reports the results of the bivariate model. Table 2 also presents the results of tests for the validity of our IV approaches. The first stage estimates of the IV approach shows that the instruments (exogenous trade intensity obtained from the gravity predictions, average GDP per capita of top five trade partners, lag of air freight capacity and average distance to the world top five traders) are significantly associated with trade intensity, indicating that the instruments are relevant (see Table A5 in the Appendix). Second, the Hansen J-test is applied to test the null hypothesis that the instruments in IV1 and IV2 are jointly valid. The p- values for the test are 0.554 for IV1 and 0.1351 for IV2, respectively, suggesting the null hypothesis cannot be rejected. Thus, the conclusion is that the overidentifying restrictions are not rejected (see Tables A5 and A7 in the Appendix). In addition, weakness identification tests were performed to test for validity of the models’ instruments. The Cragg-Donald Wald F statistic (F statistic = 47.78 for IV1, and 92 for IV2) is higher than Stock-Yogo critical values. Thus, the results are satisfactory, indicating that the instruments are not weak (see Table A7 in the Appendix).

Estimating the basic bivariate model using OLS shows that a one percentage point increase in the adjusted trade intensity leads to about 0.25 percent increase in GDP per capita (Table 2— column 1). The OLS method tends to underestimate the impact of trade intensity on GDP per capita (columns 1) compared to the IV method (columns 2 and 3). Furthermore, the IV method can estimate the causal impact of trade intensity on GDP per capita. The estimated coefficient increases when IV strategies are used with IV1, and IV2 yielding higher coefficients of 0.58 and 0.70, respectively (Table 2—columns 2 and 3). These findings align with Brueckner and Lederman (2015) who mentioned the existence of a negative reverse causality bias that causes a downward bias in the OLS estimate.

**<u>Table 2.</u>** <u>The impact of trade intensity on GDP per capita (no controls)</u>

|<br>VARIABLES|<br>(1)<br>OLS<br>LogGDP/Cap|<br>(2)<br>IV1<br>LogGDP/Cap|<br>(3)<br>IV2<br>LogGDP/Cap|
|---|---|---|---|
|Log Adj Trade|0.249***|0.577***|0.703***|
||(0.0313)|(0.0417)|(0.0480)|
|First Stage F- test|-|38.61***|42.54***|
|Hansen J test p-value|-|0.5545|0.1351|
|Observations|7,808|7,749|5,726|
|R-squared|0.842|0.753|0.726|
|Number of id|196|195|166|
|Country FE|Yes|Yes|Yes|
|TimeFE|Yes|Yes|Yes|

**Note: Adj Trade** is adjusted trade share, known as trade intensity; Robust standard errors in parentheses. *** p< 0.01, ** p< 0.05, * p< 0.1

As control variables are included in the model, the results reinforce the positive relationship between trade intensity and GDP per capita. The results in Table 3 indicate a statistically significant causal relationship (first stage results are available in Table A6). Importantly, the expected sign of the trade intensity effect is consistent with earlier studies. Notwithstanding, the coefficients of 0.54 and 0.76 for IV1 and IV2, respectively, are relatively similar to those obtained from the estimation of a basic bivariate model. With the inclusion of control variables, the positive causal effect of trade intensity on GDP per capita is 15