Local Gambling Norms and Audit Pricing

Abstract

This study investigates whether local gambling norms are associated with audit pricing. Using a religion-based measure of local social gambling norms, we find strong evidence that public firms located in U.S. counties with more liberal gambling norms exhibit higher levels of audit fees. This result is consistent with our view that, as an important external risk factor, clients’ local gambling norms influence audit pricing decisions. Our findings are robust to a battery of sensitivity tests, including non-religion based measures of liberal gambling norms and a natural experiment.

This is a preview of subscription content, log in to check access.

Notes

  1. 1.

    We do not include these additional controls in the main analysis because they reduce the sample size significantly.

  2. 2.

    Akerlof (1980) proposes a utility-maximization model that incorporates social sanctions imposed by loss of reputation from breaking custom to rationalize why social customs costly to the individual persist over time.

  3. 3.

    The evidence is not unanimous. Callen et al. (2011) do not find a relation between religiosity and earnings management in a cross-country study.

  4. 4.

    Chen et al. (2014) state that “a number of features of innovation make it unappealing for rational managers and investors. These factors include the long-term nature of research projects, the high likelihood of failure, and large information asymmetry between insiders and outside investors.”

  5. 5.

    Due to data restrictions, most empirical studies do not distinguish between the two effects—increased auditor effort and increased risk premium—with the notable exception of Bell, Landsman, and Shackelford (2001).

  6. 6.

    Firms headquartered in localities with more liberal gambling norms are also more likely to employ a larger percentage of people with higher propensity to take speculative risks, which indirectly increase firms’ risk-taking.

  7. 7.

    To control for potential outliers, we winsorize the top and bottom 1% for all variables in the regression. The regression results are quantitatively similar (untabulated) without winsorization.

  8. 8.

    https://www.opendatasoft.com/2016/11/13/2016-united-states-election-results-open-data/.

  9. 9.

    We control for this through year fixed-effects.

  10. 10.

    Since audit report lag is a count variable, we also estimated Poisson and Negative Binomial regressions. The results (untabulated) remain robust.

  11. 11.

    Because of the well-known biases in the coefficient estimates of a logistic regression that includes fixed-effects, we elected to estimate a linear probability model instead. Nevertheless, the results (untabulated) remain robust for a logistic regression without fixed-effects and for a logistic regression with year and industry fixed-effects.

  12. 12.

    We also controlled for Gompers, Ishii, and Metrick (2003)’s governance measure (G index), and the α1 coefficient remains positive and significant at less than the 1% level.

  13. 13.

    As a further sensitivity test, we follow Hall et al. (2001, 2005) and use an alternative adjustment based on the average citation count of all patents applied for in the same year and in the same technology class. The results (untabulated) remain very similar.

  14. 14.

    We also included Altman’s Z-SCORE (Altman 1968) financial distress measure to control for firms’ excessive risk-taking behavior. The results (untabulated) remain very similar.

  15. 15.

    Based on Liu et al. (2014), we also look at the time-series survey data from the Gallup Corporation regarding social attitudes toward alcohol and tobacco consumption. The survey data are subject to similar concerns as gaming consumption and casino visitations, and are also likely to be a less direct measure of social gambling norms. The untabulated results show mixed evidence when we replace the CP ratio with survey data. Thus, we suggest that readers exercise caution when using the survey data related to social attitudes toward alcohol and tobacco consumption to proxy for social gambling norms.

  16. 16.

    Since our system is just identified with one instrumental variable, we cannot implement a formal test for the validity of the instrument, which requires the system to be over-identified. In fact, the exclusion restriction cannot be tested directly (Wooldridge 2006). However, with more than one instrument, over-identification tests for exclusion restriction are possible under the assumption that one of the instruments is valid.

  17. 17.

    An alternative but equivalent difference-in-differences approach includes main effects (i.e., treatment variable and post-event variable, respectively) and an interaction term (i.e., treatment variable * post-event variable). Since our regression model (2) controls for firm and year fixed-effects, the LEG variable here is in fact equivalent to the interaction term in the alternative model. For treatment firms, LEG is switched off in the years before legalization and switched on in the years of and after legalization.

References

  1. Adhikari, B. K., & Agrawal, A. (2016). Religion, gambling attitudes and corporate innovation. Journal of Corporate Finance, 37, 229–248.

