Joint work with Helena Schweiger and Alexander Stepanov. Revision requested at the American Economic Journal: Economic Policy.

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 idiosyncratic considerations of military and strategic nature. 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 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. Within a spatial equilibrium framework, we interpret these findings as the result of the interaction between persistence and agglomeration forces. Furthermore, we rule out alternative explanations related to the differential use of public resources, and we find limited evidence of reversion to the mean. Lastly, an analysis of firm-level data suggests that locating closer to Science Cities generates localized spillover effects on firms’ innovation and performance indicators.

Joint work with Santiago Pereda Fernández. In preparation for submission.

Abstract. The estimation of spillover and peer effects presents challenges that are still unsolved. In fact, even if separate algebraic identification of the endogenous and exogenous effects is possible, these might be contaminated by the simultaneous dependence of outcomes, covariates and the network structure upon spatially correlated unobservables. In this paper we characterize the identification conditions for consistently estimating all the parameters of a spatially autoregressive or linear-in-means model in presence of linear forms of endogeneity. We show that identification is possible if the network of social interactions is non-overlapping up to three degrees of separation, and the spatial matrix that characterizes the co-dependence of individual covariates and peers’ unobservables is known to the econometrician. We propose a GMM approach for the estimation of the model’s parameters, and we evaluate its performance through Monte Carlo simulations.

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.