rodrick, D. 2001. Why is there so much economic insecurity in Latin America? CEPAL Review 73: 7-29.

Roemer, J. 1998. Equality of Opportunity. Cambridge: Harvard University Press.

World Bank. 2000. World Development Report 2000/01: Attacking Poverty. New York: Oxford University Press.

Todd, P. and P. Wolpin. 2005. Assessing the impact of a school subsidy program in Mexico. Mimeographed, University of Pennsylvania.

World Bank. 2003. Inequality in Latin America and the Caribbean: Breaking with History? Washington DC: The World Bank.

World Bank. 2005. World Development Report 2006: Equity and Development. New York: Oxford University Press.

World Bank. 2006. Poverty Reduction and Growth: Virtuous and Vicious Circles. Washington DC: The World Bank.

# **1.1 Growth and Inequality in Latin America**

*Veronica Amarantea*

# **1.1 Introduction**

Research on inequality and growth has traditionally been carried out under two approaches: one was a direct derivation from Kuznets hypothesis, estimating reduced form equations to test for the relationship between the level of income and inequality. The other has analyzed the determinants of growth (Barro and Sala-i-Martin, 1995) and inequality (Li, Squire and Zou, 1998), inequality being an independent variable. A relatively new strand of literature is trying to focus simultaneously on inequality and growth and overcome the mechanistic Kuznets view, exploring new theoretical links. Whereas Kuznets emphasized the incidence of growth on income distribution, the reconsideration of the relationship between income distribution and economic growth during the nineties is based on the links between inequality and growth. 1 Instead of focusing on the relationship alone, new developments try to explain the links that connect both variables, implying causality from inequality to growth. Among the links pointed out by this literature are the political economy, imperfect capital markets, socio-political instability and endogenous fertility (Barro, 1999; Benabou, 1996). In the framework of this literature, and boosted by the availability of better quality data on inequality and longer time series for a variety of countries, new empirical evidence has surged. Recent panel estimations do not unambiguously yield the negative relationship between inequality and growth that was found when using cross sectional data (Forbes,

a Veronica Amarante received her MSc in Economics from Universitat Pompeu Fabra in 1998. Currently she is employed as researcher at Instituto de Economia, Universidad de la Republica, Uruguay. Her research interests are poverty, inequality and the labor market.

After Kuznets', the relationship between growth and inequality was somehow left behind during the seventies and eighties, when neoclassical growth theory, and later on endogenous growth, were in the centre of academic debate. The reconsideration of the topic in the nineties is due to several factors. First, macroeconomic stabilization policies and structural reform in developing countries renewed interest on distributive aspects, because their effects on equality began to be questioned. Secondly, two important economic facts took place: the change in some countries from centrally planned systems to market oriented ones and the reverse in the downward trend in inequality in developed countries (US and many OECD countries) in recent times. Last but not least, the availability of new data sets of higher quality fostered empirical research on the topic.

2000; Li and Zou, 1998), and the debate on the role of inequality is again in the center of the academic discussion. The importance of this issue is given by the fact that the impact of different policies may depend on the underlying process that relates the two variables.

When we consider the Latin American experience, there is a consensus about the disappointing growth performance of Latin American countries from 1960 on (De Gregorio and Lee, 2000, Loayza *et al.,* 2002). Given that Latin America is the most unequal region in the world for the whole period since data is available, the idea that poverty in Latin America could be significantly reduced if income or asset inequalities were not as high has been widely discussed. But recently, there is also a growing concern about the threat that high levels of inequality may be posing on growth potentialities.

This paper summarizes recent developments in the literature related to this topic, and considers the relationship between inequality and growth for Latin American countries. Specifically, we want to analyse/study the relationship between the high levels of inequality and the disappointing growth performance of the region. The analysis is based on the estimation of a reduced form growth equation, including income inequality among the explanatory variables, using a data set for regional countries covering five years averages for the period I 960- 2000. The paper is organized as follows: section one reviews the analytical framework for the study of inequality and growth, as well as recent empirical evidence. Section two illustrates recent growth experiences of Latin American countries. It also analyses different dimensions of inequality in the region and focuses on the evolution of income inequality during the past four decades, briefly discussing the role of institutions and structural reforms. Section three discusses previous empirical evidence on the relationship between inequality and growth, highlighting potential weaknesses and focusing on existing research for Latin America. Our methodological strategy and principal results are presented in section four. Finally, section five presents some concluding remarks.

#### **1.2 Analytical framework and empirical evidence**

This section summarizes theoretical developments on the relationship between growth and inequality, as well as the main empirical evidence. Following a historical perspective, the traditional vision is briefly presented, and then new developments, which emphasize different channels of connection between inequality and growth, are summarized.

# *1.2.1 The traditional approach: an overview*

The traditional approach to the growth inequality relationship was ruled by the early research by Lewis (l 954), Kaldor (I 956) and especially Kuznets (1955). These pioneering works were the foundations for the trade-off relationship between income distribution and economic growth, which was expressed in the famous inverted U-hypothesis.

Kuznets emphasised the incidence of growth on income distribution, so it was an argument **from growth to inequality.** Based on Lewis's previous work, he argued that in the development process labour moves from low productivity sectors to higher productivity ones. Low productivity sectors (typically agricultural) have lower per capita income and probably lower inequality, whereas higher productivity sectors (typically manufacturing) have higher per capita income and higher inequality. Total inequality is the aggregation of within sectors inequality. If inequality between sectors is higher than that within sectors, during the economic development process, which implies movement of people from the agricultural to the manufacturing sector, aggregate income inequality initially raises when people move from the low productivity sector to the higher productivity one, and their per capita income increases. During the following stages of development, the size of the low productivity sector decreases, and this leads to an increase in their relative wages, and to more workers in the high productivity sector, with higher per capita income. As a consequence, in this second stage the relationship between GDP per capita and inequality is negative. This was the birth of the inverted U-hypothesis, which was tested by the author using data for five countries. The data set was composed by two developed countries (United Kingdom and USA) and three developing ones (Puerto Rico, India and Ceylon). In his article, Kuznets states that his research is *5%* empirical information and *95%* speculation. In a later study (Kuznets, 1963), he provided further support for the inverted-U hypothesis. This time data were obtained from eighteen countries, mixing developing and developed ones.

Kuznets himself did not formalize a theory on this relationship, but set an argument that later on was formalized by Fields (1979), Robinson (1976) and Anand and Kanbur (1993). The inverted U-hypothesis brought about a lot of empirical evidence, generated by the estimation of the relationship with crosscountry data sets. The view of this relation found many adepts, and was considered a stylised fact (Ahluwalia 1976) or an economic law (Robinson 1976).

#### *1.2.2 New explanations for the growth inequality relationship*

The relationship between income distribution and economic growth was carefully re-considered during the nineties. Instead of focusing on the relationship alone, new strands of literature tried to explain the links that connect both variables, implying causality **from inequality to growth.** This Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

section reviews these new developments, dividing them in the following groups: (i) political economy, (ii) imperfect capital markets and investment in education, (iii) savings, (iv) endogenous fertility, (v) socio-political instability and (vi) other explanations.<sup>2</sup>

One of the mechanisms most commonly used to establish the link from inequality to economic growth is the **political economy** channel. Literature emphasising this channel focuses on two mechanisms: the median voter theorem and the lobby activities. The original median voter theorem was proposed by Meltzer and Richard (1981 ). They developed a model where the economy is composed by individuals with different income levels and a government that imposes a proportional tax and redistributes tax revenues between people. Income distribution is asymmetric in most countries, implying a median income below mean income. As income distribution becomes more unequal, median income rises far below mean income, so the ratio median/mean decreases. If agents vote on a redistribute/progressive tax system, the theory predicts that results will correspond to the tax rate preferred by the median voter. Preferences for redistributive taxes are inversely related to the voter's income, so higher inequality implies a lower median/mean relation and a higher preferred tax rate. This literature carries a message on the harmful effect of inequality for growth, but in this case the reason for this harmful effect is the fact that higher levels of inequality (ex-ante) are associated with redistributive policies that are distortive to growth. The driving reason is that redistribution is undertaken via marginal taxes that tend to bring down the rate of investment and the rate of economic growth.

