Skip to main content
Log in

Economics at the FTC: Spatial Demand, Veterinary Hospital Mergers, Rulemaking, and Noncompete Agreements

  • Published:
Review of Industrial Organization Aims and scope Submit manuscript

Abstract

The U.S. Federal Trade Commission enforces federal competition and consumer protection laws that prevent anticompetitive, deceptive, and unfair business practices, and works to advance government policies that protect consumers and promote competition. The FTC’s Bureau of Economics performs economic analysis to support both the enforcement and policy activities of the Commission. This article discusses several examples of these activities. We first discuss some work our economists have done on spatial considerations in demand estimation, and then present an analytical approach that has been developed to assess consumer choice between service providers with the use of data on geographic variation in the location of the customers of two merging service providers. We apply this technique in the context of the analysis of the competitive effects of a merger of veterinary hospitals. Next, we discuss an important tool in the FTC’s arsenal: rulemaking. We describe the benefits and costs of rulemaking, the rulemaking process, and the role of economic analysis in that process, and then highlight recent FTC rulemaking activities and the economic analysis of a proposed rulemaking that would ban employers from imposing non-compete clauses in employment contracts.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Availability of Data and Materials

The declaration is not applicable.

Notes

  1. See FTC press release, https://www.ftc.gov/news-events/news/press-releases/2022/06/ftc-launches-inquiry-prescription-drug-middlemen-industry (last accessed July 6, 2023).

  2. Conference materials are posted at https://www.ftc.gov/news-events/events/2023/11/sixteenth-annual-microeconomics-conference.

  3. See case materials at https://www.ftc.gov/legal-library/browse/cases-proceedings/221-0040-metazuckerbergwithin-matter, https://www.ftc.gov/legal-library/browse/cases-proceedings/2210077-microsoftactivision-blizzard-matter, https://www.ftc.gov/legal-library/browse/cases-proceedings/201-0144-illumina-inc-grail-inc-matter, https://www.ftc.gov/legal-library/browse/cases-proceedings/221-0142-intercontinental-exchange-incblack-knight-inc-matter, and https://www.ftc.gov/legal-library/browse/cases-proceedings/191-0075-altria-groupjuul-labs-matter.

  4. Raval et al. (2021) find that two machine-learning models—random forests and gradient boosting trees—outperform all of the other models at individual choice prediction, although the machine-learning models’ performance suffers when addressing patients who were most likely to go to the destroyed hospital and so experience a change in their choice set.

  5. Less than 5% of dogs and 2% of cats are insured. See https://naphia.org/wp-content/uploads/2023/05/NAPHIA-SOI2023-Report-Highlights_Public-May9.pdf for data on the number of insured dogs and cats in the US in 2022, and https://ebusiness.avma.org/files/ProductDownloads/eco-pet-demographic-report-22-toc-introduction.pdf for data on the number of pet dogs and cats in 2022 in the US.

  6. See the American Veterinary Medical Association’s reports on the profession at https://www.avma.org/resources-tools/reports-statistics. Specialists include all active board-certified diplomates as of December 2021.

  7. See https://www.veterinarypracticenews.com/24-hour-emergency-vet-clinics/.

  8. See https://www.ftc.gov/news-events/news/press-releases/2017/08/ftc-requires-mars-divest-12-veterinary-clinics-condition-acquiring-pet-care-company-vca-inc.

  9. See https://www.ftc.gov/legal-library/browse/cases-proceedings/2110140-jab-consumer-partnersnational-veterinary-associatessage-veterinary-partners-matter.

  10. This distance was chosen to be over-inclusive, as the draw areas for a typical emergency or specialty veterinary practice, for any area of specialty, are generally well-contained within the area that is formed by a 50-mile radius around the hospital.

  11. Manski and Lerman (1977) also discuss the same result, which they attribute to McFadden.

  12. This assumption could be adjusted accordingly if there were evidence of a price differential between hospitals.

  13. Academic studies, such as Miller et al. (2017), have shown that under certain, reasonable, demand forms, the pass-through rate is about “one"—which means that the GUPPI is a good approximation for the predicted price increase that would occur in the absence of any marginal cost efficiencies.

  14. AMG Capital Mgmt., LLC v. FTC, 141 S. Ct. 1341 (2021).

  15. See https://www.ftc.gov/system/files/documents/public_statements/1596664/agency_priorities_memo_from_chair_lina_m_khan_9-22-21.pdf.

