With respect to EMDEs, the results confirm the positive and significant impact of trade intensity on GDP per capita. To assess the impact of trade intensity on GDP per capita for EMDEs, this study introduces an interactive variable between trade intensity and a dummy taking the value of one if the country is an EMDE and zero otherwise. The findings highlight that international trade is important the growth of GDP per capita in EMDEs. The average trade intensity effect for EMDEs is higher than the average global trade effect, underscoring the importance of trade for EMDEs’ growth (according to IV1). For EMDEs, a 1 percent increase in trade intensity, on average, leads to an increase in GDP per capita by 0.58 percent for IV1 and 0.71 percent for IV2. Based on 95% confidence intervals, on average, the impact of trade intensity in EMDEs ranges between 0.44 and 0.86 percent (Table A10 in the Appendix).

# **4.3 Testing the results for robustness**

The current study takes steps to assess the robustness of the findings by controlling for additional regional fixed effects (to consider unobserved regional specific effects during a given period) and applying a GMM approach to compare the long-term impact of trade intensity on GDP per capita with the average trade intensity impact obtained from the IV estimates.

Controlling for regional fixed effects, the results confirm that our findings are robust to the inclusion of additional regional fixed effects (see Table A11 in the Appendix). Additional regional-year fixed effects support and stabilize the results around a fixed range. While IV1 presents a significant coefficient of 0.42 percent, IV2 show that a 1 percent increase in trade intensity increases GDP per capita by 0.52 percent. Thus, the causal impact of trade on economic growth, with the inclusion of additional regional fixed effects of trade intensity, ranges between 0.395 and 0.65 percent.

Our finding is also robust to the addition of institutional quality as a control variable. Controlling for institutional quality has been a major criticism of Frankel and Romer (1999). Rodrik, Subramanian, and Trebbi (2004) indicated that Frankel and Romer’s (1999) results suffer from omitted variable bias due to excluded determinants of growth, specifically institutional quality. We therefore controlled for institutional quality, measured by the polity2 indicator per Aisen and Veiga (2013).<sup>35</sup> The results indicate that both IV1 and IV2 present significant and positive coefficients of 0.52 percent and 0.71 percent respectively (Table A14). These results are not significantly different from the baseline estimates, indicating the robustness of our finding to the addition of institutional quality as a control variable. It is important to note that, given the low variance of the polity2 indicator, country and time fixed effects likely already account for institutional quality.

This study also uses a GMM estimator as a further robustness check. The credibility of the results of the GMM estimator depends on the outcomes of diagnostic tests for overidentification and serial correlation, as presented in Table 4. In each specification, the number of instruments is lower than the number of countries. In our case, the Hansen test, which diagnoses the problem of overidentification, shows that the additional instruments associated with the system GMM estimator are valid. The two tests for serial correlation show evidence of first order autocorrelation since the null hypothesis of no autocorrelation is rejected using the AR (1) test. However, the AR (2) test cannot reject the null hypothesis and provides evidence of no second order autocorrelation. Therefore, the results of the diagnostic tests indicate that the GMM model is valid.

> 35 The Polity2 variable measures the quality of institutions. It provides a score ranging from -10 (autarchy) to 10 (democracy). It covers all major, independent states in the global system since 1800 (currently 167 countries).

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