This special issue features inequality. This is a subject that rightly draws immediate attention from both the profession and the popular press. The numbers themselves are intrinsically interesting, if not disturbing. There is, on the one hand, great variety in the distribution of earnings and an enormously right-skewed distribution of wealth. On the other hand, there is absolute and relative poverty. In developing countries there are extremes coexisting on both ends of the distribution. But developing economies also feature growth with time-varying levels of inequality. Macroeconomic growth, stability, and social policies seem correlated with poverty reduction in some instances.

This paper shows that growth and income distribution dynamics are closely linked through occupation, financial intermediation, and education. We use micro data from Thailand for 1976–1996. The compositional changes across these characteristics account for half of the Thai inequality increase and 40% of the Thai growth and poverty reduction. Financial deepening and educational expansion contributed to increasing inequality, whereas occupational transformation contributed to poverty alleviation. The changes in income gaps across the income–status groups, that is, divergence and then convergence, give rise to inverted-U inequality dynamics. These two growth-related components of inequality dynamics, composition and income-gap dynamics, explain virtually all the change in overall inequality, except its initial rise. Thus, inequality dynamics can be viewed as an integral part of the wider process of growth, as Kuznets speculated.

Brazil's Gini coefficient rose from 0.57 in 1981 to 0.63 in 1989, before falling back to 0.56 in 2004. Poverty incidence rose from 0.30 in 1981 to 0.33 in 1993, before falling to 0.22 in 2004. This paper presents a preliminary investigation of the determinants of Brazil's distributional reversal over this period. The rise in inequality in the 1980s appears to have been driven by increases in educational attainment in a context of convex returns, and by high and accelerating inflation. Although the secular decline in inequality, which began in 1993, is associated with declining inflation, it also appears to have been driven by four structural and policy changes, namely, declining returns to education; pronounced rural-urban convergence; increases in social assistance transfers targeted to the poor; and a possible decline in racial inequality. Falling inequality has made a substantial contribution to poverty reduction.

This paper evaluates two well-known models of growth with inequality that have explicit micro underpinnings related to household choice. With incomplete markets or transactions costs, wealth can constrain investment in business and the choice of occupation and also constrain the timing of entry into the formal financial sector. Using the Thai Socio-Economic Survey (SES), we estimate the distribution of wealth and the key parameters that best fit cross-sectional data on household choices and wealth. We then simulate the model economies for two decades at the estimated initial wealth distribution and analyze whether the model economies at those micro-fit parameter estimates can explain the observed macro and sectoral aspects of income growth and inequality change. Both models capture important features of Thai reality. Anomalies and comparisons across the two distinct models yield specific suggestions for improved research on the micro foundations of growth and inequality.

In the United States wealth is highly concentrated and very unequally distributed: the richest 1% hold one third of the total wealth in the economy. Understanding the determinants of wealth inequality is a challenge for many economic models. We summarize some key facts about the wealth distribution and what economic models have been able to explain so far.

This paper presents a new framework for analyzing inequality that moves beyond the anonymity postulate. We estimate the determinants of sectoral choice and the joint distributions of outcomes across sectors. We determine which components of realized earnings variability are due to uncertainty and which components are due to components of human diversity that are forcastable by agents. Using our tools, we can determine how policies shift persons across sectors and outcome distributions across sectors.