Joint work with Santiago Pereda Fernández. Published in: Econometric Reviews, 44(9), September 2025 (pp. 1321-1360).
Abstract. Conventional methods for the estimation of peer, social or network effects are invalid if individual unobservables and covariates correlate across observations. 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 covariates 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 revisit an empirical application about classmates in college. Contrasting with conventional methods, our methodology can estimate zero, non-significant peer effects on both academic performance and major choice.
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.
Abstract. We build a model of production network formation that enables econometric estimation of the determinants of supplier choice, like trade costs or matching frictions. The model informs an estimator obtained from a transformation of the multinomial logit likelihood function that conditions on two network statistics: the out-degree of sellers (a sufficient statistic for the seller marginal costs) and the in-degree of buyers (which is determined by decisions of buyers, like “make-or-buy”). In an empirical application, this estimator shows that a prominent Costa Rican highway fostered firm-to-firm connections between core and peripheral regions, and within the core regions themselves.
Abstract. We explore the effect of a reduction in overall labor costs, indirectly induced by an Italian reform that weakened employment protection legislation, on the productivity distribution of manufacturing firms. Due to the unique institutional features of the Italian collective bargaining system, in the manufacturing sector the reform led to a clean reduction in average worker compensation, without altering the average structure of employment relationships. This decrease in labor cost resulted in a reduction in average total factor productivity (TFP) among less productive firms, and an increase at the upper end of the distribution. We pair these findings with increased entry and exit dynamics among low-productivity firms, suggesting the presence of an adverse selection mechanism at the bottom of the TFP distribution, enhanced by the reform. We formalize this concept via a general equilibrium model that links productivity to frictions in the markets for inputs.
Abstract. We study the effects of acquisitions on firms and their production networks in Türkiye using rich administrative firm-to-firm transaction data. Leveraging a staggered event-study design, we compare post-acquisition outcomes of target firms and their trading partners to matched controls. Acquisitions increase the intangible intensity of target firms but have no consistent effects on conventional performance measures. A key finding is that the network consequences of acquisitions depend on the acquirer’s origin. Domestic acquisitions lead to tangible capital deepening and strengthen existing buyer-supplier relationships along the intensive margin, while foreign acquisitions tend to shift production toward outsourcing and diversify network connections. We argue that these differences stem from variation in firms’ relationship capability: their ability to sustain productive links in a network governed by incomplete contracts.
Abstract. How much wage dispersion is visible in characteristics we observe? Using Portuguese matched employer-employee records linked to firm financial data, we sort workers and firms into observable cells and decompose log wages across worker-firm cells. Worker cells account for 35.0% of the variance, firm cells 6.7%, sorting 8.8%, and a worker-firm interaction 6.7%; the remaining 42.8% lies within cells. A second exercise splits the firm component into what firms pay similar workers (pay policy) and whom they employ (workforce composition). Pay policy dominates, accounting for about two-thirds of the firm component’s variance, and the two margins reinforce: higher-paying firms employ higher-wage workforces. Holding observable cells fixed across Portugal’s recovery, the fall in log-wage variance is explained by changing cell wage schedules rather than by workforce reallocation or re-sorting. The two-sided structure usually credited to latent worker and firm effects is thus visible in observed characteristics on the worker side, much less so on the firm side.
Abstract. We study structural estimation on networks in the empirically common case of a single large observed graph. We propose an adversarial estimator that minimizes statistical distance between observed and simulated node-specific distributions of local network neighborhoods. The paper provides two theoretical results: population identification via a divergence characterization of the estimation objective, and consistency under growing-graph asymptotics with cross-observation dependence. A key contribution is computational. We provide a reproducible estimation workflow that integrates fixed-point simulation, efficient focal-neighborhood data construction, and alternating minimax training with stabilization tools suitable for large-scale runs. The workflow is model-agnostic in a broad class of network structural models and is straightforward to implement with modern software. In benchmark simulations, the procedure scales to large graphs and recovers structural parameters with high precision.
Abstract. We develop a model of staged entry: to operate, monopolistically competitive firms must pay two sequential entry costs, each time acquiring a more informative signal of future performance. This model yields two implications for fiscal policy. First, the equilibrium outcome is constrained-efficient if preferences are CES and entry costs are exogenous. Second, when entry costs depend on how many firms pass any entry stage (due to positive knowledge spillovers or negative congestion effects) the resulting externalities can be offset via Pigouvian taxes or subsidies that are timed around the relevant entry decisions. A calibration exercise based on U.S. firm-entry data shows that these policies would raise welfare through a wider pool of entrants and sharper selection of productive firms.