3 of economic growth, income inequality and, in some cases, poverty statistics.<sup>3</sup> From the point of view of validating a particular theoretical approach, the resulting empirical literature was not encouraging. As time‐series of inequality statistics became available for more countries, evidence of an “inverted U” Kuznets curve appeared to vanish, both when looking within countries over time (see Bruno et al, 1998) and across countries at different levels of GDP per capita (Ravallion and Chen, 1997). Evidence of causality from inequality to subsequent growth fared no better. Some early papers found a negative effect of initial inequality on growth in cross‐country regressions (Alesina and Rodrik, 1994; Deininger and Squire, 1998), but later studies cast doubt on the interpretation of that result by finding that, in a panel specification, the effect of lagged inequality on growth appeared to be, if anything, positive (Forbes, 2000).

The literature never converged to a consensus. It was not clear whether the panel specification yielded econometrically superior results (because it eliminated possible endogeneity biases by using lagged variables as instruments), or whether there was something substantively different about recent changes in inequality, vis‐à‐vis differences in “steady‐state” initial levels of inequality. Banerjee and Duflo (2003) argued that cross‐country data of the kind used in this literature was not capable of shedding much light on the myriad non‐linear processes that might be linking inequality and growth. Voitchovsky (2009) concludes a recent survey by noting that “the inconsistency of reported empirical findings could reflect the gap between the intricacy of the relationship, as expressed in the theoretical literature, and the simple relationships that are commonly estimated.” (p.569) To make matters worse, it seemed for a moment ‐ in the early 2000s ‐ as though even the relationship between growth and poverty – which had always been taken more or less for granted, at least among economists – was being questioned. Using quotes from policy circles, Dollar and Kraay (2002) noted that although “The world economy grew well during the 1990s [...], there is intense debate over the extent to which the poor benefit from this growth.” (p.195). While some appeared convinced that growth is “a rising tide that lifts all boats”, others insisted that, in the 1990s, “the rich were getting richer, and the poor were getting poorer”.

In the early 2000s, then, one might have been forgiven for concluding that economists had not made much progress in understanding the relationship(s) between growth and distribution since the days of David Ricardo. Our best shot at a general theory of how structural change and economic development affected the distribution of income – the Kuznets hypothesis – appeared to be rejected by more plentiful data.<sup>4</sup> Similar data appeared to generate contradictory or inconclusive results for tests of alternative theories about the reverse channel of causation: from inequality to growth. And now there was even a

> 3 The history of this growth in data availability ‐ and a discussion of how comparable the different data sets are ‐ are topics of considerable interest in and of themselves, but they fall beyond the scope of this paper.

> 4 To be fair, there was an alternative view of long‐term inequality dynamics, associated with Jan Tinbergen, which proved rather more robust. Tinbergen (1975) saw changes in earnings inequality as largely reflecting the evolution of a “race” between technological progress – which he saw as raising the demand for skills – and the expansion of formal education – which raised the supply of skills. Strangely, this view appears to have been more popular with labor economists than with development economists.