inequality and economic growth evidence from argentina's provinces using spatial econometrics

In the dissertation I have found very robust evidence that the own province i inequality, and the inequality of the neighboring provinces of province i, affects negatively the economics

INEQUALITY AND ECONOMIC GROWTH:

EVIDENCE FROM ARGENTINA’S PROVINCES USING SPATIAL

ECONOMETRICS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for The Degree Doctor of Philosophy in the Graduate School of The Ohio State University

By Alejandro A Cañadas, M.B.A

* * * * *

The Ohio State University

2008

Professor Claudio Gonzalez-Vega, Adviser

Professor Mark Partridge

3313008

3313008 2008

Copyright 2008 byCanadas, Alejandro A.All rights reserved

Copyright by Alejandro Cañadas

2008

inequality into long-run and short-run effect

To accomplish this, I based the analysis on a framework used by Partridge (2005), which starts considering a very simple model, called a “parsimonious” model with a few key variables Building on that simple model I started adding a set of important control variables in order to get a more fully specified model, called “base” model The main idea of using this methodology is that the “parsimonious” models, with only a few variables (income distribution and a few other control variables), not only reduces multicollinearity but also it is a test for robustness in the relationship between inequality and growth (Perotti, 1996; Panizza, 2002; Partridge, 2005)

In addition, following Partridge (2005), I considered that income distribution might have an entirely separate effect at the middle versus the tails of the distribution Therefore, I decided to include the Gini that controls for the overall distribution, and the third Quantile share (Q3) that controls for middle-class consensus and the role of the

median voter The purpose of having two variables of income distribution is that when the Q3 is included in the model, the Gini controls for the overall distribution, especially

at the tails, while Q3 controls for middle-class consensus and the role of the median voter

Additionally, a key difference from Partridge (2005) framework, apart from the

decoupled effect of inequality into within inequality, which is the own province i level of inequality, and the spillover of inequality from the closest provinces to province i, is the

explicit consideration of possible spatial autocorrelation in the models To achieve this, I used two of the simplest spatial specifications: the spatial lag and spatial error models

In the dissertation I have found very robust evidence that the own province i inequality, and the inequality of the neighboring provinces of province i, affects

negatively the economics growth of the provinces of Argentina in the period 1991-2002 Morerover, I have also found robust evidence that the third Quantile (Q3) affects

negatively the economics growth, which is not consistent to the vibrancy of the middle class The overall pattern of my results are not consistent with a long-run

classical/incentive interpretation but to a political economy interpretation, in which the distortionary redistribution policies and social or political conflict are generated by the difference in inequality among provinces

Dedicated to my beloved family, my lovely wife, Cynthia, my son Santiago, and my

daughter María Camila

ACKNOWLEDGMENTS

This dissertation is the end product of a five-year journey that began when

I started working toward my Ph.D at The Ohio State University Many people have walked (and stumbled) with me throughout these years First and foremost, I would like

to thank my advisor Dr Claudio Gonzalez-Vega His encouragement and guidance have been invaluable to go through some turbulent moments of the Ph.D program, particularly the first year I also want to thank Don Claudio for giving me the opportunity to work as his assistant since 2003 I learned a great deal from him and I will always remember him

as a smart thinker, generous person, and enthusiastic teacher

I also want to thank Dr Mark Partridge and Dr Joe Kaboski, who played a fundamental role in helping me develop this research They were always ready to read

my draft, give me precious advice, and offer suggestions that help me to be ready for the job market Moreover, I am very grateful to Dr Dave Kraybill and Dr Ian Sheldon for teaching the best classes I have ever had and inspiring the topics for this dissertation I

am also very thankful to Stan Thompson, Fred Hitzhusen, Mario Miranda, and specially

my advisor from the PFF Program (Preparing Future Faculty) Dr Robert Ebert, from Baldwin Wallace College, for all his support

I am very grateful to Ricardo Martinez (Ricardo.MARTINEZ@cepal.org ) from

capita GDP and to Dr Leonardo Gasparini from CEDLAS, Universidad Nacional de La Plata, Argentina (leonardo@depeco.econo.unlp.edu.ar), who offered me useful comments

in the manipulation of the survey from the EPH

Working as a staff member at AEDE, I have had the pleasure to work with Joan Weber and Susan Miller, who always have been very kind to me

During these years, I shared wonderful moments with fantastic people that I want

to mention: Franz Gomez-Soto, Francisco Monge-Ariño, Erik Davidson, Mauricio Ramirez, Maria Jose Roa, Carlos Alpizar, Jose Pablo Barquero, Malena Svarch, PaulaCordero-Salas, Carolina Castilla, Emilio Hernandez, Scott Pearson, Carolina Prado, and Marcelo Villafani

