Inferring Corporate Culture through Regulatory Violations
Ramy Elitzur#, Christopher Small#*, and Dushyant Vyas#*
# Rotman School of Management, University of Toronto, Toronto, ON
* Department of Management, University of Toronto, Mississauga, ON
“Culture eats strategy for lunch”, a phrase coined by Peter Drucker, is oft-quoted in corporate circles. However, measurement and examination of the implications of corporate culture has often eluded practitioners and academics alike. This is primarily because “corporate culture” as a construct is rather nebulous, and its effects are so pervasive that they manifest in almost all aspects of a corporation’s activities. We circumvent this measurement problem by not attempting to directly measure corporate culture, but rather inferring corporate culture from observed effects. Specifically, we argue that instances of regulatory violations (such as violations of accounting, labor, safety, and other regulatory norms) often reflect deep cultural malaise within companies. We obtain approx. 20,000 instances of operational and financial regulatory violations by U.S. firms from the Good Jobs First website1. The dataset comprises several types of violations – including accounting and financial, environmental, safety, labor, and other violations.
How do we infer that regulatory violations reflect perverse corporate culture? One way to do this is to examine whether external stakeholders, such as lenders, are able to pick up signals that are positively correlated with our measure. Lenders presumably conduct careful due-diligence before extending credit and are sensitive to any economic and/or reputational consequences that accompany extension of credit to questionable businesses. Thus, we examine the loan contracting implications of operational and financial regulatory violations by firms. Consistent with the argument that lending to such borrowers poses obvious reputational risks, using multivariate regression analyses, we find that firms with higher regulatory violation incidences in the past receive higher loan spreads in the primary syndicated loan market.
Do regulatory violations reflect innate firm characteristics? Admittedly, regulatory violations are accompanied by direct and indirect monetary penalties. We thus set out to test whether higher loan spreads are reflective of the immediate material economic consequences of regulatory violations, or innate firm cultural characteristics using an econometric approach known as the “firm fixed effects” model. This approach controls for all the time invariant factors associated with a firm – for example, unidentifiable innate characteristics. We find that firm fixed effects soak up the effects of past regulatory violations. Thus, our empirical results show that higher loan spreads likely reflect innate firm characteristics (which we label as “culture”), rather than immediate financial consequences such as regulatory fines.
Are all violations created equal? We conduct a series of cross-sectional analyses indicating that of the various types of regulatory violations, operational violations pertaining to labor and safety problems are most statistically significant. However, we caution the readers that this finding may merely be a result of statistical power, as the number of safety and labor violations reported in the Good Jobs First tracker significantly outnumber other violation types.
Further cross-sectional analyses suggest that the effect of regulatory violations on loan spreads is magnified in the presence of low financial reporting quality signals and during recessionary periods. In other words, at least in the eyes of the lenders, other signals that reflect uncertainty either at the firm- or economy-wide level reinforce the effect of negative signals embedded in regulatory violations.
Corporate culture or something else? It is difficult to answer this question emphatically. Our empirical analyses however control for standard firm-level determinants of loan spreads such as size, leverage, profitability, default risk, past restatements, disclosed internal control weaknesses, to name a few. Furthermore, we control for a number of loan characteristics (e.g., loan type, number of covenants, amount, and maturity) and macroeconomic characteristics such as term and credit spreads. We demonstrate robustness of the main findings using statistical techniques such as an entropy balancing approach. Our results suggest that these violations are picking up signals about firm characteristics that are not fully reflected in commonly observable firm-, loan-, and macro-characteristics. However, we caution the readers to interpret our results carefully. In other words, it is possible that the incremental loan spreads observed by us reflect some unobservable underlying risk factor – we call that factor “culture”, but would be satisfied with other nomenclature.
Further empirical tests are underway, and results may be subject to change. Updates and the full paper will be made available when complete.