  16. See https://www.ftc.gov/system/files/ftc_gov/pdf/565A_Complying%20with%20Funeral%20Rule_2023_508.pdf for the FTC’s guidance on complying with the Funeral Rule.

  17. See the FTC’s policy statement on unfair acts and practices, available here: https://www.ftc.gov/legal-library/browse/ftc-policy-statement-unfairness.

  18. The FTC regularly conducts undercover operations to detect funeral homes that do not abide by the rule. Violators can enter the Funeral Rule Offenders Program run by the National Funeral Directors Association in lieu of a potential FTC lawsuit. It provides participants with a legal review of the price disclosures that are required by the Funeral Rule, and on-going training, testing, and monitoring for compliance with the Rule. In addition, funeral homes that participate in the program make a voluntary payment to the U.S. Treasury in place of a civil penalty and pay annual administrative fees to the Association. See https://www.ftc.gov/news-events/news/press-releases/2010/03/undercover-inspections-funeral-homes-nine-states-washington-dc-press-funeral-homes-comply-consumer.

  19. However, if actors are risk averse, the uncertainty that occurs in the absence of rules may increase the likelihood of settlement.

  20. As FTC Chair Lina Khan states, “Although these tools [AI] are novel, they are not exempt from existing rules, and the F.T.C. will vigorously enforce the laws we are charged with administering, even in this new market.” See https://www.nytimes.com/2023/05/03/opinion/ai-lina-khan-ftc-technology.html?te=1&nl=dealbook&emc=edit_dk_20230516.

  21. However, firms still must predict accurately the requirements of the standard ex-ante for the standard to deter undesirable conduct, and the agency may have to bring enforcement actions quickly against firms that violate the standard.

  22. Diver (1983) discusses further issues when rulemaking is done by ordinary humans as opposed to a perfectly rational social planner.

  23. 15 U.S.C. § 57b-3.

  24. This guidance is provided in OMB’s Circular A-4, https://www.whitehouse.gov/wp-content/uploads/2023/11/CircularA-4.pdf.

  25.  Before the revisions, Circular A-4 stated that “You should report transfers separately and avoid the misclassification of transfer payments as benefits or costs. Transfers occur when wealth or income is redistributed without any direct change in aggregate social welfare.” After the recent revisions, Circular A-4 states that “A transfer payment, in its simplest form, is a shift in money (or other item of value) from one party to another. More generally, when a regulation generates a gain for one group and an equal-dollar-value loss for another group, the regulation is said to cause a transfer from the latter group to the former.” The recent revisions to Circular A-4 stress examining distributional effects of rules; see Section 9 and 10 of Circular A-4 for a discussion of transfers and distributional effects.

  26. Here, we rely on the Unified Agenda of Federal Regulatory and Deregulatory Actions: This is a semi-annual resource for the public to preview all federal rulemakings anticipated to occur within a 12-month time frame or beyond if they are listed as long-term actions. Our count excludes the annual “Regulatory Review” notification listing. The current Unified Agenda is available at: https://www.reginfo.gov/public/do/eAgendaMain.

  27. Reviews or modifications of existing rules are conducted as part of the FTC’s ongoing 10-year review program that is modelled after provisions in the Regulatory Flexibility Act, 5 U.S.C. 601–612 in compliance with the Small Business Regulatory Enforcement Fairness Act of 1996.

  28. See Parnes and Jennings (1997) and Lubbers (2014) for more on the history of FTC rulemaking.

  29. See, e.g., Lipsitz and Starr (2022), Starr (2019), and Johnson and Lipsitz (2022).

  30. See, e.g., Lipsitz and Tremblay (2022) and Starr et al. (2018).

  31. See, e.g., Johnson et al. (2023) and Baslandze (2022).

  32. National annual earnings of $7577.3 billion are taken from Bureau of Labor Statistics, Employment and Wages Data Viewer (last visited Dec. 9, 2022), available at https://data.bls.gov/cew/apps/data_views/data_views.htm#tab=Tables. The calculation used data from private employers in 2020 (the most recent year with finalized numbers at the time of calculation).

  33. The discount rate is the more conservative of the two options recommended in the previous version of Circular A-4, the Office of Management and Budget’s guidelines for regulatory impact analysis. The time horizon was selected to encompass a reasonable timeframe during which costs and benefits may persist, while still considering a timeframe during which the agency can reasonably make predictions. See the Appendix for more details on discounting benefits and costs in regulatory analysis.

