Poverty,inequality and ethnic minorities in Vietnam

The present study examines how and why ethnic minorities are poorer than ethnic majorities in Vietnam using the Vietnam Household Living Standards Survey data for 2002 and 2004. First, the analysis confirms that households belonging to the ethnic minority groups are not only poorer but also more vulnerable to various shocks than those in the ethnic majority groups, namely the Kinh and the Chinese. Second, household composition (e.g., dependency burden), education, land holding and location are important determinants of expenditure and poverty, whilst there is some diversity among different ethnic groups. Finally, the decomposition analyses reveal that the ethnic minorities are poorer not necessarily because they have more disadvantaged household characteristics (e.g., educational attainment or location), but, more importantly, because the returns to the characteristics are much lower for ethnic minorities than for the majorities. Government policies to reduce structural differences between ethnic majorities and minorities are imperative to address the disparities in returns to endowments between them.     

Poverty decompositions with counterfactual income and inequality dynamics

Traditional poverty accounting decomposes changes in a country's poverty headcount ratio into changes in income and inequality. We argue that this approach is unsatisfactory from the perspective of policy analysis because it compares a country in two points of time without taking the country's initial situation, and hence its potential for poverty reduction, into account. We thus suggest comparing traditional poverty decompositions with a counterfactual situation. This counterfactual indicates what a country starting from its initial situation could be expected to achieve in terms of income, inequality, and, hence, poverty developments. We construct those counterfactuals by modeling income and inequality trends characterized by convergence and a “Kuznets” relationship between inequality and development. Parameters in those relationships are estimated using PovcalNet survey data from 144 countries and we construct our counterfactual poverty predictions for 71 developing countries. While there is overall a tight relationship between actual developments and counterfactuals, we identify several cases, where both deviate from each other and discuss the policy implications. We also check for commonalities in differently performing countries and find that those who fell particularly short of expectations often underwent political transition and state fragility.     

Persistence of gender inequality: the role of ethnic divisions

Gender inequality remains a fundamental challenge for global policymakers given that it has detrimental implications for growth and human capital formation. However, studies examining the roots of gender inequality, and what determines the level of inequality are relatively scant. In this study, we seek to contribute to the literature that examines the roots of gender inequality and thus, our objective is to examine the impact of ethnic diversity on gender inequality. We argue that the level of ethnic diversity within a country plays a role in either deepening or bridging gender gaps. Using indices of ethnic fractionalization, we examine the effects of ethnic diversity on measures of gender inequality such as the gender inequality index, and its associated dimensions of empowerment, educational attainment and labour market outcomes. Our evidence suggests that ethnic diversity widens existing gender gaps. We discuss several mechanisms through which ethnic diversity may lead to the increase of existing gender gaps, and lay out various policy approaches to address gender inequality.     

A Gini decomposition analysis of inequality in the Czech and Slovak Republics during the transition1

Disposable income inequality, as measured by the Gini coefficient and using the Family Budget Survey data, increased very little, and by a similar amount, from 1989 to 1993 in the Czech Republic and Slovakia. This surprising result is examined with an analysis of changes in the channels of redistribution and Gini decomposition. We find that the sizeable increase in overall inequality due to changes in the wage earnings component is mitigated by changes in the tax and transfer components in both republics. As for the relative effects of government policies, changes in the transfer component contributed more than changes in the tax component to lowering the growth of inequality in the Czech Republic, while the reverse was true for Slovakia.     

Poverty,inequality and growth in Albania

This article studies the distribution of well‐being and, specifically, the degree of poverty and deprivation in Albania, in the years 2002 and 2005, using Living Standard Measurement Surveys (LSMS). The distribution analysis is performed by applying both one‐dimensional and multidimensional approaches, in particular to better examine the link between economic growth, inequality and poverty in Albania. Furthermore, by estimating a non‐monetary indicator, as proposed by Bossert et al. ( 2007 ), and a nonlinear principal component model together with a probit model, the paper focuses on the multidimensional measures of poverty to address the relationship between poverty and socio‐economic factors. Our evidence shows that absolute poverty decreased from 2002 to 2005 while national relative poverty increased; economic growth reduced poverty in Albania over the observed period; and living in rural and mountain areas, being female, poorly educated and with a large family increased the probability of suffering from deprivation.     

