arXiv subjects

Armando Rungi

Publications and source records attributed to Armando Rungi.

14 recordsLinked to original sources

arXiv2025

Owning the Intelligence: Global AI Patents Landscape and Europe's Quest for Technological Sovereignty

Artificial intelligence has become a key arena of global technological competition and a central concern for Europe's quest for technological sovereignty. This paper analyzes global AI patenting from 2010 to 2023 to assess Europe's position in an increasingly bipolar innovation landscape dominated by the United States and China. Using linked patent, firm, ownership, and citation data, we examine the geography, specialization, and international diffusion of AI innovation. We find a highly concentrated patent landscape: China leads in patent volumes, while the United States dominates in citation impact and technological influence. Europe accounts for a limited share of AI patents but exhibits signals of relatively high patent quality. Technological proximity reveals global convergence toward U.S. innovation trajectories, with Europe remaining fragmented rather than forming an autonomous pole. Gravity-model estimates show that cross-border AI knowledge flows are driven primarily by technological capability and specialization, while geographic and institutional factors play a secondary role. EU membership does not significantly enhance intra-European knowledge diffusion, suggesting that technological capacity, rather than political integration, underpins participation in global AI innovation networks.

econ.GN↗

arXiv2025

Seeing Through Green: Text-Based Classification and the Firm's Returns from Green Patents

This paper introduces Natural Language Processing for identifying ``true'' green patents from official supporting documents. We start our training on about 12.4 million patents that had been classified as green from previous literature. Thus, we train a simple neural network to enlarge a baseline dictionary through vector representations of expressions related to environmental technologies. After testing, we find that ``true'' green patents represent about 20\% of the total of patents classified as green from previous literature. We show heterogeneity by technological classes, and then check that `true' green patents are about 1\% less cited by following inventions. In the second part of the paper, we test the relationship between patenting and a dashboard of firm-level financial accounts in the European Union. After controlling for reverse causality, we show that holding at least one ``true'' green patent raises sales, market shares, and productivity. If we restrict the analysis to high-novelty ``true'' green patents, we find that they also yield higher profits. Our findings underscore the importance of using text analyses to gauge finer-grained patent classifications that are useful for policymaking in different domains.

econ.GN↗

arXiv2025

The Network Effects of the EU Carbon Border Adjustment Mechanism with a Quantitative Trade Model

We investigate the economic and environmental impacts of the European Carbon Border Adjustment Mechanism (CBAM) using a multi-country, multi-sector general equilibrium model with input-output linkages. We quantify the general equilibrium responses of trade flows, expenditures, and emissions. To our knowledge, we are the first to endogenize both carbon prices and the CBAM price. We find that, once fully implemented, CBAM could reduce carbon emissions embodied in EU imports by 5.19%. In the absence of global production network adjustments, this reduction would be larger (8.84%), highlighting the substitution effects along global supply chains. At the same time, CBAM slightly increases EU Gross National Expenditure (GNE) through terms-of-trade effects and induces a reallocation of sourcing toward domestic and relatively cleaner inputs. For non-EU countries, the aggregate effects are modest: GNE declines by 0.02%, and emissions fall by 0.11%. Overall, our results underscore the importance of accounting for global supply chains when evaluating border carbon policies. We conclude that policies targeting supply-chain emissions are essential for capturing the full carbon footprint of production.

econ.GN↗

arXiv2025

Procompetitive effects of vertical takeovers. Evidence from the European Union

This study investigates the causal impact of takeovers on firm-level financial accounts on a sample of 4,482 targets in the European Union in the period 2007- 2021. Findings suggest that horizontal integrations do not have a statistically significant impact, while vertical takeovers bring about a lower markup (0.7%), a larger market share (2.5%), a higher profitability (2.3%), and a lower capital intensity (7.2%). The impact of vertical integrations grows over time, and it is higher when the corporate perimeter of the acquirer is bigger. Our results point to strategies aimed at eliminating double profit margins along supply chains. Finally, we reconnect with the debate initiated by the U.S. Vertical Merger Guidelines in 2020 and 2023, where the presumption of harm after vertical deals has been softened, thus considering procompetitive effects, but the discussion of potential foreclosure risks has been expanded.

econ.GN↗

arXiv2025

Non-linear dependence and Granger causality: A vine copula approach

Inspired by Jang et al. (2022), we propose a Granger causality-in-the-mean test for bivariate $k-$Markov stationary processes based on a recently introduced class of non-linear models, i.e., vine copula models. By means of a simulation study, we show that the proposed test improves on the statistical properties of the original test in Jang et al. (2022), and also of other previous methods, constituting an excellent tool for testing Granger causality in the presence of non-linear dependence structures. Finally, we apply our test to study the pairwise relationships between energy consumption, GDP and investment in the U.S. and, notably, we find that Granger-causality runs two ways between GDP and energy consumption.

econ.EM↗

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

What do Firms Gain from Patenting? The Case of the Global ICT Industry

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.