  34. See Section 3(f)(1)–(4) for the definition of a “significant regulatory action” in Executive Order 12866, issued September 30, 1993, available at https://www.reginfo.gov/public/jsp/Utilities/EO_12866.pdf, as amended by the April 6, 2023 Executive Order on “Modernizing Regulatory Review”, available at: https://www.whitehouse.gov/briefing-room/presidential-actions/2023/04/06/executive-order-on-modernizing-regulatory-review/. On January 21, 2011, Executive Order 13,563, available at https://www.reginfo.gov/public/jsp/Utilities/EO_13563.pdf, reaffirmed the principles in E.O. 12866. The list of Executive Departments can be found in 5 U.S.C. § 101.

  35. See Section 22 of the FTC Act, which applies to all FTC rules that are promulgated under Sections 6 or 18 of the FTC Act, except for those “involving Commission management or personnel, general statements of policy, or rules relating to Commission organization, procedure, or practice”.

  36. 15 U.S.C. § 57b-3.

  37. Available at https://www.regulations.gov/.

  38. See https://www.congress.gov/93/statute/STATUTE-88/STATUTE-88-Pg2183.pdf.

  39. 15 U.S.C. 57a(a)(1)(B).

  40. Available at: https://www.whitehouse.gov/wp-content/uploads/2023/04/DraftCircularA-4Preamble.pdf.

  41. The shadow price of capital is the ratio of the gross rate of return on capital over the sum of the consumption discount rate plus the capital depreciation rate. The previous Circular A-4 guidance recommended using two default discount rates: 3 percent and 7 percent. The 3 percent discount rate approximated the rate that the average saver uses to discount future consumption based on the real rate of return on long-term government debt as a measure of a “social rate of time preference” (SRTP). The 7 percent rate was an estimate of the “average before-tax rate of return to private capital in the U.S. economy” so as to approximate the opportunity cost of capital.

  42. Available at: https://www.reginfo.gov/public/jsp/Utilities/circular-a-4_regulatory-impact-analysis-a-primer.pdf.

References

  • Ashenfelter, O. C., Hosken, D. S., & Weinberg, M. C. (2015). Efficiencies brewed: Pricing and consolidation in the US beer industry. The RAND Journal of Economics, 46(2), 328–361.

    Article  Google Scholar 

  • Balan, D. J., & Brand, K. (2023). Simulating hospital merger simulations. The Journal of Industrial Economics, 71(1), 47–123.

    Article  Google Scholar 

  • Barrette, E., Gowrisankaran, G., & Town, R. (2022). Countervailing market power and hospital competition. Review of Economics and Statistics, 104(6), 1351–1360.

    Article  Google Scholar 

  • Baslandze, S. (2022). Entrepreneurship through employee mobility, innovation, and growth. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4277191

  • Brand, K., Garmon C., & Rosenbaum T. (forthcoming). In the Shadow of antitrust enforcement: Price effects of hospital mergers from 2009–2016. Journal of Law and Economics.

  • Brand, K., & Rosenbaum, T. (2018). A review of the economic literature on cross-market health care mergers. Antitrust LJ, 82, 533.

    Google Scholar 

  • Capps, C., Dranove, D., & Satterthwaite, M. (2003). Competition and market power in option demand markets. RAND Journal of Economics, 854, 737–763.

    Article  Google Scholar 

  • Chopra, R., & Khan, L. M. (2020). The case for “unfair methods of competition” rulemaking. The University of Chicago Law Review, 87(2), 357–380.

    Google Scholar 

  • Diver, C. S. (1983). The optimal precision of administrative rules. Yale LJ, 93, 65.

    Article  Google Scholar 

  • Ehrlich, I., & Posner, R. A. (1974). An economic analysis of legal rulemaking. The Journal of Legal Studies, 3(1), 257–286.

    Article  Google Scholar 

  • Einav, L., Finkelstein, A., & Gupta, A. (2017). Is American pet health care (also) uniquely inefficient? American Economic Review, 107(5), 491–495.

    Article  Google Scholar 

  • Farrell, J., Balan, D. J., Brand, K., & Wendling, B. W. (2011). Economics at the FTC: Hospital mergers, authorized generic drugs, and consumer credit markets. Review of Industrial Organization, 39, 271–296.