A refinement of the relationship between economic growth and income inequality

There is mixed evidence in the literature of a clear relationship between income inequality and economic growth. Most of that work has focused almost exclusively on developed economies. In what we believe to be a first effort, our emphasis is solely on developing economics, which we classify as high-income and low-income developing countries (HIDC and LIDC). We make such distinction on theoretical and empirical grounds. Empirically, the World Bank has classified developing economies in this manner since 1978. The data in our sample are also supportive of such classifications. We provide theoretical scaffolding that uses asymmetric credit constraints as a premise for separating developing economies in such a way. We find strong evidence of a negative relationship between income inequality and economic growth in LIDC to be in stark contrast with a positive inequality–growth relationship for HIDC. Both correlations are statistically significant across multiple econometric specifications. Using international data from 1960 to 2010, this article explores the effect of income inequality on economic growth using dynamic panel technique, such as system generalized method of moments (GMM) that is believed to mitigate endogenous problem. These results are strikingly contrasting to the previous estimation results of Forbes (2000) displaying significant positive correlation between two variables in the short to medium term.     

Status-seeking behavior,the evolution of income inequality,and growth

Using an overlapping generations model, this paper investigates the implications of status-seeking behavior, induced by preferences for relative income, for the evolution of income inequality. When average income rises, an individual’s marginal utility of their own income may increase (keeping up with the Joneses, or KUJ), or decrease (running away from the Joneses, or RAJ). It is shown that income inequality is shrinking over time in the KUJ economy, whereas it is expanding in the RAJ economy. We also explore the implications for long-run growth and inequality, in the existence of both KUJ and RAJ agents. I am truly grateful to Koichi Futagami for his encouragement and guidance in writing this paper. I have benefitted from comments by an anonymous referee, Been-Lon Chen, Giacomo Corneo, Akiomi Kitagawa, Kazuo Mino, Kazuhiro Yuki, and seminar participants at Osaka University, the 2006 Japanese Economic Association Autumn Meeting at Osaka City University, the Far Eastern Meeting of Econometric Society 2007 at Taipei, SER Conference 2007 at Singapore, and the European Meeting of Econometric Society 2007 at Budapest. All remaining errors are, of course, my own. The financial support from JSPS Research Fellowships for Young Scientists is greatly acknowledged.     

The geography of natural resources,ethnic inequality and civil conflicts

We study whether the spatial distribution of natural resources across different ethnic groups within countries causes spatial inequality and the incidence of armed conflict. By providing a theoretical rent-seeking model and analysing a set of geo-coded data for mines, night-time light emissions, local populations and ethnic homelands, we show that the spatial distribution of resources is a major driving factor of ethnic income inequality. Moreover, a spatially unequal distribution of natural resources induces rent-seeking behaviour and thus increases the risk of civil conflicts. Consequently, we extend the perspective of the resource curse to explain cross-country differences in income inequality and the onset of civil conflicts.     

教育不平等、收入非平衡与贫困陷阱——对农村教育和农民收入的考察

研究表明,无论是在短期还是长期内,农村居民受教育水平和职业培训的差异是造成收入差距的一个重要因素.同时,教育差距既推动了收入差距的扩大,又可能伴随着收入非平衡增长的扩大而扩大,低收入农户有陷入贫困陷阱的危险,而解决问题的关键是推进农村教育的均衡协调发展.     

Unfair inequality and growth*

Fighting against economic inequality is one fundamental social goal in the agendas of most governments. However, recent studies highlight that people actually prefer unequal societies, as they accept inequality generated by an individual's effort and wish to reduce only unfair inequality (generated by factors beyond an individual's control). This distinction might help to explain the fundamental unsolved question about whether inequality is good or bad for growth: unfair inequality (UI) could be growth-deterring, while fair inequality (FI) might be growth-enhancing. We derive a reduced-form growth equation from a stylized overlapping-generations model with human capital that includes FI, UI, and poverty. Then, using an instrumental variable approach, we show for alternative samples and inequality measures at the worldwide level that the estimated coefficient associated with UI is always negative, while the coefficient of total inequality increases when UI is included in the regression. Moreover, we find that poverty mediates this relationship because the higher the poverty rate, the smaller the impact of either type of inequality on growth.     

