The wedge between private and social returns may be significant. The first reason is that labor income is only one component of GDP, even if a relevant one. The second, most important reason is that human capital investment generate _externalities_ . For example, as seen in Section 3, human capital investments may generate technological progress which in turn increases productivity of _any_ production inputs. Other examples of typical positive externalities are: education investments reducing aggregate crime rates or health investments in mothers making their children healthier [Kruger and Lindhal (2001); Glewwe (2000)]. The presence of positive externalities does not necessarily mean that the social return of a given human capital investment is always higher than the private return. In many countries investment in education is highly subsidized: if the subsidy is too high, the private return may exceed the social return and lead to overinvestment in education [Heckman and Klenow (1997)]. What it means, though, is that looking at wages instead of GDP may under- or over-estimate the overall impact of human capital investments on growth. The micro-evidence is well equipped to estimate private returns to human capital investments. How these private returns exactly relate to the social returns at the center of the growth literature depends on the human capital investment under consideration, the specific country, the specific time period, the growth model assumptions used to interpret the results. Still, impacts on wages are relevant since they are systematically correlated with growth in GDP.

The second issue with the micro level evidence concerns the identification of the effects of interest. Both econometric issues related to the quality of the estimates and modeling issues related to the interpretation of the results are as important in the micro evidence as in the macro evidence. Both concerns are discussed in Section 4.2.3.

The third and final issue concerns measurement errors. In Section 4.1 we already pointed out the importance of a correct measure of human capital variables. In the empirical micro literature, measurement errors issues also involve the outcome variables: wages and earnings. Validation studies have shown the presence of measurement errors in selfreported wages in economies with well-developed labor markets [Bollinger (1998); Bound et al. (2001)]. The problem is only amplified in less developed labor markets where the informal sector is large or the agricultural sector employs a large proportion of the labor force [Jacoby and Skoufias (1997)]. Possible solutions to measurement errors issues are the implementation of validation studies able to cross-check different data sources and the use of administrative data. In economies with large agricultural sectors, proposed solutions include changing the outcome variable by using consumption data or production data instead of labor income data.

# **4.2.2 Review of Empirical Results**

Human Capital investment is a dynamic process, involving a variety of decisions covering the entire life cycle. The following section presents empirical evidence on some of the most popular human capital investments involved in this process. It starts with early childhood 13