8 One of the first papers to estimate equation (5) on cross‐country data was Ravallion and Chen (1997). Using a compilation of household survey datasets available at the World Bank, these authors assembled a data set with household‐survey‐based poverty measures and growth rates for 67 countries over the period from 1981 to 1994. With no controls other than a country‐specific time trend, and using an international poverty line of US$1 per day at 1985 PPP exchange rates, they found a central estimate for the growth elasticity of poverty of β = ‐3.1. Excluding the then transitioning economies of Eastern Europe and Central Asia, the regression line went through the origin: the average rate of change in poverty at zero growth in their sample was zero. This reflected the fact that they could find no significant cross‐country correlation between changes in inequality and economic growth in their sample.

Although the numbers have changed as the sample expanded, and different specifications have been tried, those two basic results have been confirmed by the more recent literature. Dollar and Kraay (2002) replaced the headcount index with an alternative, slightly unconventional (inverse) measure of poverty: the average income of the poorest 20% of the population. Using a sample of 92 countries over four decades, they could not reject a positive elasticity of 1.0: growth in the incomes of the bottom quintile was, on average, equiproportional to growth in mean incomes. This result, which survived the introduction of a number of controls, once again implied that changes in inequality were not systematically correlated with economic growth. Ravallion (2007) find a slightly lower growth elasticity for the poverty headcount than the earlier Ravallion‐Chen study, and a small negative – but statistically insignificant ‐ correlation between changes in the Gini coefficient and proportional changes in mean income, in a sample of 80 countries. Kraay (2006) uses variance decompositions to understand the main drivers of poverty changes in developing countries in the 1980s and 1990s. He finds that changes in distribution account for 30% of the variance in changes in the headcount when all spells are considered, versus 70% for growth in average incomes. When the sample is restricted to long spells, those proportions change to 3% and 97% respectively.

In sum, the macroeconomic approach to the poverty‐growth ‐inequality triangle yields two basic stylized facts: _(i) Economic growth and changes in inequality are uncorrelated, at least in the sample of countries available to researchers for the two decades from 1980 to 2000._ _(ii) Poverty generally declines as the economy grows. The longer the growth spells under consideration, the larger the share of the variance in poverty that is accounted for by the growth component._ Given equation (3), (ii) is clearly implied by (i). The empirical content of the Ravallion and Chen (1997) and Dollar and Kraay (2002) results is that changes in inequality appear to be uncorrelated with growth, at least for this sample – a result that is related to the empirical rejection of the Kuznets hypothesis, documented by Bruno et al. (1998). Equation (3) tells us nothing about that relationship. It decomposes