(like the inequality index) are calculated by a jackknife estimator (i.e., quartiles are calculated excluding household _i_ ). The base regression specification for the exploration of interactions is column (5) from Table 2.

Turning first to the education and cohort interactions, they are jointly and individually insignificant for 1995-96, and 2001-02, when the overall effect of inequality is also insignificant. However, in the middle two time periods of our 1990s sample (1997-98 and 1999-00), we see that education significantly offsets the adverse effect of inequality: the impact of inequality hurts less educated households most. The timing coincides with the Asian financial crisis, and suggests that inequality hurt the least educated most during those periods, possibly due to reduced migration opportunities. The cohort interaction is also significant for 1999-00, and borderline in 1997-98. This interaction coefficient suggests that the effect of inequality was worse for the young, which seems somewhat surprising. However, it is the young that normally have the highest income growth potential and are the most mobile, and the adverse impact of inequality seems to have hurt these households most. In other words, income growth is potentially higher for younger households, but those in more unequal villages experience stunted growth.

In column (2) for each time period, we report the interaction effects for income. The most striking finding is that the interaction effects for income quartile are individually and jointly insignificant for all time periods. The adverse effects of inequality hurt rich and poor equally. In terms of implications about the mechanism, we view this as speaking most clearly to credit market explanations: the effect of inequality exists even when controlling for a rich set of household characteristics, and furthermore, affects rich as much as poor households within a village. This is not what we would expect to observe if imperfect credit markets drove the relationship. Instead, the effect of inequality spills over households across the income distribution, though with less impact on households with higher education. The only nuance of the income interactions worth noting is that in 2001-02, where we estimated an insignificant impact overall, we do estimate significant negative effects of inequality on the bottom two quartiles. While not significantly different than the effect on their richer neighbors, to the extent that there is an impact of inequality in the most recent time period, it does seem to fall on the poorest households. The effect of inequality does not completely go away for those quartiles.

# _4.4 Regression Results: Composition of Income_

We may be able to learn more about the nature of the effect of inequality by investigating links between inequality and other measures of economic development. One potentially informative dimension is the composition of household income, which will reflect changes in the economic structure of villages, and households’ ability to participate in more lucrative economic opportunities, especially in nonagricultural employment. To conduct this exercise, we make a slight modification to the base _Inequality and Growth in Rural China_ , Page 17