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Social Capital and Individual Ethics: Evidence from Financial Adviser Misconduct

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Abstract

We show that social capital has a strong mitigating effect on financial adviser misconduct in the United States. Moreover, advisers who have committed misconduct are also more likely to relocate to counties with a relatively lower level of social capital than that of his previously residing county. These findings provide support for both the deterrence and displacement effects of social capital on financial adviser misconduct, and are robust to tests that address potential endogeneity concerns. Our results shed new light on social capital as an informal governing and monitoring mechanism against individual unethical behavior.

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Notes

  1. https://www.jec.senate.gov/public/index.cfm/republicans/socialcapitalproject.

  2. Source: Boston Consulting Group: https://www.bcg.com/publications/2019/global-asset-management-will-these-20s-roar.aspx.

  3. For example, seven percent of registered advisers have misconduct records, with the number doubling at the largest firms (Gurun et al., 2018).

  4. Charoenwong, Kwan, and Umar (2019) document an approximately one-third increase in client complaints against mid-size advisers after a regulatory jurisdiction shift from the SEC to state-securities regulators. Kowaleski, Sutherland, and Vetter (2020) show that training on ethics is associated with a one-fourth reduction in advisers’ propensity to commit misconduct.

  5. Egan et al. (2019) report an increase in misconduct being disclosed in the aftermath of the recent financial crisis.

  6. For example, if a firm located in a high social capital area provides better quality financial reports (Jha, 2019), it is not clear whether the tendency for transparent disclosure comes from the corporate culture fostered in a rich social environment or is due to the managers’ personal code of conduct shaped by local social expectations. In other words, firm level evidence cannot conclusively determine whether social capital affects firm culture or individual managers’ behavior.

  7. Gennaioli et al. (2015) suggest that professional managers may exploit trust investors put on them and attribute such trust to investors’ behavioral bias. Our paper suggests that trust exerted by social norms plays a disciplinary role on investment professionals.

  8. In a perfect world, all people are able to follow social norms and create societies that require no regulations or law enforcement. In reality, however, since not all individuals are willing to hold themselves and one another to the highest standards, formal monitoring by authorities is practically necessary.

  9. Nonetheless, social capital may also carry possible negative consequences such as coercion or inequality by social exclusion. Sometimes the pursuit of the greater good may not necessarily be commensurate with the pursuit of individual goals (see Ayios et al., 2014).

  10. We thank Mark Egan, Gregor Matvos, and Amit Seru for kindly sharing this dataset with us. Egan, Matvos, and Seru (2019) collect information for all financial advisers in the United States from FINRA’s BrokerCheck database.

    Please see more details at http://eganmatvoserru.stanford.edu/index.php/download-adviser-level-data/

  11. This dataset does not have exactly the same coverage as that used in Egan et al. (2019). In particular, while the IAPD dataset covers a majority of Investment Adviser Representatives, it only includes a fraction of the individuals who registered with the Financial Industry Regulatory Authority (FINRA) as Registered Representatives (i.e., “brokers”). Nonetheless, Egan et al. (2019) note that similar inferences were obtained when using the IAPD dataset.

  12. Data is downloaded from https://aese.psu.edu/nercrd/community/social-capital-resources.

  13. For example, in our sample, the 2005 and the 2009 versions of the RGF index have a correlation of approximately 95%.

  14. It is important to note that, while we use “county” and “community” interchangeably in this paper, we acknowledge that subcommunities, based on characteristics such as religious affiliations, age groups, or socio-economic backgrounds, may exist within each county, and it is possible for social norms to vary across these subcommunities even within a county. However, the county-level social capital measure is the most granular that is available.

  15. Income per capita, population, and population growth data comes from the Bureau of Economic Analysis (BEA). The religiosity data comes from the Association of Religion Data Archives (ARDA). Population over 65 and college education data comes from the U.S. Census Bureau.

  16. Similarly, Laamanen et al. (2012) use the Heckman’s procedure to study corporate cross-border relocations of headquarters. The first stage regression is a probit model to predict the decision “whether to relocate”. The inverse Mills ratio obtained from the first stage is then included in the second stage model to estimate “where to relocate”.

