No Poaching

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Key Points

  • Fideres’s investigation found preliminary economic evidence to suggest widespread wage suppression in occupations within the healthcare and fast-food sectors between 2012 and 2016.
  • Specifically, we have found evidence of wage suppression for nurses, radiologists and food preparation supervisors in over 30 US states.
  • Highly concentrated industries, restraints caused by specialized qualifications and low unionization rates may facilitate wage suppression induced by anticompetitive measures such as ‘no-poach agreements’.

No-Poach Agreements

While stagnating wages can be a result of competitive forces, they can also be indicative of anticompetitive behavior between colluding employers. One way employers can collude are via no-poach agreements, whereby competing firms typically agree not to solicit or hire each others employees.

In recent years, no-poach agreements have drawn increased scrutiny, particularly in the healthcare and fast-food sectors. Notable recent filings in these sectors include Seaman v. Duke University et al. and Stigar v. Dough Dough, Inc, et al. respectively.

Moreover, in July 2018 seven large fast food chains including McDonald’s settled with the Washington State attorney general’s office to remove no-poach provisions from franchise agreements.

Despite continued filings and settlements, by nature these agreements are often hard to uncover without insider information. As a result, Fideres has developed a methodology that utilizes a combination of plus factors and sector-specific wage data to identify occupations in which wages may have been suppressed by no-poach agreements.

Plus Factors Analysis and Wages

Are certain occupations more vulnerable to anticompetitive no-poach agreements?

‘Plus Factors’ are criteria that, if met, not only make no-poach agreements easier to implement and enforce, but increase the potential severity of wage suppression if implemented. Examples of these criteria include:

  • A highly concentrated industry: in concentrated industries with a small number of employers, such agreements are easier to arrange and implement
  • Qualifications constraints can restrict employee movement between states within a given industry. For example, a nurse typically cannot practice in different states without having multiple licenses
  • Previous wage suppression cases filed within the same sector, but in another state for example
  • Franchising agreements, such as those observed in the fast-food sector, can have terms written into them that prevent franchises from hiring each others employees


After screening over 700 occupations, our model flagged nurses, radiologists and food preparation supervisors as having experienced significant wage suppression, relative to state wage benchmarks, in at least 30 states from 2012-16. These occupations also met at least one plus factor criteria.

Occupation No. States with Estimated Wage Supression
Registered Nurses 47
Radiologic Technologists 43
Licensed Practical and Vocational Nurses 42
First-Line Supervisors of Food Preparation/Serving Workers 32

In some states, wage growth for registered nurses was almost 10% lower than the state wage average.

Estimating Size of Suppression

While a sophisticated wage suppression model would require firm-level payroll data to calibrate, our current model utilizes public wage and employment data to estimate the size of wage suppression in the US. Accounting for state wage inflation and the number of employees within a given occupation, among other factors, we estimated the size of suppression for the previously outlined occupations.

Occupation Estimated Size of Wage Suppression ($bn)
Registered Nurses 10.2
Licensed Practical and Vocational Nurses 2.22
First-Line Supervisors of Food Preparation/Serving Workers 1.23
Radiologic Technologists 0.97

These estimates are based on US-wide wage suppression, however wage suppression induced by a no-poach agreement may have a narrower geographical impact.

Model Application

While our model can estimate wage suppression at both national and state level, it is particularly useful at analysing suppression in metropolitan statistical areas (MSAs). This is due to the fact that it is often possible to attribute MSA wage data to salaries being paid by a small number of local employers.

In light of this, we decided to take a closer look at wages for occupations covered by a historical no-poach case involving hospitals within particular MSAs.

During the alleged class period, our model found three different healthcare occupations to have suffered from significant wage suppression in the metropolitan statistical areas in which the hospitals were based:

Occupation Average Estimated Wage Suppression %
Family and General Practitioners 25.61%
Health Information Technicians 15.41%
Nursing Assistants 7.89%

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