# **1 Introduction**

One of the enduring puzzles in the study of human capital and income has been the apparent inconsistency between the empirical micro- and macro-econometric evidence. Studies using Mincer (1974)-style earnings functions generally find that the educational level—when used as a measure of human capital—is one of the strongest predictors of lifetime income, but this intuitive result does not generally survive aggregation: The cross-country evidence supporting a strong effect of educational attainment on national income is surprisingly mixed.

Indeed, while earlier studies that have considered the contribution of human capital to economic performance (Barro 1991; Mankiw, Romer & Weil 1992) have typically found a large and significant influence of such capital—as proxied by enrollment rates—on income per capita, later papers (Benhabib & Spiegel 1994; Pritchett 2001) have not only found an insignificant contribution, but in some cases have actually established a _negative_ relationship between human capital and income. This stands in stark contrast to a very large body of microeconometric labor research that has found a strong and persistent relationship between educational levels and wage rates. Although estimates are noisy and may depend on the time period chosen, the general result that earnings increase linearly with schooling completion has been found to hold for both U.S. (Heckman, Lochner & Todd 2006) as well as international (Peracchi 2006) data.

This micro-macro incongruence has led to various efforts aimed at resolving the paradox. One approach argues that human capital is either poorly measured or mismeasured. This approach stresses how existing education stock data may either suffer from systematic data deficiencies (Cohen & Soto 2007; Dom ́enech & de la Fuente 2006), fail to capture important quality dimensions (Behrman & Birdsall 1983; Hanushek & Kimko 2000), or be subject to high rates of measurement error when first-differenced (Krueger & Lindahl 2001). Accounting for these measurement issues would then resolve the paradox.

Another school of thought has stressed the importance of educational governance failures. Factors such as teacher absenteeism, informal payments, and corruption in schools can severely erode the productivity of the education sector (Reinikka & Svensson 2005; Rogers 2008) and reduce the incentives for human capital accumulation (Gupta, Davoodi & Tiongson 2001). This institutional failure has implications for growth outcomes (Acemo ̆glu, Johnson & Robinson 2005). Given the poor institutional environment in which learning occurs, the failure of traditional educational statistics to capture the actual stock of human capital is hardly surprising.

These two resolutions are not unrelated; governance failures often imply poor quality of education. Nonetheless, authors have tended to stress one approach over another.<sup>1</sup>

> 1Pritchett (2001) further argues that the results could be due to stagnant demand for education labor in developing countries. This explanation is less likely, however, given both international (Berman, Bound & Machin 1998) and plant-level evidence that suggests that the demand for skilled labor is reasonably strong in many developing countries

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