Different variants of these models may imply a negative relationship between inequality and growth (Alesina and Rodrik, 1994; Persson and Tabellini, 1994), a positive one (Saint Paul and Verdier, 1993, 1996) or even ambiguity in this relationship (Li and Zou, 1998; Banerjee and Dulfo, 2000), depending on the underlying assumptions, mainly those related to the use of the income revenue.

Other authors have emphasised the role of **capital market imperfections** to explain the growth inequality relationship. Models based on capital market imperfections point out that in the presence of credit rationing investment opportunities depend on personal income and assets. So poorer people will have fewer opportunities to invest on human capital. A redistribution of assets or income from a rich person to a poorer one, which implies inequality reduction, will tend to increase average productivity of investment and will spur economic growth. Capital market imperfections can be due to credit market imperfections or insurance market imperfections, which in tum can derive from asymmetric information and limitations of legal institutions. An example given by Barro

<sup>2</sup> A chart summarizing main theoretical channels proposed by the literature can be found in Annex Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 I . Downloaded from PubFactory at 01/11/2019 05:50:02AM via free access

(2000) is the difficulty of collecting defaulted loans with imperfect law enforcement. Among the research on capital market imperfections we find Galor and Zeira (1993) and Banerjee and Newman (1993).

The importance of **savings** as a link between inequality and growth dates back to the influential studies of Keynes (1936) and Kaldor (1956), so this strand of literature is strictly not part of the new developments. Individual saving rates depend on the level of income, if we assume that these rates rise with the level of income, then a redistribution of resources from rich to poor tends to lower the aggregate rate of saving in an economy. By the same token, a rise in inequality tends to raise investment, and so more inequality would enhance economic growth. This reasoning, which was influential some decades later, is somehow of secondary relevance due to the closed economy assumption. Most surveys on the relationship between inequality and growth do not include this channel in a direct way, an exception being Barro (1999), who mentions the saving channel in his review of these links. Nevertheless, we must keep in mind that somehow, two of the channels often mentioned act through the investment effect: capital market imperfection lead to lower investment in human capital, whereas the political economy channel (in its version of distortive taxes) also implies lower investment.

Literature relating income distribution and growth through the **fertility**  channel starts out with Barro and Becker (1988) and Becker, Murphy and Tamura ( 1990). These models are based on the idea that households face a tradeoff between quality and quantity when taking their decisions about children. The intuition implied in the recent literature that emphasises the link between growth and inequality through the fertility channel is presented by Perotti (1996) in simple terms. He points out that an increase in human capital of parents has both an income effect and a substitution effect. Higher levels of human capital cause higher demand for children by the income effect, but as the opportunity costs of having children increase, there is a substitution effect that implies lower demand for children. At low levels of human capital, the income effect prevails, but at a sufficiently higher level of human capital the substitution effect prevails, and an increase in human capital leads to less fertility. A redistribution of human capital from individuals with a high endowment to individuals with a low endowment, in case the substitution effect prevails, would imply an increase in the rate of return to education for poor people, so lower fertility. If the demand for human capital were elastic to the rate of return, this would also imply higher enrolment rates. A negative relationship between equality and fertility, and a positive one between equality and investment in human capital, result from this reasoning. This mechanism has recently been formalized by many authors (Galor and Weil, 1996; Dahan y Tsiddon, 1998; Morand, 1999).

Some authors consider that income inequality is tied to **socio political instability,** which threatens property rights and reduces investment. Along these lines, Alesina and Perotti ( 1996) argue that political instability can lead to lower growth. They consider that social conflict affects investment through three channels. First, political horizons are reduced, so traditional reputation mechanisms that prevent capital taxing are weak. This leads to an increase in the expected level of factor taxation and eventually prevents investment. Secondly, social conflict generates interruptions in productive activities and so lowers labour and capital productivity, leading to lower economic growth. Finally, it increases uncertainty, so risk aversion leads investment projects to be postponed or carried out in other countries. They argue that this can be a good explanation for the different performance between Asia and Latin America.

Finally, **other explanations** given by different authors to link growth and inequality should be mentioned. Among these possible channels, we can find the size of the market (Murphy, Shleifer and Vishny, 1989), the existence of a middle class consensus (Easterly, 2001), the role of institutions and initial conditions (Sokoloff and Engerman, 2000), the existence of "neighbourhood effects" (Durlauf, 1992; Benabou, 1994, 1996) and the importance of land inequality (Deininger and Olinto, 2000).

# *1.2.3 Empirical evidence*

The origins of the empirical literature on inequality and growth can be tracked to the initial attempts of estimating Kuznets curves, meaning the relationship between inequality and the level of income. A wide range of cross country studies look at approximately the same point in time and examine how the pattern of inequality varied when moving from lower income to higher income countries. The usual estimated regression contained income inequality as the dependent variable and per capita national income, with a non-linear specification, as the explanatory variable. 3 Among this research we find Adelman and Robinson (1989), Clarke (1995), Fishlow (1995), Bourguignon and Morrison (1990) and Jha ( 1996), who estimate this kind of cross-country equations. Most of these works find support for the Kuznets hypothesis, as well as the empirical research undertaken in the previous decades. This led Adelman and Robsinson (1988) to conclude: <sup>4</sup>

"the initial phase of the development process, during which a mostly agrarian economy starts industrialization, is necessarily marked by substantial increases in the inequality of the distribution of income, with a sharply reduced

<sup>3</sup> The two main approaches consist on including per capita income and its square on the right hand side of the equation (the inverted-U hypothesis would be consistent with a positive coefficient on income and a negative one on its square) or including per capita income and its inverse (in this case the inverted-U hypothesis would be consistent with both coefficients being negative).

<sup>4</sup> Despite finding evidence for the inverted U shape, these regressions indicate that the most important variables are not included, as variation in national income explains only a small fraction of the variation in income inequality; Ros (2000) reports an RStephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 <sup>2</sup>of the order of 0.15 to 0.20.

share of income going to the poorest 20, 40 and 60 percent of the population" (Adelman and Robinson, p. 958).

Later on, empirical research on the Kuznets curve tried to estimate the relationship between income and inequality, using panel data, a better approximation for this kind of empirical problem. As Bruno *et al.* (1995) point out, in middle-income countries, like Latin American ones, income is usually the variable used to analyse inequality, whereas in the rest of the countries, including Asian economies, consumption is generally used. Income inequality is generally higher than consumer inequality, so this kind of measurement differences could drive the inverted-U-result when considering a wide set of countries, and could disappear when using similar inequality measures. Previous results from cross-country studies have been questioned by later research that finds that when country fixed effects are included and the model is estimated using first differences, the coefficients on income and its square may become not significant. Fields and Jakubson ( l 984) show that the estimated curve can go from a statistically significant inverted U to a statistically significant U-shape when fixed effects are included.

Based on panel data, Ravalli on ( 1995) and Deininger and Squire ( l 998), among others, find no support for the Kuznets hypothesis. When allowing for country specific intercept dummies, the coefficients on income and its inverse lose significance and even reverse their sign. Considering these findings, Fields (200 l) concludes that:

"the inverted U-pattem in the cross section has nothing to do with growth per se; what it has to do is with the fact that for particular historical, political and cultural reasons, Latin American countries have higher inequality than other developing countries" (Fields 200 l, p. 45).

More recently, and before the debate about the existence of the Kuznets curve was closed, empirical research has centred on the relationship between inequality and growth (that is the change of income, not its level), in the framework of the theoretical explanations reviewed before, implying causality from inequality to growth. Again, cross sectional data was used in the first stages, mainly because of the problems with availability of data. In general terms, results from these estimations show that inequality is harmful to growth in the long run, implying a negative coefficient on the inequality variable. Among this research, studies from Persson and Tabellini (1994), Alesina and Rodrik (1994), Alesina and Perotti ( 1996), Perotti ( l 996), Keefer and Knack (2000) are included. The general conclusion is that initial levels of inequality significantly predict the subsequent rate of economic growth, with higher inequality being detrimental to economic growth. In words ofBenabou (1996):

"These regressions, which run over a variety of data sets and periods with many different measures of income inequality, deliver a consistent message: initial inequality is detrimental to long run growth" (Benabou 1996, pp. 13).