I extend my love to my family, my dad, mom, Angeles and Marita, as well as my friends, Hernan Bourbotte, Diego Sica, Octavio Groppa, Mariano Massano, Juan Pablo Tiepolt, Jill Gerschutz, Ana Maria Gilmore, and William Hamant, and I thank them for believing in me and for supporting my dreams from a distance

Nothing would have been possible without my wife’s unconditional support, care and love She gives me the strength and courage to do things I would have never imagined I could I thank God for her and for our precious little son, Santiago, and our daughter, María Camila, and for all God’s strength through all these years

VITA

March 13, 1972………Born – Jujuy, Argentina

1995 – 1996………… Economist, Arthur Andersen

1997……… B.S (Licenciatura) Economics, Universidad Católica Argentina

1996 – 2000………… Marketing Researcher, Telefónica de Argentina

2000 – 2003………… Masters of Business Administration,

University of Dayton, Ohio 2004– 2008………… Graduate Research Associate, Rural Finance Program,

Agricultural, Environmental and Development Economics, The Ohio State University

FIELDS OF STUDY

Major Field: Agricultural, Environmental and Development Economics

Minor Fields: Development Economics

TABLE OF CONTENTS

Page

Abstract ii

Dedication iv

Acknowledgements v

Vita vii

List of Tables xiii

List of Figures xvi

Chapters 1 Introduction 1

1.1 Motivation 1

1.2 Growth, Distribution and Poverty 3

1.3 Spatial Dependence and Convergence 6

1.4 Research Questions and Objectives 6

1.5 Research Strategy 8

1.6 Hypotheses 9

1.7 Contents 11

1.8 Significance and Relation to the Present State of Knowledge 11

1.9 The Influence of Inequality on Growth 13

2 Argentina 18

2.1 Argentina, a Beautiful Country 18

2.2 Initial Conditions 19

2.3 Argentina as a Puzzling Country 23

2.4 What Went Wrong, and When? 26

2.5 Volatility of Growth 27

2.6 The Argentinean Economy 30

2.7 The Argentinean Economy 32

2.8 A Caudillo Country 38

3 Economic Growth in Argentina 41

3.1 Per Capita Income in Latin America: A Long-Run Comparative Perspectives 43

3.1.1 Historical Per Capita GDP Estimates for Latin America 43

3.1.2 Income Convergence in Latin America 46

3.2 Comparative Perspective 47

3.3 The Data 49

3.3.1 Changes in geographical coverage 50

3.3.2 The New EPH Continua 51

3.3.3 Limitations 53

3.4 Convergence Concepts and Spatial Effects 54

3.4.1 Spatial Effects in the Analysis of Regional Income Convergence56 3.4.2 Exploratory Spatial Data Analysis of Argentina’s Income Convergence 57

3.5 Spatial Autocorrelation 58

3.5.1 Local spatial autocorrelation 62

4 Inequality in Argentina 68

4.1 Importance of the Study of Inequality 69

4.2 Relationships among Poverty-Growth-Inequality……… 69

4.3 Relationship between Inequality and Growth 70

4.4Inequality in Argentina 73

4.5 Inequality in Latin America 76

4.6 Regional Inequality in Argentina 81

4.7 Spatial Autocorrelation of Income Inequality 82

4.7.1 Local Spatial Autocorrelation for Income Inequality 84

5 Empirical Implementation 88

5.1Spatial Econometrics 88

5.1.1The Problem of Spatial Autocorrelation 89

5.1.2Spatial Lag Operator 93

5.1.3Spatial Autocorrelation in a Regression Model 94

5.2 Inequality-Economic Growth Models 98

5.3 Regression Specification 100

5.3.1 Spatial Econometric Model Specification 101

5.3.2 SpatialLag Model 102

5.3.3 SpatialError Model 103

5.4 Empirical Results 104

5.4.1 Parsimonious Long Run Model 104

5.4.2 Base Long Run Model 109

5.4.3 Gini Spillover Effect Model 116

5.4.5.1Durbin-Wu-Hausman Tests for Endogeneity 134

5.4.6Spatial Pooled-OLS Models 137

5.4.7Panel Data Models 138

5.4.7.1Fixed Effect Model 142

5.4.7.2Random Effect Model 144

6 Conclusions 149

6.1 Summary 149

6.2 The Main Results 150

6.3 Contributions 154

6.4 Policy Implications 157

6.5 Limitations and Future Research 157

Bibliography 168

Appendices 185

Appendix A: Tables and Figures for Chapter 1 185

Appendix B: Maps from Argentina 192

Appendix C: Geary-Khamis method of aggregation 205

Appendix D: Tables and Figures for Chapter 3 214

Appendix E: Tables and Figures for Chapter 4 238

American economies, in 2007 30 2.4 Comparison of labor force, unemployment rate, poverty and inequality for the

seven largest Latin American economies, in 2007 31 A.1 Population living below the US$ 1 poverty line, 1990 and 2001 188 A.2 Population living below the US$ 2 poverty line, 1900 and 2001 189 A.3 Indicators of inequality for selected Latin American countries, the United

