Decomposition of the Inequality of Income Distribution by Income Types - Application for Romania

This paper identifies the salient factors that characterize the inequality income distribution for Romania. Data analysis is rigorously carried out using sophisticated techniques borrowed from classical statistics (Theil). Decomposition of the inequalities measured by the Theil index is also performed. This study relies on an exhaustive (11.1 million records for 2014) data-set for total personal gross income of Romanian citizens.Comment: 12 pages, 5 figures, 4 tables, 49 reference

Decomposition of the Inequality of Income distribution by income types- Application for Romania

This paper identifies the salient factors that characterize the inequality income distribution for Romania. Data analysis is rigorously carried out using sophisticated techniques borrowed from classical statistics (Theil). Decomposition of the inequalities measured by the Theil index is also performed. This study relies on an exhaustive (11.1 million records for 2014) data-set for total personal gross income of Romanian citizens

Increasing inequality in transition economies : is there more to come?

This paper decomposes changes in inequality, which has in general been increasing in the transition economies of Eastern Europe and the former Soviet Union, both by income source and socio-economic group, with a view to understanding the determinants of inequality and assessing how it might evolve in the future. The empirical analysis relies on a set of inequality statistics that, unlike"official data", are consistent and comparable across countries and are based on primary records from household surveys recently put together for the World Bank study"Growth, Poverty and Inequality in Eastern Europe and the Former Soviet Union: 1998-2003"[World Bank (2005b)]. The increase in inequality in transition, as predicted by a number of theoretical models, in practice differed substantially across countries, with the size and speed of its evolution depending on the relative importance of its key determinants, viz., changes in the wage distribution, employment, entrepreneurial incomes and social safety nets. Its evolution was also influenced by policy. This diversity of outcomes is exemplified on the one hand for Central Europe by Poland, where the increase in inequality has been steady but gradual and reflects, inter alia, larger changes in employment and compensating adjustments in social safety nets and, on the other for the Commonwealth of Independent States by Russia, where an explosive overshooting of inequality peaked in the mid-1990s before being moderated through the extinguishing of wage arrears during its post-1998 recovery. The paper argues that the process of transition to a market economy is not complete and that further evolution of inequality will depend both on (i) transition-related factors, such as the evolution of the education premium, a bias in the investment climate against new private sector firms which are important vehicles of job creation and regional impediments to mobility of goods and labor, as well as increasingly (ii) other factors, such as technological change and globalization. The paper also contrasts key features of inequality in Russia in the context of other transition economies with trends in inequality observed in China where rapid economic growth has been accompanied by a steep increase in inequality. It argues that the latter's experience is, to a large extent, a developmental, rather than a transition-related phenomenonderiving from the rural-urban divide and is, therefore, of limited relevance for predicting changes in inequality in Russia.Poverty Impact Evaluation,Inequality,Services&Transfers to Poor,Economic Theory&Research,Equity and Development

Subgroup and Shapely Value Decompositions of Multidimensional Inequality: An Application to South East European Countries

Inequality is a multidimensional phenomenon though it is often discussed along a single dimension like income. This is also the case for the various decomposition approaches of inequality indices. In this paper we study one- and multidimensional indices on inequality on data for three large South-East European countries, Bulgaria, Romania and Serbia. We include four dimensions in our measure of multidimensional inequality: income, health, education and housing. We apply various decomposition methods to these one- and multidimensional indices. In doing so, we apply standard decomposition techniques of the mean logarithmic deviation index (I0) and decompositions based on regression analysis in conjunction with the Shapley value approach.Multidimensional inequality, Inequality decomposition, Shapley value

Inequality and Social Welfare

This paper provides a review of part of the literature on inequality and social welfare, with a special focus on the Gini index. The paper first presents the extended Gini index used for measuring inequality, as well as the source decomposition of the Gini used to analyze how changes in income and consumption sources affect overall inequality. The paper then provides a wide range of policy applications of the source decomposition of the extended Gini index, including techniques for analyzing the impact on inequality of the targeting of programs as opposed to the rules for the allocation of benefits among program participants.Inequality; Social welfare

Markets, Human Capital, and Inequality: Evidence from Rural China

Market reforms are generally credited with the rapid growth enjoyed by China's rural sector. This growth has not been without some cost, however, as inequality has also increased. Estimates suggest that the Gini rose from less than 0.20 to over 0.40 during this period. In this paper we go behind these numbers to explore the nature and causes of this inequality. To begin, we find that a considerable share of rural inequality is driven by local differences in household incomes, as opposed to regional income differences, that have been the focus of the previous literature. We then examine inter-household income differentials at the village level, exploring the links between education, market development, non-agricultural employment, and household income. To address these questions, we draw on a recently collected data set from Northeast China, that was collected by two of the authors in collaboration with Chinese colleagues in Hebei and Liaoning provinces in 1995. For purposes of comparison, we also draw on the Chinese Health and Nutrition Survey. We find that indeed, increasing rates of return to education and unevenly developed non-agricultural business opportunities contribute to the high levels of inequality in the countryside. Of most interest, however, is the implication that simultaneous improvements in educational attainment and off-farm market-development would allow more households to share in the rapid growth in rural China.http://deepblue.lib.umich.edu/bitstream/2027.42/39682/3/wp298.pd

