Use of Averaged Data; Mature Cohort Size as an Instrument for Inequality 

 In my paper with S. Khagram entitled "A comparative study of inequality and corruption" (ASR 2005, vol.70:136-157), we demonstrated that data averaged for a long period (say, 1971-1996) instead of single-year data can be useful for both reducing measurement error and capturing a long-term effect. 

 In previous empirical studies of causes of corruption, income inequality was found insignificant.  We suspected this lack of significance might be due to attenuation bias because income inequality was poorly measured. We found that using averaged data for inequality and other control variables increased the coefficient for inequality and made it significant. 

 Another result from this paper used "mature cohort size" (ratio of population 40 to 59 years old to the population 15 to 69 years old) as an instrument for inequality in IV regressions; again, inequality was found significant. Higgins and Williamson (1999) have previously studied the effect of cohort size on inequality.  Because fat cohorts tend to get low rewards, when these fat cohorts lie at the top of the age-earnings curve, earnings inequality is reduced. When the fat cohorts are old or young adults, earnings inequality is augmented.  Indeed, the mature cohort size is a powerful predictor of inequality 
across countries. 

 Note that by "fat cohorts" and "slim cohorts" I mean the relative size of the cohorts.  When the mature cohorts is fat, or the relative size of the mature cohort is large, the earns differential (earnings gap between the mature cohort and the others) is reduced and hence earnings inequality is reduced. 

 You can view my paper  here .