    Article  Google Scholar 

  2. Advisory Committee on the Auditing Profession (ACAP). (2008). Final report of the advisory committee on the auditing profession to the U.S. Department of the Treasury. Washington, DC: ACAP.

    Google Scholar 

  3. Akerlof, G. 1980. The theory of social custom, of which unemployment may be one consequence. The Quarterly Journal of Economics, 94, 749–775.

    Article  Google Scholar 

  4. Alesina, A., & Ferrara, E. L. (2000). Participation in heterogeneous communities. Quarterly Journal of Economics, 115, 847–904.

    Article  Google Scholar 

  5. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.

    Article  Google Scholar 

  6. American Institute of Certified Public Accountants (AICPA). (1992). Audit risk alert. The CPA Letter 72.

  7. American Institute of Certified Public Accountants (AICPA). (2002). Consideration of fraud in a financial statement audit. In Statement on Auditing Standards No. 99. New York: AICPA.

  8. American Institute of Certified Public Accountants (AICPA). (2006). Audit risk and materiality in conducting an audit. In Statement on Auditing Standards No. 107. New York: AICPA.

  9. American Institute of Certified Public Accountants (AICPA). (2006). Understanding the entity and its environment and assessing the risks of material misstatement. In Statement on Auditing Standards No. 109. New York: AICPA.

  10. American Institute of Certified Public Accountants (AICPA). (2012). Understanding the entity and its environment and assessing the risks of material misstatement. In Statement on Auditing Standards No. 122. New York: AICPA.

    Google Scholar 

  11. Arens, A. A. & Loebecke, J. L. (2000). Auditing: An integrated approach. Upper Saddle River: Prentice Hall.

    Google Scholar 

  12. Armstrong, C., Larcker, D., Ormazabal, G., & Taylor, D. (2013). The relation between equity incentives and misreporting: The role of risk-taking incentives. Journal of Financial Economics, 109, 327–350.

    Article  Google Scholar 

  13. Bamber, E., Bamber, L., & Schoderbek, M. (1993). Audit structure and other determinants of audit report lag: An empirical analysis. Auditing: A Journal of Practice & Theory, 12, 1–23.

    Google Scholar 

  14. Bebchuk, L., Cohen, A., & Ferrell, A. (2009). What matters in corporate governance? Review of Financial Studies, 22, 783–827.

    Article  Google Scholar 

  15. Bedard, J., & Johnstone, K. (2004). Earnings management risk, corporate governance risk, and auditors’ planning and pricing decision. The Accounting Review, 79, 277–304.

    Article  Google Scholar 

  16. Bell, T. B., Landsman, W. R., & Shackelford, D. A. (2001). Auditors’ perceived business risk and audit fees: Analysis and evidence. Journal of Accounting Research, 39, 35–43.

    Article  Google Scholar 

  17. Bertrand, M., & Mullainathan, S. (2003). Enjoying the quiet life? Corporate governance and managerial preferences. Journal of Political Economy, 111, 1043–1075.

    Article  Google Scholar 

  18. Bushee, B. (1998). The influence of institutional investors on myopic r&d investment behavior. The Accounting Review, 73, 305–333.

    Google Scholar 

  19. Callen, J. L., & Fang, X. (2015). Religion and stock price crash risk. Journal of Financial and Quantitative Analysis, 50(1/2), 169–185.

    Article  Google Scholar 

  20. Callen, J. L., Fang, X., Xin, B., & Zhang, W. 2016. Knowledge advantage and stock price crash risk: Evidence from the office size of engagement auditors. Working paper.

  21. Callen, J. L., Morel, M., & Richardson, G. 2011. Do culture and religion mitigate earnings management? Evidence from a cross-country analysis. International Journal of Disclosure and Governance (8): 103–21.

  22. Causholli, M., De Martinis, M., Hay, D., & Knechel, W. R. (2010). Audit markets, fees and production: Towards an integrated view of empirical audit research. Journal of Accounting Literature, 29, 167–215.

    Google Scholar 

  23. Chan, L. H., Chen, K. C. W., Chen, T., & Yu, Y. (2012). The effects of firm-initiated clawback provisions on earnings quality and auditor behavior. Journal of Accounting and Economics, 54, 180–196.

    Article  Google Scholar 

  24. Chen, Y., Podolski, E. J., Rhee, S. G., & Veeraraghavan, M. (2014). Local gambling preferences and corporate innovative success. Journal of Financial and Quantitative Analysis, 49(1), 77–106.

