Working Paper: CEPR ID: DP15549
Authors: Tobias Berg; Markus Reisinger; Daniel Streitz
Abstract: Despite their importance, the discussion of spillover effects in empirical research often misses the rigor dedicated to endogeneity concerns. We analyze a broad set of workhorse models of firm interactions and show that spillovers naturally arise in many corporate finance settings. This has important implications for the estimation of treatment effects: i) even with random treatment, spillovers lead to a complicated bias, ii) fixed effects can exacerbate the spillover-induced bias. We propose simple diagnostic tools for empirical researchers and illustrate our guidance in an application.
Keywords: spillovers; direct vs indirect effects; credit supply
JEL Codes: C13; C21; G21; G32; R11; R23; M41; M42
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| spillovers (O36) | bias in estimating treatment effects (C21) |
| violation of SUTVA (C52) | bias in estimating treatment effects (C21) |
| fixed effects (C23) | spillover-induced bias (C92) |
| econometric strategy (C51) | handling spillover effects (C21) |
| capacity constraints (D24) | different modeling approaches (C50) |
| marginal cost shocks (E39) | different modeling approaches (C50) |
| ignoring spillovers (D62) | misestimation of treatment effects (C21) |
| credit supply reductions (E51) | underestimate of employment effects (J68) |