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 Dissertation Committee: Approved by Professor Claudio Gonzalez-Vega, Adviser Professor Mark Partridge Professor Joseph Kaboski Adviser Graduate Program in Agricultural, Environmental and Development Economics 3313008 Copyright 2008 by Canadas, Alejandro A All rights reserved 2008 3313008 Copyright by Alejandro Cañadas 2008 iii ABSTRACT This dissertation analyzes whether or not the spatial distribution of inequality in the provinces of Argentina affects real per capita economic growth The primary objective is to decouple the 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 Furthermore, another important objective is to decouple the influence of 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 ii 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 iii Dedicated to my beloved family, my lovely wife, Cynthia, my son Santiago, and my daughter María Camila iv 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 the CEPAL office in Buenos Aires, who provided me with Argentina's provincial per v 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, Paula Cordero-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 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 vi 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 vii FIELDS OF STUDY Major Field: Agricultural, Environmental and Development Economics Minor Fields: Development Economics viii Source: Bourguignon (2004) Figure E.1: Decomposition of a change in distribution and poverty into growth and distributional effects 239 EPH-15 cities 1992 1.8 3.0 1993 1.7 3.0 1994 1.7 2.9 1995 1.4 2.7 1996 1.4 2.6 1997 1.4 2.6 1998 1.2 2.4 EPH - 28 cities 1998 1.3 2.4 1999 1.3 2.5 2000 1.2 2.3 2001 1.0 2.1 2002 1.0 2.0 2003 1.1 2.1 EPH-C 2003-II 1.0 2.1 2004-I 1.2 2.3 2004-II 1.1 2.3 2005-I 1.2 2.4 2005-II 1.2 2.3 Share of deciles 10 Income ratios 10/1 90/10 95/80 4.1 4.1 4.0 3.7 3.6 3.6 3.4 5.1 5.2 5.1 4.8 4.7 4.7 4.5 6.2 6.4 6.3 5.9 5.9 6.0 5.7 7.6 7.9 7.7 7.3 7.3 7.3 7.0 9.4 9.6 9.5 9.1 9.2 9.2 9.0 12.0 12.3 12.1 11.6 11.9 12.0 12.0 16.5 16.6 16.4 16.7 17.0 17.2 17.1 34.1 33.1 34.2 36.7 36.5 36.1 37.7 19.0 7.9 19.9 8.1 19.7 8.2 25.8 9.6 26.5 10.1 26.7 10.5 30.2 11.2 2.0 1.9 1.9 2.1 2.0 2.1 2.1 3.4 3.5 3.3 3.1 3.0 3.0 4.5 4.6 4.4 4.1 4.1 4.0 5.7 5.8 5.6 5.4 5.4 5.2 7.1 7.3 7.2 6.9 6.9 6.8 9.0 9.2 9.1 9.0 8.7 8.8 11.9 12.0 12.2 12.0 11.6 11.9 16.9 17.0 17.4 17.4 17.2 17.3 37.8 36.8 37.4 39.0 40.3 39.8 29.9 28.0 32.3 40.0 39.4 34.8 11.1 10.9 11.9 13.9 14.3 13.5 2.1 2.1 2.1 2.2 2.3 2.2 3.1 3.3 3.3 3.4 3.4 4.1 4.3 4.4 4.4 4.5 5.3 5.5 5.7 5.7 5.8 6.7 7.1 7.2 7.3 7.3 8.8 9.0 9.1 9.1 9.1 11.9 11.9 12.0 11.9 11.9 17.1 16.8 17.0 16.9 16.8 39.8 38.6 37.9 37.8 37.6 38.1 32.7 33.0 32.5 32.7 13.7 11.8 12.0 11.7 11.8 2.2 2.1 2.0 2.1 2.1 Note: Income distribution for the population in major urban cities of Argentina Source: Constructed by the author using Socio-Economic Database for Latin America and the Caribbean (CEDLAS and The World Bank) Table E.1: Distribution of household per capita income in Argentina (deciles shares and income ratios), 1992-2005 240 Gini EPH-15 cities 1992 0.450 1993 0.444 1994 0.453 1995 0.481 1996 0.486 1997 0.484 1998 0.502 EPH - 28 cities 1998 0.502 1999 0.491 2000 0.504 2001 0.522 2002 0.533 2003 0.528 EPH-C (*) 2003-II 0.537 2003-II 0.529 2004-I 0.510 2004-II 0.506 2005-I 0.502 2005-II 0.501 Theil CV A(.5) A(1) A(2) E(0) E(2) 0.370 0.359 0.378 0.430 0.442 0.422 0.471 1.101 1.077 1.112 1.205 1.260 1.146 1.300 0.165 0.162 0.168 0.190 0.194 0.190 0.207 0.299 0.297 0.303 0.340 0.349 0.346 0.369 0.510 0.517 