arXiv subjects

Federico Nutarelli

Publications and source records attributed to Federico Nutarelli.

4 recordsLinked to original sources

arXiv2025

Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes

Innovation is cumulative and interdependent: successful inventions build on prior knowledge within technological fields and may also affect success across related ones. Yet these dimensions are often studied separately in the innovation literature. This paper asks whether patent success across technological categories can be represented within a single dynamic framework that jointly captures within-category reinforcement, cross-category spillovers, and a set of aggregate regularities observed in patent data. To address this question, we propose a model of interacting reinforced Bernoulli processes in which the probability of success in a given category depends on past successes both within that category and across other categories. The framework yields joint predictions for success probabilities, cumulative successes, relative success shares, and cross-category dependence. We implement the model using granted US patent families from GLOBAL PATSTAT (1980-2018), defining category-specific success through a cohort-normalized forward-citation index. The empirical analysis shows that successful innovations continue to accumulate, but less than proportionally to the growth in patent opportunities, while technological categories remain interdependent without becoming homogeneous. Under a mean-field restriction, the model-based inferential exercise yields an estimated interaction intensity of 0.643, pointing to positive but non-maximal interaction across technological categories.

stat.AP↗

arXiv2024

Learning to Import through Production Networks

Using administrative data on the universe of inter-firm transactions in Spain, we show that firms learn to import from their domestic suppliers and customers. Our identification strategy exploits the panel structure of the data, the firm-time variation across import origins, and the network structure. We find evidence of both upstream and downstream network effects, even after accounting for sectoral and spatial spillovers. We estimate that an increase of 10 percentage points in the share of suppliers (customers) that are importing from a given region increases the probability of starting importing from that region by 10.7\% (19.2\%). Connections with geographically distant domestic firms provide more useful information to start importing. Larger firms are more responsive to this information but less likely to disseminate it.

econ.GN↗

arXiv2021

Product recalls, market size and innovation in the pharmaceutical industry

The idea that research investments respond to market rewards is well established in the literature on markets for innovation (Schmookler, 1966; Acemoglu and Linn, 2004; Bryan and Williams, 2021). Empirical evidence tells us that a change in market size, such as the one measured by demographical shifts, is associated with an increase in the number of new drugs available (Acemoglu and Linn, 2004; Dubois et al., 2015). However, the debate about potential reverse causality is still open (Cerda et al., 2007). In this paper we analyze market size's effect on innovation as measured by active clinical trials. The idea is to exploit product recalls an innovative instrument tested to be sharp, strong, and unexpected. The work analyses the relationship between US market size and innovation at ATC-3 level through an original dataset and the two-step IV methodology proposed by Wooldridge et al. (2019). The results reveal a robust and significantly positive response of number of active trials to market size.

econ.GN↗

arXiv2021

Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms

Our paper presents a methodology to study the heterogeneous effects of economy-wide shocks and applies it to the case of the impact of the COVID-19 crisis on exports. This methodology is applicable in scenarios where the pervasive nature of the shock hinders the identification of a control group unaffected by the shock, as well as the ex-ante definition of the intensity of the shock's exposure of each unit. In particular, our study investigates the effectiveness of various Machine Learning (ML) techniques in predicting firms' trade and, by building on recent developments in causal ML, uses these predictions to reconstruct the counterfactual distribution of firms' trade under different COVID-19 scenarios and to study treatment effect heterogeneity. Specifically, we focus on the probability of Colombian firms surviving in the export market under two different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual situation. On average, we find that the COVID-19 shock decreased a firm's probability of surviving in the export market by about 20 percentage points in April 2020. We study the treatment effect heterogeneity by employing a classification analysis that compares the characteristics of the firms on the tails of the estimated distribution of the individual treatment effects.

econ.GN↗