data were the _Worldwide Governance Indicators_ (Kaufmann, Kraay & Mastruzzi 2011), which not only provides disaggregation into the subcomponents that we need, but are also, in our view, the highest-quality data available. For reasons discussed in Subsection 3.2, our preferred measure is government effectiveness, although we consider aggregate measures of governance as well.

The government effectiveness measure is central to our analysis, and it is worthwhile describing it briefly here. The measure captures, _inter alia_ , perceptions regarding the quality of public services and the quality of the civil service (Kaufmann _et al._ 2011). Key for our application is that this variable is a reasonably good proxy for the quality of educational service delivery, as distinct from other institutional quality measures—such as the rule of law or voice and accountability—that are likely to have a far weaker relationship with educational attainment, if at all (further details on the construction of the variable are provided in the annex).

The benchmark sample comprises 64 developed and developing economies, for the year 2000. This year selection was dictated by data limitations: it is the only year where there is overlap between our preferred human capital measure and the governance measure used as an instrument. In Subsection 3.6, we consider an alternative setup that comprises an unbalanced panel of 445 observations, for the years 1998, 2000, 2002–2006 (annual). More details on the specific measures employed, as well as other data sources and additional controls used in the robustness tests, are described in full in the data appendix, along with additional summary statistics and the list of countries included in the sample.

# **3.4 Main Findings**

In Table 1 we report the main results of our benchmark model, which is a cross-section using 2000 data. Specification ( _B1_ ) is the least squares estimates for the augmented Solow model consistent with (7).<sup>12</sup> The sample comprises 103 countries, and the model provides a reasonably good fit. The human capital contribution is statistically significant, and enters with the expected sign. However, endogeneity concerns lead us to discount these results.

The top half of column ( _B2_ ) reports the IV estimates for the baseline specification. In this specification, we use the pupil-teacher ratio as a proxy for school inputs, and the consumptioninvestment ratio as a proxy for family inputs. Due to data limitations, the full sample falls to 64 countries. Our main coefficient of interest, _π_ 2, remains positive and statistically (and economically) significant. The contribution of physical capital is also consistent with the theoretical prior, but

> 12Breton (2010) has argued that consistency with the Mincerian framework requires that, in contrast to the Mankiw _et al._ (1992) specification, human capital be measured without transformation into logarithms. Since human capital is instrumented in our setup, the significance of the coefficient (although not the magnitude) is determined primarily by our instrument, rather than the form of the variable. We nevertheless provide estimates with a log-linear specification for human capital in the annex, and it is clear that the qualitative nature of our main result—that human capital is a significant determinant of cross-country income—remains unaffected.

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