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Ms. Evridiki Tsounta and Anayochukwu Osueke
Income inequality in Latin America has declined during the last decade, in contrast to the experience in many other emerging and developed regions. However, Latin America remains the most unequal region in the world. This study documents the declining trend in income inequality in Latin America and proposes various reasons behind this important development. Using a panel econometric analysis for a large group of emerging and developing countries, we find that the Kuznets curve holds. Notwithstanding the limitations in the dataset and of cross-country regression analysis more generally, our results suggest that almost two-thirds of the recent decline in income inequality in Latin America is explained by policies and strong GDP growth, with policies alone explaining more than half of this total decline. Higher education spending is the most important driver, followed by stronger foreign direct investment and higher tax revenues. Results suggest that policies and to some extent positive growth dynamics could play an important role in lowering inequality further.
Mr. Chris Papageorgiou, Mr. Subir Lall, and Ms. Florence Jaumotte

Front Matter Page Research Department Authorized for distribution by Jörg Decressin Contents I. Introduction II. A Look at Cross-Country Trends A. Income Inequality B. Trade Openness, Financial Openness and Technological Progress III. Empirical Analysis A. Specification B. Results C. Robustness IV. Discussion V. Conclusion Appendix I. Variable Definitions and Data Sources Appendix II. Income Country Groups and Estimation Sample References Tables 1. Income Inequality Panel Regressions 2. Quintile Income Shares

Tingyun Chen, Mr. Jean-Jacques Hallaert, Mr. Alexander Pitt, Mr. Haonan Qu, Mr. Maximilien Queyranne, Ms. Alaina P Rhee, Ms. Anna Shabunina, Mr. Jerome Vandenbussche, and Irene Yackovlev

FIGURES 1. EU: Risk of Poverty Across Generations 2. Income Inequality Across Generations in Europe 3. The Young and the Labor Market 4. Decomposing Fiscal Redistribution by Instrument TABLES 1. Income Inequality Panel Regressions 2. Absolute Poverty Panel Regressions 3. Unemployment Rate Panel Regressions APPENDICES I. Regional Disparities in Poverty and Real Income Across Europe II. The Econometrics of Assessing the Effects on Inequality, Poverty, and Labor Market Outcomes III. Data Quality and Comparability IV. Preferences for

Mr. Chris Papageorgiou, Mr. Subir Lall, and Ms. Florence Jaumotte
We examine the relationship between trade and financial globalization and the rise in inequality in most countries in recent decades. We find technological progress as having a greater impact than globalization on inequality. The limited overall impact of globalization reflects two offsetting tendencies: whereas trade globalization is associated with a reduction in inequality, financial globalization-and foreign direct investment in particular-is associated with an increase. A key finding is that both globalization and technological changes increase the returns on human capital, underscoring the importance of education and training in both developed and developing countries in addressing rising inequality.
Tingyun Chen, Mr. Jean-Jacques Hallaert, Mr. Alexander Pitt, Mr. Haonan Qu, Mr. Maximilien Queyranne, Ms. Alaina P Rhee, Ms. Anna Shabunina, Mr. Jerome Vandenbussche, and Irene Yackovlev

(2013) and Dabla-Norris and others (2015) . 7 The main results are presented in Tables 1 and 2 . 8 Control variables are included to account for other factors that could affect poverty and inequality, including globalization and technological progress ( Pavcnik 2011 ; Kanbur 2015 ). 9 The data feature harmonized measurements of income inequality and poverty. This helps overcome limitations on data comparability faced by other cross-country studies ( Clements and others, 2015 ). Table 1. Income Inequality Panel Regressions 1 (1) (2) (3

Tingyun Chen, Mr. Jean-Jacques Hallaert, Mr. Alexander Pitt, Mr. Haonan Qu, Mr. Maximilien Queyranne, Ms. Alaina P Rhee, Ms. Anna Shabunina, Mr. Jerome Vandenbussche, and Irene Yackovlev
This SDN studies the evolution of inequality across age groups leading up to and since the global financial crisis, as well as implications for fiscal and labor policies. Europe’s population is aging, child and youth poverty are rising, and income support systems are often better equipped to address old-age poverty than the challenges faced by poor children and/or unemployed youth today.
Mr. Chris Papageorgiou, Mr. Subir Lall, and Ms. Florence Jaumotte

, although puzzling at first, appeared to make a lot of sense upon examination of data on the sectoral composition of FDI. These suggest indeed that FDI mostly takes place in relatively higher skill- and technology-intensive sectors, and thereby increases the demand for and wages of more skilled workers. Table 1: Income inequality panel regressions (dependent variable: natural logarithm of Gini) Model Specification Full Model Benchmark Model Sectoral Exports Sectoral Productivity Excluding Sectoral Empl. Shares IV Estimation

Ms. Evridiki Tsounta and Anayochukwu Osueke

importance of strong economic fundamentals to boost FDI and subsequently equality ( Table 4 ). Based on the estimated model, the contributions of the various factors to the change in the Gini coefficient can be calculated as the average total change in the respective variable over the last decade multiplied by the corresponding coefficient estimate (in the spirit of Jaumotte, Lall and Papageorgiou, 2008 ). Table 4. Income Inequality Panel Regression (Dependent variable: Gini coefficient) Real effective exchange rate 0.02 (0.06)** Tax