The key observation is that, while _lS,c_ { _lU,c_ is substantially higher in rich countries, _wS,c_ { _wU,c_ is relatively flat across countries (Caselli and Coleman, 2006; Jones, 2014; Rossi, 2017). According to (12), for the range of values for _ε_ estimated in the empirical literature, this implies that _hS,c_ { _hU,c_ is higher in rich countries, i.e. that skilled workers are relatively more “efficient” in those countries. Malmberg (2018) confirms this finding through a different empirical strategy, based on the analysis of trade patterns for skilled- and unskilled-intensive manufacturing goods.

The implications for development accounting depend on how we interpret the crosscountry variation in _hS,c_ { _hU,c_ . Jones (2014) attributes this variation to differences in unmeasured human capital (for given levels of educational attainment), and shows that it can dramatically boost the contribution of human capital in accounting for cross-country income gaps. In contrast, Caselli and Coleman (2006) interpret it as reflecting skill-biased technological differences across countries (i.e. firms in rich countries adopting more skilled labor augmenting technologies compared to poor countries), and Caselli and Ciccone (2017) show that under this interpretation the role of human capital for development accounting is much more limited.

Table 6 illustrates these different results. I construct human capital per worker as in (11), where _hS,c_ { _hU,c_ is backed out from (12) using _ε_ “ 1 _._ 5 as estimated by Ciccone and Peri (2005), and where, following the baseline exercise in Jones (2014), I assume that _hU,c_ does not vary across countries. I start by classifying skilled labor as workers with some secondary education, as in Jones (2014) and Caselli and Ciccone (2017).<sup>14</sup> The second and third column of Table 6 show that the amplification of human capital gaps when incorporating the variation in _hS,c_ { _hU,c_ is dramatic. The cross-country variance in human capital is five times larger than the cross-country variance in GDP per worker. This partially reflect an extremely high value of _hS,c_ { _hU,c_ estimated for the United States, where the share of skilled labor is much higher than in poorer countries, while the skill premium is not dramatically different. However, even when taking France as a benchmark (fourth and fifth columns), human capital accounts for the majority or the entirety of income gaps with respect to other countries.

These results are even larger than those reported in Jones (2014). Given that I am using much more recent data, one valid concern is that considering skilled every worker with at least some secondary education might not be appropriate for my exercise. In 2010, according to this criterion, 97% of the US labor force is high-skilled, and in a few other countries

> 14I also follow Jones (2014)’s approach for the construction of skill premia: I impute the wage for each level of eduational attainment using country-specific Mincerian return, and compute _wS,c_ and _wU,c_ as weighted averages across the educational categories belonging to the skilled and unskilled groups. The share of skilled and unskilled workers in 2010 is computed from Barro and Lee (2013)’s data on educational attainment of the population above 25.

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