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Self-regulation and governmental oversight: a theoretical and experimental study

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Abstract

A self-regulatory organization (SRO) is a non-governmental organization owned and operated by its members, with the power to create and enforce industry regulations and standards for its members. A key question is whether oversight by an SRO can replace governmental oversight, or whether supplementary governmental oversight is necessary. Using a formal model for the financial sector, and solving simultaneous games, I show that a lack of commitment by the SRO may necessitate governmental oversight of both SRO members and the SRO itself. The core of the model is supported by economics experiments.

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Notes

  1. This is because SROs operate in closer proximity to institutions in the various sectors and can therefore more readily audit their members and better interpret the information available. Maxwell et al. (2000) show that SROs may also increase welfare by preventing a costly influence game between industry and consumers.

  2. Gunningham and Rees (1997, p. 366) also use the term “co-regulation”.

  3. For example, in the US, many brokerage firms dealing in financial securities are regulated by the Financial Industry Regulatory Authority (FINRA), an SRO. In addition, a governmental regulator, the US Securities and Exchange Commission (SEC), oversees these brokers. The SEC also oversees many SROs, including FINRA. The SEC thus exerts both public parallel regulation and meta-regulation (SEC 2019).

  4. For example, Carson (2011) and Karmel (2008) claim that the oversight of FINRA (see footnote 3) by the SEC has become excessively strict and invasive and that FINRA has become overly bureaucratic and inflexible as a result.

  5. In an economics experiment, human decision-makers are exposed to economic laboratory environments with specific embedded incentives. This enables the evaluation of a theory under the “best shot” circumstances of the laboratory (Plott 1982, p. 1520; Smith 2007).

  6. The model is basically unaltered if the GOV maximizes some combination of agents' and customers' expected utility(DeMarzo et al. 2001, p. 9).

  7. The zero initial wealth and limited liability of agents imply that the maximum penalty on agents is bound and that agents cannot compete away the rents by paying customers to do business with them. The risk neutrality of customers abstracts from their demand for insurance in the optimal contract. See also DeMarzo et al. (2005) for more details.

  8. This assumption could be rationalized assuming the SRO is quicker to react than the GOV. Alternatively, assuming that the GOV moves first does not qualitatively change the results.

  9. When the GOV decides to investigate an agent, it first checks if the agent has already been investigated by the SRO. The total probability of an investigation is then equal to the probability of the SRO investigating, \(c_{S}\), plus the probability of the SRO not investigating, \(c_{S}\), times the conditional probability of the GOV investigating conditional on the SRO not investigating. This results in \(c_{S}\). See also footnote 17 in DeMarzo et al. (2005).

  10. The main result of Lemma 1 has been presented in DeMarzo et al. (2005).

  11. http://ssrn.com/abstract_id=3734310.

  12. Ibid.

  13. Ibid.

  14. The Corollary in Appendix A (http://ssrn.com/abstract_id=3734310) presents a formal proof that such a point exists.

  15. They show the same equilibrium, but as solutions by different regulators. The point on the left (right), where \(c_{S}\) (\(c_{S}\)), shows the outcome solved by the GOV (SRO) as a function of the SRO (GOV) investigation probability.

  16. http://ssrn.com/abstract_id=3734310.

  17. Informed consent was obtained for experimentation with human subjects and the privacy rights of the participants has been observed.

  18. http://ssrn.com/abstract_id=3734310. Neutral language was used to put the explicit reward structure at the center of the driver of behavior, and to avoid priming effects towards possible “socially desirable” actions (e.g., subjects may be more familiar with notions of regulation by government than by an SRO, and thus they may tend to choose higher investigation probabilities for the government). However, recent discussions (Alekseev et al 2017; Lima and Núñez 2015) indicate that also using contextual instructions may be a useful venture for future studies.

  19. The option "None" is equal to an investigation probability of zero. The investigation probability is then increased by 16.67% for each successive option. Thus, the option "Very Low" is equal to an investigation probability of 16.67%, the option "Low" to one of 33.33%, the option "Medium" to one of 50%, and so on. See Van Koten and Ortmann for further details (2016, p. 95, Fig. 1).

  20. The occurrence of extra Nash equilibria is the result of the relatively flat payoff function for GOV and the implementation in which players’ options are discrete. See Van Koten and Ortmann (2016, pp. 92–94) for further details.

  21. In 2011, 7 sessions also took place in which players made their choices sequentially, thus testing the predictions of DeMarzo et al. (2005). See Van Koten and Ortmann (2016, pp. 92–94) for further details.

  22. For an approximate estimate of the required number of independent observations for a sufficiently powerful test, I assume that the probability of success is 0.5 for an independent group (of 6 participants). For a power of 0.95, I allow a probability of 5% that a result may be obtained with an average at the level of random play (0.11) or less. This implies that the standard deviation of the average proportion should thus be no larger than 0.2. Using the formula \({\text{sd}} = (p(1-p)/n)^{0.5}\) and solving for n gives \(n = p(1-p)/{\text{sd}}^2 = 0.5 (1-0.5)/{0.2}^2 = 6.25\). The 12 independent observations per treatment collected are thus far more than sufficient.

  23. I thank one of the anonymous referees for raising this point.

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Acknowledgements

I thank Raúl Bajo; Paola Bertoli; Andreas Ortmann; the editor, Menahem Spiegel; and two anonymous referees for their insightful and helpful comments. I also thank Pete Kyle for hosting me during an academic visit at the University of Maryland. All errors remaining in this text are the responsibility of the author. Funding: This work was supported by UJEP-IGA-TC-2019-45-01-2 for “Economic Experiments” and the REGBES program CZ.02.2.69/0.0/0.0/16_018/0002727. An early part of the research was supported by the GACR grant agency. All experimental data are available at https://github.com/slvstr1/All_Data_SRO2.

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  1. Jan Evangelista Purkyně University (FSE-UJEP), Ústí nad Labem, Czech Republic

    Silvester van Koten

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van Koten, S. Self-regulation and governmental oversight: a theoretical and experimental study. J Regul Econ 59, 161–174 (2021). https://doi.org/10.1007/s11149-020-09421-0

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