More recently, cross-country growth equations results have also been questioned by evidence based on panel data estimations. Forbes (2000) argues that there are a number of potential problems with this empirical research: the lack of robustness and the existence of measurement error and omitted variable biases. Her estimations of a growth equation (five year average growth rates from 1966-1995) show that, no matter which panel estimation technique is used, the coefficient on the income inequality variable is never negative, challenging the common belief of a negative effect of inequality on growth. 5 She concludes that in the short and medium term an increase in a country's level of income inequality has a significant positive relationship with subsequent economic growth. Country specific, time invariant omitted variables were generating the significant negative bias in the estimated inequality coefficient from crosscountry data.6 Li and Zou (1998) also find evidence of a positive relationship between changes in inequality and changes in growth, using the Deininger and Squire ( 1996) data set. Barro (2000) uses a panel of countries and considers average growth rates and average ratios of investment over three decades to capture long run effects of growth, using three stage least squares for his estimation. He finds that for the growth rate, the expected coefficient on the Gini variable is zero, so overall differences in Gini coefficients for income inequality have no significant relation with subsequent economic growth. He states that one possible interpretation of this is that the various theoretical effects of inequality on growth are nearly fully offsetting. He also finds that when the effect of the Gini coefficient on economic growth is allowed to depend on the level of economic development, the coefficients are jointly and individually significant and the estimated relation implies that the effect of inequality on growth is negative for low values of GDP per capita and positive for higher ones. Finally, Banerjee and Dulfo (2000) find that past variation in inequality is related to subsequent growth in a non-linear way. While the linear term is insignificant, the quadratic term is negative and significant. Their results do not support the conclusion that increases in inequality are followed by increases in growth, as argued by Forbes (2000). Indeed, increases in inequality, like reductions in inequality, seem to be associated with a fall in growth.

Summarizing, it can be said that in general terms, recent **panel data**  estimations do not yield a clear negative relationship between inequality and growth that was found when using cross sectional data. Therefore, the need to advance in more careful research on the sign and explanation of the inequalitygrowth relationship emerges as a result of the survey of existing evidence.

*<sup>5</sup>* She estimates the model with random effects, fixed effects and using generalized method of moments **(GMM),** the method proposed by Arellano and Bond (1991).

<sup>6</sup> She suggests as possible omitted variables levels of corruption, share of government spending on basic health care or primary education, quality of public education. Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

Moreover, empirical evidence on this issue is mainly based on reduced form equations of growth, adding income distribution and some variable related to the hypothesis they want to test on the right hand side. The scarce research that tries to find support for any of the theoretical explanations discussed above is based on cross country data sets and so is likely to suffer from omitted variable bias.<sup>7</sup>

# **1.3 Growth and inequality in Latin America**

# *1.3.1 Economic growth and income distribution*

The theoretical and empirical debate about the relationship between inequality and growth is especially relevant for the Latin American case. The idea that poverty in Latin America could be significantly reduced if income or asset inequalities were not as high has been widely discussed and is fundamental for the study of inequality. But recently, there is also a growing consensus about the threat that high levels of inequality may be posing on growth potentials. The question that has recently been formulated on the role of inequality *(ls inequality harmful for growth?,* Persson and Tabellini 1994) seems particularly important for the region. Although some authors have formulated this hypothesis (Birsdall, Ross and Sabot 1995), focused empirical analysis seems to be necessary in order to assess both the role of inequality on economic growth and the specific channels that explain the relationship between these two variables.

Previous research based on world data sets suggests the importance of distinguishing different regional behaviours (Barro, 2000; Deininger and Squire 1998; Fields and Jakubson, 1994), or recognize the fact that world wide data sets may not yield results valid for less developed countries (Fishlow, 1995; Forbes, 2000). Given the presumption of a connection between high levels of inequality and disappointing growth performance in Latin American countries, this research will try to shed light on this relationship and the possible factors that explain the link. Data on income distribution by country also shows important variation, ranging from 0.625 (Brazil) to 0.44 (Uruguay) according to last figures from ECLAC (2003). Growth rates are also considerable different between countries. So data exhibits enough variation to pose the question whether there is a common relationship between inequality and growth in the region. Moreover, the availability of panel data for the region is crucial to understand this phenomenon, in the light of recent controversies on empirical evidence reported in the previous section. This section briefly presents the main facts that arise when considering the economic situation of Latin America during the last four decades: high levels of inequality and a lack of dynamic growth performance.

<sup>7</sup> A chart summarizing Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 empirical findings is presented in Annex 2. Downloaded from PubFactory at 01/11/2019 05:50:02AM via free access

Growth performance has been considerably divergent in different regions of the world during the last decades. There is a consensus about the disappointing growth performance of Latin American countries from 1960 on (De Gregorio and Lee, 2000; Loayza *et al.,* 2002)8• Average growth rates in the region have been lower than the world ones for the whole period up to 1990, but clearly the eighties were particularly bad years. The "lost decade" deepened the gap between the region and the developed world; only two countries in the region (Colombia and Chile) were able to reach positive growth rates. Even when economic growth became positive during the nineties, it did not recover the levels prior to the debt crises.

Inequality is probably the strongest feature of Latin America as a region; it permeates several dimensions of economic and social life.

The study of inequality in Latin America from 1960 on faces an important problem related to data, as no systematic statistics existed for many countries up to the 70s. This section summarizes the evolution of inequalitf in the region and considers its different dimensions based on existing research . Table 2 presents the evolution of the Gini coefficient on income for different regions of the world.

The region's relative disadvantage is present since data has been available, Latin America is the most unequal region in the world10• Londono and Szekely ( 1997) point out that not only does Latin America have the highest inequality level in absolute terms, but that it is much higher than expected given the level of development of the region. They estimate the "excess inequality" and argue that, in 1995, the region registered a Gini coefficient that was 25% higher than what one would expect given its GDP per capita. This estimation may be subject to criticism, as it implicitly assumes the existence of some kind of Kuznets relationship; nevertheless it is illustrative of the regional situation. Londono and Szekely interpret poverty in the region, to a large extent, as a distributive problem.

Szekely and Londono (1998) point out that, on aggregate terms, macroeconomic expansion during the 70s took place jointly with an

<sup>8</sup> Some authors even point out that a misleading optimistic view about the region prior to the debt crisis ( 1982) can be obtained when considering weighted averaged growth rates, because among the best performers were the biggest countries of the region, Mexico and Brazil, which explain 25 and 30% of regional GDP respectively.

<sup>9</sup> This section is based on Gasparini (2003), Justino, Litchfield and Whitehead (2003), Londono y Szekely (1997), Londono y Szekely (2000), Szekely y Hilgert (1999).

<sup>10</sup> Szekely and Hilgert (1999) argue that Latin American household surveys severely underestimate inequality because they do not include information on the richest sectors of society. But even with these failures, they argue that inequality in the region is mainly Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 the consequence of income concentration among the top deciles.

improvement in income distribution. Apparently this decade was characterized by an expansion of the incomes of the poor and the middle classes at the expense of the richest population. Several countries in the region experienced equalizing changes (Mexico, Colombia, Peru and Venezuela among others) while others showed stable distributions. Only in the southern cone (Argentina, Chile and Uruguay) inequality increased during the seventies (Gasparini, 2003).

The eighties were a lost decade not only in terms of growth, but also in terms of equality. Most countries increased their income inequality, and this is mainly explained by higher concentration in the top decile, according to Londono y Szekely (2000).

After carefully analysing a set of household surveys for different Latin American countries, Szekely (2001) argue that there is no country in the region where income inequality improved during the nineties, despite the moderate economic growth. The increasing trend that inequality showed during the 80s continues in the 90s, although the authors point out that there seems to be a slight deceleration in deterioration. Lack of improvement on distributional aspects during the nineties is attributed to the fact that individuals located at the lower tail of the distribution do not seem to have benefited from growth to the same extent as other sectors of the population. Gasparini (2003) somehow disagrees with Szekely (2001 ), as he points out that this study overlooked the two most relevant distributional changes in the region: the large increase in inequality in Argentina and the distributional improvement in Brazil. Decrease in Brazil's inequality was small and did not change its position as the most unequal country in the region. The author also states that, if instead of considering simple averages, a population weighted average Gini coefficient was computed for the region during the nineties, it would show a small decrease because of the positive performance of Brazil and Mexico and the stability of Colombia, the three most populated countries in the region. Analysis of inequality during the whole period shows that both the level and the change of overall inequality are mainly due to differences within rather than across countries. A slow convergence in per capita income has occurred among countries in the region, so the increase in inequality is due to disequalizing changes in the income distributions within countries. The author stresses that less unequal countries have performed worse on average than more unequal ones. While inequality increased in Argentina, Uruguay and Venezuela, which are economies with low levels of inequality, it has not changed or even become more equal in Brazil, Colombia, Mexico and Panama. The standard deviation for the distribution of the Gini coefficients in the region fell substantially in the last decade, from 6.1 to 4.6. This implies that the region has become even more homogenous in distributional terms.

