where _rc_ is the average health status as proxied by the afore-mentioned survival rate.<sup>11</sup> Implementing the development accounting exercise requires picking a value for _γ_ . One cannot rely directly on microeconomic evidence in this case, since the survival rate is an aggregate statistics and by definition does not vary within countries. Weil (2007) bypasses this problem by exploiting the fact that, over time, the survival rate is strongly correlated with average height, and that height does vary within countries. Specifically, he postulates a linear cross-country relationship between the survival rate _rc_ and average height _vc_ , _vc_ “ _αr_ ` _γrrc_ , as well as a within-country log-linear relationship between wages and individual height, Since _γr_ and _γv_ can be estimated directly, one can identify _γ_ in (7) as _γ_ “ _γrγv_ . I use the estimates in Weil (2007), which imply _γ_ “ 0 _._ 65. Moreover, I use _β_ “ 0 _._ 10 as before.<sup>12</sup> Table 3 shows the development accounting results. Compared to Table 1, human capital varies more between the United States and poor countries. The measure of _Success_ increases to 11.9%, suggesting that accounting for health differences moderately increases the inferred contribution of human capital.

Another potentially important dimension of human capital is labor market experience. The process of learning does not stop at the end of one’s schooling career, and it is reasonable to think that rich and poor countries might offer differential opportunities for human capital accumulation even at a later age.

Traditionally, the development accounting literature dismissed this dimension of human capital on the ground that the _quantity_ of potential experience is not positively correlated with GDP per worker. While life expectancy is higher in rich countries, longer schooling careers imply that individuals in those countries enter later in the labor market. Klenow and Rodr ́ıguez-Clare (1997b) and Caselli (2005) document that on balance this leads, if anything, to a higher average experience in poorer countries. Since the micro evidence suggests that experience is positively correlated with earnings, adding this component by assuming a common return across countries unavoidably leads to a smaller contribution of human capital to income differences.

Lagakos et al. (2016) argue however that returns to experience do vary across countries. The authors estimate the experience-wage profiles of US immigrants, and show that those

> 11I take the survival rate to 65 by gender from the World Development Indicators, and use the gender composition of the population to compute an average rate. All data refer to 2010.

> 12This is potentially problematic as educational attainment and health (or height) are likely to be positively correlated, and therefore the estimate of _β_ “ 0 _._ 10 from Mincerian regression without health controls might already partially reflect the effect of health. While the estimates Weil (2007) refers to for the calibration of _γ_ are conditional on educational attainment, he does not report the corresponding estimates for the education controls. However, reasonable changes to the value of _β_ do not massively affect the conclusions that follow.

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