States, and Italy, late 1990s 190 D.1 Average per capita income growth rates for the seven major Latin American

economies, 1810-2004 215 D.2 Average annual rates of growth of per capita GDP for regions of the world,

1820-2004 (percentage) 221 D.3 Economic Regions and Provinces in Argentina EPH 223

D.5 Moran’s I statistics for the provincial per capita GDP of Argentina, 1980-

2002……… 228 D.6 Summary of local Moran statistic as a measure of spatial association: real per

capita GDP by quadrants, 1980-2002 232 E.1 Distribution of household per capita income in Argentina (deciles shares and

income ratios), 1992-2005 240 E.2 Inequality Indices from household surveys in major provincial cities in

Argentina, 1992-2005 241 E.3 Inequality in Latin America between 1950 and 2000 Measured by Gini

coefficients 243 E.4 Changes in inequality measured by percentage points of Gini Coefficient using

household surveys in each country 244 E.5 Bonferroni and the Tukey’s tests to determine means differ in Gini coefficient

among regions in Argentina, 1991-2002 247 E.6 Changes in Gini coefficient, third quantile (Q3), top 10 percent and bottom 20

percent shares in income of the population by region, between 1991 and 2002 (percentage) 248 E.7 Estimates of the Moran’s I statistic for the provincial Gini coefficients of

Argentina, 1991-2002 250 5.1 Results from parsimonious long-run models 108

5.3 Growth Model with Gini Spillover Effects 117 5.4 Pooled-OLS Models 126 5.5 Robust, Clusters, FGLS, IV, and GMM Models 133 5.6 Spatial autocorrelation test statistic for the spatial lag and error models 140 5.7 Descriptive Statistics of the Panel Data Variables 139 5.8 Panel Data Models of Real Per Capita Growth 147 5.9 Fixed Effect and Random Effect Models of Real Per Capita Growth 148

LIST OF FIGURES

Figure Page

2.1 Map of the Republic of Argentina, its main cities, and neighboring countries …… 21

2.2 Comparison of per capita GDP relative to the US for Argentina, Brazil and Mexico, 1820-1990 …… 24

2.3 Annual rate of growth of per capita GDP for Argentina, 1810-2004 (percentage) …… 27

2.4 Coefficient of variation of the log of inter-annual per capita GDP for each decade in Argentina, 1810-2004 …… 29

A.1 Gini coefficient for the income distribution in Latin America, 1950-2000 186 A.2 Poverty rates in Latin America, 1950-2000 …… 187

A.3 Trends in inequality in major Latin American countries from the early 1980s to mid-2000s (Gini coefficients) 191

B.1 Satelital map of Argentina using Google Earth 193

B.2 Satelital map of Argentina in South America using Google Earth 194

B.3 Satelital map of the provinces of Argentina using Google Earth 195

B.4 Map of the provinces of Argentina 196

B.5 Map of the regions of Argentina 197

B.7 Gini Coefficient by province in 1991 using STATA 199

B.8 Gini Coefficient by province in 2002 using STATA 200

B.9 Area of provinces in Argentina using STATA 201

B.10 Density of population in the provinces of Argentina using STATA 202

B.11 Two main clusters for the real per capita income in the provinces of Argentina using the Moran I, 1980-2002 203

B.12 Two main clusters for the Gini in the provinces of Argentina using the Moran I, 1991-2002 204

D.1 Per Capita GDP for seven major Latin America economies, 1900-2004 216 D.2 Per Capita GDP for Argentina, 1810-2004 217

D.3 Average annual GDP growth rates experienced by the seven largest Latin American economies between 1900 and 2004, with their corresponding (logged) initial per capita income level in 1900 218

D.4 Cross-country standard deviation of per capita GDP for the seven largest Latin America economies, 1900-2004 219

D.5 Coefficient of variation of log of per capita GDP for the seven largest Latin America economies, 1900-2004 220

D.6 Per capita GDP for the United States, United Kingdom, Germany, Norway, Australia, Japan and Argentina, 1820-2004 222