# *1.3.2 Other dimensions of inequality*

Income inequality is just one aspect of inequality. Social, cultural and political aspects do also play an important role in the region. Justino, Litchfield and Whitehead (2003) analyse the multidimensional aspects of inequality, pointing out the importance of inequality in employment conditions, access to land and physical assets, use and access to social services and access to political power in the region. With regards to health care, education and social security benefits, inequalities are determined not only by the access, but also by the quality of these services. These inequalities are mainly dysfunctional 11 and arise as a consequence of political connections, inherited wealth and power and discriminatory acts. The authors also argue that race and ethnicity are among the most important correlates of inequality in Latin America. With regards to political inequalities they point out that, despite an appearance of pluralism and even ideological discord, dominant elites concentrate political power, and this is accentuated by the low organisational capacity of poorer groups. Justino and Acharya (2003) point out that social and political policy decisions are most of the times determined by the interests of elites. Following Frances Stewart's work, they emphasize the importance of focusing on groups, rather than on individuals, to understand inequality, as they argue that the group dimension of inequality is very high in Latin America.

The importance of asset inequality for the region, and its potential effects on economic growth have also been analysed in Birdsall and Londono (1997). They find that higher initial income inequality is negatively associated with long-term growth, but once that variables measuring initial asset inequality (land distribution and human capital) are included, income inequality itself is no longer statistically significant. So, according to these authors, what really matters is asset distribution in the region.

Gasparini (2003) analyses non-income measures of inequality, including health status, crime victimization, political influence and access to basic services. He stresses that in health status, the region is highly unequal by international standards, and probably the same happens with political influence, although information is weaker in this area. Cunningham *et al* (2003) find that educational attainment is unequal by race and gender. Accordingly, wage differentials between racial and ethnic groups are driven more by productivity related characteristics than by differences in the returns to those characteristics. Two characteristics are most responsible for the wage gap: inequality in education and racial and ethnic disparities in urban-rural residence. Nevertheless, national wage inequality is mostly explained by within group differences, instead of between group ones.

<sup>11</sup> They consider as dysfunctional inequalities those that do not arise as a result of rewards to risk taking, enterprise, skill acquisition and saving but as a consequence Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 oflack of opportunities and social and political exclusion.

Justino and Acharya (2003) argue that the tax system, unequal initial opportunities and various fonns of discrimination are among the factors that detennine high and persistent inequality in the region, so regional inequality is mainly dysfunctional. The prevalence of regressive tax systems is related to the existence of weak governments influenced by elites. Macroeconomic crises and high inflation also explain the weakness of the tax systems in the region. The importance of unequal initial opportunities is illustrated by the fact that returns to higher education have been very high for a long time. This reveals the relative scarcity of human capital and suggests that the acquisition of human capital may be closely related to family and community connections. The authors quote different research pointing to a high intergenerational transmission of inequality in Latin America. Neighbourhood effects tend to reinforce these mechanisms. The authors also suggest that discrimination, especially against indigenous people, may explain inequalities between different groups. The persistence of high inequality is related, according to these authors, to the fact that inequality is considered undesirable but politically tolerable. Civil society in the region has been concerned about other issues other than redistribution, mainly security and fiscal stabilization.

### *1.3.3 The role of institutions*

The importance of economic and political institutions to understand the persistence of high regional inequality has been stressed in a number of recent papers (Engennan and Sokoloff, 2002; Acemoglu and Robinson, 2002). With a historical perspective, Sokoloff and Engennan (2000) compare the evolution of European colonies in North and South America. Whereas in the beginning North America was of relatively marginal economic interest when compared to the Caribbean and Latin America, nowadays it is clear that US and Canada have proved to be far more successful in economic tenns. Traditional explanations for these facts have highlighted differences in the security of property rights, levels of corruption, structures of the financial sector, investment in public infrastructure and social capital, and even the inclination to work hard or be entrepreneurial. But the evidence of wide disparities among economies of the same European heritage led scholars to explore the role of factor endowments in the consolidation of paths of economic and institutional development. Although the role of factor endowments has been previously stressed, thise new vision emphasizes how the different environments of the European colonies may have led to societies with different levels of inequality, stressing the evolution of institutions that consolidated this inequality. Departing from previous explanations that emphasize the importance of exogenous differences in religion or national heritage in the fonnation of institutions, they suggest that initial conditions could have had a significant impact on long run paths of institutional and economic development.

Economies of scale in the production of sugar and other crops, jointly with their intense use of slave labor supply, led to an extremely unequal distribution of wealth and human capital in countries dedicated to this type of production (Cuba, Barbados, Jamaica and Brazil). This determined the evolution of institutions that protected the privileges of the elites and restricted opportunities of participating in the commercial economy for the major part of the population, even after the abolition of slavery. Other categories of colonies are the ones rich in mineral resources (like Mexico and Peru), where natives survived the contact with colonizers. In this case elite families acted as representatives of the Spanish government, and their power was protected by restrictive immigration policies applied by Spain to their colonies. The third group of colonies is integrated by the actual United States and Canada. They did not have a substantial population of natives nor a comparative advantage in the production of crops, so their development was based on workers of European descent who had relatively high and similar levels of human capital. The abundance of land and low capital requirements made it possible for adult men to operate as independent proprietors.

These initial conditions in the distribution of wealth and power were reproduced by government policies and other institutions that developed in the colonies. The authors argue that elites in societies which began with greater inequality were able to influence the choice of legal and economic institutions in their favor. 12 Such biases in the path of institutional development may explain the persistence of inequality in the long run in Latin America.

#### *1.3.4 Structural reforms and their impact on income inequality*

The impact of structural reforms on inequality has also generated debate. Many researchers have tried to assess the impact of different reforms on inequality. This section summarizes some of the results.

Morley (2001) presents the expected results of reforms on theoretical grounds. The effects of trade openness on inequality have been widely discussed in economic theory. The liberalization of the capital account integrates the local and international capital markets, bringing local interest and profit rates closer to world ones. If this induces an inflow of foreign capital, the distributional effects are ambiguous. Wage to profit ratios should rise because of the rise in capital to labor ratio, so a potential progressive effect can be found. But if capital and skilled labor are complementary, the skill differential will rise, with a regressive effect. The behavior of domestic owners of capital also counts. If the demand for foreign exchange was excessive under capital controls, reforms should cause a capital outflow with the opposite results. Besides, opening the capital account

<sup>12</sup> In this regard, they examine the institution of public primary schools, which was widely developed in North American since colonial times. They also analyse the role ofland Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 ownership and the extension of the franchise.

shifts the balance of power in favor of capital holders, as both government and labor are forced to accept arrangements favorable to capital owners in order to attract foreign capital. Financial reforms are supposed to increase private savings and investment, with a progressive result, although the distributional effect is probably small. Tax reforms shifted the tax burden away from the wealthy and towards the middle and lower classes, with a regressive effect. Finally, the expected impacts of privatizations are ambiguous, as they depend on three aspects. First, if the sales price of the assets of the state owned enterprises is less than their true market price, there is a transfer from taxpayers to buyers. Second, the effect on costumers depends on the previous situation. If publicly owned utilities subsidized their customers by selling below costs, the elimination of the subsidy could have progressive or regressive effects depending on who the customers were. The third effect is the impact on labor demand and employment. As employment in public enterprises followed frequently political objectives, privatization implied job destruction. Morley (200 I) suggests that privatization is likely to have mainly hurt the middle class, which represented both the main users of subsidized services and their main employees.

