The choice of control variables in our model is informed by past research, e.g., Balassa (1978), Calderón, Cantú, and Zeufack (2020), Cavallo and Frankel 2008, Chang, Kaltani, and Loayza (2009), Dollar (1992), Dollar and Kraay (2003), Easterly and Levine (2001), Eaton and Kortum (1996), Fosu (2011), Frankel and Romer (1999), Greenaway, Morgan, and Wright (1998), Levine and Renelt (1992), Rodrik, Subramanian, and Trebbi (2004), and Sachs and Warner (1995). The literature has mentioned additional determinants of growth; multiple control variables were considered and tested. For instance, simultaneous inclusion of both labor quality and availability proved unnecessary econometrically.

# **3.2. Model estimation techniques**

As mentioned earlier, the estimation of equation (1) using OLS presents some empirical challenges, mainly the problem of endogeneity. This study addresses this problem by using an IV approach with innovative instruments to test the hypothesis that trade intensity has a significant and positive causal effect on growth. The estimation follows a two-step process to assess causal relationship between trade and growth. First, we examine the correlation between trade intensity and growth using a bivariate model and then we add control variables. We also deploy a Generalized Method of Moments (GMM) approach as a robustness check specifically to test the sensitivity of the results to a different estimation method.

# 3.2.1. Instrumental Variable Approach

To address endogeneity, our study estimates equation (1) using an IV approach. The strength of an IV approach is heavily reliant on the identification of suitable instruments for endogenous regressors (Acemoglu, Johnson, and Robinson, 2001; Frankel and Romer 1999). Our study relies on two separate IV strategies, obtained through the combination of several different trade instruments: “lagged air transport freight capacity,” “the mean GDP of top five trade partners,” and “the mean distance to the world top five largest traders.” The use of these instruments is justified on both an intuitive and technical grounds below.

# _The IV1 strategy – Integrating Air transport freight capacity_

The economic intuition for the study’s choice of instruments is predicated on refinements of the Frankel and Romer (1999) instrument by Feyrer (2018). <mark>Trade by air represents a material component of trade.</mark> While <mark>over 80% of the volume of international trade in goods is carried by sea,</mark><sup>23</sup> <mark>air cargo transports over US $6 trillion worth of goods, including those where transport speed is critical, accounting for a material 35% of world trade by value.</mark><sup>24</sup> <mark>F</mark> eyrer (2018) incorporated both sea and air distance to create an instrument. However, he relied on flight distance as a proxy for air-channeled trade. This instrument includes passenger flight and introduces the contribution of non-trade growth factors (e.g., tourism, migration) into the analysis, and distorts the coefficient estimating the effect of trade on GDP growth. Feyrer (2018) considers this inflated coefficient as a ‘globalization effect’ which comprises pure trade effects, and other effects associated with changes in factors such as technology transfer or foreign direct investment (FDI).

Our study further refines the “predicted exogenous trade instrument” by focusing purely on air-freight capacity, which measures only the transportation of freight/cargo and excludes passenger air travel. Instrumenting trade with air freight captures the change in trade logistics, spurred, in part, by a ten-fold reduction in the cost of air freight, as aeronautical technology has improved over time (Hummels 2007).

> 23 <u>UNCTAD Review of Maritime Transport 2021.</u>

> 24 <u>IATA Value of Air Cargo.</u>

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