    Article  Google Scholar 

  • Garmon, C. (2017). The accuracy of hospital merger screening methods. The RAND Journal of Economics, 48(4), 1068–1102.

    Article  Google Scholar 

  • Gowrisankaran, G., Nevo, A., & Town, R. (2015). Mergers when prices are negotiated: Evidence from the hospital industry. American Economic Review, 105(1), 172–203.

    Article  Google Scholar 

  • Hiraiwa, T., Lipsitz, M., & Starr, E. (2023). Do firms value court enforceability of noncompete agreements? A revealed preference approach. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4364674

  • Ho, K. (2006). The welfare effects of restricted hospital choice in the US medical care market. Journal of Applied Econometrics, 21(7), 1039–1079.

    Article  Google Scholar 

  • Hosken, D., & Tenn, S. (2016). Horizontal merger analysis in retail markets. In E. Basker (Ed.), Handbook on the economics of retailing and distribution. Edward Elgar Publishing.

    Google Scholar 

  • Jaffe, S., & Weyl, E. G. (2013). The first-order approach to merger analysis. American Economic Journal: Microeconomics, 5(4), 188–218.

    Google Scholar 

  • Johnson, M. S., Lavetti, K., & Lipsitz, M. (2021). The labor market effects of legal restrictions on worker mobility. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3455381.

  • Johnson, M. S., Lipsitz, M., & Pei, A. (2023). Innovation and the enforceability of noncompete agreements (No. w31487). National Bureau of Economic Research.

  • Johnson, M. S., & Lipsitz, M. (2022). Why are low-wage workers signing noncompete agreements? Journal of Human Resources, 57(3), 689–724.

    Article  Google Scholar 

  • Kaplow, L. (1992). Rules versus standards: An economic analysis. Duke LJ, 42, 557.

    Article  Google Scholar 

  • Lau, C. (2023). DIVRATIO: Stata module for estimating diversion ratios and willingness-to-pay from a semiparametric choice model. https://EconPapers.repec.org/RePEc:boc:bocode:s459169

  • Lipsitz, M., & Tremblay, M. J. (2021). Noncompete agreements and the welfare of consumers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975864

  • Lipsitz, M., & Starr, E. (2022). Low-wage workers and the enforceability of noncompete agreements. Management Science, 68(1), 143–170.

    Article  Google Scholar 

  • Lubbers, J. S. (2014). It’s time to remove the mossified procedures for FTC rulemaking. George Washington Law Review, 83, 1979.

    Google Scholar 

  • Manski, C. F., & Lerman, S. R. (1977). The estimation of choice probabilities from choice based samples. Econometrica, 1977–1988.

  • McFadden, D. L. (1984). Econometric analysis of qualitative response models. Handbook of Econometrics, 2, 1395–1457.

    Article  Google Scholar 

  • Miller, N. H., Remer, M., Ryan, C., & Sheu, G. (2017). Upward pricing pressure as a predictor of merger price effects. International Journal of Industrial Organization, 52, 216–247.

    Article  Google Scholar 

  • Panhans, M. T. (2023). healthcare.antitrust: Healthcare antitrust analysis. https://cran.r-project.org/web/packages/healthcare.antitrust/index.html

  • Parnes, L. B., & Jennings, C. J. (1997). Through the looking glass: A perspective on regulatory reform at the federal trade commission. Administrative Law Review, 49, 989.

    Google Scholar 

  • Grosz & Raval (2023). Fraud across borders. SSRN 4333120.

  • Raval, D., & Rosenbaum, T. (2018). Why do previous choices matter for hospital demand? Decomposing switching costs from unobserved preferences. Review of Economics and Statistics, 100(5), 906–915.

    Article  Google Scholar 

  • Raval, D., & Rosenbaum, T. (2021). Why is distance important for hospital choice? Separating home bias from transport costs. The Journal of Industrial Economics, 69(2), 338–368.

    Article  Google Scholar 

  • Raval, D., Rosenbaum, T., & Tenn, S. A. (2017). A semiparametric discrete choice model: An application to hospital mergers. Economic Inquiry, 55(4), 1919–1944.

    Article  Google Scholar 

  • Raval, D., Rosenbaum, T., & Wilson, N. E. (2021). How do machine learning algorithms perform in predicting hospital choices? Evidence from changing environments. Journal of Health Economics, 78, 102481.

