25 and negatively correlated with changes in poverty. However, (iii) the higher a country’s initial level of inequality, the higher the growth rate needed to obtain a given amount of poverty reduction.

But economic growth, changes in inequality and changes in poverty are actually just three different aggregations of information about individual income dynamics. They are therefore jointly determined (by the general equilibrium of the economy), and macroeconomic estimates of the reduced‐form relationships between them – however useful in identifying empirical regularities – were never likely to shed much light on the fundamental factors underlying distributional change.

Two alternative approaches have been more successful in doing that. The first, which I have called “mesoeconomic”, uses sub‐national panel data on poverty and on economic growth rates disaggregated by sector, to investigate the role of different growth patterns and initial conditions on poverty reduction. The second approach, which is microeconomic in nature, investigates distributional change at a fully disaggregated level, by decomposing changes in the growth incidence curve. These decompositions have not resolved the identification problems inherent in studying distributional dynamics either. The first crop of studies in this tradition are essentially generalizations of the Oaxaca‐ Blinder decomposition to a full‐distribution, dynamic setting. The counterfactual income distributions on which they rely suffer from the usual problems of equilibrium‐inconsistency and path dependence.

Nevertheless, they have succeeded in shedding some light on the nature of distributional change in countries ranging from Indonesia to the United States, in a set of quite informative ways. These studies have employed various different methods, parametric and otherwise. They have generally focused on a set of key factors, including (i) the dynamics of the distribution of educational attainment; (ii) changes in the returns to education (and, less prominently, other covariates); (iii) changes in the structure of occupations, including female labor force and the extent and nature of the informal sector(s); (iv) the links between education, labor force participation, and demographic change; (v) changes in labor market institutions, including unionization and minimum wages. Although the topics are often similar across countries, as the number of studies expands, one interesting result has been just how different each country’s specific story is.<sup>25</sup> It appears that the basic pieces of the income distribution dynamics puzzle can be combined in a multiplicity of ways.

A second crop of growth‐incidence based studies attempts to get nearer to a causal interpretation of the GIC decompositions, by deriving counterfactual income distributions from models of behavior, or of the general equilibrium of the economy. This paper briefly summarized a few such studies, which were remarkable, if for nothing else, at least for their methodological diversity – ranging from old‐fashioned CGEs linked to micro‐simulations, to fully structural models of dynamic household behavior.

Despite this great methodological diversity, there are some shared findings and areas of common ground in the multifaceted literature on the poverty‐growth‐inequality triangle. Growth is good for the poor, and it is particularly good when it is the incomes of the poor that are growing...<sup>26</sup> This is most likely

> 25 See, for example, the concluding chapter in Bourguignon, Ferreira and Lustig (2005).

> 26 When the incomes of the poor grow faster than those of the non‐poor, inequality generally declines, and poverty mechanically falls by more than if growth was uniformly distributed.