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Management’s personal ideology and financial reporting quality

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Abstract

We investigate the relation between managers’ personal ideologies and financial reporting quality. We use Federal Elections Commission data to develop three proxies for managers’ personal ideologies and test their relations with two financial reporting quality metrics: discretionary accruals and financial statement restatements. We find that both the absolute value of discretionary accruals and the probability of restatement decrease in the degree to which firms’ managers have conservative ideologies. These results are robust in the post-SOX period, to controls for potential self-selection bias, to alternative measures of both ideology and financial reporting quality, and to controls for firm political sensitivity, lobbying activity, governance, operational complexity and auditor strength. Our findings contribute to a growing literature demonstrating that business outcomes are partially explained by manager-specific factors including managers’ personal views and priorities.

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Notes

  1. We use the term ideology to refer to “an interrelated set of moral and political attitudes that possesses cognitive, affective, and motivational components (Jost 2006).” This definition makes clear that the concept of ideology is much broader than an individual’s political views. However, consistent with prior work in finance (Hutton et al. 2014; Jiang et al. 2012; and Hutton et al. 2015), social psychology (Jost 2006), and political science (Gerring 1997) we simplify this multidimensional concept by assuming that individual ideologies all lie somewhere on a single ideological continuum running between conservative and liberal. Therefore, our use of the terms “conservative” and “liberal” refers to the direction or position of an individual’s personal ideology on this continuum.

  2. We use the term “earnings quality” to refer to the subset of “financial reporting quality” that is related to amounts included in the financial statements and specifically in the determination of net income. We use the term “financial reporting quality” to refer to the overall quality of the financial statements and the related disclosures.

  3. Although Bamber et al. (2010) cite research in psychology, they motivate their investigation, at least in part, by the finding that humans develop long-lived, individual communication styles (e.g., Pennebaker and King 1999). Results of Bamber et al. (2010) are consistent with persistent differences in manager-specific disclosure styles. This finding is consistent with the theory of behavioral consistency.

  4. It is possible that some managers’ social views dominate their fiscal views, allowing fiscally liberal (conservative) managers who vote and contribute as social conservatives (liberals) to appear in our dataset differently than their fiscal views would dictate. However, this should only bias against finding results consistent with our hypotheses.

  5. To aggregate contribution data by individual donor, we construct an individual identifier based on first name, last name and employer name. We use these three variables because they are the only common variables across the FEC contributions files and the EXECUCOMP database used to link the contributions data to COMPUSTAT financial data. Because of the size of the databases, we use a matching algorithm created in SAS. Due to the particulars of SAS, we have to thoroughly clean the data for non-letter characters, titles, etc. For firm name, we choose the longest name of the employer after removing several common terms (e.g., CORP, CO., etc.).

  6. Since it is not straightforward to identify and classify the conservatism level of minor or third parties, we exclude all contributions that are not to candidates identified as Republican and Democrat.

  7. If George Smith had made contributions to both Republican and Democrat candidates in the same election cycle (1999–2000 in this case), the conservatism score would have been coded zero since not all contributions were to Republican candidates.

  8. It is essential to code managers in EXECUCOMP but not matched to FEC contributions data as zero rather than missing when constructing the CONSRVTOP5 proxy since this metric is an average of the top managers’ individual scores. If missing values were not coded to zero an extreme constraint would be placed on sample size (i.e., only cases where all listed managers were matched with FEC contributions data would be included).

  9. Following Hutton et al. (2014), the weight assigned to the ith (of 5) highest paid manager’s political conservatism wi, is: \(w_{i} = \frac{{i^{ - 1} }}{{\sum\nolimits_{i = 1}^{5} {i^{ - 1} } }}\)

  10. We also estimated our discretionary accruals models using discretionary accruals from (a) a cross-sectional modified Jones model, as recommended by Keung and Shih (2014) and (b) a cross-sectional performance adjusted modified Jones model, as recommended by Kothari et al. (2005). Our results were qualitatively unaffected by the use of these alternative discretionary accruals models.

  11. We also considered the relation between financial reporting quality and conditional conservatism. However, upon further investigation we discovered that other researchers question the connection between financial reporting quality and conditional conservatism. Ruch and Taylor (2015) said, “In general, it is unlikely that conditional conservatism would have a significant relationship with earnings management.” Therefore, we do not investigate the relation between management’s ideology and conditional conservatism.

  12. As a specification check, we also performed analyses using an alternative restatement sample that excludes restatements related to “Accounting Rule Application Failures”. This is a slightly more conservative definition of restatement than that used in our main analyses, and is consistent with that used by Hennes et al. (2008) and in the dataset available on Andrew Leone’s website at https://sbaleone.bus.miami.edu/. We find significantly negative coefficients on the CONSRV measures for the CFO and the top five managers. Results for the CEO CONSRV variable are insignificant, but this may be explained by the restricted sample size that this definition of restatement imposes.

  13. Altman’s (1983) Z score is equal to 0.717 * working capital/total assets + 0.847 * retained earnings/total assets + 3.107 * EBIT/total assets + 0.42*Book Value of Equity/total liabilities + 0.998 * Sales/total assets. A lower Z-score indicates a higher risk of bankruptcy.