    Article  Google Scholar 

  • Raval, D., Rosenbaum, T., & Wilson, N. E. (2022). Using disaster-induced closures to evaluate discrete choice models of hospital demand. The RAND Journal of Economics, 53(3), 561–589.

    Article  Google Scholar 

  • Sacher, S., & Simpson, J. (2020). Estimating incremental margins for diversion analysis. Antitrust Law Journal, 83(2), 527–556.

    Google Scholar 

  • Starr, E. (2019). Consider this: Training, wages, and the enforceability of covenants not to compete. ILR Review, 72(4), 783–817.

    Article  Google Scholar 

  • Starr, E., Balasubramanian, N., & Sakakibara, M. (2018). Screening spinouts? How noncompete enforceability affects the creation, growth, and survival of new firms. Management Science, 64(2), 552–572.

    Article  Google Scholar 

  • Wollmann, T. G. (2020). How to get away with merger: Stealth consolidation and its effects on us healthcare (No. w27274). National Bureau of Economic Research.

Download references

Acknowledgements

We thank Marie Choi, Rich Gold, Louis Kaplow, Josephine Liu, Sarah Mackey, Aviv Nevo, Karuna Patel, Dave Schmidt, Aileen Thompson, James Weiss, Larry White, and Maggie Yellen for helpful comments. We especially acknowledge the contributions of BE economists Ted Rosenbaum and Viola Chen in developing the approach to veterinary hospital merger analysis. The views that are expressed in this article are those of the authors and do not necessarily reflect those of the Federal Trade Commission or any of the individual commissioners.

Funding

The declaration is not applicable.

Author information

Authors and Affiliations

Authors

Contributions

Aviv Nevo is currently the Director of the Bureau of Economics, but much of the work that underlies this article preceded his tenure in that role. Devesh Raval is the Deputy Director who was overseeing the Bureau’s research while this article was being drafted and is the corresponding author. All authors reviewed the manuscript. A.F. wrote the veterinary merger section, D.R. wrote the spatial demand section, N. L. and D.R. wrote the rulemaking section, N.L. wrote the rulemaking appendix, and M.L. wrote the non-compete section.

Corresponding author

Correspondence to Devesh Raval.

Ethics declarations

Conflict of interest

The authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Ethical Approval

The declaration is not applicable.

A Appendix: Rulemaking Details

A Appendix: Rulemaking Details

1.1 A.1 Elements of the Rulemaking Process

Most federal rulemakings, including certain types of FTC rulemakings, are governed by the notice-and-comment process under the Administrative Procedures Act (APA). Under the APA, rulemakings usually begin with a general “notice of proposed rulemaking” (NPRM), or “proposed rule”, which is published in the Federal Register so as to let the public know that it plans to commence rulemaking. The preamble of the proposed rule: explains an agency’s statutory authority for rulemaking; summarizes the issues that it seeks to address and why a rule is necessary; and provides the details of its proposal. The NPRM also includes the proposed “regulatory text”, which sets out the amendments to the standing body of law in the Code of Federal Regulations. The NPRM also invites the public to provide comments, supporting evidence, and data to inform the rulemaking.

A key feature of many federal rulemakings is the requirement for agencies to consider both costs and benefits of the rule before implementing new regulatory actions and to make that underlying analysis public. If a rule is determined to be a “significant regulatory action”, the executive branch or cabinet-level departments and agencies are required to submit their rulemaking along with its assessment and underlying analysis of costs and benefits for review by the Office of Management and Budget.Footnote 34 While “independent” agencies, such as the FTC, are not subject to the same review and analytical requirements as are agencies that are within the executive branch of the federal government, the rulemaking provisions that are covered by the FTC Act embed analytical requirements into its own rulemaking procedures.Footnote 35 Accordingly, rules that have “an annual effect on the national economy of $100,000,000 or more” should include a regulatory analysis that projects benefits and any adverse economic effects or consequences, as well as that of any alternative approaches.Footnote 36

The purpose of a regulatory analysis is to inform the development and design of the regulation with consideration of social benefits and social costs and for transparency to the public of the likely effects of the proposal. A prospective cost–benefit analysis provides a systematic approach for comparing benefits and costs of a policy intervention and uses techniques and methods from economics and statistics to predict the impacts of the rule—including any unintended consequences. The economic analysis (which is also commonly known as a “regulatory impact analysis” or RIA) of the proposed rule is made available for public review and comment. It is usually either found in a section of the preamble or referenced as a standalone document that is included in the public docket for the rulemaking.Footnote 37

Before issuing a proposed rule, an agency may also gather information and increase public participation through an “advance notice of proposed rulemaking” (ANPRM) in the Federal Register as a preliminary step. This may be done to engage industry, consumer groups, and any other interested parties in a public dialogue on specific needs for a rulemaking or to gather additional data, when it is not yet clear what should be proposed. Any interested individual or group may submit: comments on their perspectives on whether a rule is needed; their concerns; and any supporting data so that the agency can consider these comments as part of the rulemaking record in developing the draft proposal.

