We also develop a placebo-treatment exercise to understand the relationship between trade and innovation. The idea behind the placebo treatment exercise is to target the potential self -selection of most productive (and innovative) firms into trade. We do this by predicting the firm’s trader status using firm productivity as an ancillary statistic in a Probit model. We choose the cut-off point in the predicted trade status probability distribution so that the share of pseudo-traders in the sample matches that of the underlying data. Finally, we re-estimate the innovation and trade relationship using the firm's pseudo-trader status instead of the actual one. We keep the same control variables and our results confirm those obtained with the CEM model. If trade is an important determinant of innovation, not only due to productivity, we would expect the estimated coefficient to decline significantly.

The baseline estimation suggests a positive and significant relationship between innovation and trade, as traders are about 9 percentage points more likely to be also innovators (Column (1) in Table 5). However, this estimate could be biased either via a common force that drives both trade and innovation, such as a talented manager or other unobserved firm characteristics, or via the reciprocal effect on innovation on trade. The results that control for endogeneity through CEM estimation are reported in column (2) and confirm that trade participation has a positive effect on innovation, increasing innovativeness by 11%. Column (3) shows that the estimated marginal effect of pseudotrader is low and not significant. This suggests that trade has an important impact on firms' innovation. It also suggests that the relationship is not entirely is not driven entirely by reverse causality and the self-selection of the most productive firms into international trade.

Table 5 Trade as a driver of innovation: Logit, coarsened exact matching (CEM) and Placebo estimation

||(1)<br>Logit|(2)<br>CEM|(3)<br>Placebo|
|---|---|---|---|
|Trader|0.079**|0.106***|0.045|
||(0.033)|(0.024)|(0.048)|
|Foreign ownership|0.034|-0.069**|0.067|
||(0.040)|(0.033)|(0.046)|
|Foreign licensed tech|0.173***|0.122***|0.159***|
||(0.033)|(0.021)|(0.041)|
|Firm size (log)|0.039**|0.043**|0.048**|
||(0.011)|(0.008)|(0.012)|
|Observations|5,305|3,746|4,886|

_Note:_ The dependent variable is innovation. Marginal effects from logit estimation using sampling weights. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Labour productivity is used as an ancillary variable both for CEM (column 2). Firm size is defined as the log of the number of employees. Other firm characteristics included in the regression but not reported in the table include: country, industry, binary variables for whether the firm was formally registered when it began operations, whether the firm has a written business strategy with clear key performance indicators, whether the top manager is female, whether annual financial statements are checked and certified by an external auditor, whether the firm is a young firm (under five years old), and the years of experience of the top manager (in log).

# **A gravity model of trade combining bilateral data on trade flows and the Enterprise Surveys**

France, Spain, Italy and Germany are among the top five export destinations of Morocco and Tunisia, But Egypt, Jordan, Lebanon and the West Bank and Gaza are more exposed to countries that are 13