    Article  Google Scholar 

  25. Chenhall, R. H., & Moers, F. (2007). The issue of endogeneity within theory-based, quantitative management accounting research. European Accounting Review, 16(1), 173–195.

    Article  Google Scholar 

  26. Christensen, D. M., Jones, K. L., & Kenchington, D. G. (2018). Gambling attitudes and financial misreporting. Contemporary Accounting Research, 35(3), 1229–1261.

    Article  Google Scholar 

  27. Colbert, J., Luehlfing, M., & Alderman, C. (1996). Auditing—Engagement risk. CPA Journal. Retrieved Sept 25, 2018, from http://archives.cpajournal.com/1996/mar96/depts/auditing.htm.

  28. Coles, J., Daniel, N., & Naveen, L. (2006). Managerial incentives and risk-taking. Journal of Financial Economics, 79, 431–468.

    Article  Google Scholar 

  29. Core, J., & Guay, W. (2002). Estimating the value of employee stock option portfolios and their sensitivities to price and volatility. Journal of Accounting Research, 40, 613–630.

    Article  Google Scholar 

  30. Coval, J., & Moskowitz, T. (1999). Home bias at home: local equity preference in domestic portfolios. Journal of Finance, 54, 2045–2073.

    Article  Google Scholar 

  31. DeFond, M. L., & Zhang, J. (2014). A review of archival auditing research. Journal of Accounting and Economics, 58(2–3), 275–326.

    Article  Google Scholar 

  32. Diaz, J. (2000). Religion and gambling in sin-city: A statistical analysis of the relationship between religion and gambling patterns in Las Vegas residents. Social Science Journal, 37, 453–458.

    Article  Google Scholar 

  33. Dyreng, S. D., Mayew, W. J., & Williams, C. D. (2012). Religious social norms and corporate financial reporting. Journal of Business Finance and Accounting, 39, 845–875.

    Article  Google Scholar 

  34. Eadington, W. R. (1999). The economics of casino gambling. The Journal of Economic Perspectives, 13(3), 173–192.

    Article  Google Scholar 

  35. El Ghoul, S., Guedhami, O., Ni, Y., Pittman, J. A., & Saadi, S. (2012). Does religion matter to equity pricing? Journal of Business Ethics, 111, 491–518.

    Article  Google Scholar 

  36. Ellison, C. G., & Nybroten, K. A. (1999). Conservative Protestantism and opposition to state-sponsored lotteries: Evidence from the 1997 Texas poll. Social Science Quarterly, 80, 356–369.

    Google Scholar 

  37. Elster, J. (1989). Social norms and economic theory. Journal of Economic Perspectives, 3(4), 99–117.

    Article  Google Scholar 

  38. Fama, E., & MacBeth, J. (1973). Risk, return and equilibrium: Empirical tests. Journal of Political Economy, 81, 607–636.

    Article  Google Scholar 

  39. Fang, V. W., Tian, X., & Tice, S. 2014. Does Stock Liquidity Enhance or Impede Firm Innovation? Journal of Finance (Oct): 2085–125.

  40. Festinger, L. A. (1957). A theory of cognitive dissonance. Stanford: Stanford University Press.

    Google Scholar 

  41. Festre, A. (2010). Incentives and social norms: A motivation-based economic analysis of social norms. Journal of Economic Surveys, 24(3), 511–538.

    Article  Google Scholar 

  42. Francis, J., LaFond, R., Olsson, P., & Schipper, K. (2005). The market pricing of accruals quality. Journal of Accounting and Economics, 39(2), 295–327.

    Article  Google Scholar 

  43. Francis, J. R. (1984). The effect of audit firm size on audit prices: A study of the Australian market. Journal of Accounting and Economics, 6, 133–151.

    Article  Google Scholar 

  44. Francis, J. R., Michas, P. N., & Yu, M. D. (2013). Office size of big 4 auditors and client restatements. Contemporary Accounting Research, 30(4), 1626–1661.

    Article  Google Scholar 

  45. Francis, J. R., & Yu, M. D. (2009). The effect of Big 4 office size on audit quality. The Accounting Review, 84(5), 1521–1552.

    Article  Google Scholar 

  46. Ghosh, A., & Tang, C. Y. (2015). Assessing financial reporting quality of family firms: The auditors’ perspective. Journal of Accounting and Economics, 60(1), 95–116.