  17. Our definition of regions is based on Jha & Chen (2015).

  18. As a robustness check, we control for two-dimensional fixed effects such as firm-year fixed effects, region-year fixed effects, and firm-region fixed effects with the following rationale. Firm-year fixed effects control for attributes of advisory firms that may vary over time, such as the evolution of firms’ culture and leadership. Region-year fixed effects capture changes in geographic and demographic characteristics (e.g., the passage of legislation or the flow of labor force) in a region. Firm-region fixed effects account for the possibility that firms with geographically dispersed operations may adopt policies that are location-specific. Our results are robust to these alternative fixed effects.

  19. In untabulated robustness checks, we cluster standard errors by financial adviser or by county and financial adviser, and obtain very similar results.

  20. Since all specifications include fixed effects, we no longer report the constant in the table. We also subsume the constant in subsequent tables wherever it is appropriate.

  21. The number, -8.7%, is obtained as -0.0932 (coefficient of RGF; Table 2, Column 2) multiplied by 0.8702 (standard deviation of RGF; Table 1) and then divided by 0.9276 (mean of Misconduct; Table 1).

  22. In untabulated analysis, we find that including the lagged value of $$Misconduct$$ as an additional control variable has no material effect on the economic and statistical significance of RGF. The sample size is reduced by about 18% in this analysis.

  23. As a robustness check, we construct an alternative racial heterogeneity measure based on a Shannon index (\(-\sum {k}_{i}\mathrm{ln}\left({k}_{i}\right))\), Race_1970_Shannon, and find very similar result.

  24. In an untabulated analysis, we include Experience2 in the second stage regression, and find its coefficient estimate to be statistically insignificant, suggesting that the exclusion restriction is satisfied.

  25. As a robustness check, we further control for the distance between relocated financial advisers’ current location and previous location as well as the cosine similarity in county characteristics between the two locations and find consistent results.

  26. Literature in the corporate arena reinstates these findings, with Kedia and Rajgopal (2007) and Kedia et al. (2015) finding geographical spillover of corporate financial misreporting practices.

  27. Given that the average area of U.S. counties is 1,091 square miles and assuming counties are square-shaped, 50 miles is roughly the length of the diagonal of a county (more precisely, \(\sqrt{2\times 1091 }=46.7\)).

  28. The main effect of Crisis is absorbed by fixed effects.

  29. The fourteen survey questions regard club meetings attended, community projects worked on, times entertained at home, times volunteered, time spent visiting friends, agreeing that most people are honest, serving on committees for local organizations, servicing as officer of clubs or organizations, attending meetings on town or school affairs, organizations per capita, mean number of group memberships, agreeing that most people can be trusted, civic and social organizations per 1,000 population, and voter turnout. Putnam’s (2000) measures of the different attributes of social capital are downloaded from http://bowlingalone.com/bowlingalone.com/.

  30. Note that rare event Logit model does not comply with fixed effects.

References

  • Alesina, A., & La Ferrara, E. (2000). Participation in heterogeneous communities. Quarterly Journal of Economics, 115(3), 847–904.

    Article  Google Scholar 

  • Anagol, S., Cole, S., & Sarkar, S. (2017). Understanding the advice of commissions-motivated agents: Evidence from the indian life insurance market. Review of Economics and Statistics, 99(1), 1–15.

    Article  Google Scholar 

  • Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.

    Book  Google Scholar 

  • Ayios, A., Jeurissen, R., Manning, P., & Spence, L. (2014). Social capital: A review from an ethics perspective. Business Ethics: A European Review, 23, 108–124.

    Article  Google Scholar 

  • Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261–292.

    Article  Google Scholar 

  • Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust difference-in-differences estimates? Quarterly Journal of Economics, 119(1), 249–275.

    Article  Google Scholar 

  • Brockman, P., El Ghoul, S., Guedhami, O., & Zheng, Y. (2020). Does social trust affect international contracting? Evidence from foreign bond covenants. Journal of International Business Studies. https://doi.org/10.1057/s41267-020-00348-4

    Article  Google Scholar 

  • Byrnes, J., Miller, D., & William, S. (1999). Psychological bulletin. Psychological Bulletin, 125(3), 367–383.

    Article  Google Scholar 

  • Charoenwong, B., Kwan, A., & Umar, T. (2019). Does regulatory jurisdiction affect the quality of investment-adviser regulation? American Economic Review, 109(10), 3681–3712.

