William Grieser

We show that a single firm's financial constraints trigger a series of investment disruptions that propagate through direct and indirect customer-supplier relationships. Quantitatively, propagation effects are responsible for roughly half of the total reduction in investment spending stemming from constraints. Firms with higher input specificity generate larger spillovers and rely more on trade credit. We employ a Network-RDD that accounts for investment spillovers to bolster identification. Our estimates are robust to network measurement error, endogenous selection effects, and various constraint measures. Our results demonstrate that interdependent investments amplify the consequences of capital market frictions in production networks.

Using a network approach that circumvents well-known challenges in estimating peer effects, we show that interactions with a firm’s geographic neighbors play a significant causal role in corporate investment behavior and a modest role in financial policies and firm performance. Moreover, these geography network effects are almost entirely driven by propagation effects through product market and supply chain networks. We corroborate our findings in a quasi-experimental framework that allows for spillovers in treatment effects. Our findings help rationalize industrial clusters (e.g., Silicon Valley), as they illustrate that agglomeration economies are substantial and operate predominantly within industry boundaries.

We study the role of peer effects in capital structure decisions by exploiting the heterogeneous and intransitive nature of product market networks combined with spatial econometric techniques that account for these features. In contrast to prior work, this approach allows us to provide economically meaningful estimates of the magnitude of the causal role of peer effects in capital structure decisions. Our estimates indicate an initial sensitivity of a firm’s leverage choice to peer average leverage on the order of .20, indicating a substantive but moderate level of strategic complementarity in capital structure decisions. Our modeling allows peer effects to vary by a firm’s location in the product market network, with more central firms having relatively larger aggregate effects on their set of network peers. Our evidence appears most likely to reflect strategic behavior. Extensions of our modeling framework to related finance questions are also briefly considered.

A large literature explores whether asset returns comove in excess of what can be explained by fundamentals, thus suggesting the existence of frictions or behavioral biases. However, we show that comovement is a ubiquitous feature of asset returns that will arise in the presence of latent or mismeasured systematic factors. Therefore, existing empirical tests cannot distinguish between alternate sources of comovement, and several documented interpretations of comovement in favor of particular explanations are premature and warrant reconsideration. We propose new statistical tests of excess comovement that account for latent factors and that exploit additional implications of market efficiency.

We study the relation between investment behavior and competitor financial constraints. Using interfirm patent citations and text-based product market similarities to identify intransitive competitor networks, we find that firms increase investment spending, patenting activity, and opportunistic hiring when competitor constraints become more binding. In addition, firms shift their investment composition (product market and patent portfolios) to compete more aggressively with relatively constrained competitors. To mitigate endogeneity concerns, we exploit the 2004 AJCA tax holiday and the 1989 junk bond crisis as exogenous shocks to competitor constraints, and we find similar effects.

We investigate the strict-exogeneity assumption, a necessary condition for estimator consistency in many finance panel-data applications. We outline tests for strict exogeneity in both traditional (non–instrumental variable (IV)) and IV settings. When we apply these tests in common traditional finance panel regressions, we find that the strict-exogeneity assumption is often strongly rejected, suggesting large inference errors. We test for strict exogeneity in specific finance panel-data IV settings and illustrate the potential for these tests to help confirm, or rule out, the validity of common panel-data IV estimators. We offer recommendations to address the strict-exogeneity issue in finance research.