Markets, Human Capital, and Inequality: Evidence from Rural China

Market reforms are generally credited with the rapid growth enjoyed by China's rural sector. This growth has not been without some cost, however, as inequality has also increased. Estimates suggest that the Gini rose from less than 0.20 to over 0.40 during this period. In this paper we go behind these numbers to explore the nature and causes of this inequality. To begin, we find that a considerable share of rural inequality is driven by local differences in household incomes, as opposed to regional income differences, that have been the focus of the previous literature. We then examine inter-household income differentials at the village level, exploring the links between education, market development, non-agricultural employment, and household income. To address these questions, we draw on a recently collected data set from Northeast China, that was collected by two of the authors in collaboration with Chinese colleagues in Hebei and Liaoning provinces in 1995. For purposes of comparison, we also draw on the Chinese Health and Nutrition Survey. We find that indeed, increasing rates of return to education and unevenly developed non-agricultural business opportunities contribute to the high levels of inequality in the countryside. Of most interest, however, is the implication that simultaneous improvements in educational attainment and off-farm market-development would allow more households to share in the rapid growth in rural China.

The Measurement of Educational Inequality: Achievement and Opportunity

This paper proposes two related measures of educational inequality: one for educational achievement and another for educational opportunity. The former is the simple variance (or standard deviation) of test scores. Its selection is informed by consideration of two measurement issues that have typically been overlooked in the literature: the implications of the standardization of test scores for inequality indices, and the possible sample selection biases arising from the Program of International Student Assessment (PISA) sampling frame. The measure of inequality of educational opportunity is given by the share of the variance in test scores that is explained by pre-determined circumstances. Both measures are computed for the 57 countries in which PISA surveys were conducted in 2006. Inequality of opportunity accounts for up to 35 percent of all disparities in educational achievement. It is greater in (most of) continental Europe and Latin America than in Asia, Scandinavia, and North America. It is uncorrelated with average educational achievement and only weakly negatively correlated with per capita gross domestic product. It correlates negatively with the share of spending in primary schooling, and positively with tracking in secondary schools.educational inequality, educational achievement, inequality of opportunity

The measurement of educational inequality : achievement and opportunity

This paper proposes two related measures of educational inequality: one for educational achievement and another for educational opportunity. The former is the simple variance (or standard deviation) of test scores. Its selection is informed by consideration of two measurement issues that have typically been overlooked in the literature: the implications of the standardization of test scores for inequality indices, and the possible sample selection biases arising from the Program of International Student Assessment (PISA) sampling frame. The measure of inequality of educational opportunity is given by the share of the variance in test scores that is explained by pre-determined circumstances. Both measures are computed for the 57 countries in which PISA surveys were conducted in 2006. Inequality of opportunity accounts for up to 35 percent of all disparities in educational achievement. It is greater in (most of) continental Europe and Latin America than in Asia, Scandinavia, and North America. It is uncorrelated with average educational achievement and only weakly negatively correlated with per capita gross domestic product. It correlates negatively with the share of spending in primary schooling, and positively with tracking in secondary schools.Teaching and Learning,Secondary Education,Education For All,Poverty Impact Evaluation,Tertiary Education

The measurement of educational inequality: Achievement and opportunity

This paper proposes two related measures of educational inequality: one for educational achievement and another for educational opportunity. The former is the simple variance (or standard deviation) of test scores. Its selection is informed by consideration of two measurement issues that have typically been overlooked in the literature: the implications of the standardization of test scores for inequality indices, and the possible sample selection biases arising from the Program of International Student Assessment (PISA) sampling frame. The measure of inequality of educational opportunity is given by the share of the variance in test scores that is explained by pre-determined circumstances. Both measures are computed for the 57 countries in which PISA surveys were conducted in 2006. Inequality of opportunity accounts for up to 35 percent of all disparities in educational achievement. It is greater in (most of) continental Europe and Latin America than in Asia, Scandinavia, and North America. It is uncorrelated with average educational achievement and only weakly negatively correlated with per capita gross domestic product. It correlates negatively with the share of spending in primary schooling, and positively with tracking in secondary schools.Educational inequality, educational achievement, inequality of opportunity.