    Article  Google Scholar 

  • Clifford, C. P., Ellis, J. A., & Gerken, W. C. (2019). Born to be bad. Working Paper.

  • Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, 95–120.

    Article  Google Scholar 

  • Coleman, J. S. (1990). Foundations of Social Theory. Belknap Press of Harvard University Press.

    Google Scholar 

  • Dimmock, S. G., Gerken, W. C., & Graham, N. P. (2018). Is fraud contagious? Coworker influence on misconduct by financial advisers. Journal of Finance, 73(3), 1417–1450.

    Article  Google Scholar 

  • Dimmock, S. G., Gerken, W. C., and Van Alfen, T. D. (2019). Real estate shocks and financial adviser misconduct, Working paper.

  • Egan, M., Matvos, G., & Seru, A. (2018). When Harry Fired Sally: The double standard in punishing misconduct. NBER Working Paper No. w23242.

  • Egan, M., Matvos, G., & Seru, A. (2019). The market for financial adviser misconduct. Journal of Political Economy, 127(1), 233–295.

    Article  Google Scholar 

  • El Ghoul, S., Guedhami, O., Kwok, C. C. Y., & Zheng, X. (2016). Collectivism and corruption in commercial loan production: How to break the curse? Journal Business Ethics, 139, 225–250.

    Article  Google Scholar 

  • Fukuyama, F. (1997). Social capital and the modern capitalist economy: Creating a high trust workplace. Stern Business Magazine, 4, 1–16.

    Google Scholar 

  • Fuller, T., & Tian, Y. (2006). Social and symbolic capital and responsible entrepreneurship: an empirical investigation of SME narratives. Journal of Business Ethics, 67(3), 287–304.

    Article  Google Scholar 

  • Gao, Z., Li, L., & Lu, L. Y. (2019). Social capital and managers’ use of corporate resources. Journal of Business Ethics, 168, 593–613.

    Article  Google Scholar 

  • Gennaioli, N., Shleifer, A., & Vishny, R. (2015). Money doctors. The Journal of Finance, 70(1), 91–114.

    Article  Google Scholar 

  • Glaeser, E. L., Sacerdote, B., & Scheinkman, J. A. (1996). Crime and social interactions. Quarterly Journal of Economics, 111(2), 507–548.

    Article  Google Scholar 

  • Guiso, L., Sapienza, P., & Zingales, L. (2004). The role of social capital in financial development. American Economic Review, 94(3), 526–556.

    Article  Google Scholar 

  • Guiso, L., Sapienza, P., & Zingales, L. (2009). Cultural biases in economic exchange? Quarterly Journal of Economics, 124, 1095–1131.

    Article  Google Scholar 

  • Gupta, A., Raman, K., & Shang, C. (2018). Social capital and the cost of equity. Journal of Banking and Finance, 87, 102–117.

    Article  Google Scholar 

  • Gupta, A., Raman, K., & Shang, C. (2020). Do informal contracts matter for corporate innovation? Evidence from social capital. Journal of Financial and Quantitative Analysis, 55(5), 1657–1684.

    Article  Google Scholar 

  • Gurun, U. G., Matvos, G., & Seru, A. (2016). Advertising expensive mortgages. Journal of Finance, 71(5), 2371–2416.

    Article  Google Scholar 

  • Gurun, U. G., Stoffman, N., & Yonker, S. E. (2018). Trust Busting: The Effect of Fraud on Investor Behavior. Review of Financial Studies, 31(4), 1341–1376.

    Article  Google Scholar 

  • Hasan, I., Hoi, C. K., Wu, Q., & Zhang, H. (2017a). Does social capital matter in corporate decisions? Evidence from corporate tax avoidance. Journal of Accounting Research, 55(3), 629–668.

    Article  Google Scholar 

  • Hasan, I., Hoi, C. K., Wu, Q., & Zhang, H. (2017b). Social capital and debt contracting: Evidence from bank loans and public bonds. Journal of Financial and Quantitative Analysis, 52(3), 1017–1047.

    Article  Google Scholar 

  • Hawes, D. P. (2019). Coming together to punish others: Social capital, racial context, and social control. Social Science Quarterly, 100(4), 1094–1111.

    Article  Google Scholar 

  • Heckman, J.J. (1979). Sample selection bias as a specification error. Econometrica: Journal of the Econometric Society, 153–161.

