Economics at the FTC: Office Supply Retailers Redux, Healthcare Quality Efficiencies Analysis, and Litigation of an Alleged Get-Rich-Quick Scheme


We discuss in this essay three of the matters on which economists in the Bureau of Economics (BE) at the Federal Trade Commission have worked this past year. BE revisited familiar ground in the first matter, a proposed merger of office supply retailers. The second part of the essay considers efficiency claims in health care mergers, with focus on the acquisition of a physician group by a health care system in Idaho. The final part of the essay discusses empirical work that was undertaken by the Bureau to investigate claims made by marketers of an alleged get-rich-quick scheme.

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  1. 1.

    See, for instance, a policy paper on the regulation of nurse practitioners (FTC 2014b) and a letter to the Centers for Medicare & Medicaid Services about the potential anticompetitive impact of proposed modifications of Medicare Part D rules,

  2. 2.

    Federal Trade Commission & US Department of Justice (2014c).

  3. 3.

    FTC Act, 15 USC § 46(f).

  4. 4.

    Including an ongoing study of Patent Assertion Entities,, and a study of self-regulation in the alcohol industry (FTC 2014a).

  5. 5.

    For example, a study of authorized generic drugs (FTC 2011) and a study of the use of credit scores in the pricing of automobile insurance policies (FTC 2007).

  6. 6.


  7. 7.


  8. 8.

    Check for the program.

  9. 9.

    See, for example, a Wall Street Journal study from December 12, 2012, that suggested Staples’ online prices were lower when the requesting computer was located near an ODP or OMX retail store, Valentino-DeVries et al. (2012).

  10. 10.

    See Murphy (2007).

  11. 11.

    See Ashenfelter et al. (2006) for a summary of the analyses that were conducted in Staples. The approach has the useful attribute of not requiring an a priori market definition. The set of stores included on the right-hand side need not be limited to those within any purported product market.

  12. 12.

    See Ashenfelter et al. (2006) for a description of the relative strengths of these approaches.

  13. 13.

    Intuitively, there is likely meaningful interaction between the brick-and-mortar and online retail segments. While our analyses did not explicitly model this interaction, the potential effect of online competition nonetheless was captured in our reduced-form results. For example, if consumers viewed brick-and-mortar and online suppliers as highly substitutable, this would have been reflected in our results since margins and prices would be less responsive to the entry/exit of competing brick-and-mortar stores.

  14. 14.

    We aggregated the weekly SKU-level data for two reasons: First, our SKU-level analyses involved estimating tens of thousands of fixed-effects panel regression models under the time constraints imposed by the HSR Act. Aggregating to 4-week periods significantly reduced the computational burden. Second, aggregating to 4-week periods reduced the number of missing observations in the price/cost time series within SKU-store combinations. Of course, the aggregation may have made the estimated treatment effects less precise. In addition, the aggregation may have engendered a bias towards zero in our results since some prices from the post-entry/exit period may have been averaged into the pre-entry/exit period. However, since, as discussed below, we controlled for the 4-week period that captured the entry/exit event, as well as the preceding 4-week period, we believe that the likelihood of meaningful bias due to aggregating to the 4-week period is minimal.

  15. 15.

    For example, we can analyze margins using only a single product category: office supplies, from which we cannot separate copy paper. Moreover, we cannot separately analyze margins for all office consumables, including ink/toner, because those products are not separated from other products in the technology department in the department level data.

  16. 16.

    Throughout all of our analyses, we constructed standard errors and p values of our estimates from an estimated covariance matrix that allows for arbitrary forms of correlation in the error term within stores, across time periods.

  17. 17.

    We also estimated regression models that specified a single marginal effect for entry/exit across a 30-min threshold. However, the implied parameter restrictions were generally rejected by the data.

  18. 18.

    In Staples, the FTC’s econometric expert captured the effect of local competition using the natural log of the number of competitors, as opposed to the square root. Because the natural log is not defined at zero, an indicator variable was added for the outcome in which there were no local stores of a given competitor. However, there was no within-store variation in this indicator variable in some of our specifications. Under this circumstance, the effect of closing all OMX (ODP) stores on ODP (OMX) prices or margins could not be predicted. Since the square root function is defined at zero, it does not require this added indicator variable. Hence, we adopted the square root specification here.

  19. 19.

    We also investigated larger time frames and found similar results.

  20. 20.

    Note that we did not apply this formula to the predicted margin changes constructed from the SKU-level data since, as described above, those margin levels were likely measured with significant error.

