poorer countries for each capital category.<sup>7</sup> The share of intangible capital in total wealth is also positively correlated with income, as shown in Figure 2b. For the OECD countries, this share is on average 78 percent and always larger than 60 percent. Panel B in Table 2 also shows IC increasing over time, in absolute value and as a percentage of total wealth. The _z_ -statistic for a test of a difference between the means in the IC shares in years 2005 and 1995 is 1.12.

# _2.2 Human capital_

The analysis in World Bank (2006) suggests that the value of the stock of human capital is a large component of a country's wealth. This fits well with the intuition that an important asset of a country is its people. Not surprisingly, the statistical agencies in some developed countries are starting to systematically compile human capital accounts to monitor the evolution of the stock of human capital. A method typically employed to value the stock of human capital in dollars is the lifetime income approach of Jorgenson and Fraumeni (1989, 1992a, 1992b). Under this approach, human capital is valued as the net present value of the income flow it produces over an assumed lifetime. In practice, the application of the method requires information on survival, enrollment and employment probabilities as well as earnings, by sex, age and educational attainment. Although these are relatively modest informational requirements, applying this method to a cross section of countries is impracticable.

Most of the empirical studies studying the role of human capital on growth and development accounting have focused instead on a cost-based approach to measure human capital. In order to increase future labor productivity and future income, people forgo consumption and invest in education. The human capital embodied in the labor force can then be seen as a function of education. Even accepting education as a valid proxy for investment in human capital, the relationship between education and human capital needs to be correctly specified.<sup>8</sup> The current consensus relies on a log-linear specification between earnings and years of schooling first formulated by Mincer (1974), to express the human capital per worker as an exponential function of the years of schooling, _h_  _e_  ( _N_ ) , where the function  ( _N_ ) represents the efficiency of a unit of labor with _N_ years of schooling relative to one with no schooling (for reviews see Krueger and Lindahl, 2001; Woessman, 2003). We follow common practice and use  ( _N_ )  _re N_ , where _re_ is the rate of return to education.

We made different assumptions regarding the returns to education. Our benchmark is _re_ =8.5 percent return on years of schooling as in Arrow et al. (2010) and Klenow and Rodriguez-Clare

> 7 This is also true for aggregate natural resource stocks; the simple correlation between GNI per capita and the natural resource stock per capita for the countries in our sample is 0.44.

> 8 For example, Klenow and Rodriguez-Clare (1997) show that using secondary school enrolment rates as proxies for human capital in Mankiw, Romer and Weil (1992) exaggerates the role of human capital in explaining differences in output per worker, as secondary enrolment varies more than other (preferable) measures of human capital. The results of Mankiw Romer and Weil (1992) are not robust to expanding the human capital estimator to include primary and terciary education or to using an alternative human capital estimator based on Mincerian regressions.

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