    Article  Google Scholar 

  47. Gompers, P., Ishii, J., & Metrick, A. (2003). Corporate governance and equity prices. Quarterly Journal of Economics, 118, 107–155.

    Article  Google Scholar 

  48. Graham, J. R., Harvey, C. R., & Puri, M. (2013). Managerial attitudes and corporate actions. Journal of Financial Economics, 109, 103–121.

    Article  Google Scholar 

  49. Grinblatt, M., & Keloharju, M. (2009). Sensation seeking, overconfidence, and trading activity. Journal of Finance, 64, 549–578.

    Article  Google Scholar 

  50. Grullon, G., Kanatas, G., & Weston, J. 2010. Religion and corporate (mis)behavior. Working Paper, Rice University.

  51. Guiso, L., Sapienza, P., & Zingales, L. (2006). Does culture affect economic outcomes? Journal of Economic Perspectives, 20, 23–48.

    Article  Google Scholar 

  52. Hall, B., Jaffe, A., & Trajtenberg, M. 2001. The NBER patent citations data file: Lessons, insights and methodological tools. Working Paper, University of California, Berkeley.

  53. Hall, B., Jaffe, A., & Trajtenberg, M. (2005). Market value and patent citations. RAND Journal of Economics, 36, 16–38.

    Google Scholar 

  54. Hausman, J. (1978). Specification tests in econometrics. Econometrica, 46, 1251–1273.

    Article  Google Scholar 

  55. Hay, D., Knechel, W. R., & Wong, N. (2006). Audit fees: A meta-analysis of the effect of supply and demand attributes. Contemporary Accounting Research, 23(1), 141–191.

    Article  Google Scholar 

  56. He, J., & Tian, X. (2013). The dark side of analyst coverage: The case of innovation. Journal of Financial Economics, 109, 856–878.

    Article  Google Scholar 

  57. Hilary, G., & Hui, K. W. (2009). Does religion matter in corporate decision making in America? Journal of Financial Economics, 93, 455–473.

    Article  Google Scholar 

  58. Hirshleifer, D., Low, A., & Teoh, S. H. (2012). Are overconfident CEOs better innovators? Journal of Finance, 67, 1457–1498.

    Article  Google Scholar 

  59. Hoffman, J. P. (2000). Religion and problem gambling in the U.S. Review of Religious Research, 41, 488–509.

    Article  Google Scholar 

  60. Holmstrom, B. (1989). Agency costs and innovation. Journal of Economic Behavior and Organization, 12, 305–327.

    Article  Google Scholar 

  61. Hong, H., Scheinkman, J. A., & Xiong, W. (2006). Asset float and speculative bubbles. Journal of Finance, 61, 1073–1117.

    Article  Google Scholar 

  62. Houston, R., Peters, M., & Pratt, J. (1999). The audit risk model, business risk and audit planning decisions. The Accounting Review, 74(3), 281–298.

    Article  Google Scholar 

  63. Ivkovic, Z., & Weisbenner, S. (2005). Local does as local is: Information content of the geography of individual investors’ common stock investments. Journal of Finance, 60, 267–306.

    Article  Google Scholar 

  64. Jaggi, B., & Xin, H. (2017). Impact of religiosity on auditors’ behavior and audit fees. Journal of Accounting, Ethics, and Public Policy, 18(3), 439–493.

    Google Scholar 

  65. Jha, A., & Chen, Y. (2015). Audit fees and social capital. The Accounting Review, 90(2), 611–639.

    Article  Google Scholar 

  66. Johnstone, K. (2000). Client-acceptance decisions: Simultaneous effects of client business risk, audit risk, auditor business risk, and risk adaptation. Auditing: A Journal of Practice & Theory, 19(1), 1–25.

    Article  Google Scholar 

  67. Kennedy, E. J., & Lawton, L. (1998). Religiousness and business ethics. Journal of Business Ethics, 17, 163–175.

    Article  Google Scholar 

  68. Knechel, W., & Payne, J. (2001). Additional evidence on audit report lag. Auditing: A Journal of Practice & Theory, 20, 137–146.

    Article  Google Scholar 

  69. Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N. (2017). Technological innovation, resource allocation, and growth. Quarterly Journal of Economics, 132(2), 665–712.

    Article  Google Scholar 

  70. Kumar, A. (2009). Who gambles in the stock market? Journal of Finance, 64, 1889–1933.

    Article  Google Scholar 

  71. Kumar, A., Page, J., & Spalt, O. (2011). Religious beliefs, gambling attitudes, and financial market outcomes. Journal of Financial Economics, 102, 671–708.

