arXiv subjects

Francesca Micocci

Publications and source records attributed to Francesca Micocci.

3 recordsLinked to original sources

arXiv2025

Learning by exporting with a dose-response function

This paper investigates the causal effect of export intensity on productivity and other firm-level outcomes with a dose-response function. After positing that export intensity acts as a continuous treatment, we investigate counterfactual productivity levels in a quasi-experimental setting. For our purpose, we exploit a control group of non-temporary exporters that have already sustained the fixed costs of reaching foreign markets, thus controlling for self-selection into exporting. Our findings reveal a non-linear relationship between export intensity and productivity, with small albeit statistically significant benefits ranging from 0.1% to 0.6% per year only after exports reach 60% of total revenues. After we look at sales, variable costs, capital intensity, and the propensity to filing patents, we show that, before the 60% threshold, economies of scale and capital adjustment offset each other and induce, on average, a minimal albeit statistically significant loss in productivity of about 0.01% per year. Crucially, we find that heterogeneous export intensity is associated with the firm's position on the technological frontier, as the propensity to file a patent increases when export intensity ranges in 8%-60% with a peak at 40%. The latest finding further highlights that learning-by-exporting is linked to the building of absorptive capacity.

econ.GN↗

arXiv2024

The heterogeneous impact of the EU-Canada agreement with causal machine learning

This paper introduces a causal machine learning approach to investigate the effects of free trade agreements and applies it to the EU-Canada Comprehensive Economic and Trade Agreement (CETA). Previous estimates of the impact of trade liberalization have been found to be unstable and contradictory, possibly due to the presence of heterogeneous treatment effects. The matrix completion estimator computes multidimensional counterfactuals in trade data at the firm, product, and destination levels. Compared with other estimators, it relies on a weaker exogeneity assumption and a more general functional form. In the case of CETA, we obtain both positive and negative idiosyncratic treatment effects at the product-destination level, although the sales-weighted average treatment effect is 6.4% in the year after the agreement. At the same time, we can estimate idiosyncratic treatment effects for the extensive margin at the product-destination level; thus, we find product churning beyond regular entry-exit dynamics: 8.1% that were not previously exported, and about 7.3% that are no longer exported. Finally, we consider the case of multiproduct firms after ranking product portfolios. After CETA, we observe a reallocation of French exports toward the first and most exported products, possibly driven by increased competition in the local market by other European producers after trade liberalization.

econ.GN↗

arXiv2021

Predicting Exporters with Machine Learning

In this contribution, we exploit machine learning techniques to evaluate whether and how close firms are to becoming successful exporters. First, we train and test various algorithms using financial information on both exporters and non-exporters in France in 2010-2018. Thus, we show that we are able to predict the distance of non-exporters from export status. In particular, we find that a Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than other techniques with an accuracy of up to 0.90. Predictions are robust to changes in definitions of exporters and in the presence of discontinuous exporting activity. Eventually, we discuss how our exporting scores can be helpful for trade promotion, trade credit, and assessing aggregate trade potential. For example, back-of-the-envelope estimates show that a representative firm with just below-average exporting scores needs up to 44% more cash resources and up to 2.5 times more capital to get to foreign markets.

econ.GN↗