  • Hoi, C. K., Wu, Q., & Zhang, H. (2019). Does social capital mitigate agency problems? Evidence from Chief Executive Officer (CEO) compensation. Journal of Financial Economics, 133(2), 498–519.

    Article  Google Scholar 

  • Jacobs, J. (1961). The Death and Life of Great American Cities. Vintage.

    Google Scholar 

  • Jha, A. (2019). Financial reports and social capital. Journal of Business Ethics, 155(2), 567–596.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Jha, A., & Cox, J. (2015). Corporate social responsibility and social capital. Journal of Banking & Finance, 60, 252–270.

    Article  Google Scholar 

  • Kedia, S., Koh, K., & Rajgopal, S. (2015). Evidence on contagion in earnings management. The Accounting Review, 90(6), 2337–2373.

    Article  Google Scholar 

  • Kedia, S., & Rajgopal, S. (2007). Geography and the incidence of financial misreporting. Working paper.

  • Knack, S., & Keefer, P. (1997). Does social capital have an economic payoff? A cross-country investigation. Quarterly Journal of Economics, 112(4), 1251–1288.

    Article  Google Scholar 

  • Kowaleski, Z. T., Sutherland, A. G., & Vetter, F. W. (2020). Can ethics be taught? Evidence from securities exams and investment adviser misconduct. Journal of Financial Economics, 138(1), 159–175.

    Article  Google Scholar 

  • La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1997). Trust in large organizations. American Economic Review, 87(2), 333–338.

    Google Scholar 

  • Laamanen, T., Simula, T., & Torstila, S. (2012). Cross-border relocations of headquarters in Europe. Journal of International Business Studies, 43(2), 187–210.

    Article  Google Scholar 

  • Law, K. K. F., & Mills, L. F. (2019). Financial gatekeepers and investor protection: Evidence from criminal background checks. Journal of Accounting Research, 57(2), 491–543.

    Article  Google Scholar 

  • Law, K. K. F., & Zuo, L. (2018). How does the economy shape the financial advisory profession? Working paper.

  • Li, P., Tang, L., & Jaggi, B. (2018). Social capital and the municipal bond market. Journal of Business Ethics, 153(2), 479–501.

    Article  Google Scholar 

  • Parsons, C. A., Sulaeman, J., & Titman, S. (2018). The geography of financial misconduct. Journal of Finance, 73(5), 2087–2137.

    Article  Google Scholar 

  • Posner, E. A. (2000). Law and social norms: The case of tax compliance. Virginia Law Review, 86, 781–1819.

    Article  Google Scholar 

  • Putnam, R. D. (1993). Making Democracy Work: Civic Traditions in Modern Italy. Princeton: Princeton University Press.

    Google Scholar 

  • Putnam, R. (2001). Social capital: Measurement and consequences. Canadian Journal of Policy Research, 2, 41–51.

    Google Scholar 

  • Putnam, R. D. (2000). Bowling alone: America’s declining social capital. Culture and Politics (pp. 223–234). Palgrave Macmillan US.

    Google Scholar 

  • Qureshi, H., Sokobin, J. S. (2015). Do Investors Have Valuable Information About Brokers? FINRA Office of the Chief Economist Working Paper.

  • Realo, A., & Allik, J. (2009). On the relationship between social capital and individualism-collectivism. Social and Personality Psychology Compass, 3, 1–16.

    Article  Google Scholar 

  • Rupasingha, A., Goetz, S. J., & Freshwater, D. (2006). The production of social capital in US counties. Journal of Socio-Economics, 35(1), 83–101.

    Article  Google Scholar 

  • Sapienza, P., Zingales, L., & Maestripieri, D. (2009). Gender differences in financial risk aversion and career choices are affected by testosterone. Proceedings of the National Academy of Sciences of the United States of America, 106(36), 15268–15273.

    Article  Google Scholar 

  • Stock, James H., and Motohiro Yogo, 2005, Testing for weak instruments in linear iv regression, in Donald W. K. Andrews, and James H. Stock, eds.: Identification and inference for econometric models: Essays in honor of Thomas Rothenberg (Cambridge University Press, Cambridge).

  • Uhlaner, C. J. (1989a). Rational Turnout: The Neglected Role of Groups. American Journal of Political Science, 33(2), 390–422.