    Article  Google Scholar 

  72. Leventis, S., Dedoulis, E., & Abdelsalam, O. (2018). The impact of religiosity on audit pricing. Journal of Business Ethics, 148, 53–78.

    Article  Google Scholar 

  73. Leventis, S., Hasan, I., & Dedoulis, E. (2013). The cost of sin: The effect of social norms on audit pricing. International Review of Financial Analysis, 29, 152–165.

    Article  Google Scholar 

  74. Liu, Y., Lu, H., & Veenstra, K. (2014). Is sin always a sin? The interaction effect of social norms and financial incentives on market participants’ behavior. Accounting, Organizations and Society, 39, 289–307.

    Article  Google Scholar 

  75. Loughran, T., & Schultz, P. 2004. Dissemination of information: urban versus rural stock return patterns. Working Paper, University of Notre Dame.

  76. Ittner, C. D., & Larcker, D. F. (2001). Assessing empirical research in managerial accounting: A value-based management perspective. Journal of Accounting and Economics, 32(1–3), 349–410.

    Article  Google Scholar 

  77. Lyon, J. D., & Maher, M. W. (2005). The importance of business risk in setting audit fees: Evidence from cases of client misconduct. Journal of Accounting Research, 43(1), 645–673.

    Article  Google Scholar 

  78. Malmendier, U., & Tate, G. (2005). CEO overconfidence and corporate investment. The Journal of Finance, 60(6), 2661–2700.

    Article  Google Scholar 

  79. Malmendier, U., & Tate, G. (2008). Who makes acquisitions? CEO overconfidence and the market’s reaction. Journal of Financial Economics, 89, 20–43.

    Article  Google Scholar 

  80. McGuire, S. T., Omer, T. C., & Sharp, N. Y. (2012). The impact of religion on financial reporting irregularities. The Accounting Review, 87(2), 645–673.

    Article  Google Scholar 

  81. Messier, W. F., Glover, S. M., & Prawitt, D. F. (2012). Auditing and assurance services: a systematic approach. New York: McGraw-Hill Education.

    Google Scholar 

  82. Mikesell, J. (1994). State lottery sales and economic activity. National Tax Journal, 47, 165–171.

    Google Scholar 

  83. Miller, A., & Hoffmann, J. (1995). Risk and religion: An explanation of gender differences in religiosity. Journal for the Scientific Study of Religion, 34, 63–75.

    Article  Google Scholar 

  84. Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review, 115, 502–517.

    Article  Google Scholar 

  85. Osoba, B. 2003. Risk preferences and the practice of religion: evidence from panel data. Working Paper, West Virginia University.

  86. Ozment, S. (1991). Protestant: the birth of a revolution. New York: Doubleday.

    Google Scholar 

  87. Perkins, H. W., & Berkowitz, A. D. (1986). Perceiving the community norms of alcohol use among students: Some research implications for campus alcohol education programming. International Journal of the Addictions, 21, 961–976.

    Article  Google Scholar 

  88. Pirinsky, C., & Wang, Q. H. (2006). Does corporate headquarters location matter for stock returns? Journal of Finance, 61(4), 1991–2015.

    Article  Google Scholar 

  89. Public Company Accounting Oversight Board (PCAOB). (2010). Auditing standards related to the auditor’s assessment of and response to risk. Release No. 2010-004. New York

  90. Schneider, C., & Spalt, O. 2013. Acquisitions as lotteries: Do managerial gambling attitudes influence takeover decisions?” Working Paper, Tilburg University.

  91. Scott, J., & Marshall, G. (2005). A dictionary of sociology (3rd edn.). Oxford: Oxford University Press.

    Google Scholar 

  92. Shu, T., Sulaeman, J., & Yeung, P. E. (2012). Local religious beliefs and mutual fund risk-taking behaviors. Management Science, 58, 1179–1796.

    Article  Google Scholar 

  93. Simunic, D. A. (1980). The pricing of audit services: Theory and evidence. Journal of Accounting Research, 18(1), 161–190.

    Article  Google Scholar 

  94. Simunic, D. A., & Stein, M. T. (1996). The impact of litigation risk on audit pricing: A review of the economics and the evidence. Auditing: A Journal of Practice and Theory, 15, 119–134.

