Statistical Approaches to Pay Equity Issues: A Q&A with Affiliate Anthony Lo Sasso
In this Q&A, Vice President Mark Gustafson and Tony Lo Sasso, Professor in Health Policy and Administration at the University of Illinois at Chicago, discuss the recent wave of litigation and publicity related to alleged pay disparities, and the challenges companies face in accurately assessing their own pay practices.
Mr. Gustafson : It is well known that companies face the possibility of discrimination lawsuits whenever there are pay disparities between employees of different gender, race, age, etc. Beyond the scope of such litigation, are there other reasons for companies to address pay disparities?
Professor Lo Sasso: Yes. First, if a company is underpaying employees of a particular demographic, it may be hard to attract and retain quality employees.
Pay disparities can also have reputational consequences. Given the recent increased media coverage in the US on pay equity issues, companies with potential pay equity disparities run the risk of negative publicity. Many of these articles highlight examples of large differences in pay for women compared to men, typically looking at workers in a particular profession or at a particular company. For example, one article discusses a survey from the social networking platform, Doximity, that shows that women doctors earn 28 percent less than their male counterparts. Another recent article discusses Goldman Sachs’s apparent gender wage gap in Britain, where female employees receive an average wage and average bonus that are 44 percent lower and 72 percent lower than for male employees, respectively. Notably, such articles often do not consider those employees’ experience, hours worked, or other factors that may explain the difference, which can further exacerbate public perception of a company's pay practices.
Anthony Lo Sasso: Professor in Health Policy and Administration, University of Illinois at Chicago
On a more positive note, companies’ efforts to achieve pay equality can provide a significant asset for PR and marketing campaigns. In 2016, President Obama announced an Equal Pay Pledge, to which many businesses signed on. As part of this initiative, Apple stated that it had achieved “pay equity in the United States for similar roles and performance” and that “[w]omen employees at Apple earn one dollar for every dollar male employees earn.” In March 2018, Starbucks stated that it had achieved 100% pay equity by gender in the USand would maintain pay equity domestically as well as achieve it abroad. Starbucks stated that it achieved 100% pay equity by running regular checks on compensation and statistically analyzing raises and bonuses to ensure that they do not reflect bias.
Mr. Gustafson: How can companies proactively capitalize on the current attention this issue is receiving, while also preventing negative media coverage or discrimination lawsuits?
Professor Lo Sasso: A company can perform statistical analyses of its compensation using data it already has available. If differences are found in compensation between certain groups of employees, the company can use these findings to proactively target these disparities, potentially preventing future legal and PR issues and possibly even benefitting from positive PR.
Mr. Gustafson: What characteristics of employees or their jobs do researchers typically look at in examining pay equity?
Professor Lo Sasso: Hours worked, position, geography, and experience, among others. For doctors and other medical professionals, specialty and organizational structure (sole practitioner, group, etc.) are frequently analyzed as well.
Mark Gustafson: Vice President, Analysis Group
Mr. Gustafson: Are there any characteristics of jobs or employees that are difficult to observe and that can cause problems in correctly identifying pay disparities?
Professor Lo Sasso: Companies often struggle to properly account for employee productivity and experience. Other characteristics that pose a challenge might include any requirements to be “on call” or to work night and weekend shifts, as well as the quality, size, and exact location of the employer.
Mr. Gustafson: Can you describe an example of your research on pay equity issues?
Professor Lo Sasso: A paper I published in Health Affairs analyzed differences in pay by gender for newly trained physicians using several years of data from a survey of graduating residents and fellows in New York. Analyzing physicians who have just completed training programs ensures that the physicians studied have similar levels of experience. To ensure that the types of jobs are comparable, the paper studied only physicians who took clinical, non-training positions. The paper considered other factors as well, including patient care hours per week, specialty, geography, and the type of practice.
Mr. Gustafson: What characteristics of employees or their jobs might a company have information on that are not typically available to researchers? How could this information improve the analysis?
Professor Lo Sasso: Companies can increase the accuracy of pay equity analyses by including more specific information, such as: job title or a similar measure of the seniority of specific positions held by employees; experience in the current position and profession as a whole; productivity measures, such as amount billed or the amount the company was paid for a person’s work; employee reviews or other measures of employee quality; and the specific location where an employee works.
Mr. Gustafson: What types of companies can test for pay disparities between groups of employees, and which cannot? What distinguishes them?
Professor Lo Sasso: Generally, any company that has available data containing information related to employees and their pay can test for pay disparities. The biggest distinguishing factor between those companies that can do preliminary, imprecise studies and those that can more accurately analyze pay equity is the quantity and quality of the data available to the company. The larger the number of employees for whom data are available and the more precise and comprehensive the data collected, the more accurate the statistical analysis of pay practices will be.
Mr. Gustafson: Are methods similar or different between health care and other industries?
Professor Lo Sasso: The methods and general employee attributes of interest are similar, but the specific attributes needed to properly analyze pay equity may be different. Given that health care services can often be discretely measured and are associated with specific revenue amounts, more precise measurement of a worker’s productivity is feasible. However, health care pay equity analyses can also be complicated; for example, quality is highly valued by patients but frequently not directly observed by payers. Moreover, health care is often characterized by team-based work, and teasing out an individual’s contribution to a team can be challenging. Similar issues arise for other professionals such as dentists and lawyers. ■