These three authors, among many others, have chipped away at the primary problems associated with tradegrowth modeling, especially the problem of endogeneity. However, the issue has not been completely laid to rest. For example, even Feyrer’s instrument does not fully satisfy the exclusion restriction as he accepted that his measure of air transport includes the movement of people, which can affect GDP via non-trade channels (e.g., tourism, migration, etc.). This raises an attribution problem because it becomes difficult to separate the ‘pure’ effect of trade from that of non-trade related travel. In line with Feyrer’s concerns, Campante and Yanagizawa-Drott (2018) found that business links and capital flows have increased as air travel became cheaper. Furthermore, biases such as Tang’s country-size bias, remain. Therefore, further work is needed to refine such instruments further.

# **3. METHODOLOGY**

## **_3.1._ Model specification**

This study leverages the Mankiw, Romer, and Weil (1992) model which proposes three fundamental determinants of economic growth: human and physical capital accumulation and labor. The main empirical model for our study is specified in equation (1) which augments the Mankiw, Romer, and Weil (1992) model by including inflation: Where ln Yi,t represents the logarithm of GDP per capita (in US dollars), AdTrade i,t is adjusted trade share (adjusted ratio of the sum of imports and exports to GDP; see Table A4 for more details) and α is the target parameter which measures the relationship between economic growth and trade. The use of AdTrade i,t allows our study to depart from earlier literature which has commonly relied on the trade intensity as a proxy for trade (Irwin and Tervi ö 2002; Dollar and Kraay 2004; Cavallo and Frankel 2008; Brueckner and Lederman 2015; Didier and Pinat 2017). Xi,th is a vector of control variables (h=3) including investment, human capital and inflation, and εi,t represents an error term. Each ηi represents an unobserved individual country-specific effect and each φt is a time-specific effect; i= 1, ... , N denotes the country ( N= 197) and t= 1, ... , T denotes time in years (T= 51). δ is a constant which estimates the average economic growth related to the base country and base year (1970) when the rest of the regressors are not statistically different from zero. Table 1 provides a list of variables used in the estimation of equation (1) including the description of how each is measured as well as the sources of data.

**Table 1.** <u>Variables, measures, and sources</u>

|Variables|Measures|Sources|
|---|---|---|
||**Dependent variable**||
|**GDP per capita**|Gross domestic product divided by midyear population.|WDI|
||**Independent variables**||
|**Adjusted Trade**<br>**share (% GDP)**|Adjusted Trade Share: measured as total trade (i.e., export plus<br>imports) as a percentage of GDP, the ratio is then adjusted for<br>each country’s share of global trade (see Table A4)|WDI|
|**Inv (% GDP)**|Grossfixed capital formation(% GDP)|WDI|
|**Human cap (%)**|Secondary school enrollment ratio (% total enrollment)|WDI|
|**Inflation**|Consumer price index (base year=2010)|WDI|

**Note:** WDI refers to the World Bank World Development Indicators databases.

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