Under informal rulemaking procedures under the APA, which applies to rules that address “unfair methods of competition”, it would not be necessary to issue an ANPRM prior to an NPRM; however, under the Magnuson-Moss Warranty—Federal Trade Commission Improvement Act of 1975, Congress imposed more stringent rulemaking procedures for rules that address “unfair and deceptive practices” as described in Section 18 of the FTC Act.Footnote 38 With “Section 18 rulemakings” or “trade regulation rules” that address “unfair or deceptive” practices, the FTC must issue an ANPRM and take additional steps under the Magnuson-Moss rulemaking procedures.Footnote 39

For these rules, the FTC must publish an ANPRM before publishing the NPRM, as well as provide an opportunity for an informal hearing after publishing the proposed rule. The ANPRM engages the public early in the process before the agency has reached its tentative conclusion on the proposal and usually includes a series of open-ended questions to which the public may respond. Those comments are reviewed and considered through the process of developing specific provisions of the proposal and may inform development of the preliminary regulatory impact analysis for the proposed rule.

Comments that are filed during the open comment period, data, and other evidence that is collected during the ANPRM and NPRM stages contribute to the “rulemaking record”, which forms the basis for the agency’s reasoned decision-making for a final rule that would then become legally binding upon its effective date. A published final rule notice is structured similarly to the NPRM—except that it also includes the agency’s summary and assessment of the significant issues that were raised by public comments in response to the NPRM and a final regulatory analysis. The final rule may differ from the proposed rule so long as it is a “logical outgrowth” of the approach discussed in the NPRM.

As with the ANPRM stage, public comments and data that are submitted during the public comment period for the proposed rule can be useful for filling in data gaps and informing the final regulatory impact analysis. Therefore, the changes that are made between the proposed rule and the final rule that are based on consideration of public comments may also result in corresponding changes to the economic analysis of the final rule.

1.2 A.2 Economic Analysis of Rulemakings

The text described five key components to the economic analysis of rulemakings. These components are discussed in more detail below:

1.2.1 A.2.1 Identifying the Need for Federal Regulatory Action

A regulatory impact analysis usually begins with a section that characterizes the nature of the underlying problem that the rule addresses and an economic rationale for why a regulatory intervention would be necessary. An economic framing of the problem involves describing any market failures—such as an externality, public goods, market power, or inadequate or asymmetric information—and the degree to which the regulatory action may correct such distortions. The recent revisions to Circular A-4 (this document is referenced in the main body of the text above) go beyond the list of traditional sources of market failures: for example, to include behavioral biases—where there may be limitations in information processing and systematic decision-making biases—and other social purposes, such as equity and fairness considerations.

The purpose of the “need for regulation” discussion is to develop the theoretical and conceptual framework as a starting point for further examination of the evidence, data, and empirical literature on the magnitude of the problem and to determine the linkage between the proposed regulation and its potential effects. While there may be other reasons for requiring a regulatory action that are discussed in the preamble of a rule—such as a statutory mandate or other legal purposes—they may be less informative for grounding the economic analysis and identifying potential sources for welfare gains.

1.2.2 A.2.2 Defining the Analytical Baseline

All costs, benefits, and transfers of a rule are measured against a baseline that represents the future state of the world in the absence of the rule; this is sometimes referred to as the “no action baseline”. While the status quo or current conditions are used as a proxy to forecast the future, the baseline is dynamic and should ideally account for any market trends, technological advancements, and other changes that would have occurred in the counterfactual. Selecting a time horizon for the analysis should balance the length of time that is needed to capture the effects of the rule with the ability to forecast accurately the future baseline as uncertainty grows with a longer time horizon. Circular A-4 doesn’t prescribe a specific time horizon for regulatory analyses but recommends that it should be long enough to encompass most of the important effects—this would mean 10–20 years for most rules that have more immediate effects.