Based on an econometric estimation of the predictive power of the reform indexes over changes in inequality, Morley (2001) concludes that in the aggregate, reforms seem to have a regressive effect on distribution, but the effect is small and marginally significant. This result hides the fact that different reforms had different effects on inequality. He finds that trade reform has no significant effects, tax reform is unambiguously regressive (shifting the tax burden away from the rich), and opening the capital account is unambiguously progressive. These last two results are, in his opinion, the more robust.

Berry (1998), based on the study of nine countries in the region, concludes that in every case except for Costa Rica and Colombia, reforms took part jointly with an increase in inequality. Contrary to the expectation, trade reform did not narrow wage differentials, but these were increased in the period, suggesting that the region's comparative advantage is not in unskilled labor.

Behrman, Birdsall and Szekely (2001) analyze the effects of trade and financial liberalization on poverty and income inequality. They use the reform indexes proposed by Lora (1997) and rely on first-difference econometric estimations to test the impact of reforms. They also construct different inequality measures apart from the Gini coefficient, using micro-data for a wide set of countries. They find that financial liberalization seems to have positively affected inequality (meaning an increase in inequality), whereas commercial liberalization does not seem to have a significant effect. They also find that the other reforms (tax reform, international financial liberalization, and privatization) do not seem to have affected income inequality in the region, but volatility and inflation do affect inequality positively. They also argue that trade liberalization seems to have multiple effects with different signs, that tend to

cancel out on the aggregate. Financial liberalization implies greater capital inflow, and is therefore associated with a decline in the price of capital, which is relatively scarce in the region. If capital and skilled labor are complementary factors, this reduction in the price of capital generates higher demand for skilled labor, and this causes the increase in inequality.

Spilimbergo, Londono and Szekely (1999) and Londono (2002) explore the relationship between inequality and trade openness. They find that, empirically, there is not a close link between these variables; differences in trade openness only explain a minor part of the change in inequality. Nevertheless, inequality seems to be closely related to geographical factors. They find that trade openness is associated with higher inequality, for given factor endowments, but the effect depends on the relative abundance of each type of factor. Inequality increases in countries well endowed with skills and declines in countries abundant in physical capital and land. The effect of opening on inequality in the region is modest, as Latin America does not have a high level of unskilled labour relative to the world. Londono (2002) concludes that inequality in the region is associated, not with trade openness, but with the slow accumulation of capital (human and physical) in relation with the world.

#### **1.4 Empirical evidence on the relationship between inequality and growth in Latin America**

Low growth performance combined with high levels of inequality has implied that the region was not able to reduce poverty significantly. On these grounds, inequality has been identified as a major problem for Latin American countries. The re-discussion of the theoretical relationship between economic growth and inequality, and the new empirical evidence on these issues, have generated new interest in the role of economic inequality, questioning the economic wisdom of continued high levels of inequality in the region (De Janvry and Sadoulet, 2000). This section reviews existing empirical evidence on the relationship between inequality and growth in the region, and presents new empirical evidence based on a panel data base for 1960-2000.

#### *I .4.1 Previous evidence*

A number of papers that analyze the role of economic growth and inequality with a long-run perspective found that poverty and inequality have been closely linked to the economic cycle in the region, rising during recessions and falling during recoveries (Psacharopoulos *et al.,* 1993; Iglesias, 1998). Considering only the last two decades, Szekely and Londono ( 1998) found that inequality increased in the eighties, during recession, and went on increasing during the nineties, when economic growth took place. This suggests that the pro-cyclical pattern may have been broken during recent years. Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2

Research on the theoretical links between inequality and growth is not abundant. Most studies analyze changes in GDP per capita and changes in inequality across countries, looking for correlation as evidence of a relationship. Most research considers changes in poverty and inequality during globally defined historical periods broadly associated with growth or recession, without considering countries idiosyncratic phases of growth and recession. An exception can be found in DeJanvry *et al* (2000), who carry out a detailed analysis of episodes of growth and recession specific to each country in the region. They define 48 spells in twelve countries, classified into three groups according to Gross National Income per capita growth (GNI pc): early growth (spells with positive GNI pc before reforms), recession (spells with negative GNI pc) and late growth (spells with positive GNI pc originated after the reforms). Their results indicate that growth only reduces urban poverty if the initial levels of inequality and poverty are not too high. They also find that there is an asymmetry in the effect of changes in income on poverty, and this may lead to misleading conclusions since the overall relation between income and poverty is principally obtained through spells of recessions . Another important result refers to the relationship between inequality and growth: growth was always ineffective in reducing inequality, both under early and late growth. They also find that the services sector has been crucial in reducing both urban and rural poverty. They stress the need to attack inequality through direct policies, as growth alone might not be able to reduce inequality.

Previous studies that use econometric models to estimate the relationship between income or growth as independent variables and inequality as dependent variable are scarce. Morley (200 I) estimates the following equation:

where i denotes country, *t* denotes year, *a* is a regression constant which may vary across countries, *Y* is income, *Z* is a vector of variables such as inflation, land distribution and education, *R* is an index of reform, *S* is a vector of dummies which reflect various sample characteristics, *T* is a trend variable. He estimates this Kuznets equation under alternative specifications: fixed effects, random effects, urban and nationwide samples and so on. The general model explains between 85 and 97% of the total variance of Gini over time and across countries. The coefficients on income and its inverse are negative and significant in the fixed effects specifications, indicating that there is a stable and identifiable relation between income and inequality in the region, displaying the inverted-U shape. The hypothesis of a single Kuznets curve common to different countries is rejected.

He also finds that a high percentage of workers with primary school have a large and regressive effect on the distribution, and that increasing the share of university graduates in the adult population is progressive. The effect of expanding university education is lower than that of reducing the size of the group with Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 primary education, suggesting that a bigger distributional impact is

obtained by spending in reducing the size of the group with low education. The estimations also show that high inflation is regressive, and that sample characteristics have a significant effect on the level of the Kuznets curve.

Morley also finds that reforms have had, on aggregate, a small regressive effect, but the effect of each component is different. Trade reform was regressive; whereas opening the capital account has been progressive ( capital inflow produces lower profit rates and increases the demand for labour, causing a progressive effect). Tax reforms were regressive as they shifted the tax burden away from the rich. Another finding from this research is that the Kuznets curves for the high income countries tend to get flatter over time, and the low income curves are getting steeper, meaning that growth is getting less and less progressive. The author suggests that the impact of growth on inequality is likely to be more regressive in the futures, indicating the need to undertake specific policies. Finally, Morley (2001) estimates the same regression using as dependent variable the change in the Gini coefficient, finding that changes in inequality are negatively related to changes in income. So inequality falls during recovery and rises during recessions.

Garcia and Furquim (200 I) evaluate the relationship between income inequality and economic growth in Latin America, based on a 13 country panel, from 1970 to 1995. They estimate an equation for per capita product, relating it to the savings rate, the demographic growth rate, the technological innovation rate and the depreciation rate, and adding the Gini index. The coefficient for the Gini index is positive and significant at 5%, showing that, according to the author's words, for the sample of Latin American countries, higher concentration of income allows a larger per capita income. They also estimate a traditional growth equation, under the following procedure: first they estimate the convergence equation, without human capital. Then they add average schooling, and finally, they add the inequality index. They find that the coefficient associated to inequality is positive: income inequality has positively affected the rate of economic growth of the sample countries. According to their estimates, a 0.10 increase in the Gini index (from 0.4 to 0.5, for example) is associated with a very high growth rate of per capita income for the countries of this sample: 15.6% in five years. In a later paper, Garcia, Bandeira and Furquim (2002) present a growth model where economic reforms promote capital effectiveness, increasing both growth and income inequality as effective capital productivity grows faster than labour effectiveness. They find that positive and significant inequality coefficients on income and growth equations become insignificant when economic reform indices are included. According to the authors, this suggests that there is no direct "causal" relation between inequality and growth; economic reforms would be the proper mechanism which explains both growth and inequality trends.

The short review presented in the above paragraphs indicates that there is no strong evidence about the relationship between income inequality and economic growth. Evidence on this relationship is contradictory and moreover, research on the potential links between inequality and growth seems to be at a very early stage.

