Paper: arXiv 2511.08591

Authors: F. Javier Sánchez-Vidal

Abstract

This paper investigates a fundamental methodological flaw in the investment-cash flow sensitivity model of Fazzari, Hubbard, and Petersen (1988). The model comes from a full accounting identity in which some components are missing, generating what I term an Accounting Semi-Identity, that mechanically links investment and cash flow, and this could bias coefficients, making the estimation difficult if not impossible. I propose an augmented specification including a variable that captures this arithmetic bias and test it across multiple firm-level databases. Results show that the ASI distortion is universal and severe: the ASI issue is present in 100% of the databases and explains more than 83% of the total explained variance, while the standard Fazzari, Hubbard, and Petersen (1988) model only accounts for approximately 17%. These findings suggest that a substantial body of prior empirical research based on this model may have reported spurious results rather than evidence of underlying economic behavior. This finding provides a compelling explanation for the substantial body of literature surrounding this model that has reported anomalous and incoherent results.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a significant methodological critique of the widely used Fazzari, Hubbard, and Petersen (1988) model by demonstrating how accounting semi-identities introduce severe mechanical bias. While the mathematical framework is elementary, the empirical evaluation across nine distinct firm-level databases provides strong, practical evidence that the accounting distortion accounts for the vast majority of explained variance.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess Accounting Semi-Identity bias in Investment-Cash Flow Models] --> B[Data: Firm-level Databases across Corporate Finance]
    B --> C[Methodology: Develop Augmented Specification with ASI Bias Variable]
    C --> D[Empirical Analysis: Compare Fazzari, Hubbard, & Petersen Model vs. Augmented Model]
    D --> E[Finding 1: ASI Distortion is Universal - Present in 100% of Databases]
    D --> F[Finding 2: ASI Explains >83% of Variance vs. ~17% by Standard Model]
    E & F --> G[Conclusion: Prior Empirical Results may be Spurious due to Arithmetic Bias]