Table 4 Productivity, size and growth of sales premia of traders and innovators

||(1)<br>Productivity|(2)<br>Firm size (log)|(3)<br>Sales growth (%)|
|---|---|---|---|
|Trader|0.253***|0.290***|1.730|
||(0.090)|(0.053)|(1.434)|
|Innovator|0.388*|0.420***|2.656|
||(0.224)|(0.132)|(4.209)|
|Trader & innovator|0.520***|0.661***|3.802*|
||(0.132)|(0.084)|(2.216)|
|Observations|5,017|5,539|4,779|
|R-squared|0.286|0.190|0.227|

_Note:_ OLS regressions using sampling weights. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. “Superstar” exporters are defined as firms above the 95th percentile of the distribution of export sales, big player exporters are firms between the 50th and 94th percentile, and small player exporters are firms below the median. Other firm characteristics included in the regression but not reported in the table include: country, industry, a binary variable whether the firm is foreign-owned, firm size (included as explanatory variable in columns 1 and 3), and labour productivity (in column 2).

# **Effects of trade on innovation activity**

To analyze the learning by exporting effect, we need to assess whether trade participation has a causal effect on innovation, which is not obvious. We use two different estimation methods to control for endogeneity between international trade and innovation and mitigate the potential sample selection bias (based on observable characteristics, including labor productivity) between trading and non-trading firms. The effect of internationalization on the probability of being innovative is estimated using the following equation: P P (I ) 1 2 The dependent variable is a binary taking value 1 if firm IIPPI IIP ii = ββ TTPPIIT PPii _i_ is an innovator (defined as a firm that + ββ XXii + εε The dependent variable is a binary taking value 1 if firm _i_ is an innovator (defined as a firm that introduces new or improved products and processes or invests in R&D). Trader is a binary variable taking value 1 if the firm participates in trade (by importing, exporting or participating in GVCs). is a set of explanatory variables, which includes various firm characteristics, country and sector fixed XX effects, and is a disturbance term. The firm characteristics include binary variables for whether the firm was formally registered when it began operations, whether the firm has a written business εε 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 (less than 5 years old), and the years of experience of the top manager (in log).

To address the endogeneity issues of omitted variable bias and reverse causality, which cannot be addressed directly, we use two indirect approaches: 1) coarsened exact matching (CEM) and 2) a placebo-treatment exercise. Coarsened exact matching (CEM) is a non-parametric estimation method that establishes a covariate balance between treated and control units (Lamperti et al., 2017; Blackwell et al., 2009; Iacus et al., 2012). It creates different strata based on the covariates included in the analysis. CEM thus meets the congruence principle and restricts the matched data to areas of XX common support.

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