  14. McGuire et al. (2012) use responses to a Gallup survey about religiosity. We use the American Religion Data Archive Churches and Church Membership surveys following Hilary and Hui (2009). This allows us to also analyze the robustness of the results of McGuire et al. (2012) using an alternative measure of geographical religiosity.

  15. We thank the American Religion Data Archive (www.theARDA.com) for making this data available for download. We use the Churches and Church Membership in the United States, 1990 (principal investigators are the Association of Statisticians of American Religious Bodies and the Lily Foundation), Churches and Church Membership in the United States, 2000 (principal investigators are the Association of Statisticians of American Religious Bodies), and the U.S. Religion Census: Religious Congregations and Membership Study, 2010 (County file) (principal investigators are Clifford Grammich, Kirk Hadaway, Richard Houseal, Dale E. Jones, Alexei Krindatch, Richie Stanley, Richard H. Taylor).

  16. We considered using CEO equity pay in the CONSRVCEO regressions, CFO equity pay in the CONSRVCFO regressions and the proportion of equity pay for the top 5 executives in the CONSRVTOP5 regressions. However, data losses due to missing compensation data for CFO and others led us to use only CEO equity pay in all specifications. We believe that the CEO equity pay ratio sufficiently measures the relevant construct, which is the firm’s philosophy on managerial pay.

  17. We could not perform firm-level fixed effects analysis for our restatement sample because there is no firm level variation in the value of RESTATE. Due to sample selection criteria all firms are either RESTATE = 1 or RESTATE = 0. Therefore, controlling for firm level effects removes all variation in RESTATE.

  18. Lennox et al. (2012) caution researchers about using an Instrumental Variables approach when controlling for self-selection bias. These researchers suggest that selection models frequently suffer from multicolinearity and generally fail to base 2nd stage regression exclusions on theory. These issues can result in spurious findings.

  19. We do not remove utility firms or financial service firms from the restatements and control samples to be consistent with prior studies utilizing restatement methodologies (Paterson and Valencia 2011; Kinney et al. 2004; Raghunandan et al. 2003; Aier et al. 2005).

  20. We follow Paterson and Valencia (2011) elimination of restatements not categorized within Audit Analytics as either “Financial Fraud, Irregularities, or Misrepresentations” or “Accounting Rule-Application Failures”. This data screen is designed to remove less severe restatements.

  21. The one exception is our measure of the market to book ratio, MB, computed as the natural log of the market value of equity divided by the book value of equity. Our sample mean (median) was 0.83 (0.82), but the sample mean (median) for Francis and Yu (2009) was 1.367 (0.943). We believe this difference can be explained by the difference in economic conditions during Francis and Yu’s sample period (2003–2005) and ours (1992–2010).

  22. We tested for multicolinearity using VIFs. All were below 2, suggesting that multicolinearity is not a problem.

  23. Chen et al. (2015) suggest that firm level fixed effects may be insufficient controls when estimating the effect of political contributions variables since political contributions rise and fall with election cycles.

  24. We do not use a changes specification for the restatement model, since there is no firm-level variation in the dependent variable.

  25. Coefficients on control variables are generally consistent with predictions or insignificant. The only exceptions are SIZE, DEBT, VOLRet and CFO, where some specifications include significant coefficient estimates with signs inconsistent with predictions for these variables.

  26. We thank an anonymous referee for making this point.

  27. We sincerely thank anonymous referees for identifying these issues and suggesting the related analyses.

  28. We acquire this data from the Lobbying Disclosure database that is maintained by the U.S. Senate’s Office of Public Records. This database covers all federal lobbyists, not just those that lobby the U.S. Senate. This database is available at http://www.senate.gov/legislative/Public_Disclosure/database_download.htm.

  29. For pre-2009 fiscal years, these inputs are all reported in EXECUCOMP. For fiscal years 2009 and after, EXECUCOMP changed some of the option-related variables it reported. From data available in CRSP, we obtained or computed stock price, dividend yield, and stock return volatility following the method EXECUCOMP used for the pre-2009 fiscal years.

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Authors and Affiliations

  1. Accountancy Department, University of North Dakota, Gamble 220 Centennial Drive, P.O. Box 8097, Grand Forks, ND, 58202-8097, USA

    Matthew Notbohm & Katherine Campbell

  2. Finance, Investment & Banking, Wisconsin School of Business at the University of Wisconsin-Madison, 5275 Grainger Hall, 975 University Ave., Madison, WI, 53593, USA

    Adam R. Smedema

  3. Department of Accounting, Florida State University, 420 Rovetta Business Building, Tallahassee, FL, 32306-1110, USA

    Tianming Zhang

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  1. Matthew Notbohm
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Notbohm, M., Campbell, K., Smedema, A.R. et al. Management’s personal ideology and financial reporting quality. Rev Quant Finan Acc 52, 521–571 (2019). https://doi.org/10.1007/s11156-018-0718-5

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