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.