countries and over long time periods.<sup>12</sup> In a typical specification, the human capital investment of interest is represented by the number of years of schooling completed. Other specifications look at the highest degree completed, occasionally at the type of degree within the same level. Typical controls include a quadratic term in labor market experience (generating the so called _mincerian regression,_ from Mincer (1974)) and demographic variables such as marital status and gender. For case studies on specific countries the set of controls may become particularly large, including measure of individual ability (such as test scores) or family background variables (such as parents' education).

Particularly relevant for the growth literature are papers estimating returns to education on a large number of countries using comparable data.<sup>13</sup> Working with micro data from 149 countries, Montenegro and Patrinos (2014) estimate that, on average, one additional year of schooling is associate with a 9.7% increase in earnings; with higher increases for women than men (11.4% with respect to 9.1%). However, they find wide variations between countries and over time: returns range from more than 25% to less than 1%. Similar results are found by Caselli (2015) which collects estimation results from different authors on 87 countries in the mid-1990s and 91 countries in the mid-2000s. Regional patterns by world regions over time show stable returns in Europe and Central Asia (about 7%) and in Africa and the Middle East (about 8%); a decrease in returns in Latin America and the Caribbean (from more than 10% in the 1990s to about 8% in the 2000s); and an increase in returns in South-East Asia and the Pacific (from about 8% in the 1990s to more than 10% in the 2000s.) Again, country variation within world regions is substantial. A different way to summarize estimates extracted from a large set of different papers is proposed by Ashenfelter et al. (2001). They acknowledge the importance of potential bias in returns to schooling estimates, collecting works using different samples and estimation method. They then conduct a meta-analysis based on about a hundred estimates on nine different countries. The meta-analysis aggregates the estimation results using different weight based on estimation method and publication bias. The average returned obtained is comparable to those reported above: about 7% from estimates based on ordinary least squares (OLS) and about 9% from estimates attempting to reduce estimation bias (instrumental variable estimators (IV) and estimators based on sample of twins).

Given the importance accorded to education in building human capital, the empirical literature has also attempted to estimate the interaction between education and other human capital components. Heckman, Humphries, and Veramendi (2016) estimate causal consequences of schooling decisions on health status and smoking behavior using a dynamic model of investment in schooling. Two main results emerge from their work. First, returns to schooling investments are very heterogeneous in the population. For highability individuals, completing High School or College has not only high direct returns but also improves the option of acquiring additional valuable schooling. For low-ability

> 12 A partial list of important contributions include: Griliches (1977); Mincer (1974); Card (2001); Heckman, Lochner, and Todd (2006); Goldin and Katz (2008).

> 13 Recent examples presenting estimates for a variety of world regions include Caselli (2015), Montenegro and Patrinos (2014) and World Bank (2012). Peet, Fink and Fawzi (2015) present results on 25 developing countries. Flabbi, Paternostro, and Tiongson (2008) focus on 8 ECA countries and estimate returns over two decades.

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