In the past two decades, the literature has obtained mixed results. Mankiw et al. (1992) use secondary school enrolment to proxy for the investment rate in human capital and attribute 50% of income differences in their sample of 98 non-oil countries to differences in human capital. Klenow and Rodriguez-Clare (1997) use years of schooling _attainment_ as inputs to a human capital aggregator and find instead that human capital accounts for 10 to 30 percent of income differences. When taking into account various measures of school quality, Caselli (2005) concludes that improving measurement of human capital by including school quality changes only slightly the conclusion that human capital explains a small share of income differences. Caselli and Ciccone (2013) compute the maximum impact that can be obtained by giving to the country under examination the same level of schooling as the US and they find similar results as Caselli (2005). For example, if Brazil, India, and Mexico were to move to the same education attainment of the US, the output gap that would be covered would only be equal to, respectively, 22.4%, 5.4% and 20.1%.<sup>6</sup> These are estimated as upper bounds, resting on the assumption of perfect substitutability of skilled and unskilled labor.

Contributions removing the perfect substitutability assumption arrive at different conclusions. Jones (2014) advocates the use of more general form of human capital aggregators than those used in the previous development accounting literature. Human capital aggregators are those functions that aggregate the heterogeneous human capital in the economy in one factor of production to be inserted in the production functions described by equation [1] and [2]. He proposes aggregators taking into account the different returns of the different types of human capital as a function of: (i) their relative scarcity, and (ii) the possible complementarities between them. Using the same data used by Caselli (2005), Jones (2014) estimates that physical and human capital variations can fully explain output differences between countries. Malmberg (2016) follows a similar approach by removing other restrictions from the standard development accounting model and by using different functional forms inspired by theory. Specifically, he allows for imperfect substitutability between skilled and unskilled labor and uses a broader definition of skills which is not simply based on education levels but also on the skill contents of specific occupations as described in the _Occupational Employment Statistics_ (OES) survey maintained by the US Bureau of Labor Statistics (BLS).<sup>7</sup> He concludes that the human capital accounts for the majority of income differences across countries.

Development accounting has also been used to study the impact of other human capital components in addition to education and schooling. Health is an area of recent interest. For example, Galasso and Wagstaff (2016) compute that the per capita income penalty a country incurs for not having eliminated stunting when today’s workers were children is, on average, 6% of GDP per capita. Weill (2013) uses a decomposition à la Caselli (2005) and estimates that the cross-section income variance explained by health is about 10%.

> 6All data refer to the year 2000.

> 7 Sasso and Ritzen (2016) use cognitive skills data from the _Programme for the International Assessment of Adult Competences_ (PIAAC) and confirm the importance of a definition of skills that goes beyond the simple education level when performing developing or growth accounting exercises.

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