consistent with human capital driving the whole cross-country variation.<sup>7</sup> In the next subsections, I consider different alternatives to measure _h_ . The approach is cumulative: I start from from measures based only on educational attainment, and I gradually introduce additional dimensions of human capital to evaluate their relative importance. I use recent data to replicate the main measurement strategies that have been proposed in the literature, and to illustrate their implications in terms of development accounting results.<sup>8</sup>

# **4.1 Educational Attainment**

The first contributions in the development accounting literature focused on years of schooling as a proxy for human capital. As discussed above, this approach holds some promise as educational attainment is positively correlated in GDP per worker. However, by construction it ignores other potentially important sources of human capital accumulation, such as the quality of schooling, domestic upbringing and on-the-job learning.

I consider the following specification, proposed by Bils and Klenow (2000). Human capital per worker is where _sc_ is the average years of schooling completed in country _c_ . The logarithm of human capital per worker depends linearly on _s_ , with _β_ being the slope of this relationship.<sup>9</sup> The calibration of _β_ is based on micro-economic evidence on returns to education. Under the assumption of perfectly competitive labor markets, (5) implies a log-linear relationship between wages and years of schooling. This equation, often referred to as Mincerian after the seminal work of Mincer (1974), has been widely estimated in the labor economics literature. Reviews of the international evidence (Psacharopoulos, 1994; Psacharopoulos and Patrinos, 2004; Caselli et al., 2016) suggest a Mincerian return of 10%, or _β_ “ 0 _._ 10.

Table 1 illustrates the development accounting results.<sup>10</sup> I display _Share_<sup>_US_</sup> _c_ for countries at the 15<sup>_th_</sup> , 30<sup>_th_</sup> , 45<sup>_th_</sup> , 60<sup>_th_</sup> , 75<sup>_th_</sup> and 90<sup>_th_</sup> percentiles of the income distribution as well as

> 7The cross-country variance of log GDP can be additively decomposed between the variances of log human capital, physical capital and technology, as well as all the covariances. Some authors, including Klenow and Rodr ́ıguez-Clare (1997b), consider measures of success for human capital which include some of the covariance terms. Given that these covariances are positive, this would result in a larger success term for human capital.

> 8To measure _y_ , I use GDP per worker in PPP terms for 2010, which I take from version 9.0 of the Penn World Tables. The baseline sample is made of 130 countries for which the data to conduct exercises on educational attainment, health, migration outcomes and imperfect substitution are available. I consider smaller samples when I look at test scores and returns to experience.

> 9Several authors, including Hall and Jones (1999) and Caselli (2005), consider versions of (5) where years of schooling enter non-linearly in the exponent, to match the fact that returns to schooling are often found to be different across levels of educational attainment. These modifications have limited impact on the development accounting results.

> 10I use average years of schooling for the population above 25 from Barro and Lee (2013).

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