D.7 Average annual GDP growth rates experienced by 23 provinces of Argentina,

1980-2002, with their corresponding (logged) initial per capita income level in 1980 225 D.8 Coefficient of variation of the log of provincial real per capita GDP for 23

provinces and the capital city (Ciudad Autónoma de Buenos Aires) in

Argentina, 1980-2002 226 D.9 Moran's I statistic for the provincial real per capita GDP of Argentina, 1980-2002

227 D.10 Provincial coefficient of variation of the log of provincial real per capita GDP

for 23 provinces and the capital city of variation and the Moran's I statistic, Argentina, 1980-2002 229 D.11 Local Moran’s I statistic for the provincial real per capita GDP in 1980 230 D.12 Local Moran’s I statistic for the provincial real per capita GDP in 2002 231 D.13 GDP participation of the five richest provinces in Argentina, 1980, 1991 and

2002 234 D.14 Population participation among the five largest regions 235 D.15 Population shares of the five largest provinces in Argentina, 1980, 1991 and

2002 236 D.16 Comparison of provincial per capita income relative to the country’s average,

for 1980, 1991 and 2002 237

E.1 Decomposition of a change in distribution and poverty into growth and

distributional effects 239

E.2 Gini Coefficient for Argentina, from the distribution of per capita household income, 1992-2005 242

E.3 Provincial Gini coefficients for Argentina Averages for 1991-2002 245

E.4 Regional Inequality in Argentina, as shown by Gini coefficients Averages for 1991-2002 246

E.5 Moran’s I statistic for the provincial Gini coefficients of Argentina, 1980-2002 249

E.6 Local Moran’s I statistic for the Gini coefficients provincial in 1991 251

E.7 Local Moran’s I statistic for the Gini coefficients in 2002 252

E.8 Local Moran’s I statistic for the Gini coefficients in 2001 253

CHAPTER 1

INTRODUCTION

1.1 Motivation

This dissertation is about the effects of the inequality in the income distribution

on per capita GDP growth in the provinces of Argentina

A recent report of the World Bank claims that the levels of inequality in Latin America are well above those of developed countries The Gini coefficient for the region

is about 0.55, compared to 0.37 for developed countries (de Ferranti et al., 2004) The report also indicates that, together with Sub-Saharan Africa, Latin America has long been known as the region with the highest inequality in the world, with a Gini coefficient above 0.50 since the 1960s, as shown in Figure 1 in Appendix A While inequality in Argentina has been below the region’s average, it has been increasing over time

The fact that the first of the Millennium Development Goals is to “eradicate extreme hunger and poverty by 2015” shows that poverty is perceived as one of the most important problems in the world Both poverty and persistent inequality are major challenges in Latin America

Tables 1 and 2 in Appendix A show that, by 2001, some 128 million people in Latin America lived below the US$2-a-day poverty line Of those, 10 million lived below

the US$1-a-day poverty line Nevertheless, if we look at the incidence of poverty in Latin America and the Caribbean, as a share of the total population, the region’s poverty levels are among the lowest, well below those for East Asia, South Asia, and Sub-Saharan Africa In contrast to those regions, poverty in Latin America is less a rural and more an urban phenomenon

In the long term, the region has experienced important gains in alleviating poverty Figure 2, in Appendix A, shows that, while measured using a poverty line of US$ 2 per person a day, in Latin America the incidence of poverty declined rapidly between 1950 and 1980, but this process slowed down and the poverty rate was still above 20 percent by 2000

Despite this progress, the problem continues to be acute According to estimations

of the Economic Commission for Latin America and the Caribbean for 2006, 71 million persons (13.4 percent of the total population of Latin America) were extremely poor, while the number of poor people (including those 71 million) was estimated at 194 million, equivalent to 36.5 percent of the region’s population (ECLAC, 2007)

According to some authors, in Latin America the problems of poverty and inequality have been related to weak economic growth (Perry et al., 2006) Inequality, in turn, may have also influenced economic growth itself Thus, inequality matters per se and because of its association with other critical outcomes, such as poverty and a country’s growth performance

Both within the region and in comparison to other regions, inequality varies a lot

In most Latin American countries, the richest 10 percent of the individuals earned

between 2 to 4 percent (Table 3 in Appendix A) In contrast, the richest 10 percent in the United States received 31 percent of total income while, in Italy, individuals in this decile earned 27 percent Even the most egalitarian countries in Latin America (Costa Rica and Uruguay) show comparatively high levels of income inequality This concentration has been substantially higher than in OECD countries, Eastern Europe, and most of Asia Only some countries in Africa and the successor states of the former Soviet Union show comparable inequality (de Ferranti et al., 2004) Moreover, when inequality is measured

as the shares of the richest and the poorest quintiles, Latin America is the least equitable region in the world

