(1997, 2005) -this is the average of returns to education in Psacharopoulos and Patrinos (2004). The results did not change when we adjusted the returns to education to account for differences in educational quality across countries. In this case,  ( _N_ )  _reQN_ , where _Q_ is a country-specific educational quality index, as in Hanushek and Kimko (2000).<sup>9</sup> We also used two alternative data sources for years of schooling, _s_ , as an indicator of the level of educational attainment in the labor force: Barro and Lee (2001) and Cohen and Soto (2007).<sup>10</sup> In this paper we present the results for the human capital indicator based on Barro and Lee's years of schooling. This reduces our sample to 112 countries and 309 observations. When the human capital indicator was based in Cohen and Soto's _N_ , the estimates were robust, if a bit less precise, but the sample size further dropped, by 20 percent (from 112 to 88 countries, or from 309 to 253 observations).

Finally, we augment our indicator of human capital to account for the health of the population and of the working force; _Ah_ can be expressed as _Ah_  _e_  _ASR ASR_ as in Caselli (2005), where _ASR_ is the adult survival rate. Shastry and Weil (2003) argue that differences in health status proxied by adult mortality rates map into substantial differences in energy and capacity for effort. Caselli (2005) finds that correcting the standard human capital measures to account for health, proxied, as in our case, by adult survival rates ( _ASR_ ), increases the percentage of cross-country income variance attributed to physical and human capital by almost one third. In Weil (2007) accounting for health differences reduces the variance of log GDP per worker by 9.9 percent.

> 9 Hanushek and Kimko (2000) combine the results of cognitive achievement tests in mathematics and science into two educational quality indexes.  We take an average of the two indexes and, as Woessman (2003), normalize the educational quality index for each country relative to the US. Woessman argues that the US is a good reference country as the returns to schooling should be relatively undistorted in the competitive US labor market. Data are available for 80 countries in our sample.

> 10 From both data sets we take the years of schooling in the population aged 15 and over because this age group corresponds better to the labor force for most developing countries (the majority in our sample) than the population aged 25 and over (Woessman 2003, p.256). The Barro and Lee dataset contains the average years of schooling in 5- year intervals from 1960 to 1995 and projections for 2000. Values for 2005 were predicted by using a linear trend fitted from the observations between 1960 and 2000. When a country-specific growth rate in the years of schooling could not be calculated, the regional average growth rate was used. A visual inspection of the data suggests that a linear trend is a good approximation. A caveat, however, is that educational attainment is asymptotically a constant, and by fitting a linear trend we may overstate the educational attainment for the countries that have reached the asymptote. This would affect mainly OECD countries.  Our second source for schooling data (Cohen and Soto, 2007) suggests that this may not be a problem. Virtually all the developed countries with the exception of Norway and Sweden exhibit more years of schooling in this second dataset for all the years considered, including 2005. For Norway and Sweden, the largest difference between the two datasets is 0.52 years of schooling. Cohen and Soto (2007) report educational attainment for the years 1990, 2000, and a projection for 2010. Since we are interested in the years 1995 and 2005, averages of 1990 and 2000 values were taken for 1995 and of 2000 and 2010 for 2005.

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