Endogeneity in CEO power: A survey and experiment

By F. Li. 2016

252 citations recorded by the source corpus.

ABSTRACT The endogeneity problem has always been one, if not the only, obstacle to understanding the true relationship between different aspects of empirical corporate finance. Variables are typically endogenous, instruments are scarce, and causality relations are complicated. As the first attempt to summarise different econometric methods that are commonly used to address endogeneity concerns in the context of corporate governance, we explore the relation between CEO power and firm performance, as an experiment, to illustrate how these methods can be used to mitigate the endogeneity problem and by how much. After carefully dealing with the endogeneity issues, we find strong evidence that the true relationship between CEO power and subsequent firm performance is negative, suggesting CEOs are overpowered in some firms. Furthermore, we show that all the prevailing econometric remedies are generally effective in mitigating the endogeneity problem to some degree (i.e., to correct the sign from positive to negative), but quantitatively the effects vary considerably. Among all the remedies, GMM has the greatest correction effect on the bias, followed by instrumental variables, fixed effect models, lagged dependent variables and the addition of more control variables. As for a combination of the methods, firm fixed effects, year fixed effects and the addition of more meaningful control variables appear to work as well, even without a valid instrumental variable.

Subfields: Accounting, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/5d5cb2a27f5c628f3dd7be3a06df27d14dc3cd0c

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Citations in the corpus

The Impact of Working Capital Management on Firm Profitability: Empirical Evidence from the Polish Listed Firms (2020, methodology): “Endogeneity is recognized as being a challenge in corporate finance, and thus Li (2016) proposes several methods to deal with it, as follows: GMM, instrumental variables, fixed effects models, lagged dependent variables, and control variables.”
Corporate Social Responsibility and Financial Performance: The Moderating Role of Ownership Concentration in Turkey (2019, methodology): “To address the endogeneity issue in corporate finance, one of the methods suggested by Li [52] is the instrumental variable (IV) technique, which focuses on finding a variable which is correlated with CSR but not correlated with ROA.”
Corporate Social Responsibility and Market Efficiency: Evidence from ESG and Misvaluation Measures (2020, background): “However, potential endogeneity concerns may arise from measurement errors in the explanatory variable, omitted variables or reverse causality (e.g. Roberts and Whited, 2013; Li, 2016).”
Corporate Governance and Executive Compensation for Corporate Social Responsibility (2015, unknown): “This model is used to mitigate the endogeneity problem if there is persistence in a firm’s level of CSR over time (Li 2015).”
The Effects of Market Competition, Capital Structure, and CEO Duality on Firm Performance: A Mediation Analysis by Incorporating the GMM Model Technique (2020, methodology): “Therefore, to tackle this endogeneity problem, many econometrics methods have been applied to solve endogeneity issues such as instrumental, lagged dependent variables, random effects, control variables, fixed effects, and the GMM model [96].”
Study on the Relationship between CSR and Financial Performance (2019, background): “Book-to-market ratio is also considered because it is related to CSR [117].”
Governing Interfirm Relationships for Social Sustainability: The Relationship between Governance Mechanisms, Sustainable Collaboration, and Cultural Intelligence (2018, methodology): “A model that consists of a common method factor, following the lead of Li (2016) [73], was speciﬁed.”
An Analysis of Corporate Social Responsibility and Firm Performance with Moderating Effects of CEO Power and Ownership Structure: A Case Study of the Manufacturing Sector of Pakistan (2019, methodology): “Moreover, we employed Generalized Method of Moments (GMM) to control the endogeneity between stochastic error terms and explanatory variables, in line with prior studies conducted by [25,162,163].”