rationale. For example, educational achievements might be a better proxy for human capital than attainments. Unfortunately, data on such a level of quality is not available for a broad range of countries. Researchers thus face a trade-off between data sophistication and country coverage. Since data quality is especially poor in lower income countries, while our goal is to include them in the sample to obtain most appropriate estimates for Ethiopia, we are thus limited to the mentioned data at hand. On the upside, this leaves us with a panel of 126 countries for the 19702010 period (see Annex Table A.3 for country coverage).

# **2.2 Estimation**

We use System GMM estimation with a limited set of internal instruments. This method is appropriate as some of the explanatory variables, _Xct_ , may themselves be a function of the dependent variable and because dynamic panel estimation in the presence of country fixed effects generally yields biased estimates (e.g. Nickel, 1981; Wooldridge, 2002). Our estimator uses internal instruments to avoid endogeneity biases. More specifically we use one first-differenced lag of the explanatory variables as instrument for those variables in levels which is valid under some assumptions (see Arellano and Bover, 1995, and Blundell and Bond, 1998).<sup>8</sup> Our estimation strategy addresses most conventional methodological pitfalls. The inclusion of country fixed effects avoids unobserved heterogeneity across countries while the use of internal instruments avoids endogeneity biases. By limiting the instrument set to one lag we avoid the wellknown problems associated with too many instruments (Roodman, 2009). Since the outset of the Brueckner (2013) study, recent findings have shown that this might give rise to the opposite problem of suspiciously weak instruments (Bazzi and Clemens, 2013; Kraay, in progress) which we briefly address in the robustness section.

Our econometric model is considered most appropriate to estimate neoclassical growth models over a policy-relevant horizon and has been tested for robustness on several fronts. Despite the limitations and concerns about System GMM, we find it to be the most appropriate estimation method for our purpose as it can incorporate a wide range of (linear) relationships among current and lagged values of economic variables, helps isolate exogenous changes in a variable from automatic reactions of that variable to other variables in the system and is careful to not confuse the change in a variable with a temporary shock. Furthermore, the identification strategy over time variation makes it appropriate to assess our periods of interest for one country, as opposed to identification strategies using cross-country variation (like the between-effects estimator) that also potentially suffer from unobserved cross-country heterogeneity. Given the dimension of our panel data set (especially the focus on a relatively short period) and some data gaps, we also find it superior to cointegration methods. To address remaining concerns, we also perform several

> 8 We limit the instrument set to one lag in the baseline model in order to ensure that the number of instruments does not grow too large in the System GMM estimation and furthermore avoid over-fitting the model by using the ‘collapse’ sub-option in the STATA _xtabond2_ command. Commodity prices, terms of trade and time dummies are treated as exogenous. We also use the one-step estimator as the two-step estimator is infeasible given the dimension of our data set. This also avoids severely downward biased standard errors associated with the two-step estimator (Blundell and Bond, 1998). See Brueckner (2013) for further details and discussions.

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