世界経済・社会統計 = World development indicators

We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

https://www.econstor.eu/bitstream/10419/81822/1/02-28.pdf

Economic growth and income inequality

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups' income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

A New Data Set Measuring Income Inequality

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups' income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect inter temporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

/pdf/a-new-data-set-measuring-income-inequality-547mfhckz4.pdf

sector employment, uncovered sector employment, and unemployment. The impact of these labor market adjustments on absolute poverty will depend on how the pattern of employment composition changes within households and on how income is shared within households. An earlier paper (Fields and Kanbur, 2007) focused on the income-sharing dimension of the problem. The present paper focuses on household employment composition. For a particular structure of the labor market— one with good jobs, bad jobs, unemployment, and adult and youth workers— and with a particular model of how the sectoral patterns of employment are translated into household employment composition, we analyze the impact of minimum wages on a class of absolute poverty measures. The precise characterizations demonstrate the need for a nuanced appreciation of the impacts of a minimum wage increase, since they depend intricately on the values of key parameters (the poverty line, poverty aversion, labor demand elasticity, and the starting level of the minimum wage). Moreover, the relationship between poverty and the minimum wage is in general nonmonotonic, so that local effects can be quite different from the effects of large changes in the minimum wage.

Poverty Effects of the Minimum Wage: The Role of Household Employment Composition

This Paper uses a new data set drawn from official earnings records kept by the French national statistical agency, INSEE, and builds a time series on various mobility indices for the first time. Using six mobility concepts, we chart wage mobility trends for the working population and compare mobility rates in various population subgroups differentiated by gender, and education. We then compare mobility trends over time for each population subgroup. Next, we relate the extent of mobility using each of these concepts to measures of macroeconomic conditions including GNP growth, unemployment, inflation, and change in the minimum wage. The results show that the answers to even the most fundamental of mobility questions depend on the mobility concept used. Specifically, we find: over time, income mobility in France has risen for some concepts and fallen for others; comparing genders, women have higher income mobility for some concepts and lower income mobility for others; looking across educational groups, for some mobility concepts it is the best-educated workers who have the highest mobility, while for other concepts, it is the least-educated; in general, the indices are affected by demographic variables, macroeconomic conditions, and changes in employment composition, but these patterns are not uniform across the different concepts; changes in ranks track only imperfectly changes in francs, and the relationships are far from linear. The implication is that before labour economists ‘do a mobility study,’ they need to be very clear about the mobility concept or concepts they wish to study. As our work shows, the choice can and does make a vital difference.

Francs or Ranks? Earnings Mobility in France, 1967-1999

When economic growth (or economic decline) takes place, who benefits and who is hurt how much? The more traditional way of answering this question is to compare two or more comparable cross sections and gauge changing income inequality among countries or individuals. A newer way is to utilize data on a panel of countries or a panel of people and assess the pattern of panel income changes. How do these two approaches relate to one another? This paper shows, first, that it is possible to have all four combinations – rising or falling inequality and divergent or convergent panel income changes, and second, under what conditions, for various measures of rising/falling inequality and various measures of divergent/convergent income changes, each of the four possible combinations can arise.

Changing Income Inequality and Panel Income Changes

Between 2000 and 2012, Panama boasted the strongest economic growth in Latin America. The growth experience was not uniform: the 2000.02 period was marked by slow or negative growth rates, after which growth was exceptionally rapid. Although the international economic crisis of 2008 slowed the growth process down, it rebounded in the years following the crisis. Labour market indicators have clearly improved. The economic crisis led to a temporary increase in unemployment, a drop in the share of paid employees, and a rise in some poverty indicators; all these effects were reversed by the end of the period studied.

/pdf/the-growth-employment-poverty-nexus-in-latin-america-in-the-2mue3z9awj.pdf

Gary S. Fields

Papers

Poverty Effects of the Minimum Wage: The Role of Household Employment Composition

Francs or Ranks? Earnings Mobility in France, 1967-1999

Changing Income Inequality and Panel Income Changes

The growth-employment-poverty nexus in Latin America in the 2000s: Panama country study

Discontinuous Losses from Poverty, Generalized P& Measures and Optimal Transfers to the Poor