Joint work with Helena Schweiger and Alexander Stepanov. Published in: the American Economic Journal: Economic Policy, 14(3), August 2022 (pp. 322-351). VoxEU summary here.

Abstract. We study the long-run effects of historical place-based policies targeting R&D: the creation of Science Cities in former Soviet Russia. The establishment of Science Cities and the criteria for selecting their location were largely guided by military and strategic considerations. We compare current demographic and economic characteristics of Science Cities with those of appropriately matched localities that were similar to them at the time of their establishment, and had similar pre-trends. We find that in present-day Russia, despite the massive cuts in government support to R&D that followed the dissolution of the USSR, Science Cities still host more highly skilled workers and more developed R&D and ICT sectors; they are the origin of more international patents; and they generally appear to be more productive and economically developed. We also rule out alternative explanations related to the differential use of public resources, and we find limited evidence of reversion to the mean. By estimating a spatial equilibrium model in our matched sample, we interpret these findings as the result of the interaction between persistence and agglomeration forces.

Published in: the Review of Economic Studies, 84(7), July 2020 (pp. 1989-2018)

Abstract. In this article, I directly test the hypothesis that interactions between inventors of different firms drive knowledge spillovers. I construct a network of publicly traded companies in which each link is a function of the relative proportion of two firms’ inventors who have former patent collaborators in both organizations. I use this measure to weigh the impact of R&D performed by each firm on the productivity and innovation outcomes of its network linkages. An empirical concern is that the resulting estimates may reflect unobserved, simultaneous determinants of firm performance, network connections, and external R&D. I address this problem with an innovative IV strategy, motivated by a game-theoretic model of firm interaction. I instrument the R&D of one firm’s connections with that of other firms that are sufficiently distant in network space. With the resulting spillover estimates, I calculate that among firms connected to the network the marginal social return of R&D amounts to approximately 112% of the marginal private return.

 

Published in: Research Policy, 47(5), June 2018 (pp. 992-1005)

Abstract. In this paper I examine episodes in which superstar inventors relocate to a new city. In particular, in order to assess whether the beneficial effects of physical proximity to a superstar have a restricted network dimension or a wider spatial breadth (spillovers), I estimate changes in patterns of patenting activity following these events for two different groups of inventors: the superstar’s close collaborators, and all the other inventors in a given urban area, for both the locality where the superstar moves to and for the one that is left behind. In the case of collaborators, I restrict the attention to patents realized independently from the superstar. The results from the event study register a large and persistent positive effect on the collaborators in the city of destination, as well as a simultaneous negative trend affecting those still residing in the previous location. In the long run, these effects translate into an increased difference between the two groups of about 0.16 patents per inventor. Conversely, no city-wide spillover effect can be attested, offering little support to place-based policies aimed at inducing a positive influx of top innovators in urban areas.

Joint work with Santiago Pereda Fernández. Second round of revision at the Journal of Applied Econometrics.

Abstract. Researchers interested in the estimation of peer and network effects, even if these are algebraically identified, still need to address the problem of correlated effects. In this paper we characterize the identification conditions for consistently estimating all the parameters of a spatially autoregressive or linear-in-means model when the structure of social or peer effects is exogenous, but the observed and unobserved characteristics of agents are cross-correlated over some given metric space. We show that identification is possible if the network of social interactions is non-overlapping up to enough degrees of separation, and the spatial matrix that characterizes the co-dependence of individual unobservables and peers’ characteristics is known up to a multiplicative constant. We propose a GMM approach for the estimation of the model’s parameters, and we evaluate its performance through Monte Carlo simulations. Finally, we show that in a classical empirical application about classmates our approach might estimate statistically non-significant peer effects when conventional approaches register them as significant.

Joint work with Francesco del Prato. Comments are welcome!

Abstract. We provide evidence that increased labor flexibility, through a more liberal use of temporary contracts by firms, adversely impacted the total factor productivity (TFP) in the lower segments of the productivity distribution across manufacturing industries, while leaving the rest of the distribution largely unaltered. Specifically, we show that following an Italian labor market reform from 2001, firms at the bottom of the TFP distribution are less productive than the counterfactual firms, with a difference of 4-to-5 percentage points. This adverse effect monotonously decreases along the distribution itself. Moreover, these firms’ exit rates were reduced by 20-to-30% within two years after the reform. Instead, firms in the middle-to-high segments of the productivity distribution experienced no sizable impact on the TFP as well as an increase in labor productivity by 5-to-8% within three years. We build a general equilibrium model with monopolistic competition to argue about what mechanisms can rationalize the empirical evidence. Our model, which relates the equilibrium productivity distributions across sectors to frictions in both labor and capital markets, highlights how labor wedges may have heterogeneous effects and ambiguous net impact, as they can potentially mitigate misallocation effects due to distortions of other kinds.

Joint work with Alonso Alfaro-Ureña and Jose Vasquez. New version coming soon.

Abstract. Using administrative data for the universe of firm-to-firm transactions in Costa Rica, we study the role and prevalence of “good suppliers”, defined as those upstream firms that provide better, more valuable inputs to their downstream buyers. We then investigate the frictions that might prevent buyers from matching with good suppliers and thus become more productive. Our analysis proceeds in three phases. First, we adapt standard machine learning techniques to the estimation of production functions with many inputs in order to identify the good suppliers in the economy. Next, we quantify the frictions that may preclude buyers from matching with the good suppliers. We do so by empirically estimating a production network formation model through a conditional likelihood approach specifically suited to this problem. Finally, we perform economy-wide counterfactual simulations of industrial policies aimed at supporting good suppliers. The objective of this paper is to study matching distortions in input markets as a microeconomic origin of misallocation in developing economies and to suggest adequate policy responses.

Preliminary and incomplete draft available on request.

Abstract. I provide an economic interpretation to the entropy-based probabilistic models of network formation used in statistical physics. Specifically, I show how these models are nested in a wider class of network formation models where agents are rationally inattentive about the characteristics of other agents. I develop conditions for estimating these models and provide an application about the formation of R&D alliance networks.

Joint work with Francesco del Prato.

Abstract. In local labor markets, workers often move at early stages of their careers from lower-paying firms that provide them training, to better-paying, specialized fi rms. We call this mechanism “human capital value chain” and we document its implications on both workers’ page paths and local agglomeration externalities.