  21. 21.

    See Ashenfelter et al. (2006). The econometric evidence in Staples was consistent with the parties’ documents with regard to pricing strategies, the parties’ marketing materials, and the testimony of non-OSS vendors.

  22. 22.

    See Farrell et al. (2011) and Carlson et al. (2013).

  23. 23.

    Federal Trade Commission vs. St. Luke’s Health System, Ltd., Findings of Fact and Conclusions of Law, Case No. 1:13-CV-00116-BLW (D. Idaho Jan. 24, 2014); Alphonsus Medical Center—Nampa, Inc., et al. vs. St. Luke’s Health System, Ltd., 2014-1 Trade Cas. (CCH) P78,667.

  24. 24.

    See Pate (2013).

  25. 25.

    See, e.g., Gaynor (2014a, b), Feinstein (2014), and Brill (2014).

  26. 26.

    US Department of Justice and Federal Trade Commission (2010).

  27. 27.

    See Hussey et al. (2013).

  28. 28.

    Gaynor (2007), Vogt and Town (2006), and Gaynor and Town (2012).

  29. 29.

    See Romano and Balan (2011) for the retrospective quality analysis performed by Dr. Romano as an expert for the FTC in its successful retroactive suit against the Evanston–Northwestern hospital merger: In re Evanston Northwestern Healthcare Corp. FTC No. 9315 (August 6, 2007). See also the prospective merger analysis in the FTC’s administrative complaint in Inova Health System Foundation–Prince William Health System, which asserted .at ¶35: “Currently, the quality of PWHS’ services is comparable to, and at times superior to, the quality of Inova’s services, as measured by numerous objective quality criteria. Accordingly, Inova is unlikely to improve PWHS’ quality of service or to help generate other efficiencies sufficient to offset the Merger’s anticompetitive effects.”

  30. 30.

    See Romano and Balan (2011). It is important to look at both versions of statistics because differences or changes in coding of patient co-morbidities can make comparisons of risk-adjusted metrics misleading.

  31. 31.

    See, for example, the National Quality Forum’s endorsed quality measures for improving the quality of care.

  32. 32.

    See Halm et al. (2002). Economists have also looked at this; see Ho et al. (2007), Gowrisankaran and Town (2003), and Gaynor et al. (2005).

  33. 33.

    See Berwick et al. (2008).

  34. 34.

    See World Health Organization (2008).

  35. 35.

    See Jha et al. (2003).

  36. 36.

    See L&M Policy Research and Partners (2013). In the executive summary, the authors state that “The 8 (of 32) ACOs that reduced spending growth varied in geographic location, size, organizational structure, and average Medicare spending in their markets, suggesting that ACOs can achieve lower spending growth under a range of market conditions and organizational structures.”

  37. 37.

    The HITECH Act includes an incentive program for providers’ meaningful use of electronic health records. Stage 2 meaningful use criteria include interoperability measures. See

  38. 38.

    FTC vs. Dalbey, No. 11-cv-01396-RBJ-KLM (D. Colo.) (Final Pretrial Order) (filed May 9, 2013, Stipulation #30).

  39. 39.

    FTC vs. Dalbey, No. 11-cv-01396-RBJ-KLM (D. Colo.) (Final Pretrial Order) (filed May 9, 2013, Stipulation #39).

  40. 40.

    FTC vs. Dalbey, No. 11-cv-01396-RBJ-KLM (D. Colo.) (Defendants’ Motion to Exclude Plaintiffs’ Experts Dr. Manoj Hastak and Dr. Frederica Conrey) (filed Nov. 6, 2012).

  41. 41.

    We stratified at the $500 cutoff because: (1) given the pricing of DEI’s up-sells, spending more than $500 required purchasing at least one substantial additional training product or service; and (2) there was a sharp increase in the c.d.f. of customer expenditures around $500, naturally dividing customers into two distinct groups.


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We thank Michael Vita for helpful comments. The views 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.

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Correspondence to Martin Gaynor.

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Brand, K., Gaynor, M., McAlvanah, P. et al. Economics at the FTC: Office Supply Retailers Redux, Healthcare Quality Efficiencies Analysis, and Litigation of an Alleged Get-Rich-Quick Scheme. Rev Ind Organ 45, 325–344 (2014).

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  • Antitrust
  • Consumer protection
  • Fraud
  • FTC
  • Healthcare
  • Retailing