0.510 0.569 0.607 0.586 0.608 0.355 0.352 0.361 0.416 0.429 0.424 0.461 0.606 0.580 0.618 0.726 0.793 0.656 0.845 0.472 0.443 0.464 0.497 0.530 0.519 1.307 1.213 1.231 1.264 1.356 1.343 0.207 0.197 0.208 0.224 0.233 0.227 0.368 0.356 0.377 0.404 0.412 0.401 0.605 0.606 0.647 0.675 0.657 0.637 0.458 0.440 0.474 0.517 0.530 0.512 0.854 0.735 0.757 0.798 0.920 0.902 0.625 0.532 0.507 0.499 0.473 0.480 3.056 1.457 1.714 1.550 1.306 1.418 0.244 0.231 0.216 0.213 0.208 0.209 0.417 0.407 0.380 0.379 0.373 0.373 0.673 0.672 0.621 0.624 0.624 0.624 0.539 0.522 0.478 0.476 0.466 0.467 4.671 1.061 1.469 1.201 0.853 1.005 Note: (*) this calculation uses the EPH weights corresponding to the 28 major provincial cities CV=coefficient of variation A(e) refers to the Atkinson index with a CES function with parameter e E(e) refers to the generalized entropy index with parameter e E(1)=Theil Source: Constructed by the author using Socio-Economic Database for Latin America and the Caribbean (CEDLAS and The World Bank) Table E.2: Inequality Indices from household surveys in major provincial cities in Argentina, 1992-2005 241 0.540 0.520 0.500 0.480 0.460 0.440 0.420 0.400 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003-II * 2003-II 2004-I 2004-II 2005-I 2005-II Note: (*) this calculation uses the EPH weights corresponding to the 28 major provincial cities Source: Constructed by the author using EPH Figure E.2: Gini Coefficient for Argentina, from the distribution of per capita household income, 1992-2005 242 Country Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic El Salvador Honduras Mexico Panama Paraguay Peru Uruguay Venezuela LAC LAC LAC 15 Spain 1950 0.396 0.51 1960 0.414 0.57 0.482 0.54 0.5 0.424 0.55 0.613 0.505 0.606 0.5 0.61 0.37 0.462 0.532 0.548 1970 0.412 0.53 0.571 0.474 0.573 0.445 0.455 0.465 0.618 0.579 0.584 0.485 0.428 0.48 0.531 0.548 0.539 0.457 1980 0.472 0.534 0.571 0.531 0.488 0.485 0.421 0.484 0.549 0.509 0.475 0.451 0.43 0.436 0.447 0.491 0.532 0.519 0.363 1990 0.477 0.545 0.573 0.547 0.503 0.46 0.481 0.505 0.57 0.531 0.563 0.57 0.464 0.406 0.459 0.507 0.542 0.532 0.347 Note: LAC = population-weighted average of Brazil, Chile, Mexico and Venezuela LAC = population-weighted average of LAC + Argentina and Uruguay LAC 15 = population-weighted average of LAC + Colombia, Cuba, Ecuador, Peru, Costa Rica, El Salvador, Guatemala, Honduras, and Panama Source: Constructed by the author using Perry (2006); Altimir (1987); Lodoño and Szekely (2000) Table E.3: Inequality in Latin America between 1950 and 2000 Measured by Gini coefficients 243 Country Argentina Period 1992-1998 1998-2002 2002-2004 1992-2004 Bolivia 1993-1997 1997-2002 1993-2002 Brazil 1990-1995 1995-2003 1990-2003 Chile 1990-1996 1996-2003 1990-2003 Colombia 1992-2000 2000-2004 Costa Rica 1992-1997 1997-2003 1992-2003 Dominican Republic 2000-2004 Ecuador 1994-1998 Change in Gini points Country 0.05 El Salvador 0.03 Honduras -0.02 Jamaica 0.06 Mexico 0.03 0.02 -0.01 Nicaragua -0.02 -0.03 Panama Paraguay -0.01 Peru 0.07 Uruguay 0 Venezuela 0.04 0.04 -0.01 0.02 Period 1991-2003 1997-2003 1990-1999 1990-2002 1992-1996 1996-2002 1992-2002 1993-1998 1998-2001 1993-2001 1995-2002 1997-2002 1997-2002 1989-1998 1998-2003 1989-1995 1995-2003 1989-2000 1989-2003 Change in Gini points -0.02 0.01 -0.02 0.02 -0.02 -0.03 -0.04 -0.02 -0.02 0.01 0.01 0.01 0.02 0.01 0.04 0.02 0.04 Source: Constructed by the author using Gasparini, Gutierréz and Tornarolli (2007) Table E.4: Changes in inequality measured by percentage points of Gini Coefficient using household surveys in