economically significant predictor of slower growth. If he made the same bet for growth fifteen years out (to 2002), it would be a closer call, though a better than fair bet. Beyond that, however, if he was greedy enough to ride the inequality horse in other dimensions, placing money on rising inequality as a marker of lower growth, it would be no better than a crap shoot. The preponderance of evidence we find suggests that inequality did not have a reliable causal impact on growth. We do not believe that higher inequality impeded growth in rural China.

This conclusion rests on several strands of evidence. First, by using household-level data we are able to rule out imperfect credit markets as the source of the causal relationship between observed inequality and growth. Instead, the results using household-level data point to institutional features of villages at the outset of reforms as underlying the correlation between inequality and growth. Important though those institutions may have been, whatever they were, there is little to suggest that they were a _causal_ consequence of inequality. This is most clearly seen when we exploit time-series variation of inequality within-villages (in the panel), and all links between growth and inequality disappear. Even if we keep the cross-section approach, but use village inequality from the 1990s as the basis of predicting growth, we find that inequality ceases to matter: inequality in the 1990s is different from inequality earlier in the reform period. Even focusing on 1987 inequality, the source of a safe bet for growth to 1997, we find that the effect of inequality fades by 2002.

We still learn something about the inequality-related determinants of growth in rural China. For example, our evidence points to the likelihood of growth-impeding polices that were associated with higher inequality. The policies seemed to trap their victims, especially the lower-educated in agriculture (a relative dead end during much of this period), and impeded their movement into more lucrative labor markets. We cannot be sure exactly what these institutions might be, but likely candidates include those affecting household returns to running small businesses, the flexibility accorded to households in meeting local grain quotas (i.e, the degree to which these commitments could be made with cash instead of grain), and the costs of getting local government permission to migrate to take advantage of newly emerging job opportunities (deBrauw and Giles, 2008). Whatever the policies or institutions were, their effects eroded very quickly in the reform period. Our data do not allow us to determine whether high inequality caused such policies, or reflected a more general level of dysfunction at the local level as villages embarked on the reform process.

More generally, and setting aside the profound problem of causality, our results show the value of using household-level data to address the question of whether inequality has a purely external impact on growth. Indeed, there is no better way to distill the external effect from that due to aggregation. Furthermore, the household-level data can be used to explore the heterogeneity of responses that may be informative about the underlying economic mechanisms. Such evidence is especially useful in ruling out _Inequality and Growth in Rural China_ , Page 22