with unobservable determinants of cross-country differences in economic performance. As pointed out in Klenow and Rodr ́ıguez-Clare (1997b), countries with policies discouraging capital accumulation are also likely to have in place policies or institutions discouraging technology adoption. This would lead to a violation of identification assumption in Mankiw et al. (1992), where the level of technology is treated as unobservable.

Various approaches have been proposed to overcome these identification challenges. Islam (1995) adopts a panel data approach, where time invariant “country effects” are added to the dynamic specification of Mankiw et al. (1992). To the extent that the common drivers of capital accumulation and technology adoption are indeed fixed over time, they are captured by these country effects and do not spuriously inflate the contribution of physical and human capital to output per worker. In this setting, Islam (1995) finds that proxies for human capital are not strongly related to output per worker, questioning the empirical relevance of the Solow model with human capital accumulation. While levels of school enrollment are positively related to levels of GDP per worker, the relationship is much weaker when introducing the temporal dimension.

This approach is itself subject to a number of issues. First, the least squares estimation of dynamic panel model including fixed effects is generally biased, as showed by Nickell (1981). Second, temporal comparisons are particularly sensitive to problems in the measurement of human capital. Given that average school attainment at the country level changes slowly over time, short-run fluctuations are likely to be mostly driven by measurement error. Indeed, Lindahl and Krueger (2001) argue that commonly used international data on educational attainment are subject to problems of incompleteness, imprecision and lack of consistency across countries. Cohen and Soto (2007) show that when using a more accurate dataset on average years of schooling across countries, increases over time in this measure of human capital are in fact positively related to GDP growth.

Other authors attempted to solve the identification problem through instrumental variables. This approach consists of finding variables (instruments) that are correlated with the variables of interest, but not with the unobservable confounders that bias the regression results to start with. By focusing on variation “induced” by these instruments, in principle one can separate the effect of the variables of interest from the one of unobservable factors.

Unfortunately, finding suitable instruments in the context of cross-country regression is a challenging task. A seminal contribution in this direction is Acemoglu et al. (2001). The paper focuses on political institutions, and proposes to identify the importance of institutional quality for economic development by using the mortality rate among European colonizers as an instrumental variable. The logic is that colonizers created extractive institutions in colonies where they could not easily settle, whereas they attempted to recreate European institutions in environments more favourable to their inhabitancy. The main result of the paper 6