Income distribution and economic development: Insights from machine learning

We draw upon recent advances that combine causal inferences with machine learning, to show that poverty is the key income distribution measure that matters for development outcomes. In a predictive framework, we first show that LASSO chooses only the headcount measure of poverty from 37 income distribution measures in predicting schooling, institutional quality, and per capita income. Next, causal inferences with post‐LASSO models indicate that poverty matters more strongly for development outcomes than does the Gini coefficient. Finally, instrumental variable estimates in conjunction with post‐LASSO models show that compared to Gini, poverty is more strongly causally associated with schooling and per capita income, but not institutional quality. Our results question the literature's overwhelming focus on the Gini coefficient. At the least, our results imply that the causal link from inequality (as measured by Gini) to development outcomes is tenuous.     

On the impact of innovation and inequality in economic growth

We present robust results on the empirical relationship among income inequality, innovation, and economic growth for a panel dataset of 74 countries over the period 1996–2014. We estimate pairwise causality tests to show that there is bidirectional causality between GDP per capita and R&D, while R&D causes the Gini index of income inequality, and it causes human capital. Allowing coefficients to be different across cross-sections of countries, we get in any case a pairwise bi-directionality. By dynamic panel data estimations, when regressing R&D on GDP per capita, we obtain a threshold value of 0.16% of R&D such that for values above it there is economic growth. While regressing R&D on the Gini index, we get a threshold of 0.10% of R&D above which, the income distribution begins to improve. Finally, we estimate a growth equation that depends on R&D, income inequality, and physical capital. We obtain two thresholds, one of 38.79 for the Gini (above which the economic growth decreases), and one of 0.06% for R&D such that above it, economic growth is rising.     

Poverty among minorities in the United States: explaining the racial poverty gap for Blacks and Latinos

The two largest minorities in the United States, African Americans and people of Hispanic origin, show official poverty rates at least twice as high as those of non-Hispanic Whites. These similarly high poverty rates between the two minorities are, however, the result of different combinations of factors related to the specific characteristics of these two groups. In this article, we analyse the role of demographic and labour-related variables in the current differential of poverty rates among racial and ethnic groups in the United States and its recent evolution. Our results show, first, that these differentials are largely explained by differing family characteristics of the ethnic groups. Furthermore, we show that while labour market activity of family members and a preponderance of single mothers play a more significant role in the higher poverty rates of Blacks, a larger number of dependent children is closely associated with higher poverty among Latinos, who also suffer from a larger educational attainment gap and higher immigration rates.     

Wealth inequality,governance and economic growth

Links between economic growth and inequality are of growing interest for researchers and policy makers. Previous studies of this relationship have focused mainly on inequalities in income rather than in wealth. Yet from many perspectives wealth inequality is arguably more important. Using a new panel data set from Credit Suisse for 45 sample countries over the period 2000–2012, this study investigates the effects of wealth inequality on economic growth. Empirical results from system GMM estimation suggest that the wealth inequality is negatively associated with cross-country economic growth. This result is robust to alternative estimators and measures of wealth inequality, as well as the econometric specification. Further empirical investigation reveals that impact of wealth inequality on growth is mitigated by better governance.     

Income inequality and economic growth

Despite the extensive existing literature on income inequality and economic growth, there remains considerable disagreement on the effect of inequality on economic growth. Existing literatures find either a positive or a negative relationship. In this paper, we attempt to theoretically examine that relationship with a stochastic optimal growth model. We make the disagreement clear within a single model. We conclude (i) that both are possible – that is, higher inequality can retard growth in the early stage of economic development, and can encourage growth in a near steady state, (ii) that income redistribution by high income tax does not always reduce income inequality. Income inequality can be reduced by higher income tax in a near steady state, but it cannot be reduced in the early stage of economic development, and (iii) that two government polices – rapid economic growth and low income inequality – can be achieved by low income tax in the early stage of economic development, but both cannot be achieved simultaneously in a near steady state.     