each country 244 en os Ca Ai ta r es m ar C h ca a C h co ub ut C Có BA C o rd o r r ba E n i e nt t re e s F o R ío rm s os a Ju La ju Pa y m La p a R M io j en a d M oz isi a o Ne ne uq s ué n Sa Sa lt n a J S a u an n Sa L n t u ís Sa a C nt ia San ru z go t Ti d e a F e er l E s d e t er l o T u fue g cu o m án Bu 0.500 0.400 0.300 0.200 0.100 0.000 Source: Constructed by the author using EPH Figure E.3: Provincial Gini coefficients for Argentina Averages for 1991-2002 245 0.490 0.484 0.482 0.480 0.469 0.470 0.460 0.454 0.450 0.445 0.442 0.440 0.430 0.420 Pampeana Cuyo NE NW Patagonia BA Source: Constructed by the author using EPH Figure E.4: Regional Inequality in Argentina, as shown by Gini coefficients Averages for 1991-2002 246 Subset Region N 44047753 48 44218363 36 45430459 72 12 48243993 Tukey HSD(a,b,c) 60 48 48440541 Sig 45430459 46939949 418 317 46939949 323 Notes: Means for groups in homogeneous subsets are displayed Based on Type III Sum of Squares The error term is Mean Square(Error) = 001 a Uses Harmonic Mean Sample Size = 32.727 b The group sizes are unequal The harmonic mean of the group sizes is used Type I error levels are not guaranteed c Alpha = 05 Regions: 1) Capital City, 2) Pampeana, 3) Cuyo, 4) Northwest, 5)Northeast and 6) Patagonia Table E.5: Bonferroni and the Tukey’s tests to determine means differ in Gini coefficient among regions in Argentina, 1991-2002 247 Change Change Change Change Bottom Gini Q3 Top 10% 20% 91-02 Region 91-02 91-02 91-02 -44.32 Buenos Aires 17.06 -11.26 12.52 19.28 Pampeana -10.42 -2.59 -29.71 Cuyo 14.21 -6.23 12.45 -28.39 -15.05 14.59 Northeast 15.23 -33.54 Northwest 11.94 -7.49 12.32 -19.21 Patagonia 9.32 -12.07 9.77 -18.61 Argentina 16.22 -7.82 15.30 -35.56 Source: Constructed by the author using EPH Table 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 0.655 0.555 0.455 0.355 0.255 0.155 0.055 -0.045 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Source: Author’s calculation using the EPH Figure E.5: Moran’s I statistic for the provincial Gini coefficients of Argentina, 19802002 249 Year Moran's I 1991 0.015 1992 0.325 1993 0.25 1994 0.246 1995 0.057 1996 0.105 1997 0.333 1998 0.327 1999 0.152 2000 0.082 2001 0.59 2002 -0.114 *Two-tail test E(I) -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 -0.045 sd(I) 0.164 0.164 0.169 0.167 0.168 0.168 0.169 0.168 0.166 0.16 0.167 0.165 z 0.372 2.25 1.75 1.746 0.608 0.894 2.244 2.221 1.188 0.795 3.801 -0.417 p-value* 0.71 0.024 0.08 0.081 0.543 0.371 0.025 0.026 0.235 0.427 0.000 0.676 Source: Author’s calculation using the EPH Table E.7: Estimates of the Moran’s I statistic for the provincial Gini coefficients of Argentina, 1991-2002 250 Moran scatterplot (Moran's I = 0.015) gini_91 16 10 21 23 19 Wz 11 18 20 17 13 14 12 22 -1 15 -2 -3 -3 -2 -1 z Source: Author’s calculation using the EPH Figure E.6: Local Moran’s I statistic for the Gini coefficients provincial in 1991 251 Moran scatterplot (Moran's I = -0.114) gini_02 10 1 16 21 20 19 Wz 618 23 15 17 13 11 14 12 -1 -2 22 -3 -3 -2 -1 z Source: Author’s calculation using the EPH Figure E.7: Local Moran’s I statistic for the Gini coefficients in 2002 252 Moran scatterplot (Moran's I = 0.590) gini_01 10 14 23 Wz 15 -2 17 13 12 18 11 19 -1 21 20 16 22 -3 -3 -2 -1 z Source: Author’s calculation using the EPH Figure E.8: Local Moran’s I statistic for the Gini coefficients provincial in 2001 253 ... 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? ... 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. .. 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