Armando Rungi

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.

This study investigates the causal relationship between patent grants and firms' dynamics in the Information and Communication Technology (ICT) industry, as the latter is a peculiar sector of modern economies, often under the lens of antitrust authorities. For our purpose, we exploit matched information about financial accounts and patenting activity in 2009-2017 by 179,660 companies operating in 39 countries. Preliminarily, we show how bigger companies are less than 2% of the sample, although they concentrate about 89% of the grants obtained in the period of analyses. Thus, we test that patent grants in the ICT industry have a significant and large impact on market shares and firm size of smaller companies (31.5% and 30.7%, respectively) in the first year after the grants, while we have no evidence of an impact for bigger companies. After a novel instrumental variable strategy that exploits information at the level of patent offices, we confirm that most of the effects on smaller companies are due to the protection of property rights and not to the innovative content of inventions. Finally, we never observe a significant impact on either profitability or productivity for any firm size category. Eventually, we discuss how our findings support the idea that the ICT industry is a case of endogenous R&D sunk costs, which prevent profit margins from rising in the presence of a relatively high market concentration.

In this contribution, we exploit machine learning techniques to evaluate whether and how close firms are to become successful exporters. First, we train various algorithms using financial information on both exporters and non-exporters in France in 2010–2018. Thus, we show that it is possible to predict the distance non-exporters are 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.

In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithms on disclosed financial information and non-random missing values of 304,906 firms active in Italy from 2008 to 2017. Then, we spot the highest financial distress conditional on predictions that lies above a threshold for which a combination of false positive rate (false prediction of firm failure) and false negative rate (false prediction of active firms) is minimized. Therefore, we identify zombies as firms that persist in a state of financial distress, i.e., their forecasts fall into the risk category above the threshold for at least three consecutive years. For our purpose, we implement a gradient boosting algorithm (XGBoost) that exploits information about missing values. The inclusion of missing values in our predictive model is crucial because patterns of undisclosed accounts are correlated with firm failure. Finally, we show that our preferred machine learning algorithm outperforms (i) proxy models such as Z-scores and the Distance-to-Default, (ii) traditional econometric methods, and (iii) other widely used machine learning techniques. We provide evidence that zombies are on average less productive and smaller, and that they tend to increase in times of crisis. Finally, we argue that our application can help financial institutions and public authorities design evidence-based policies-e.g., optimal bankruptcy laws and information disclosure policies.

In this contribution, we investigate the role of ownership chains developed by multinational enterprises across different national borders. First, we document that parent companies control a majority (58%) of foreign subsidiaries through indirect control relationships involving at least two countries along an ownership chain. Therefore, we hypothesize that locations along ownership chains are driven by the existence of communication costs to transmit management decisions. In line with motivating evidence, we develop a theoretical model for competition on corporate control that considers the possibility that parent companies in the origin countries can delegate their monitoring activities in final subsidiaries to middlemen subsidiaries that are located in intermediate jurisdictions. Our model returns a two-step empirical strategy with two gravity equations: i) a triangular gravity for establishing a middleman by the parent, conditional on final investments’ locations; ii) a classical gravity for the location of final investments. Estimates confirm the predictions that ease of communication at the country level shapes the heterogeneous locations of subsidiaries along global ownership chains

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.