    Google Scholar 

  95. Starkey, L. M. (1964). Money, mania, and morals: The churches and gambling. New York: Abingdon Press.

    Google Scholar 

  96. Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103, 193–210.

    Article  Google Scholar 

  97. Weaver, G. R., & Agle, B. R. (2002). Religiosity and ethical behavior in organizations: A symbolic interactionist perspective. Academy of Management Review, 27, 77–97.

    Article  Google Scholar 

  98. Wooldridge, J. (2006). Introductory econometrics: A modern approach (3rd edn). Cincinnati, OH: South-Western College Pub.

    Google Scholar 

Download references

Acknowledgements

We are thankful to the editor, Steven Dellaportas, and two anonymous reviewers of this journal for their helpful and constructive comments. We also wish to thank participants at the 2016 CAAA Annual Conference and 2017 Annual Hawaii International Conference on Arts & Humanities for their remarks.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xiaohua Fang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Appendix A: Variable Definitions

Appendix A: Variable Definitions

Main Dependent Variable

LN_AUDIT_FEE is the natural logarithm of total audit service fees.

Test Variables

LN_CPRATIO is the natural logarithm of the ratio of Catholic residents to Protestant residents in the county where the firm is headquartered.

Other Variables

LN_ASSETS is the log value of total assets at the end of the fiscal year.

QUICK is current assets minus inventory, divided by current liabilities at the end of the fiscal year.

INV is the ratio of total inventory to total assets at the end of the fiscal year.

REC is the ratio of accounts receivable to total assets at the end of the fiscal year.

LEVERAGE is the book value ratio of liabilities to total assets at the end of the fiscal year.

MB is the ratio of the market value of equity to the book value of equity measured at the end of the fiscal year.

ROA is income before extraordinary items divided by the book value of total assets at the end of the fiscal year.

LOSS is an indicator variable that equals one if the company reports a loss, and zero otherwise.

LN_SEGMENT is the natural logarithm of the number of business segments.

FOREIGN is an indicator variable that equals one if the company has foreign assets, and zero otherwise.

INST is the percentage of the firm’s equity held by institutional investors at the end of the fiscal year.

ANA is the log value of one plus the number of analysts that issue earnings forecasts for a given firm during the fiscal year.

BIG is an indicator variable that equals one if the engagement auditor is a Big 4 or Big 5 auditor, and zero otherwise.

R_OFFICE_SIZE is the percentile rank of the number of clients a local auditor has for each county-year.

AUDIT_LAG is the number of days between the date of a company’s fiscal year- end and the signature date of the audit opinion.

LN_TENURE is the natural logarithm of auditor tenure.

SPECIALIST is an indicator variable that equals one if engagement auditor is a local industry specialist, and zero otherwise.

GOING_CONCERN is an indicator variable that equals one if a going-concern opinion is issued, and zero otherwise.

UNQUALIFIED is an indicator variable that equals one if an unqualified opinion without any additional language is issued, and zero otherwise.

AUDITOR_CHANGE is as an indicator variable that equals one if there is an auditor change, and zero otherwise.

REP is an indicator variable that equals one if a firm’s headquarter is in a county dominated by the Republican Party, and zero otherwise.

CSR is an index of corporate social responsibility (CSR) for each firm-year, based on CSR strengths and concerns reported by the KLD database.

RURAL is an indicator variable that equals one if a firm is headquartered in a rural area, and zero otherwise.

REL is the ratio of religious adherents to total population in the county in which the firm is headquartered.

LN_POP is the natural logarithm of the population in the county.

LN_AGE is the natural logarithm of the median age of people in the county.

MARRIED is the percentage of married people in the county.

LN_INCOME is the natural logarithm of the average income in the county.

MALE is the male-to-female ratio in the county.

MINORITY is the percentage of minorities in the county.

EDU is the percentage of people aged 25 years and older who have a bachelor’s, graduate, or professional degree.

LAWSUIT is an indicator variable that equals one if litigation is filed against the firm and its auditor during the year, and zero otherwise.

LEG is equal to one if the firm is headquartered in a state that passed commercial casino legislation, and zero otherwise.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Callen, J.L., Fang, X. Local Gambling Norms and Audit Pricing. J Bus Ethics 164, 151–173 (2020). https://doi.org/10.1007/s10551-018-4079-8

Download citation

Keywords

  • Gambling
  • Social norms
  • Audit pricing

JEL Classification

  • M14
  • M42
  • Z12