In Argentina, inequality has dramatically increased since 1950 The Gini coefficient for the household per capita income distribution in the urban areas increased from 0.421 in 1992 to 0.535 in 2003 (CEDLAS, 2003) Even if observations for the recent years of economic crisis are ignored, the trend toward increased inequality is evident This trend has steadily reduced the difference between Argentina and other major countries with more concentrated distributions in Latin America (Figure 3 in Appendix A) No other Latin American country has experienced such deep distributional changes as Argentina (Gasparini and Sosa Escudero, 2001) This dissertation addresses the consequences of this inequality in distribution on regional economic growth

1.2 Growth, Distribution and Poverty

The performance of an economy is usually described in terms of mean variables,

such as per capita GDP or disposable income If per capita GDP increases, namely, if

“the economy grows,” performance is considered to be positive While important, this

evaluation is incomplete, because it does not consider the distribution of income Actually, an increase in mean income can reflect different combinations of poverty and distributional changes However, the linkages among growth, poverty, and income distribution are complex (Ferreira, 1998) Some authors (Ravallion, 2001; Besley and Burgess, 2003) illustrate the relationships between poverty, income growth, and inequality by providing examples of how both growth and changes in inequality influence poverty

A widespread concern for pro-poor growth has resulted in part from evidence showing that, in some countries, the fruits of economic growth have not been equally shared by the population and from evidence that during some growth events the well-being of the poor actually decreased (Perry et al., 2006) The relationship is even more relevant if an economy experiences negative growth, when poverty deepens further In Latin America, growth and poverty patterns have differed substantially across countries and within countries over time There are cases of sustained growth and poverty reduction, like Chile, along with unfavorable experiences in terms of poverty, such as Argentina and Venezuela

This mix of experiences makes the analysis of pro-poor growth particularly rich Moreover, the issue has been at the core of political debates Recent episodes of economic growth combined with unchanged or even increasing poverty have made some people question the proposition that growth is strongly linked to poverty reduction Others have been concerned about the ways in which initial poverty influence growth and have explored the persistence of poverty traps (Azariadis, 1996a, 1996b) This

dissertation is concerned with how inequality may, in turn, influence the process of growth itself

A multiplicity of factors induce changes in incomes These changes usually modify several dimensions of the income distribution, like the mean income, its degree of dispersion, and the mass below certain cut-off income level In this sense, growth (linked

to shifts in the mean of the distribution), changes in inequality (linked to variations in the dispersion of income), and changes in poverty (linked to movements toward the lower tail

of the distribution) are all particular manifestations of changes in the whole distribution Thus, in a given period, one should not think of changes in poverty as being caused by

growth and changes in inequality but, rather, all three are outcomes of changes in the distribution caused by other determinants Current inequality may have consequences, however, on future growth and other outcomes This dynamic relationship constitutes the concern of this dissertation

Researchers have found it useful, nevertheless, to decompose changes in the distribution of income in a given period into two dimensions: changes in its central position (growth) and changes in its dispersion (inequality) Each of these dimensions implies, in turn, changes in the concentration in the lower tail of the distribution (poverty) From this static perspective, changes in poverty are presented as “the result” of growth and changes in inequality (Bourguignon, 2004)

This decomposition does not predict, however, how different degrees of inequality may influence the rate of economic growth over time or the reverse question of how different patterns of growth may result in diverse paths of inequality This dissertation focuses on the first issue: the relationship between income inequality and

income growth over time; that is, it examines the dynamic relationship between previous levels of inequality and current growth, in a regional context and taking into account the

spillover effects of inequality among neighboring provinces in Argentina

1.3 Spatial Dependence and Convergence

In the regional economics literature, the role of spatial relationships has only in recent times caught attention, while the earlier literature on regional inequality was practically silent on the difficulties that spatial data create and on the insights to be gained from them These spatial dimensions are not ignored here

Over the last decade, there have been an increasing number of empirical studies

on regional convergence As Rey and Janikas (2005) emphasize, there are promising opportunities for integration of the inequality and convergence literatures as well as for a reassessment of the relationship between growth and inequality (Benabou, 1996) The possibility of integration is currently considered modestly at the international level, while such articulation at the sub-national and regional levels remains essentially unexplored I would like to fill this gap by analyzing the relationships among inequality, per capita GDP growth, and spatial dependence, applied to the regions of Argentina

1.4 Research Questions and Objectives

The basic research questions that I address in the dissertation are:

1) Does inequality in income distribution affect economic growth in the provinces of Argentina?