Behind rising inequality and falling growth

This paper investigates why the upsurge of top income shares has coincided with economic slowdowns in the US since the late 1970s. I argue that a fast-growing unearned income from ‘wealth residual’ – the unexplained increase in wealth that is not accompanied by any increase in real output – lies behind them. To support this hypothesis, I measure wealth residual from the national accounts and associated statistics, and then perform a set of panel regressions using a comprehensive panel dataset of the US at the state level. The estimation results demonstrate that the rapid growth of wealth residual during the last four decades has contributed to a co-evolution of fast-growing inequality and falling growth.     

Income inequality and education revisited: persistence,endogeneity and heterogeneity

This article presents new results on the relationship between income inequality and education expansion – that is, increasing average years of schooling and reducing inequality of schooling. When dynamic panel estimation techniques are used to address issues of persistence and endogeneity, we find a large, positive, statistically significant and stable relationship between inequality of schooling and income inequality, especially in emerging and developing economies and among older-age cohorts. The relationship between income inequality and average years of schooling is positive, consistent with constant or increasing returns to additional years of schooling. While this positive relationship is small and not always statistically significant, we find a statistically significant negative relationship with years of schooling of younger cohorts. Statistical tests indicate that our dynamic estimators are consistent and that our identifying instruments are valid. Policy simulations suggest that education expansion will continue to be inequality reducing. This role will diminish as countries develop, but it could be enhanced through a stronger focus on reducing inequality in the quality of education.     

How large is income inequality in China: assessment on different estimates of Gini coefficient

The rising income inequality in China has attracted social attentions, especially since SWUFE published the unbelievably high Gini coefficient in December 2012. In order to answer the question how large income inequality in current China is, this paper compares existing different Gini coefficients estimated from four different household surveys, which are the NBS household survey, the CHIP survey, the CHFS of SWUFE, and the CFPS of Peking University, and then assesses these household surveys themselves. The relevant evidences indicate that the national Gini coefficient in current China is between 0.47~0.52. The sampling defects of the CHFS are significantly major and that the national Gini coefficient of 0.61 published by SWUFE is seriously overestimated.     

The impact of unemployment on inequality and poverty in OECD countries

The purpose of this research is to examine the contribution of unemployment to income inequality and poverty in various OECD countries. These relationships have been explored using Luxembourg Income Study micro-data. Considerable differences across OECD countries are revealed through the use of within-household unemployment distributions. These differences help to explain most of the observed divergences in the relationship between unemployment and income distribution, in conjunction with the heterogeneous influence of social benefits on the economic position of the unemployed in these countries. A sub-group decomposition analysis corroborates the limited effect of unemployment on income distribution in most of the considered countries. However, it seems clear that the unemployed are among those with the highest risk of experiencing poverty.
JEL classification: D31, I32, J31.     

Land inequality and the transition to modern growth

Can the initial distribution of land, in a country's early history, affect its subsequent economic development? In this paper, I show that when land ownership is sufficiently concentrated, the landed elite will lobby the government to raise barriers to industrialization in order to protect its rents in the rural economy. I develop a small open economy model in which barriers take the form of tariffs on the imports of intermediate inputs used in industry. Such tariffs can affect both the timing and the pace of industrialization. The quantitative application of the theory is motivated by an important question in economic history: why did Argentina not replicate Canadian economic success, despite reasonable expectations to the contrary in the late 19th century? I provide evidence that Argentina had a markedly higher inequality in land ownership than Canada. Taking as given the observed differences in land distributions in the early 20th century, the model produces differences in equilibrium tariffs similar to the ones observed at the time, and the ones required to account for the Canadian–Argentine income gap until 1950. Over time however, as land becomes unimportant in production, land inequality ceases to be a source of policy disparities and income gaps.