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in which we (with Daniel Lewis) propose a new model in which relationship-specific effects or shocks are identified in a bipartite network under mild covariance restrictions. For example, separate demand shocks are identified for each bank from which a firm borrows. .We show that a simple estimator is consistent, derive its limiting distribution, and illustrate its performance in simulations. We document considerable bias in Abowd et al. (1999) style estimates and associated regressions, while finding significant deleterious effects of the post-2022 monetary contraction on exposed firms. We highlight novel heterogeneity in the transmission of monetary policy.

CODE: A Python script to implement the decomposition is available



with Yushi Peng and Tong Zhao

Using firm-to-firm transaction and credit registry data from Belgium, we show that banks' common lending to supply chain partners is persistent and widespread. To study the benefits and costs of this lending specialization along supply chains, we develop and estimate a structural model of credit demand and supply in imperfectly competitive markets, where firms are connected through the production network. Our estimation results reveal that firms prefer to borrow from the same bank as their suppliers or customers, which gives common lenders market power and enables them to charge higher markups. At the same time, the network effects of common lending give banks an incentive to offer lower interest rates to maintain their role as common lenders. Exploiting the closure of a large manufacturing plant as an exogenous shock, we show that common lending also creates costs: the shock propagates through the production network, reducing credit demand along the supply chain and among common lenders, with banks more exposed to the affected network experiencing significantly larger declines in lending. A simulated counterfactual shows that the preference for borrowing from common lenders shapes how the losses are distributed across banks, concentrating them on the banks that lend directly to the disrupted firm and have a high exposure to the network.