2) Is the relationship between inequality and growth influenced by the spatial

distribution of inequality in the provinces of Argentina?

To answer these research questions, I approach them in several steps:

1 Does spatial clustering help in explaining differences in the growth of per capita income across regions in Argentina?

2 Does spatial clustering help in explaining differences in the inequality of the distribution of per capita income across regions in Argentina?

3.a Are the relationships between the own province i inequality and economic

growth, on the one hand, and the inequality of the neighboring provinces and

economic growth in province i, on the other, negative or positive for

Argentina’s provinces?

3.b Is this relationship between distribution and growth reinforced when controlling for spatial dependence, by using spatial econometrics procedures?

3.c What are likely explanations of the relationship found?

The general and specific objectives of the dissertation are closely interrelated These objectives are:

1 To compare the path of economic growth in Argentina with economic growth in selected countries in Latin America and regions of the world and gain an understanding of the country’s overall growth experience

2 To determine whether there has been convergence in per capita income across regions in Argentina

3 To assess the importance of spatial clustering on the processes of growth convergence and income inequality

4 To identify the relationship between inequality and economic growth in Argentina In particular, the purpose is to isolate the influence of provincial inequality on the current differential rates of growth across regions Moreover, I want to determine whether or not inequality has a short-run or a long-run effect

on growth

5 To consider some potential explanations of the results as a gateway towards future research

These are the main purposes of the dissertation The results are expected to assist

in identifying pro-poor growth policies for Argentina

1.5 Research Strategy

Most of the empirical income distribution and growth literature relates to comparisons across countries, while the literature that investigates the inequality-growth relationship across regions within countries has mostly focused on developed economies This dissertation applies these approaches to a middle-income country that has experienced substantial volatility in GDP growth rates, Argentina

The strategy for the empirical analysis relies on a framework used by Partridge (2005), which starts by considering a very simple model of regional growth, called a

“parsimonious” model, with only a few key variables Building on this simple model, I

called the “base” model In addition, I apply some spatial econometrics techniques in order to test for the presence of spatial autocorrelation among the units of analysis and, if

it is appropriate, to control for it

To gain further understanding about the relationship between distribution and growth, this dissertation attempts to test some hypotheses Since the research question is broken into several parts, the hypotheses follow a four-step scheme If any one of the null hypotheses is rejected, alternative hypotheses that reflect my expectations are proposed

Second step:

Null hypothesis

H0: If one takes into account spatial clustering in the provinces of Argentina, this will not help to explain better the inequality in the distribution of per capita income in each region

Alternative hypothesis

H 1: If one takes into account spatial clustering in the provinces of Argentina, this will help to explain better the inequality in the distribution of per capita income in each region (a more significant and more powerful influence)

Third step:

Null hypothesis

H0: Higher levels of inequality of the per capita income distribution in each different province and its neighboring provinces will not be associated with differences in the provincial rates of growth of per capita income

Alternative hypothesis

H 1: Higher levels of inequality of the per capita income distribution in each different province and its neighboring provinces will be negatively associated with the provincial rates of growth of per capita income

Fourth step:

Null hypothesis

H0: Controlling for spatial dependence, the negative relationship between

inequality and growth will remain unchanged

Alternative hypothesis

H 1: Controlling for spatial dependence, the negative relationship between inequality and growth will be stronger (more significant and more powerful)

1.8 Significance and Relation to the Present State of Knowledge

Today, there is much interest in the spatial patterns of inequality and in the dynamics of geographic income disparities Since Krugman (1999), there have been concerns with levels of spatial income inequality, their persistence, and the fundamental processes that give rise to them These issues have been investigated across the global economy down to the level of the neighborhood Surveys of the literature on convergence

at the international and regional level can be found in: de la Fuente (1997), Durlauf and Quah (1999), Temple (1999), Florax and Folmer (2002), Fingleton (2003), Islam (2003), and Magrini (2004)

Despite the reappearance of interest in regional economic growth and inequality, the geographical dimensions of the data underlying empirical analyses have received much less consideration This has been the role of the literature on spatial econometrics (Anselin and Florax, 1995; Anselin, 2001; Anselin, 2002) and spatial statistics (Getis et al., 2004)

Evidence suggesting that physical location and geographical spillovers matter more than traditional macroeconomic factors are grounded in Quah (1996) and Moreno and Trehan (1997) Modern applications of formal spatial econometrics methods to the question of regional convergence have produced new insights about the nature of spatial economic change (Rey and Montouri, 1999; Fingleton and López-Bazo, 2006)

