growth space, Frankel and Romer’s (1999) publication emerges as an early influential study. Frankel and Romer (1999) addressed the endogeneity problem by deploying a gravity model using the (static) distance between countries (among other factors) to predict trade between bilateral country pairs. Then they used their bilateral trade predictions to construct an exogenous instrument for total trade in each country. They found that trade has a significant impact on growth, with the elasticity of “trade openness” to GDP/capita ranging from 1.5 percent to 2 percent.<sup>18</sup> While their exogenous geography-based instrument addresses the problem of reverse causality, their approach was criticized on the grounds that it does not meet the exclusion restriction (Rodriguez and Rodrik 2000; Rodrik, Subramanian, and Trebbi 2004). 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, and distance from the equator which affect GDP through non-trade channels.<sup>_19_</sup> Brueckner and Lederman (2015) addressed the challenge of endogeneity by using two different IV strategies, focusing on Sub-Saharan Africa (SSA). For their first instrument, they estimated the effect of GDP growth on trade, i.e., the response of “trade openness" to variations in GDP per capita, using rainfall as an instrument for GDP per capita.<sup>20</sup> They reasoned that by construction, the model’s residuals obtained from their regression are exogenous to within-country variations (i.e., non-trade-related) in GDP per capita. As a second step, they used their resulting residual as an instrument to estimate the effect of trade openness on GDP growth. Brueckner and Lederman’s second IV strategy uses GDP growth rates of approximately 30 OECD countries as an instrument for trade openness of selected economies in SSA. This assumes that these countries are the mainstay of trading partners for the SSA countries included in their sample. They argue that this instrument avoids reverse causality because African countries are less likely to influence the domestic economies of OECD nations. Their results suggest that the elasticity of GDP per capita to trade openness is approximately 0.5 – 0.6 percent, depending on the IV strategy.

Building on Frankel and Romer (1999), Feyrer (2018) attempted to address the recalcitrant trade-growth omitted variable bias with an instrument that sought to satisfy the exclusion condition. He noted the rise in air transport over time as documented by Hummels (2007). Feyrer also noted that evolving technology affects the ease, speed, and cost of transporting goods between trade partners providing a practical way of conceptualizing distance in the trade-growth model in a dynamic, rather than static, manner. He generated a time-varying geographic instrument using the distance between countries (sea distance and air distance) to predict trade between bilateral pairs.<sup>21,22</sup> Feyrer generated time series predictions for bilateral trade based on exogenous “effective distance,” i.e., distance scaled by the propensity of each trade partner to trade (average aggregate trade), the population at the beginning of the sample period, and the log of country land area. Next, he summed the bilateral predictions for trade to generate a panel of predictions for overall trade for each country in the sample over time. Then, he used these trade predictions as an instrument in panel regressions of trade on income per capita, including country-specific effects to control for time-invariant factors that are correlated with GDP. His findings suggest that trade has a significant effect on income with an elasticity of roughly one half.

> 18 Trade as a percentage of GDP.

> 19 Remoteness also correlates with being nearer to the equator, which is associated with worse health conditions and institutions.

> 20This IV strategy builds on prior literature that has established a robust effect of rainfall on SSA countries' GDP per capita (Barrios, Bertinelli, and Strobl 2010; Brueckner and Ciccone 2011; Miguel, Satyanath, and Sergenti 2004).

> 21 Feyrer relies on bilateral great circle distances (the measure of air distance) from the CEPII.

> 22 Feyrer (2009) uses a similar methodology to estimate the impact of trade on GDP by using the temporary closure of the Suez to construct an instrument that is entirely about trade by sea.

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