The baseline usually includes estimates (over the period of analysis) of: the number of regulated entities and industries that would be affected; the size of the market that would be affected; and the number of individuals that would be affected. To the extent that more granular data are available, disaggregating by subgroups and categories can be useful for refining the scope of the rule and identifying potential alternatives. If the rule addresses specific adverse outcomes, the analysis should provide evidence about those baseline risks and quantify them to the extent possible. To lay the groundwork for estimating benefits, it is important to define the baseline for any relevant endpoints, measures, and outcomes that will be used to characterize the effectiveness of a rule.

Another consideration for the baseline is the degree to which regulated entities may already comply through state and local rules, international standards and regulations, current industry best practices, or other market pressures. Since the goal of a regulatory analysis is to estimate the incremental effects that are attributable to the rule, any voluntary compliance—if independent of the rule,—should be reflected as part of the baseline rather than counted as an incremental effect (costs, benefits, and transfers) that is attributable to the rule. When the timing of voluntary compliance overlaps with announced plans for a potential rulemaking or when there has been a substantial amount of public discourse about a forthcoming policy, it can be challenging to distinguish movement towards compliance that is due to anticipation of the rulemaking—in which case corresponding costs and benefits should be attributable to the rule—from compliance that would have occurred in the absence of a rule.

The preamble to the proposed revisions to and the updated Circular A-4 highlight some of these issues and suggest that when there is uncertainty about the correct baseline, the effects could be assessed against multiple baselines so as to determine the sensitivity of the results.Footnote 40

1.2.3 A.2.3 Identifying Regulatory Approaches

In considering potential regulatory alternatives, the initial baseline analysis sets the foundation for assessing how variations and altering parameters of the rule will affect benefits and costs. Feasible regulatory alternatives should consider different ways to achieve the regulatory objectives with the least amount of burden and unintended consequences. Recognizing the practical limitations on the number of alternatives that can be realistically assessed, Circular A-4 recommends assessing at least one option that is more stringent and one that is less stringent as compared with the preferred option.

If there are multiple distinct provisions or requirements within a rule, ideally the analysis would assess the costs and benefits that are attributable to each discrete provision. A provision-by-provision analysis of the incremental costs and benefits would facilitate a comparison of alternatives in determining which subset of provisions may be the most net beneficial. In some circumstances, the costs and benefits of individual provisions may not be distinguishable from that of others or only partially separable. It is also useful to analyze the rule’s effects according to its key provisions so as to show how the costs, benefits, and transfers of the rule would change if any were to be eliminated. With the goal of helping agencies identify potential regulatory alternatives that may maximize net benefits, Circular A-4 describes the following approaches and dimensions of a rule that could be varied to reduce burden:

  • Market-oriented approaches and direct controls

  • Performance standards and design standards

  • Informational measures and nudges

  • Different choices defined or identified by statute

  • Different methods to ensure compliance

  • Different degrees of stringency

  • Different compliance dates

  • Pilot projects, data collection, and learning through variation

  • Requirements that are based on geographic regions

1.2.4 A.2.4 Measuring Benefits, Costs, and Transfers of the Rule and Alternatives

The regulatory analysis should describe anticipated incremental benefits, costs, and transfers that are associated with the preferred regulatory option and any reasonable alternatives. Estimating costs, benefits, and transfers involves predicting the behavioral changes that are likely to arise as a consequence of a rule’s requirements and valuing those changes. The analysis should consider direct compliance costs as well as any important indirect costs that are attributable to the rule, such as any adverse or countervailing effects that are not captured in the direct costs. Benefits of a rule are usually favorable effects that correspond with the overall objectives of the rule. For example, a rule that addresses information asymmetry may result in benefits that include reduced consumer search cost and welfare gains that arise from eliminating any distortions in equilibrium pricing.

Benefits and costs should reflect changes in real resource use, whereas transfers reflect effects on one group that are offset by its opposite effects on another group and do not affect net gains in social welfare. While a transfer is not counted as a net cost or benefit, the regulatory analysis should provide a separate description of the distributional effects to show whose losses may be offset by another group’s gains, as well as the incidence of costs and benefits since those who bear the costs may be different than the ones accruing the benefits.