These trends can also be found in the literature on convergence and inequality in Latin America For example, in the past decade there has been a growing literature on the empirical analysis of regional growth and inequality in many countries of the world, including some from Latin America, such as Dobson and Ramlogan (2002) and Serra et

al (2006) for Latin America; Gerber (2002) and García-Verdú (2002) for Mexico; Aroca and Bosch (2000) for Chile; Ferreira (1998) and Azzoni (2001); for Brazil; and Utrera and Korosch (1998) and Marina (2002) for Argentina Despite this rich empirical literature, relatively few studies have compared the rates of growth of per capita income and inequality within national systems Moreover, comparative studies have tended to concentrate on the more advanced economies, such as the European countries, the US, Canada and Australia, among others

The underlying geographic dimensions of regional growth processes have not

studies has used any spatial econometrics methods to analyze the geographical dimensions of the data

I expect that, in Argentina, not only inequality within the province but also the spillovers of inequality from neighboring provinces will affect economic growth

Furthermore, there are short-run and long-run effects of inequality on economic growth I attempt to disentangle these effects in this dissertation While, chapter 4 focuses on the

provinces own inequality in a long-run framework, chapter 5 specifies econometric

models to capture the effects of inequality in neighboring provinces as well as their

short-run and long-short-run effects on economic growth

In summary, due to the fact that there is a lack of spatial considerations in the recent literature on inequality and growth in Latin America and, specifically, in Argentina, this dissertation attempts to close the gap The goal is to reconsider the relationships between inequality and growth, taking into account the impact of spatial location Thus, a contribution of this dissertation is the association of physical location and geographical spillovers to the regional levels of inequality and growth

1.9 The Influence of Inequality on Growth

The key question for this dissertation is how inequality influences growth This is

a complex relationship, as there are multiple channels through which inequality affects growth with a positive influence and multiple channels through which inequality affects growth with a negative influence The net outcome depends on which influences dominate Thus, if the relationship between inequality and growth is ambiguous at the

theoretical level, the actual impact of inequality on economic growth is an empirical question

The literature review identifies several of these channels On the one hand, there are several positive influences of inequality on growth, usually associated with a

“classical” economic approach This approach stresses how inequality enhances incentives that increase efficiency, growth and capital accumulation For example:

1) Inequality in income distribution may increase the rate of savings

(through different propensities to save across income classes) and capital accumulation and, thereby, accelerate growth (Kuznets, 1955; Kaldor, 1956).1

2) Inequality may signal opportunities to improve one’s income (or one’s

position in the income distribution), through greater work effort and diligence, as well as Schumpeterian factors such as entrepreneurship, risk-taking, and innovation, which are also sources of growth (Siebert, 1998; Bell and Freeman, 2001)

On the other hand, inequality may have negative effects on growth There are at least five conceptual reasons why this might be the case

1) If credit or insurance markets are imperfect, economic agents may

depend entirely on their initial wealth to undertake important investment projects Given these imperfections, the poor would be

1 This view suggests that inequality may have a positive effect on income growth if it is conducive to greater savings Research by the Rural Finance Program at The Ohio State University has accumulated evidence that the poor save a higher proportion of their income than the rich, contrary to Kaldor (Adams,

unable to invest in socially efficient (that is, profitable) projects (Galor and Zeira, 1993; Banerjee and Newman, 1993; Aghion et al., 1999; Levine, 2004)

2) The second conceptual reason why inequality may lead to lower

growth rates includes several political economy dimensions In societies with high degrees of concentration of power and wealth, the elites may have more success in selecting economic strategies that benefit them rather than middle and lower income groups Their rent-seeking behavior usually distorts resource allocation in the economy, affecting factor productivity and hurting economic growth (Perotti, 1993; Bertola 1993; Alesina and Rodrik, 1994: Persson and Tabellini, 1994; Benabou, 1996; Barro, 2000; Leon 2007)

3) The third conceptual reason why inequality may lead to lower rates of

economic growth emerges when income inequality encourages social conflict and results in more crime and illegal activities, which discourage investment and weaken property rights (Hibbs, 1973; Venieris and Gupta, 1986; Gupta, 1990, Alesina and Perotti, 1996; Benhabib and Rustichini, 1996; Banerjee and Duflo, 2000).Inequality

of wealth and income motivates the poor to engage in crime, riots, and other disruptive activities The stability of political institutions may even be threatened by revolution, so that laws and other rules have shorter expected duration and greater uncertainty The participation of the poor in crime and other antisocial actions represents a direct waste

of resources because the time and energy of the criminals and of the law enforcers are not devoted to productive efforts Moreover, the threats to property rights deter investment Through these various dimensions of socio-political unrest, more inequality tends to reduce the rate of growth of an economy

