Joint work with Alonso Alfaro-Ureña. Revision solicited (R&R) at the American Economic Journal: Microeconomics.
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 major Costa Rican highway contributed to shuffle the spatial distribution of firm-to-firm linkages.
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. Social and economic interactions on networks are inherently complex, and econometric methods offer only a limited toolkit for empirically studying highly nonlinear theories of social behavior, such as learning models. To address this challenge, we bridge Graph Neural Networks with the adversarial framework for structural econometric estimation by Kaji, Manresa, and Pouliot (2023). Specifically, we adapt adversarial estimation to graph-structured data by integrating geometric deep learning into the discriminator and introducing a surrogate model for the outer minimization problem. We demonstrate the feasibility of our approach on the canonical linear-in-means model of peer effects, and provide an open-source Python implementation available as a lightweight prototype on GitHub and PyPI.