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↗

arXiv2023

Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values

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.

econ.EM↗

arXiv2023

One Call Away. Ownership Chains and Ease of Communication in Multinational Enterprises

This study examines how multinational enterprises structure ownership chains to coordinate subsidiaries across multiple national borders. Using a unique global dataset, we first document key stylized facts: 54% of subsidiaries are controlled through indirect ownership, and ownership chains can span up to seven countries. In particular, we find that subsidiaries further down the control hierarchy tend to be more geographically distant from the parent and operate in different time zones. This suggests that the ease of communication along ownership chains is a critical determinant of their structure. On the other hand, tax optimization strategies are not correlated with locations along ownership chains. Motivated by previous findings, we develop a location choice model in which parent firms compete for corporate control of final subsidiaries, but monitoring is costly, and they can delegate control to an intermediate affiliate in another jurisdiction. The model generates a two-stage empirical strategy: (i) a trilateral equation that determines the location of an intermediate affiliate conditional on the location of final subsidiaries; and (ii) a bilateral equation that predicts the location of final investment. Our empirical estimates confirm that the ease of communication at the country level has a significant influence on the location decisions of affiliates along ownership chains. Our findings underscore the importance of communication frictions in shaping global corporate structures, and provide new insights into the geography of multinational ownership networks.

econ.GN↗

arXiv2022

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↗

arXiv2020

Measuring the Input Rank in Global Supply Networks

We introduce the Input Rank as a measure of relevance of direct and indirect suppliers in Global Value Chains. We conceive an intermediate input to be more relevant for a downstream buyer if a decrease in that input's productivity affects that buyer more. In particular, in our framework, the relevance of any input depends: i) on the network position of the supplier relative to the buyer, ii) the patterns of intermediate inputs vs labor intensities connecting the buyer and the supplier, iii) and the competitive pressures along supply chains. After we compute the Input Rank from both U.S. and world Input-Output tables, we provide useful insights on the crucial role of services inputs as well as on the relatively higher relevance of domestic suppliers and suppliers coming from regionally integrated partners. Finally, we test that the Input Rank is a good predictor of vertical integration choices made by 20,489 U.S. parent companies controlling 154,836 subsidiaries worldwide.

econ.GN↗

arXiv2020

Talents from Abroad. Foreign Managers and Productivity in the United Kingdom

In this paper, we test the contribution of foreign management on firms' competitiveness. We use a novel dataset on the careers of 165,084 managers employed by 13,106 companies in the United Kingdom in the period 2009-2017. We find that domestic manufacturing firms become, on average, between 7% and 12% more productive after hiring the first foreign managers, whereas foreign-owned firms register no significant improvement. In particular, we test that previous industry-specific experience is the primary driver of productivity gains in domestic firms (15.6%), in a way that allows the latter to catch up with foreign-owned firms. Managers from the European Union are highly valuable, as they represent about half of the recruits in our data. Our identification strategy combines matching techniques, difference-in-difference, and pre-recruitment trends to challenge reverse causality. Results are robust to placebo tests and to different estimators of Total Factor Productivity. Eventually, we argue that upcoming limits to the mobility of foreign talents after the Brexit event can hamper the allocation of productive managerial resources.

econ.EM↗

arXiv2014

Reputation and Impact in Academic Careers

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here we develop an original framework for measuring how a publication's citation rate $Δc$ depends on the reputation of its central author $i$, in addition to its net citation count $c$. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly-cited scientists, using the total citations $C_{i}$ of each scientist as his/her reputation measure. We find a citation crossover $c_{\times}$ which distinguishes the strength of the reputation effect. For publications with $c < c_{\times}$, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in $C_{i}$. However, the reputation effect becomes negligible for highly cited publications meaning that for $c\geq c_{\times}$ the citation rate measures scientific impact more transparently. In addition we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.

physics.soc-ph↗