4) Inequality adversely affects social capital, which is typically defined as

the level of trust, civic norms, and social networks in a society that facilitate contracts and transactions and maintain social stability (Knack and Keefer, 1997; Kawachi et al., 1997; Nan, 2000; Caramuta, 2005)

5) Inequality adversely affects the set of opportunities specifically related

to education, distribution of assets and land, political influence, public infrastructure and health (Sen, 1992; Roemer, 1998; Ferreira, 2001; Bourguignon, Ferreira, and Menéndez, 2003) Thus, more inequality implies a more narrow set of opportunities in society and less productivity of resources, which tends to reduce the rate of growth of

an economy

6) Inequality may have an entirely separate effect at the middle versus the

tails of the distribution For example, Easterly (2001) argues that a middle-class consensus promotes growth by encouraging stability, mass education, better public services, and property rights This

“consensus” appears closely related to the social capital literature that

Glaeser at al., 2002), and to the existence of income mobility across society (Partridge, 2005)

The influence of inequality in the income distribution on economic growth may have different short-run and long-run effects (Forbes, 2000; Partridge, 2005), and different effects in the rural versus the urban areas.2 For example, Fallah and Partridge (2006) have found for the U.S that in the urban areas there is a positive relationship between inequality and growth, while in the rural areas there is a negative relationship between inequality and growth The authors reinvestigated the inequality-growth relationship using U.S county-level data and they found that the inequality-growth relationship could completely vary even within states In urban areas, because factors such as agglomeration economies and specialization of labor play a primary role in generating economic growth, greater income inequality intensifies the market rewards for the most able, attracting more skilled and specialized workers Conversely, in smaller rural communities, more intimate personal relationships and lack of anonymity mean that greater income inequality takes on a personal nature that weakens social cohesion and, in turn, economic growth

My expectation is that, for environments like Argentina in recent times, the net effect of inequality of growthis likely to be negative This dissertation attempts to

establish if this is the case

2

Following Kucera (2002), suppose a ‘‘middle-class consensus’’ leads to greater taxes used to fund an improved education system Short-run growth may decline through higher taxes, but the long-run effects are positive, as the workforce becomes more productive Similarly, because increased inequality is associated with liquidity/ credit constraints, it can produce greater cyclical volatility, which can depress short-run investment and growth (Aghion, Caroli, and García-Peñalosa, 1999)

CHAPTER 2

ARGENTINA

2.1 Argentina, a Beautiful Country

Argentina is a beautiful, wealthy country with abundant natural resources, a large territory with low density of population, and a fascinating economic history Argentina’s experience of economic growth has been perplexing Scholars like della Paolera and Taylor (2003) have examined “the Argentina puzzle,” wondering why the country was very rich until at least around 1913 and today it is relatively poor This deterioration may help to explain why psychoanalysis and the nostalgia of the tango music are so popular in Argentina Its affluent past is revealed by the “Belle Époque splendor” of Buenos Aires, which was developed as the economic, cultural, and political center of the country with a French accent In contrast, the number of scavengers that comb its streets reveals its current poverty

Argentina’s economic history has been marked both by the impressive performance of its per capita income growth from 1880 to 1913 and by its ups and downs afterwards This volatility has reflected the fragility of its political economy The

institutions have broken down many times during the 20th century For this reason, the state has been able to expropriate private savings often, through hyperinflation or devaluation Cavallo, Minister of Finance in the early 1990s, felt the need to set up a currency board in order to gain the people’s confidence in the economic system

This dissertation considers how one particular initial condition, income inequality, affects the process of economic growth within and among economic regions, while controlling for regional spillovers High levels of poverty and an unequal distribution of income may create poverty traps or vicious circles that negatively affect growth (Perry et al., 2006)

This is not, however, a dissertation about the economic history of Argentina Instead, the goal of this chapter is to describe some facts of Argentina’s economic history directly related to its economic growth and income distribution Further, Argentina’s growth record will be compared to the record of other countries, to highlight its unique evolution

2.2 Initial Conditions

The sources of economic growth can be traced to a variety of factors: by and large, investment that increases the quantity and improves the quality of existing physical, human, and natural resources and that raises the productivity of resources through institutional change and technological progress These sources of growth are influenced by differences in a country’s initial conditions:

a Endowments of physical, human, and natural resources

b Levels of per capita income, poverty, and the distribution of wealth