Policy evaluation and machine learning in international economics

Abstract

This thesis explores innovative empirical models in interna- tional economics, leveraging machine learning techniques and a dose-response method to address issues of multidimension- ality, heterogeneity, and nonlinearity, while exploiting detailed firm- and product-level microdata. Firstly, we investigate the capacity of machine learning tech- niques to forecast the firm’s exporting status. Analyzing com- prehensive financial accounts and firm-and industry-specific data from French manufacturing firms (2010-2018), we demon- strate that machine-learning methodologies can accurately fore- cast a firm’s exporting status with up to 90% accuracy. Unlike traditional econometrics, our method handles multidimen- sional data and exploits it to model non-linear relationships among endogenous predictors, thus proving a valuable tool for targeted trade promotion programs. Next, we assess the heterogeneous impacts of the EU-Canada Comprehensive Economic and Trade Agreement (CETA) on French trade using a causal machine learning approach. Em- ploying a non-parametric matrix completion algorithm rooted in potential outcome models, we predict multidimensional counterfactuals at the firm, product, and destination levels, capturing complex interactions without assuming functional forms. Using predicted potential outcomes allows us to un- cover significant heterogeneity in the trade agreement’s ef- fects, which conventional average effects models might over- look. Furthermore, our methodology is suitable to evaluate spillover effects. Within our framework, these manifest as classical Vinerian diversion effects, wherein trade to Canada partially substitutes for trade outside Canada, especially for products with a higher elasticity of substitution. Lastly, we examine the learning-by-exporting phenomenon by isolating the effect of export intensity on firm productivity from the endogenous selection into exporting status. Using a dose-response model that treats export intensity as a contin- uous treatment affecting firm productivity, we move beyond traditional binary treatment models to provide insights into how this relationship evolves across the full spectrum of ex- port intensity values. Our findings indicate that productiv- ity gains from exporting are non-linear, with firms needing to achieve a 60% export intensity threshold to fully capitalize on knowledge spillovers and effectively compete in interna- tional markets. Overall, this research expands the frontier of empirical re- search in international economics, revealing insights into the complex dynamics of trade through innovative methodolo- gies