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CEO political orientation, risk taking, and firm performance: evidence from the U.S. property-liability insurance industry

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Abstract

We examine how CEOs’ political orientation can affect risk-taking behavior and firm performance in U.S. property-liability insurance companies. Using information on political donations made by CEOs to measure their political identity, we document a strong relationship between CEOs’ political conservatism and risk-averse behavior in insurers’ decision-making. We find that the more Republican leaning (or more politically conservative) a CEO is, the less risk a property-liability insurer tends to take in the capital market and underwriting business. We also provide evidence that insurers managed by Republican-oriented CEOs are more likely to achieve better financial profitability. The overall findings lead to the conclusion that property-liability insurers with politically conservative CEOs tend to have lower variability in their asset investments and underwriting business but are more likely to generate sufficient corporate value to satisfy their shareholders and policyholders. Unlike other relevant studies, our research attempts to address impacts of corporate governance and potential causality issues and shows that an insurer with a politically conservative CEO and more board members having multiple directorships is likely to take more risks. Our findings can offer important implications for property-liability insurers’ leadership in managing corporate risks and core business activities.

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Notes

  1. The political preferences of managers and political connections of firms differ in their definitions although both address the effect of political ideology in corporate management. The key difference between the two concepts is whether a direct connection to politics functions in corporate decision-making. Political connections account for the fact that such direct political connections enable firms to enjoy economic advantages in their corporate management, whereas the political preferences of managers can have an ideological impact only on corporate management. Our focus is more on the political preferences of decision-makers (i.e., CEOs or corporate managers of insurers).

  2. As discussed in Sect. 2.1, while conservatism typically leads its adherents to seek to preserve the status quo, liberalism tends to involve embracing changes and seeking novelty (Jost et al. 2003). Moreover, the features of political conservatism represent a reluctance to seek strong external stimulation (Wilson 1973), an unwillingness to accept unconventional views (Jost and Thompson 2000) and avoidance toward making major changes in life (Feather 1979).

  3. Ho, Lai and Lee (2013) point out that in the examination of insurers’ risk-taking behavior, using different risk measures is better than using one risk measure.

  4. In studies on the insurance sector, ROE has been widely used to represent a firm’s financial performance (see, e.g., Altuntas et al. 2017; Banerjee and Savitha 2021).

  5. The literature documents how the corporate governance of property-liability insurers influences firms’ risk taking or performance. For example, Ho, Lai and Lee (2013) identify that stock insurers with limited liability tend to take more risks whereas mutual insurers forcing policyholders to have more liabilities tend to take less risks. Eling and Marek (2014) also find a positive relationship between corporate governance (compensation, monitoring and blockholders) and risk taking with corporate data from the U.K. and Germany. With respect to the impact of corporate governance on firm performance, Wang, Jeng and Peng (2007) determine using Taiwanese property-liability insurer data that positive impacts of corporate governance variables (i.e., insider ownership and outside directors) on firms’ efficiency performance are present whereas board size and CEO duality have negative impacts.

  6. For example, Miller (2011) uses the board size, duality of a CEO’s role with the chairperson position and the percentage of independent directors on a board, and He and Sommer (2011) also use the percentage of outside directors. Huang et al. (2011) and Milidonis et al. (2019) consider the board size and the percentage of independent directors on a board.

  7. One may argue that the reinsurance ratio can represent a measure of risk taking and thus should not be considered an explanatory variable for risk-taking models. However, as we explained, the reinsurance variable in our study indicates the ratio of reinsurance ceded to total direct premiums written plus reinsurance assumed, a variable that has been widely used in the relevant literature to examine the relationship between the impact of reinsurance and firm riskiness (see, e.g., Ho et al. 2013; Milidonis et al. 2019). This implies that the reinsurance ratio does not directly measure the amount of underwriting risk facing an insurer but addresses its decision-making behavior. Following the literature, we consider the reinsurance ratio as a control variable.

  8. The Z-score can be used as a proxy measure of financial stability and shows how likely an insurer is to default, where the higher the score is, the lower the default probability (Shim 2019). The score is measured by taking the sum of ROA and the capital-to-asset ratio divided by the standard deviation of ROA. We can expect that the presence of conservative CEOs should positively affect the Z-score, thereby reducing the probability of default (i.e., low risk-taking action).

  9. For example, the standard deviation of the return on assets (ROA) for 2003 is calculated with ROAs from 1999 to 2003.

  10. The FEC makes public contributors' identities and donation size and statistics at the aggregate level (Cooper et al. 2010). All the information can be accessed via www.fec.gov.

  11. Our final dataset contains 32 US property-liability insurers to be used in the models, a number of firms that is comparable with other samples in the relevant literature on the property-liability insurance industry; for example, Milidonis et al. (2019) have 29 PL insurers representing 30% of the total assets in the industry.

  12. For corporate governance variables, we manually collect information from annual proxy statements in the Securities and Exchange Commission EDGAR database (DEF 14A). This process leads us to have only 64 firm–year observations matched to the main dataset.

  13. We implement the variance inflation factor (VIF) test to examine multicollinearity among explanatory variables. We find that the VIFs of all independent variables in our models are less than four, which leads us to conclude that we have no evidence of multicollinearity. This result can be provided upon request.

  14. We conduct a Hausman test of the null hypothesis that the firm-specific error term is uncorrelated with the residuals to determine whether to use a fixed or random effects model. The Hausman test rejects the null hypothesis for all the estimations, suggesting that the fixed effects model fits the data better.

  15. Table 3 also shows the Republican CEO dummy (REPCEO) and its interactions with other control variables to examine how particular firm-specific factors and policies (i.e., firm size, reinsurance purchase, diversifications, long-tailed business concentration and leverage) in tandem with a conservative CEO can affect firms’ risk-taking behavior. We do not take into account interaction terms for the models with the two other political preference variables (REPFRQ and REPINT). This is because the other two variables are not binary and thus may not exactly reflect the interactive effects with other firm characteristics.

  16. Note that the statistical significance of this key variable tends to be smaller for the model with interaction terms, in particular in the case of underwriting risk, where there is no evidence of statistical significance.

  17. Long-tailed lines are represented by accident, automobile liability, product liability, medical malpractice, health and worker compensation, whereas short-tailed lines include fire, automobile damage and homeowner insurance (Miller 2011; Milidonis et al. 2019).

  18. Table 5 also shows the Republican CEO dummy (REPCEO) and its interactions with other control variables as in Table 3; we again do not take into account interaction terms for the models with two other political preference variables (REPFRQ and REPINT) in Table 6 for the same reason as in the case of risk taking (Table 4).

  19. Although due to the space limit we only present the models with firms’ total risk taking and ROE for the 2SLS analyses in Tables 9 and 10, we also implemented the models with other risk-taking measures (i.e., underwriting risk and investment risk) and ROA. The results are in line with those presented in Tables 9 and 10 and they can be provided upon request.

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Authors and Affiliations

  1. Korea Institute of Finance, Seoul, South Korea

    Sangyong Han

  2. Department of Industrial and Management Engineering, POSTECH, Pohang, South Korea

    Kwangmin Jung

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Han, S., Jung, K. CEO political orientation, risk taking, and firm performance: evidence from the U.S. property-liability insurance industry. Econ Gov 24, 1–39 (2023). https://doi.org/10.1007/s10101-022-00281-2

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