# *1.4.2 Methodology*

Until the development of panel data methods, growth equations were traditionally estimated using cross-country data sets, based on an equation like the following:

$$
\partial\_{\nu} y\_{\nu} - y\_{\nu - 1} = \alpha + \delta y\_{\nu - 1} + \beta \mathcal{K}\_{\nu - 1} + \varepsilon\_{\nu} \tag{l}
$$

where y;, denotes the logarithm of per capita GDP, and is included as a lagged variable on the right hand side (convergence hypothesis) and *Xif./* denotes a vector of country specific time varying variables that affect growth. Under the assumption that the error tenn is independent of the explanatory variables, this equation was commonly estimated by Ordinary Least Squares (OLS). But the disturbance tenn captures the effect of unobserved time invariant and time variant country characteristics, so it can be decomposed in a country specific time-invariant effect, *U;,* and a time variant disturbance, *eit.*

$$\mathbf{e}\_{it} = \mathbf{u}\_i + \mathbf{e}\_{it} \tag{2}$$

Equation ( **l)** can be re-written as:

$$
\delta\_i \nu\_{\pi} - \nu\_{\pi \vdash 1} = \alpha + \delta \nu\_{\pi \vdash 1} + \beta X\_{\mu \vdash 1} + u\_i + e\_{\mu} \tag{3}
$$

so the estimation of the parameters of()) by Ordinary Least Squares (OLS) will be biased and inconsistent as the tenn *u;,* which accounts for unobserved country specific factors that both drive growth and are potentially correlated to the explanatory variables is not considered in ( l ). This problem is named as omitted variable bias. Either first differencing or the inclusion of fixed country effects will eliminate the bias that arises from time-invariant but country-dependent omitted variables, and indeed constitutes the main reason for using one of these techniques. 13

Fixed effects estimates are calculated from differences within each country across time. A traditional fonnulation implies the inclusion of dummies that represent time invariant country omitted variables *(u;)* and estimating the equation by OLS. This method is known as Least Square Dummy Variable Approach (LSDV).

If, instead of assuming (2), we consider that the regression model given by ( l) has a two-way error component disturbance, that is the disturbance tenn is

<sup>13</sup> This methodology does not completely eliminate omitted variable bias, as it does not control for omitted variables that change over time, but it improves crosscountry estimations.

formed by unobservable country effects *µ;,* unobservable time effects J1• and a remainder stochastic disturbance term *eu,* we have:

$$
\mathbf{e}\_{\mu} = \mu\_{\uparrow} + \lambda\_{\uparrow} + \mathbf{e}\_{\mu} \tag{4}
$$

In this formulation, J1 is country invariant and accounts for any time-specific effect that is not included in the regression. If A and J1 are assumed to be fixed parameters to be estimated and the remainder disturbances are stochastic with *eu*  - IID (0, cr2), then we have the two way fixed effects error component model. In this way, under the assumption that all regressors are independent of *eu* for all i and *t* and that *e;,s* are not autocorrelated, the following equation can be estimated byOLS:

$$
\mu\_{\rm jet} - \nu\_{\rm it-l} = \alpha + \delta\nu\_{\rm it-l} + \beta\mathbb{X}\_{\rm it-l} + \mu\_{\rm j} + \mathbb{X}\_{\rm t} + e\_{\rm u} \tag{5}
$$

This fixed effects model suffers from a large loss of degrees of freedom because of the inclusion of time and country dummies. The effects of time invariant and individual invariant variables are not estimated.

The loss of degrees of freedom of fixed effects models can be avoided if the *µ;* can be assumed random. In this case, A - IID (0, cr2) and *e;1* - IID (0, cr2), and the A are independent of the *eu,* for all i and *t.* Additionally, the *Xu* are independent of the A and *c ;1* for all *i* and *t.* In this case, the model is estimated by Generalized Least Squares (GLS). Random effects are consistent only if the country-specific effects are uncorrelated with other explanatory variables.

The issue of whether to use fixed or random effects, specially in growth equations, has generated debate. Some authors argue that individual effects should always be treated as random, but on the other hand there is no justification for treating the individual effects as uncorrelated with the other regressors, as assumed in the random effects model.

Probably, the question of whether to use fixed or random effects is particularly relevant in growth equations if the number of countries in the panel is large relative to the time dimension of the panel, as the inclusion of dummy variables reduces the degrees of freedom. This argument could favour the random effects model, but it must be kept in mind that it is very likely that country-specific characterstics are correlated with other variables if country effects represent omitted variables. This has led many authors to prefer the fixed effects model. In our case, where a non-aleatory sample of countries is included, it seems more appropriate to consider fixed effects by countries when choosing between this two alternatives. If we believed that our sample cross sectional units were drawn from a large population, it would be reasonable to prefer the modelization of individual or country specific constant terms as ranomly distributed across cross sectional units. Nevertheless, we report different estimations but prefer the fixed over the random effects model.

The estimation of (5) using panel data methods does not solve all the problems, as it Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 may present two kinds of econometric problems. First of all, the underlying relationship is dynamic in nature, and is characterized by the presence of a lagged dependent variable among the regressors. If the error terms are autocorrelated this may lead to important biases in the coefficient of the lagged variable which will then be automatically correlated with the error terms.

The second problem is the presence of endogenous regressors, due to the problem of reverse causation. In our case, for example, we can not discard that growth rates do not determine inequality. The assumption of strict exogeneity may lead to biases and inconsistency even in the panel data estimatons. Another potential source of persistence over time is the presense of heterogeneity among individuals.

Several solutions have been proposed for this problem, usually using GMM methods (Arellano and Bond, 1991; Arellano and Bover, 1995). The most extended is Arellano and Bond (199 I), that suggests an alternative estimation technique that corrects for the bias introduced by the lagged endogenous variable and permits a certain degree of endogeneity of the other regressors. **It** is based on the Generalized Method of Moments **(GMM)** where all variables are considered in first differences to eliminate the country specific effect, and all possible lagged values of each of the variable are used as instruments. The basic idea is to write the regression equation as a dynamic panel data model, take first differences to remove unobserved time invariant country specific effects, and then instrument the right hand side variables in the first differenced equations using levels of the series lagged two periods or more, under the assumption that the time-varying disturbances in the original levels equations are not serially correlated. The method is known as first differenced generalized method of moments. <sup>14</sup>

Two assumptions must be satisfied in order for this estimator to be consistent and efficient. The first assumption is that the error terms are serially uncorrelated, that is *E(c;,,c;,)* = o for *t/=s.* The second assumption implies that *x*  are weakly exogenous, that is *E(x;,,c;,) \** o for all *s<.::.t.* and zero otherwise. Then lagged values of *x* can be used as instruments. We also report results using this econometric technique.

The estimations presented in this paper are based on panel data covering a wide range of variables, including income inequality, for Latin American countries in the period 1960-2000. Economic variables (GDP per capita, exports/GDP, etc.) come from the World Development Indicators, World Bank. Data on income inequality stem from the World Income Inequality Dataset,

<sup>14</sup> Another solution proposed to deal with lagged endogenous variable bias is the correction proposed by Kiviet (l 995). He developed a corrected within estimator that substracts a consistent estimator of this bias from the original within estimator, assuming serially uncorrelated disturbances and strongly exogenous

UNDP. Data on schooling is taken from the Barro-Lee data set. The data contains periods of five years.

