Measurements of income inequality: a critique. Inflation and income inequality: a link through the job- search process

In this paper I claim that, in a long-run perspective, measurements of income inequality, under any of the usual inequality measures used in the literature, are upward biased. The reason is that such measurements are cross-sectional by nature and, therefore, do not take into consideration the turnover in the job market which, in the long run, equalizes within-group (e.g., same-education groups) inequalities. Using a job-search model, I show how to derive the within-group invariant-distribution Gini coefficient of income inequality, how to calculate the size of the bias and how to organize the data in arder to solve the problem. Two examples are provided to illustrate the argument.

Neighborhood feedbacks, endogenous stratification, and income inequality

This paper explores the evolution of the cross-section income distribution in economies where endogenous neighborhood formation interacts with positive within-neighborhood feedback effects. We study an economy in which the economic success of adults is determined by the characteristics of the families in the neighborhood in which a person grows up. These feedbacks take two forms. First, the tax base of a neighborhood affects the leveI of education investment in offspring. Second, the effectiveness of education investment is affected by a neighborhood's in come distribution, reflecting factors such as role model or labor market connection effects. Conditions are developed under which endogenous stratification, defined as the tendency for families wi th similar incomes to choose to form common communities, will occur. When families are allowed to choose their neighborhoods, wealthy families will have an incentive to segregate themselves from the rest of the population. This resulting stratification is supported by house price differences between ricli and poor communities. Endogenous stratification can lead to pronounced intertemporal inequality as different families provide very different interaction environments for offspring. When the transformation of human capital into in come exhibits constant retums to scale, cross-section in come differences may also grow across time. As a result, endogenous stratification and neighborhood feedbacks can interact to produce long run inequality.

Financial frictions, informality and income inequality

We studied the effects of changes in banking spreads on distributions of income, wealth and consumption as well as the welfare of the economy. This analysis was based on a model of heterogeneous agents with incomplete markets and occupational choice, in which the informality of firms and workers is a relevant transmission channel. The main finding is that reductions in spreads for firms increase the proportion of entrepreneurs and formal workers in the economy, thereby decreasing the size of the informal sector. The effects on inequality, however, are ambiguous and depend on wage dynamics and government transfers. Reductions in spreads for individuals lead to a reduction in inequality indicators at the expense of consumption and aggregate welfare. By calibrating the model to Brazil for the 2003-2012 period, it is possible to find results in line with the recent drop in informality and the wage gap between formal and informal workers

Wealth composition, endogenous fertility and the dynamics of income inequality

This paper analyzes how differences in the composition of wealth between human and physical capital among families affect fertility choices. These in tum influence the dynamics of wealth and income inequality across generations through a tradeoffbetween quantity and quality of children. Wealth composition affects fertility because physical capital has only a wealth effect on number of children, whereas human capital increases the time cost of child-rearing in addition to the wealth effect. I construct a model combining endogenous fertility with borrowing constraints in human capital investments, in which weaIth composition is determined endogenously. The model is calibrated to the PNAD, a Brazilian household survey, and the main findings of the paper can be summarized as follows. First, the model implies that the crosssection relationship between fertility and wealth typically displays a U-shaped pattem, reflecting differences in wealth composition between poor and rich families. Also, the quantity-quality tradeoff implies a concave cross-section relationship between investments per child and wealth. Second, as the economy develops and families overcome their bOlTowing constraints, the negative effect of weaIth on fertility becomes smaller, and persistence of inequality declines accordingly. The empirical evidence presented in this paper is consistent with both implications .

Income inequality in Central America, Dominican Republic and Mexico: assessing the importance of individual and household characteristics

Includes bibliography

The evolution of world income inequality: assessing the impact of globalization

Includes bibliography

Income inequality in Latin America: Data challenges and availability from a comparative perspective

Includes bibliography

Income inequality and credit markets

Includes bibliography

Income inequality in Latin America. Recent decline and prospects for its further reduction

Incluye bibliografía.

Income inequality in Brazil: What has changed in recent years?

Includes bibliography.

Poverty Reduction and the Income Inequality in China

In 2000, the United Nations adopted the Millennium Development Goals which set targets for raising living standards in low-income countries. The first goal was to “eradicate extreme poverty and hunger” (United Nations). The World Bank defines extreme poverty as income of less than $1.25 per day (World Bank, 2010a). Based on this definition, the World Bank estimates that the percentage of the population in China living in extreme poverty has fallen from 84 percent in 1981 to about 16 percent in 2005, a period during which China’s population grew by more than 300 million people (see Table 1 on last page). Because China is a very large country with a current population approaching 1.4 billion (more than four times the United States population), its dramatic reduction in poverty over the past 30 years has had a profound effect on global poverty measures. In fact, poverty reduction in China is the main reason that the incidence of extreme poverty in developing countries has fallen from about 52 percent in 1981 to 25 percent in 2005 (Table 1). While the absolute number of poor in China fell by some 627 million, the number of poor in other developing countries actually grew slightly (from 1,065 million to 1,166 million). These figures represent a decline in the percentage of the total population in poverty in other developing countries because of general population growth over that 25-year period (World Bank, 2010b).