    Article  Google Scholar 

  • Uhlaner, C. J. (1989b). “Relational Goods” and participation: Incorporating sociability into a theory of rational action. Public Choice, 62, 253–285.

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to Mark Egan, Gregor Matvos, and Amit Seru for kindly sharing with us their data on financial adviser misconduct and other adviser characteristics. We thank Nicole Boyson, Xavier Giroud, and seminar participants at Northeastern University for helpful comments and suggestions. Priya Garg provided excellent research assistance.

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Authors and Affiliations

  1. D’Amore-McKim School of Business, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA

    John (Jianqiu) Bai

  2. Sawyer Business School, Suffolk University, 8 Ashburton Pl, Boston, MA, 02108, USA

    Chenguang Shang

  3. College of Management, University of Massachusetts Boston, 100 Williams T. Morrissey Blvd, Boston, MA, 02125, USA

    Chi Wan

  4. College of Management, University of Massachusetts Boston, 100 Williams T. Morrissey Blvd, Boston, MA, 02125, USA

    Yijia Eddie Zhao

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  1. John (Jianqiu) Bai
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Correspondence to John (Jianqiu) Bai.

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Appendix: Key variable definitions

Appendix: Key variable definitions

Misconduct

Following Egan et al. (2019), Misconduct is defined as a dummy variable that equals one in a year if a financial adviser has engaged in actions that have led to criminal, regulatory, internal, civil or customer-related investigations that were resolved against the adviser, and zero otherwise. For ease of presentation of regression coefficients, we multiply the dummy variable by 100. Accordingly, the summary statistics of Misconduct shown in Table 1 are interpreted as percentage points

RGF

RGF is a county-level index of social capital obtained from a principal component analysis on individual participation in associational activities, presidential elections voter turnout, census response rate, and the number of tax-exempt non-profit organizations. For more details, please refer to Rupasingha et al. (2006). The RGF measure is obtained from the Northeast Regional Center for Rural Development in the College of Agricultural Sciences at Pennsylvania State University. The data is publically available at https://aese.psu.edu/nercrd/community/social-capital-resources. This county-level social capital index is available for the years 1990, 1997, 2005, and 2009. For the years with missing data, we assume that the social capital value in a given county remains the same until new data becomes available

Move to Different County

A dummy variable that is equal to one if a financial adviser has moved to a different county in a given year, and zero otherwise

Move to Lower SC County

A dummy variable that is equal to one if a financial adviser has moved to a lower social capital county in a given year, and zero otherwise

Adviser-level controls

Experience

The number of years since the adviser first started working with an investment adviser firm

Male

A dummy variable indicating whether an adviser is a male or nota

Series 63/65/66

A dummy variable indicating whether an adviser holds a series 63/65/66 license or not

County-level controls

Income Per Capita

The natural logarithm of income per capita in a county. Data comes from the Bureau of Economic Analysis (BEA)

Population

The natural logarithm of population in a county. Data comes from the Bureau of Economic Analysis (BEA)

Population Growth

population growth rate in a county. Data comes from the Bureau of Economic Analysis (BEA)

Religiosity

The number of adherents divided by the population in a county. Data comes from the Association of Religion Data Archives (ARDA)

Population Over 65

The percent of population older than 65 in a county. Data comes from the U.S. Census Bureau

College

The percent of college-educated population in a county. Data comes from the U.S. Census Bureau

Instruments

Race_1970

Race_1970 is calculated as \(\left(1-{\sum }_{k}{s}_{ki}^{2}\right)\), where i represents state, and k represents the following races: (i) Black, (ii) White and other races. Each term in \({s}_{ki}\) represents the share of race \(k\) in the population of state i. The data are obtained from the U.S. Census Bureau

Distance

The natural logarithm of the closest distance between a U.S. county and the US-Canadian border. County coordinates are obtained from

https://www.census.gov/geo/maps-data/data/gazetteer2017.html

Coordinates of country borders are obtained from http://www.mappinghacks.com/data/

  1. aGender is inferred based on the adviser’s first name using gender/name data from the Social Security Administration (SSA) (1932–2012)

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Bai, J.(., Shang, C., Wan, C. et al. Social Capital and Individual Ethics: Evidence from Financial Adviser Misconduct. J Bus Ethics 181, 495–518 (2022). https://doi.org/10.1007/s10551-021-04910-4

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