#### *1.4.3 Results: Kuznets curve*

Equations (6) and (7) were estimated in order to assess whether the level of income and inequality are related, in the fashion proposed by Kuznets. As mentioned, one of the most common criticisms to cross-countries estimations of Kuznets curves is that results can be driven by the choice of the sample. Especifically, the inclusion of Latin American countries, mainly middle income countries with high levels of inequality, may yield the inverse U shaped result, as suggested by the loss of significance of the estimation when including dummy variables to distinguish Latin American countries in world wide data sets. The two more common specifications of the Kuznets curve (using income and its quadratic expression, or alternatively using income and its inverse) are as follows:

$$\text{Gini}\_{\boldsymbol{\mu}} = \alpha\_{i} + \boldsymbol{\beta}\_{i} \, ^{\bullet}Y\_{\boldsymbol{\mu}} + \boldsymbol{\phi}\_{i} \, ^{\bullet} (1/Y\_{\boldsymbol{\mu}}) + \boldsymbol{\delta}\_{i} \, ^{\bullet}Z\_{\boldsymbol{\mu}} + \boldsymbol{\varepsilon}\_{\boldsymbol{\mu}} \tag{6}$$

$$Gini\_{\iota} = \alpha\_{i} + \beta\_{i} \, \text{\*} \, Y\_{\iota} + \phi\_{i} \, \text{\*} \, Y\_{\iota} \, \text{\*} + \delta\_{i} \, \text{\*} \, Z\_{\iota i} + \mathcal{S}\_{i\iota}$$

$$\left(\tau\right)$$

There are different possibilities that can be tested:

The first of these possibilities, i.e. the estimation of the Kuznets curve based on cross sectional data, does not yield significant results. Estimations using fixed effects panel data show the existence of a Kuznets curve, using the two more common specifications (income and its quadratic expression, income and its inverse). Results indicate that around 65% of total variance of inequality over time and across countries is explained by the included variables. The F value for the hypothesis of fixed effect being zero allows rejection, suggesting that country effects are essential in the link between income and inequality. These results coincide with previous evidence on the existence of a Kuznets type relationship (Morley, 2001; Furquim and Garcia, 2001).

For the second specification, the estimation indicates that the relationship between inequality and income is positive for values of GDP per capita below 3,526 (1985 US dollars). The following chart presents those countries and periods above that threshold, corresponding to a negative relationship between income and inequality. Above the threshold of 3,526 US-\$, growth has reduced inequality.

#### *1.4.4 Results: the relationship between growth and inequality*

Following the common practice in the literature, we estimated a basic growth regression including as dependent variable the average growth rate of GDP per capita for a five year period. The right hand side variables are GDP per capita (log) in the beginning of the period, exports as a fraction of GDP, and consumption as a fraction of GDP. The Gini coefficient was the independent variable.

The basic regression (1 ), using pooled OLS estimations, suggests that inequality does not have a significant impact on subsequent growth in our sample, whereas exports do have a positive effect on subsequent growth, and government consumption a negative one. Panel data estimation using random effects (2) yields the same results. The inclusion of fixed effects changes our results regarding inequality, as now the Gini coefficient does have a positive and significant effect on subsequent growth (3), and so helps to understand differences in economic performance among Latin American countries. Again exports have a positive impact on subsequent growth, whereas government consumption exhibits a negative sign. The Hausman test performed to test whether fixed or random effects are more appropriate, suggests that the correct specification corresponds to fixed effects estimation. Finally, estimation using the Arellano-Bond methodology do yield similar results (4), except for the loss of significance of exports. This first evidence is in line with that reported in Forbes (2000) and Li and Zou (1998) for a world data set, as it implies that inequality may have been favorable for subsequent growth in Latin American countries.

These results may have a theoretical foundation, since as Forbes (2000) argues, theoretical papers that predict a positive relationship between inequality and growth (Saint Paul and Verdier, 1993) have received less attention because of the negative sign of the relationship reported by empirical research based on cross sectional data bases.

Nevertheless, in his research for a world data set, Barro finds that the expected coefficient of the Gini variable on the growth rate is zero, so overall differences in Gini coefficients for income inequality have no significant relation with subsequent economic growth. He states that one possible interpretation of this is that the various theoretical effects of inequality on growth are nearly fully offsetting. He also finds that when the effect of the Gini coefficient on economic growth is allowed to depend on the level of economic development the coefficients are jointly and individually significant and the estimated relation implies that the effect of inequality on growth is negative for low values of GDP per capita and positive for higher ones. A possible interpretation of this result involves the idea that credit market constraints would be more serious in poorer countries.

Following Barro's strategy, we included an interaction term between inequality and initial GDP per capita in the fixed effect and GMM estimations15 • Results change dramatically, the coefficient of inequality changes its sign, suggesting that the level of inequality has a different effect on growth depending on the level of GDP per capita of the countries. For the GMM estimation, inequality and the interaction of inequality and the level of GDP lose significance.

If we believe in fixed effects results, the impact of inequality on growth depends on the initial level of income. For poorer countries, the effect of inequality on growth is negative, whereas for richer ones there is a change in the sign of the coefficient. So even when restricting the sample to Latin American countries, where we have less variance than in a world data set, results found by Barro (2000) seem to hold.

#### *1.4.5 Sensitivity analysis*

Many authors have emphasized the need for careful analysis of existing empirical evidence on these topics, as inequality may be measured in an inconsistent manner. In effect, inequality can be measured using gross income, net income or expenditure. As Knowles (2003) points out that lack of comparable data usually leads to the use of mixed data, and this can lead to biased results. As he remarks, when testing different hypotheses about the link between inequality and growth, the definition of the measure of income to include (gross or net) will depend on the hypothesis that we are trying to test. In general terms, when considering data on net distribution of income (that is after tax distribution of income) or distribution of expenditure, it is more appropriate to test whether redistribution has effects on growth. In this research, we tried to construct a consistent data base. For each country, all observations are referred to the same income variable and unit ofreference (household or person).

Results do not change with the exclusion of any particular country of the sample, with the exception of Ecuador. The exclusion of this country of the sample does not change the signs of the estimations, but the significance is considerably reduced: the Gini coefficient and the interaction between Gini and the level of **GDP** are significant only at 80% .

With respect to variables, this kind of regression generally includes some measure of human capital. These variables, alternatively measured as female and male years of schooling at the beginning of the period, average years of schooling of the population, percentage of population with secondary school, etc. do not yield significant results for this sample.

It has also been argued that the potential channels that may link inequality and growth may operate both in the short and in the long run (Forbes, 2000;

<sup>15</sup> We followed this strategy only for these two estimation techniques as they seem to be the most Stephan Klasen and Felicitas Nowak-Lehmann - 978-3-631-75368-2 suitable for the kind of model we are estimating. Downloaded from PubFactory at 01/11/2019 05:50:02AM via free access

Knowles, 2003), as investment and savings may be affected in short periods of time. Empirical studies that focus on short run correlations find a positive impact of inequality on growth (Forbes, 2000; Li and Zou, 1998). In our case, the initially detected positive impact disappears once the different levels of GDP from countries are taken into account. This result is similar to that found by Barro (1999) for a world data set, although he uses ten-year periods. When tenyear periods are considered for our data, the significance of inequality is lost. Both for the pooled estimation and for the panel estimation using random effects, the impact of inequality is negative in the long run, but not significant. With fixed effects the impact of inequality on long-run growth is positive but not significant, although when the interaction between inequality and GDP is included the sign of inequality reverses (that is becomes negative) but is still not significant. These results suggest that, in any case, the potential impacts of inequality on growth seem to operate in the short run, but are not significant to explain long-run performance of Latin American countries.

These considerations suggest that more research on the robustness of these results should be carried out. Specifically, more efforts in improving the quality of data on income distribution should be made, and tests on the sensitivity of results with respect to the consideration of different measures of inequality should be undertaken. This research sheds some light on the robustness of the results obtained. In that sense, the proposal of Bourguignon (2003) of using microeconomic data for the study of this issue seems promising, as micro simulations techniques may help to understand specific country cases.

# **1.5 Final comments**

The potential link between inequality and economic performance has led economists to focus their attention on distributional aspects, trying to assess the underlying process that relates both variables. Empirical research on this relationship, based on reduced form estimations, is really abundant, going from cross-sectional evidence to more recent research based on panel data techniques. Unfortunately, results are not conclusive at all, and the debate in the empirical literature is open. Whereas most cross-sectional studies find a negative impact of inequality on growth, estimations controlling for country fixed effects suggest that an increase in the level of income inequality in a country may enhance growth in the short and medium run. Finally, some authors suggest that the contradicting results can be explained by the common practice of estimating linear models, when the true relationship might be non-linear.

This paper analyses the relationship between inequality and growth for Latin American countries, using different specifications and econometric techniques. Both fixed effects and GMM estimations suggest a positive impact of inequality on economic growth. But the introduction of an interaction term between the level of GDP and inequality changes significantly these results. Fixed effect estimation suggests that the level of inequality has a different effect on growth depending on the level of GDP per capita of the countries. For the GMM estimation, inequality and this interaction variable lose significance. These results are not conclusive, but shed light on the need for further research on this issue, testing different specifications and econometric techniques.

