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Do political connections reduce earnings management?

Review of Quantitative Finance and Accounting volume 59, pages 273–310 (2022)Cite this article

Abstract

This study examines whether political connections are associated with earnings management (both accrual-based and real) and whether the association is influenced by corporate governance and external auditing qualities. Empirical evidence on the association between political connections and earnings management remains unclear and offers mixed results. Using a sample of Indonesian firms, we find that political connections are negatively related to accrual-based (AEM) and real (REM) earnings management. In addition, the negative relationship between political connections and earnings management is more pronounced in better-governed firms and those audited by one of the Big 4 auditors. The results are robust to alternative measures of earnings management, endogeneity, and subsample tests. Our results extend the literature by shedding additional light on the governance role and benefits of political connections.

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Notes

  1. Law No. 2/2002, Law No. 34/2008, and Law No. 25/2009 prevent incumbent politicians (except members of parliament) from holding a board membership position in publicly listed firms. As a result, out of 265 firm samples in our data, only three firms have connections with active members of parliament. We do not have sufficient data to expand the sample into close relationships with incumbent politicians. Based on our observations, incumbent politicians, especially members of parliament, prefer to be involved in business in private rather than public listed firms because private firms would allow these incumbent politicians more secrecy and avoid public scrutiny.

  2. Although current accruals could be a superior proxy for earnings because managers may have more discretion over current accruals than total accruals, we follow Choi et al. (2018) and Sohn (2016) and use total accruals to measure AEM. Sohn (2016) explains that the aggregate REM proxy includes R&D expenses, which is an investment in intangible assets, as one component. Since amortization expense is directly related to intangible assets and R&D, incorporating depreciation and amortization expenses when measuring AEM more closely matches the method used to measure REM. Unreported results are qualitatively similar when current accruals are employed.

  3. Almost identical results are obtained when modified Jones model of Dechow et al. (1995) is employed.

  4. Following prior studies (e.g., Achleitner et al. 2014; Kothari et al. 2005; Roychowdhury 2006; Zang 2012), we include unscaled intercept β0 in Eqs. (1) - (4) when estimating the expected levels of operating cash flows, production costs, discretionary expenses, and accruals to reduce misspecification in these models and ensure that the mean abnormal levels of these metrics for every industry-year are zero. However, removing the unscaled intercept does not substantially change our results.

  5. Provided that SG&A expenditure is available, we set advertising expenditures and R&D to zero if they are unavailable.

  6. We use the Durbin-Wu-Hausman test to check for this endogeneity, and F-statistics for both earnings management models are consistently significant at the 1% level, suggesting that political connectedness is an endogenous variable and hence using the OLS method would yield biased estimates.

  7. Although it is technically possible to estimate the choice model with no exclusion restrictions, it is not a recommended practice because the choice model is likely to suffer from multicollinearity problems which make Mills ratio close to be linear over a broad range of its values (Lennox et al. 2012; Puhani 2000). Including the inverse Mills ratio in the second-stage model is more likely to reduce the multicollinearity problem, which arises naturally from the correlation between Mills ratio and independent variables in the second stage. For more discussion on the importance of exclusion restrictions, see Lennox et al. (2012) and Tucker (2010).

  8. A firm’s geographic location may also affect the company’s ability to attract politically connected board members (Guedhami et al. 2014; Houston et al. 2014). Some studies use the distance of the firm headquarters from the capital city as an alternative instrument (Habib et al. 2017a; Kim and Zhang 2016). In our study, while this instrument passes the Durbin-Wu Hausman test for endogeneity and the F-test rejects the null hypothesis that the instrument is weak, the Hansen J-test rejects the null hypothesis that the instrument is exogenous.

  9. Choi et al. (2018) arrgue that it is possible that the dependent variables (i.e., REM and AEM) may be a component of contemporaneous profitability or loss. In the main analysis, we use the contemporaneous one, but untabulated results show that using lagged values of ROA and Loss does not alter our inferences.

  10. Big 4 auditors are PricewaterhouseCoopers (PwC), Klynveld Peat Marwick Goerdeler (KPMG), Ernst & Young (E&Y), and Deloitte Touche (D&T).

  11. Our results without incorporating cluster effects (untabulated) yield qualitatively similar inferences.

  12. Lennox et al. (2012) contend that an insignificant Mills coefficient does not necessarily prove the absence of selection bias as lack of statistical significance could be caused by high multicollinearity.

