Testing the sunk cost effect in publicly traded manufacturing companies

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Type of the article: Research Article

Abstract
Investment efficiency is crucial for investors and creditors. This study examines whether managers of publicly traded manufacturing firms exhibit sunk-cost bias in capital expenditure decisions. Economic theory dictates that sunk costs – unrecoverable past expenditures – should be ignored in forward-looking decisions. Depreciation expense, which allocates historical capital expenditures under GAAP, represents such a sunk cost, yet limited empirical research tests whether it systematically influences capital replacement decisions.
Using regression analysis with Compustat data for U.S. manufacturing firms from 2003–2024, we examine whether capital expenditures are predicted by past depreciation expenses, controlling for established economic determinants including Tobin’s Q, cash flow, growth opportunities, and firm characteristics. Manufacturing firms are selected due to their heavy reliance on capital assets and substantial depreciation expenses.
The results provide strong evidence of sunk-cost bias: higher depreciation expense predicts significantly larger future capital investments, controlling for economic fundamentals. The depreciation coefficient is positive (0.0158) and highly significant (p < 0.01), contributing meaningfully to explained variance beyond traditional predictors. These findings suggest managers allow accounting allocations to influence economic decisions, affecting optimal asset replacement timing.
The findings alert investors that managerial capital allocation may be suboptimal and influenced by accounting measures rather than purely economic considerations. This bias may cause over-investment when depreciation is high and under-investment when depreciation is low, potentially affecting firm value and competitive positioning.

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    • Table 1. Sample distribution across industries
    • Table 2. Descriptive statistics and Pearson correlation coefficients
    • Table 3. Regression results
    • Table 4. Granger causality test
    • Conceptualization
      Atul Rai, Joseph Kerstein
    • Data curation
      Atul Rai, Joseph Kerstein
    • Formal Analysis
      Atul Rai, Joseph Kerstein
    • Investigation
      Atul Rai, Joseph Kerstein, Steven M. Farmer
    • Methodology
      Atul Rai, Joseph Kerstein, Steven M. Farmer
    • Project administration
      Atul Rai
    • Validation
      Atul Rai, Joseph Kerstein, Steven M. Farmer
    • Visualization
      Atul Rai, Joseph Kerstein, Steven M. Farmer
    • Writing – original draft
      Atul Rai, Joseph Kerstein
    • Writing – review & editing
      Atul Rai, Joseph Kerstein, Steven M. Farmer