The analysis should capture all important practical effects and consequences of the rule. Circular A-4 recommends that the analysis present separate schedules of monetized benefits, costs, and transfers to show the type and timing of undiscounted impacts. Benefits, costs, and transfers should be quantified and monetized to the extent possible. The analysis should also identify benefits and costs that can be quantified, but not monetized, including their timing. Circular A-4 recommends presenting “benefits and costs in physical units in addition to monetary units” for transparency of the analysis. For benefits and costs that cannot be quantified, the analysis should provide a qualitative description of those effects and explain why they cannot be quantified.

The following are examples of effects that should be considered, quantified, and monetized where possible:

  • Private sector, including industry, compliance costs and savings

  • Government administrative costs and savings

  • Gains or losses in consumers’ or producers’ surpluses

  • Discomfort or inconvenience benefits and costs

  • Gains or losses of the opportunity cost of time such as work or leisure

To account for differences in the timing of the effects, the costs, benefits, and transfers are normalized across multiple time periods with the use of “discount rates” and expressed as “discounted present values” and “annualized values”. The current Circular A-4 guidance, published on November 9, 2023, recommends using a default discount rate of 2 percent, which reflects the real rate of return on long-term government debt over the last thirty years as a measure of the “social rate of time preference” (SRTP)-the rate at which society is willing to trade current consumption for future consumption. OMB indicates that they plan to publish updates to this estimate every three years in the Appendix to Circular A-4. 

If there is substantial displacement of capital anticipated, Circular A-4 recommends using a “shadow price of capital” approach to generate consumption-equivalent effects before discounting.  As a default, the guidance recommends applying 1 as a lower value and 1.2 as a high value.Footnote 41

The prospective nature of regulatory analyses makes it particularly challenging to quantify and monetize the anticipated consequences of a rule. The analysis should rely on “the best reasonably obtainable scientific, technical, economic, and other information to quantify the likely benefits and costs of each regulatory alternative.”Footnote 42 Data sources for the analysis may be publicly available or from confidential agency or proprietary data sources with the appropriate level of aggregation. While primary research and pilot studies that are specific to the rule’s context would be most informative for predicting the effects of a rule, usually time and resource constraints necessitate relying on existing studies and benefit-transfer methods for key parameters and values for the analysis.

Interrelationships between key parameters to develop estimates of costs and benefits may also involve making reasonable assumptions when there is a lack of data or when extrapolations from similar contexts are needed. A best practice for regulatory analysis is to ensure that all methods, data sources, assumptions, and any limitations or caveats are transparent. Specific references where available and technical appendices should be provided. When assumptions are necessary, it is important to provide the basis and rationale for those assumptions. For a preliminary regulatory impact analysis, agencies can also explicitly request public comment supported by data on any uncertain parameters and assumptions to inform changes for the final regulatory analysis. If there is uncertainty in underlying estimates or values, Circular A-4 recommends presenting a range of plausible values in addition to a primary estimate.

A supplemental section with an analysis of uncertainty and sensitivity could examine potential scenarios that encompasses the range of how the benefits and costs of the rule could vary. Sensitivity analysis is useful for identifying key drivers of costs and benefits and how the results change when those parameters vary. When important costs and benefits are difficult to quantify or monetize, there should be an explanation of why they cannot be fully monetized and a presentation of any available quantitative information.

Often it is easier to quantify and monetize compliance costs than the benefits of the rule. In such circumstances, agencies can consider conducting a breakeven analysis or a threshold analysis so as to find the value of a key parameter that yields positive net benefits. While a breakeven analysis addresses the question of how large non-quantified benefits would need to be for total benefits to equal costs, it cannot quantify its likelihood. If feasible and appropriate, a more formal treatment of uncertainty may involve probabilistic analysis of the key uncertainties using simulation models.

1.2.5 A.2.5 Summarizing the Regulatory Analysis

A regulatory analysis should include a plain language summary of the effects of the rule and include summary tables with estimates of benefits, costs, and transfers for the preferred regulatory option and alternatives considered. In organizing categories of benefits and costs, many agencies use a standardized accounting statement, as provided in Circular A-4, where benefits and costs are reported separately from transfers and other distributional effects. Benefits and costs are further categorized as: (1) monetized; (2) quantified but non-monetized; and (3) unquantified.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ferguson, A., Lew, N., Lipsitz, M. et al. Economics at the FTC: Spatial Demand, Veterinary Hospital Mergers, Rulemaking, and Noncompete Agreements. Rev Ind Organ 63, 435–465 (2023). https://doi.org/10.1007/s11151-023-09930-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11151-023-09930-0

Keywords

Navigation