  13. Leuz and Oberholzer-Gee (2006) and Belghitar et al. (2019) show that the effect of political connections depends on whether the politician’s political party is still in power. However, the number of firm in our sample with such affiliation is inconsequential. The number of firm samples with such affiliation is inconsequential. Out of the politically connected board members, 30.6% are former military/police generals with no political party affiliation, 50.8% who must remain neutral, are career civil servants have no political party affiliation, and only 13.6% who are former ministers have political party affiliation, representing 0.3% from the total of the entire board members.

  14. One potential reason for the conflict with Chaney et al. (2011) is that they measure earnings quality at a country level. However, this measure may obscure managerial reporting incentives across firms and disregards that a single measure for earnings quality represents neither financial reporting practices across institutions nor the firms’ fundamental performance. Although connected firms in Indonesia share similar characteristics (i.e., weak legal protection, high ownership concentration, board structure), they are likely to differ in their reporting incentives and transparency to equity markets.

  15. We are very grateful to an anonymous reviewer for this insightful suggestion.

  16. It should be noted that we are not using PSM as an alternative to the Heckman selection model. Rather, we use it to control for the potential endogeneity that may arise from observables (Lennox et al. 2012; Shipman et al. 2017; Tucker 2010).

  17. Despite the advantages of matching using PSM, it is not without its limitations. For more discussion of the limitations of matching methods, including PSM, see Minutti‐Meza (2013) and Shipman et al. (2017).

  18. The nearest neighbor matching within a specified caliper distance is similar to the nearest neighbor matching method with the additional further restriction that the absolute difference in the propensity scores of matched subjects must be below the caliper distance as a threshold. Results (untabulated) are also qualitatively similar when the Kernel matching method is used.

  19. We are very grateful to an anonymous reviewer for suggesting this analysis to us.

  20. The results remain the same when we use indicator variables to replace the continuous ownership variables.

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Funding

Funding was provided by the Indonesia Endowment Funds for Education (LPDP) at the Ministry of Finance.

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Authors and Affiliations

  1. Hull University Business School, University of Hull, Hull, UK

    Mohamed Khalil

  2. Faculty of Commerce, Tanta University, Tanta, Egypt

    Mohamed Khalil

  3. Universitas Prasetiya Mulya, Jakarta, Indonesia

    Sandy Harianto

  4. Centre for Financial and Corporate Integrity, Coventry University, Coventry, UK

    Yilmaz Guney

Authors
  1. Mohamed Khalil
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  2. Sandy Harianto
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  3. Yilmaz Guney
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Correspondence to Yilmaz Guney.

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Appendices

Appendix A

See Table 8.

Table 8 Definitions of the variables
Full size table

Appendix B

See Table 9.

Table 9 Corporate governance index
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We follow the Institute of Director Corporate Governance Index measurement to construct an overall CG score for each company. For indicators with “Yes/no” answer, if an affirmative value of the indicator is considered to be positive for governance, such as disclosing auditor fee, we assign a score of one for “Yes” and zero for “no”. If, however, an affirmative value of the indicator is considered to be negative for governance, such as a board size with “fewer than eight or more than 15 directors”, then the score is zero for “Yes” and 1 for “no”. For continuous indicators, such as “Return on Equity”, we rely on a process known as a minimum–maximum normalization. If a higher value of the indicator is considered to be positive for governance, the company with the highest value is set equal to one, and zero for the company with the lowest value. For all other companies, the score is equal one times the difference between the actual and the minimum values divided by the difference between the maximum and minimum values according to the following formula:

$$\mathrm{Indicator} \mathrm{Score}= \frac{\mathrm{Company} \mathrm{Indicator} \mathrm{Value} - \mathrm{min} (\mathrm{Indicator} \mathrm{Value})}{\mathrm{max} (\mathrm{Indicator} \mathrm{Value}) - \mathrm{min} (\mathrm{Indicator} \mathrm{Value})}$$

If higher values of the indicator are seen as a negative barometer of corporate governance—for example, an indicator which measures share price volatility, we follow the same process, but we subtract the factor score from one. Where data for an indicator is not available for a particular company, they are assigned the average factor score. We then calculated the arithmetic average of each of the standardized indicator scores for each of the five broad corporate governance categories.

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Khalil, M., Harianto, S. & Guney, Y. Do political connections reduce earnings management?. Rev Quant Finan Acc 59, 273–310 (2022). https://doi.org/10.1007/s11156-022-01062-y

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  • DOI: https://doi.org/10.1007/s11156-022-01062-y

Keywords

  • Political connections
  • Accrual-based earnings management
  • Real earnings management
  • Corporate governance
  • Audit quality
  • Indonesia

JEL Classification

  • G30
  • M4