## **5.4 Additional Robustness Checks and Analysis**

We report additional robustness checks and analysis in an Online Appendix, available in the GitHub repository _KMWacker/growthdata_ . Online Appendix A.3, for example, checks for influential outliers and provides estimation results without those observations and checks for changes in parameters or model performance over time. Online Appendix A.4 provides results from System-GMM estimation which is a possibility to use internal instruments (i.e., lags and leads of potentially endogenous variables) for parameter identification. This possibility, however, comes with the well-known downside that results can be susceptible to instrument choice. It is now standard to restrict the instrument set to avoid instrument proliferation (Roodman, 2009) but this does not resolve (or even aggravate) problems of weak identification. Kraay (2015) provides a discussion of this problem and some solutions, which are not very promising for the large set of variables we consider. Given that problems of ordinary least square regressions and associated fixed effects are much betterunderstood, we consider them more appropriate for our baseline. The most relevant difference that our System-GMM crosscheck provides to this baseline is that the correlation between income p.c. and temperature variability becomes positive in the System-GMM results. Moreover, the coefficient for the lagged dependent variable, _θ_ , is estimated to be larger under System-GMM, which is expected due to the “Nickell bias” in fixed effects estimation of dynamic models. On average, across all three main models considered (small, medium, large), this difference is about 15%. In other words, the System-GMM results suggest stronger persistence effects. 23

# **6. Practical Aspects for Country-Specific Growth Analysis**

This section illustrates in more detail how typical country-specific growth analysis is conducted in studies covering a wide range of different countries and regions such as Araujo et al. (2016) for Latin America and the Caribbean, Moller and Wacker (2017) for Ethiopia, Haile and Moller (2018) for the West African Economic and Monetary Union, Geiger et al. (2019) for Ghana, or Beyer and Wacker (2023) for Bangladesh.

## **6.1 Analyzing Country-Specific Growth Correlates: An Illustration**

Recall equation (2) from section 3: ∆ c = ∆ 1 1,c c , which can now be parameterized with the results from the previous section to analyze country-lnyy θθ lnyycc,cc−1 + ββ ∆xx + ⋯+ ββkk∆xxkk,c + ∆δδcc + ∆εε specific growth performances. Specifically, we have obtained the relevant parameter estimates , , and in section 5 (although the latter were not displayed in table 2). We illustrate this procedure based on the small model (1) in table 2 for the case of Bangladesh between period 7 (2000-2004 ββ̂ θθ<sup>�</sup> averages) and period 8 (2005-2009 averages), when growth was above 4% p.a. Table 3 first δδ̂ reproduces the parameter estimates from table 2 in column (1). As equation (2) highlights, those need to be multiplied with changes in the explanatory _x_ variables. Column (4) contains those variable changes between period 7 and 8. For example, the inflation variable _linflation_na_ increased from 0.035 in period 7 to 0.101 in period 8 – a difference of 0.066, which gets multiplied with the

> 23 The implication of this difference in persistence for the short-run and long-run parameter estimates of other variables is not clear and can go both ways. See Keele and Kelly (2005) for an instructive discussion.

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