Income inequality and elderly self-rated health in Sao Paulo, Brazil

Purpose: To test the association between income inequality and elderly self-rated health and to propose a pathway to explain the relationship. Methods: We analyzed a sample of 2143 older individuals (60 years of age and over) from 49 distritos of the Municipality of Sao Paulo, Brazil. Bayesian multilevel logistic models were performed with poor self-rated health as the outcome variable. Results: Income inequality (measured by the Gini coefficient) was found to be associated with poor self-rated health after controlling for age, sex, income and education (odds ratio, 1.19; 95% credible interval, 1.01-1.38). When the practice of physical exercise and homicide rate were added to the model, the Gini coefficient lost its statistical significance (P>.05). We fitted a structural equation model in which income inequality affects elderly health by a pathway mediated by violence and practice of physical exercise. Conclusions: The health of older individuals may be highly susceptible to the socioeconomic environment of residence, specifically to the local distribution of income. We propose that this association may be mediated by fear of violence and lack of physical activity. (C) 2012 Elsevier Inc. All rights reserved.

Cause-specific mortality and income inequality in Sao Paulo, Brazil

OBJECTIVE: To analyze cause-specifi c mortality rates according to the relative income hypothesis. METHODS: All 96 administrative areas of the city of Sao Paulo, southeastern Brazil, were divided into two groups based on the Gini coefficient of income inequality: high (>= 0.25) and low (<0.25). The propensity score matching method was applied to control for confounders associated with socioeconomic differences among areas. RESULTS: The difference between high and low income inequality areas was statistically significant for homicide (8.57 per 10,000; 95% CI: 2.60; 14.53); ischemic heart disease (5.47 per 10,000 [95% CI 0.76; 10.17]); HIV/AIDS (3.58 per 10,000 [95% CI 0.58; 6.57]); and respiratory diseases (3.56 per 10,000 [95% CI 0.18; 6.94]). The ten most common causes of death accounted for 72.30% of the mortality difference. Infant mortality also had signifi cantly higher age-adjusted rates in high inequality areas (2.80 per 10,000 [95% CI 0.86; 4.74]), as well as among males (27.37 per 10,000 [95% CI 6.19; 48.55]) and females (15.07 per 10,000 [95% CI 3.65; 26.48]). CONCLUSIONS: The study results support the relative income hypothesis. After propensity score matching cause-specifi c mortality rates was higher in more unequal areas. Studies on income inequality in smaller areas should take proper accounting of heterogeneity of social and demographic characteristics.

Propensity score matching approach to test the association of income inequality and mortality in Sao Paulo, Brazil

Background Support for the adverse effect of high income inequality on population health has come from studies that focus on larger areas, such as the US states, while studies at smaller geographical areas (eg, neighbourhoods) have found mixed results. Methods We used propensity score matching to examine the relationship between income inequality and mortality rates across 96 neighbourhoods (distritos) of the municipality of Sao Paulo, Brazil. Results Prior to matching, higher income inequality distritos (Gini >= 0.25) had slightly lower overall mortality rates (2.23 per 10 000, 95% CI -23.92 to 19.46) compared to lower income inequality areas (Gini <0.25). After propensity score matching, higher inequality was associated with a statistically significant higher mortality rate (41.58 per 10 000, 95% CI 8.85 to 73.3). Conclusion In Sao Paulo, the more egalitarian communities are among some of the poorest, with the worst health profiles. Propensity score matching was used to avoid inappropriate comparisons between the health status of unequal (but wealthy) neighbourhoods versus equal (but poor) neighbourhoods. Our methods suggest that, with proper accounting of heterogeneity between areas, income inequality is associated with worse population health in Sao Paulo.

Cause-specific mortality and income inequality in São Paulo, Brazil

OBJECTIVE: To analyze cause-specific mortality rates according to the relative income hypothesis. METHODS: All 96 administrative areas of the city of São Paulo, southeastern Brazil, were divided into two groups based on the Gini coefficient of income inequality: high (>0.25) and low (<0.25). The propensity score matching method was applied to control for confounders associated with socioeconomic differences among areas. RESULTS: The difference between high and low income inequality areas was statistically significant for homicide (8.57 per 10,000; 95%CI: 2.60;14.53); ischemic heart disease (5.47 per 10,000 [95%CI 0.76;10.17]); HIV/AIDS (3.58 per 10,000 [95%CI 0.58;6.57]); and respiratory diseases (3.56 per 10,000 [95%CI 0.18;6.94]). The ten most common causes of death accounted for 72.30% of the mortality difference. Infant mortality also had significantly higher age-adjusted rates in high inequality areas (2.80 per 10,000 [95%CI 0.86;4.74]), as well as among males (27.37 per 10,000 [95%CI 6.19;48.55]) and females (15.07 per 10,000 [95%CI 3.65;26.48]). CONCLUSIONS: The study results support the relative income hypothesis. After propensity score matching cause-specific mortality rates was higher in more unequal areas. Studies on income inequality in smaller areas should take proper accounting of heterogeneity of social and demographic characteristics.