#### **References**

Acemoglu D., S. Johnson and J. Robinson (2002). "Reversal of Fortune: Geography and Institutions in the Making of the Modem World Income Distribution" in *Quarterly Journal of Economics* 118: 1231-1294.

Adelman I. and S. Robinson (I 988). "Income distribution and development". In Chenery and Srinvasan (eds.), *Handbook of Development Economics,* Volume II, North Holland, Amsterdam.

Ahluwalia M. (1976). "Income distribution and development: some stylized facts" in *American Economic Review* 66(2): 128-135.

Alesina A. and D. Rodrik (1994). "Distributive politics and economic growth" in *The Quarterly Journal of Economics,* 109: 465-490.

Alesina A. and R. Perotti (1996). "Income distribution, political instability and investment" **in** *European Economic Review* 40: 1203-1228.

Anand S. and S. Kan bur ( 1993). "The Kuznets process and the inequalitydevelopment relationship" in *Journal of Development Economics* 40: 25-52.

Arellano M. and S. Bond (1991 ). "Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations" in *Review of Economic Studies* 58: 277-297.

Arrelano M. and 0. Bover (1995). "Another look at the instrumental variables estimation of error components models" in *Journal of Econometrics* 68 (1 ): 29-52.

Banerjee A. and A. Newman (1993). "Occupational choice and the process of development" in *Journal of Political Economy,* Volume 101, Number 2: 274-298.

Banerjee A. and E. Duflo (2000). "Inequality and growth: what can data say". *Working Paper* 00-09. Mimeo.

Barro R. (2000). "Inequality and growth in a panel of countries" in *Journal of Economic Growth* 5 (I): 5-32.

Barro R.(1999). "Inequality, growth and investment" in *National Bureau of Economic Research,* Working Paper N° 7038.

Barro R. and G. Becker (1988). "A reformulation of the economic theory of fertility" in *Quaterly Journal of Economics* 103: 1-25.

Barro R. and X. Sala i Martin (1995). "Economic growth". New York: Mc Graw Hill.

Becker G., K. Murphy and R. Tamura (1990). "Human capital, fertility and economic growth" in *Journal of Political Economy* 98: S l2-S37.

Benabou R. ( 1994 ). "Education, income distribution and growth: the local connection". *National Bureau of Economic Research,* Working Paper N° 4798.

Benabou, R. ( 1996). "Inequality and growth" in *National Bureau of Economic Research, Working Paper* N° 5658.

Behrman, J., N. Birdsall and M. Szekely (2001). "Pobreza, Desigualdad, y Liberalizacion Comercial y Financiera en America Latina" in (eds) Ganuza, *Paes de Barros, Taylor and Vos. Liberalizacion, Desigualdad y Pobreza: America Latina y el Caribe en los 90s.* United Nations Development Program. Editorial Universitaria de Buenos Aires. Buenos Aires.

Berry, A. (1998). "Confronting the Income Distribution Threat in Latin America" in Albert Berry (ed.) *Poverty, Economic Reform, and Income Distribution in Latin America.* Boulder, CO: Lynne Rienner.

Birdsall, N., D. Ross and R. Sabot ( 1995). "Inequality and growth reconsidered: lessons from East Asia" in *World Bank Economic Review* 9(3): 477-508.

Birdsall N. and J. Londoflo (1997). "Asset inequality matters: an assesment of the World Bank's approach to poverty reduction" in *American Economic Review.* Vol. 82, N° 2: 32-37.

Bourguignon F. and C. Morrison (1998). "Inequality and development: the role of dualism" in *Journal of Development Economics.* Vol. 57: 233-257.

Bourguignon F. (2004). "The poverty-growth-inequality triangle". Paper prepared for the Conference on Inequality, Poverty and Growth, Agence Francaise de Developpement/EU Development Network, Paris, November 2003.

Bruno M., M. Ravallion and L. Squire (2000). "Equity and growth in developing countries: old and new perspectives on the policy issues" in Solimano A., N. Birdsall and E. Aninat ( eds.), *Distributive Justice and Economic Development.* The University of Michigan Press.

Clarke G. (I 995). "More evidence on income distribution and growth" in *Journal of Development Economics.* Vol. 47: 403-427.

Cunningham W. and J. Jacobsen (2003). "Group based inequalities: the roles of race, ethnicity and gender" in Inequality in *Latin America and the Caribbean: breaking with history?,* Chapter 3, Washington D.C: The World Bank.

Dahan M. and D. Tsiddon ( 1998). "Demographic transition, income distribution and economic growth" in *Journal of Economic Growth* 3, March 1998: 29-52.

De Gregorio J. and J.W. Lee (2000)- "Economic growth in Latin America: sources and prospects" in Paper prepared for the Global Development Network. Mimeo.

Deininger K. and L. Squire (1996). "A new data set measuring income inequality" in *World Bank Economic Review* I 0: 565-591.

Deininger K. and L. Squire (1998). "New ways of looking at old issues: inequality and growth" in *Journal of Development Economics.* Vol. 57: 257-287.

Deininger K. and P. Olinto (2000). "Asset distribution, inequality and growth". World Bank. Mimeo.

De Janvry A. and E. Saudolet (2000). "Growth, poverty and inequality in Latin America: a causal analysis" (1970-1994) in *Review of Income and Wealth,* Serie 46,

Durlauf S. (1992). "A theory of persistent income inequality" in *National Bureau of Economic Research,* Working Paper N° 4056.

Easterly W. (2001). "The middle class consensus and economic development" in *Journal of Economic Growth,* VI (2001): 317-335.

Fields G. (1979). "A welfare economic approach to growth and distribution in the dual economy" in *Quarterly Journal of Economics* XCIII, No. 3: 325-353.

Fields G. and G. Jakubson (1994). "New evidence on the Kuznets curve". Cornell University.

Fields G. (2001). "Distribution and development". Russell Sage Foundation, New York. The MIT Press. Cambridge and London.

Fishlow A. (1995). "Inequality, poverty and growth: where do we stand?" in Annual World Bank Conference on Development Economics 1995, The World Bank.

Forbes K. (2000).A reassessment of the relationship between inequality and growth. *American Economic Review,* Vol. 90, No 4: 869-887.

Galor 0. and Zeira J. (1993) Income distribution and macroeconomics. *Review of Economic Studies* 60: 35-52.

Galor 0. and Weil N. (1996) The gender gap: fertility and growth. *American Economic Review,* Vol. 86, No3: 374-387.

Galor 0. and Moav 0. (2003) From physical to human capital accumulation: inequality and the process of development. Mimeo

Garcia F. and Furquim L. (2001) Inequality and economic growth in Latin America. Mimeo. Presented in the conference: "Old and new growth theories: an assesment". Pisa, Italy, October 2001.

Garcia F., Furquim L. y Bandeira A. (2002) Economic reforms, inequality and growth in Latin America. Mimeo.

Gasparini L. (2003). Different lives: inequality in Latin America and the Caribbean. In Inequality in Latin America and the Caribbean: breaking with history?, chapter 2. Washington D.C: The World Bank.

Iglesias E. (1998) Income distribution and sustainable growth: Latin American perspective. In Vito Tanzi and Ke-Young Chu, eds., Income distribution and high quality growth. MIT Press, Cambridge and London.

Jha, S. (1996). "The Kuznets curve: a reassessment." in *World Development,* Vol. 24, No. 4: 773-780.

Justino, **P., J.** Litchfield, and L. Whitehead, (2003). "The Impact of Inequality in Latin America." *Working Paper no. 21,* Poverty Research Unit at Sussex, University of Sussex.

Justino P. and Acharya (2003). "Inequality in Latin America: processes and inputs." in *PRUS Working Paper N° 22.* University of Sussex.

Kaldor N. (1956). "Alternative theories of distribution." in *Review of Economic Studies* 23 (2): 83-100.

Keefer P. and Knack S.(2000) Polarization, politics and property rights. Links between inequality and growth. Mimeo.

Keynes **J.M.** (1936). "The General Theory of Employment, Interest and Money." in Macmillan Cambridge University Press.

Kiviet ( 1995). "On bias, inconsistency and efficiency of various estimators in dynamic panel data models." in *Journal of Econometrics,* 68 (I): 53-78.