Growth, Poverty and Inequality in the Caribbean

.--Director’s Desk: Growth, Poverty and Inequality in the Caribbean.--Non-Comunicable Diseases: A Growing Epidemic.--Policies and Programmes for an Ageing Population.--Sexual and Reproductive Health and HIV in the Caribbean.--International Migration and Development: Challenges and Opportunites.--Gender Based Violence

Report of the AD HOC Expert Group meeting on social exclusion, poverty, inequality, crime and violence: Towardsa research agenda for informed public policy for Caribbean SIDS

The Economic Commission for Latin America and the Caribbean (ECLAC), Subregional Headquarters for the Caribbean convened an expert group meeting on Social Exclusion, Poverty, Inequality – Crime and Violence: Towards a Research Agenda for informed Public Policy for Caribbean SIDS on Friday 4 April 2008, at its conference room in Port of Spain. The meeting was attended by 14 experts drawn from, the University of the West Indies (UWI), St. Augustine, Trinidad and Tobago; and Mona Campus, Jamaica; the St. Georges University, Grenada; the Trinidad and Tobago Crime Commission and the Ministry of Social Development, Government of Trinidad and Tobago and representative of Civil Society from Guyana. Experts from the United Nations System included representatives from the United Nations Fund for Women (UNIFEM), Barbados; the United Nations Development Programme (UNDP), Port of Spain and UNDP Barbados/SRO and the Organisation of Eastern Caribbean States (OECS). The list of participants appears as an annex to this report. The purpose of the meeting was to provide a forum in which differing theories and methodologies useful to addressing the issues of social exclusion, poverty, inequality, crime and violence could be explored. It was expected that at the end of the meeting there would be consensus on areas of research which could be pursued over a two to four-year period by the ECLAC Subregional Headquarters for the Caribbean and its partners, which would lead to informed public policy in support of the reduction of the growing violence in Caribbean society.

Determinants of unfair inequality in Brazil, 1995 and 2009

This article analyses the trend of unfair inequality in Brazil (1995-2009) using a nonparametric approach to estimate the income function. The entropy metrics introduced by Li, Maasoumi and Racine (2009) are used to quantify income differences separately for each effort variable. A Gini coefficient of unfair inequality is calculated, based on the fitted values of the non-parametric estimation; and the robustness of the estimations, including circumstantial variables, is analysed. The trend of the entropies demonstrated a reduction in the income differential caused by education. The variables “hours worked” and “labour-market status” contribute significantly to explaining wage differences imputed to individual effort; but the migratory variable had little explanatory power. Lastly, the robustness analysis demonstrated the plausibility of the results obtained at each stage of the empirical work.

Inclusive social development: The next generation of policies for overcoming poverty and reducing inequality in Latin America and the Caribbean

Foreword by Alicia Bárcena

Economic growth, urbanization and social inequality: Reflections on the conservative Modernization

China is now facing a sudden change of redistribution of population in space as her urban population exceeds rural population. It seems necessary to learn others’ lessons by analyzing the urbanization of other developing countries, especially Brazil’s. To an extent, Brazil and some other developing countries have been unsuccessful in coordination for urbanization and improving living quality. The megacities in Latin America are the examples of swollen cities, and large groups excluded from the system of public services. It reflects in both short of infrastructures in many areas and high-levels of violence unique in the big cities in Latin America. Then the author summarizes Brazil’s lessons. Firstly, he describes the determinants in Brazil’s urbanization, especially the industrialization between 1930 and 1980. Secondly, the incentives for internal migration are analyzed, especially the industrial centralization in the southeast and the recessions in other areas. Finally, the characteristics of the present round of absorption of labor and the roots for the severe social inequality are discussed.

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).

SOCIAL POLICY AS REGIONAL POLICY: MARKET AND NONMARKET FACTORS DETERMINING REGIONAL INEQUALITY

We decompose the recent changes in regional inequality in Brazil into its components, highlighting the role of spatially blind social programs. We aggregate personal income micro data to the state level, differentiating nine income sources, and assess the role of these components in the observed changes in regional inequality indicators. The main results indicate that the largest part of the recent reduction in regional inequality is related to the dynamics of the market-related labor income, with manufacturing and services favoring deconcentration. Labor income in agriculture, retirement and pensions, and property rents and other sources favored concentration. The social programs Bolsa Familia and Beneficios de Prestacao Continuada are responsible for more than 24 percent of the reduction in inequality, although they account for less than 1.7 percent of the disposable household income. Such positive impact on regional concentration is impressive, since the goals of the programs are clearly nonspatial.

Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Demography and evolution of regional inequality

This paper addresses the importance of life cycle aspects in explaining the evolution of regional income inequality. The analysis of household microdata organized in age cohorts shows that Brazilian regional income inequality has different dynamics across generations, with income convergence being observed only for the older generations. The larger income share of younger generations produces a low speed of convergence in the country. When retirement payments, pensions, and other government transfers are excluded from income, convergence is not observed even for the older generations.

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.