The Gender Pay Gap ... Would YOU Accept this Compensation Data?

This November politicians will be considering legislation to address what they claim to be a 23% pay gap between males and females.  The politicians now also claim there is a 19% gender pay gap in the job family of "manager."  The proposed Paycheck Fairness Act will significantly reduce employer defenses under the Equal Pay Act and make unlimited punitive and compensatory damages available for violations, even if they are unintentional.

So let's get to the very heart of this latest pay gap claim and the underlying calculations to see if there is a credible foundation for these conclusions.

The gender pay gap calculations come from the US Census Bureau data collected under the American Community Survey.  The survey form can be found on-line, but I have provided the key survey questions #39-41 on this blog.

Job Content Information  

Respondents provide job content data on Questions #39 and 40, with enough space for perhaps 2 or 3  words at most for each question.  Two filters occur in the collection and analysis of this data.

The first set of filters is from the person(s) completing the survey form.  There are enough spaces on the form for 5 people in the household.  There is no certainty who in the household is completing the form -- does he/she fully understand questions #39 and 40; and does he/she knows each resident's job content.  What if some of the household residents work two or more jobs that are dissimilar in nature - what will the person report in the space provided?

The second filter is the US Census analyst's ability to take the brief information and categorize it into 1,000 different pigeon-hole jobs that the US Census provides for their data.  Compensation Cafe reader, would you have enough information from Questions #39 and 40 to understand the job(s) that the respondent is reporting?  Seasoned compensation professionals know that you need to dig further than one or two words to understand a job's content.

Compensation Data 

Survey repondents provide pay data in the space provided on Question #41 for up to 5 residents in the  household.  Note the directions for Question #41:

• "Give your best estimate ..."  Well strict accuracy isn't necessary is it?!   
• "For income received jointly, report the appropriate share for each person - or, if that's not possible, report the whole amount for only one person and mark the "No" box for the other person."  Huh?!  Have you ever completed a census form?  Did you always read the fine-print instructions?   
• "Wages, salary, commissions, bonuses, or tips from all jobs.  Report amount before deductions for taxes, bonds, dues, or other items."  How comfortable are you that the respondent will accurately report the compensation according to the instructions?  Will the respondent report net pay or gross pay?  Include just wages/salaries or include overtime/shift premiums/bonuses?  How will the Census analyst know if the respondent comprehended the instructions to provide the best estimate?

Do you have confidence in the number provided in Question #41 to represent it as the compensation that the person received?  Self-reported compensation data is notoriously unreliable.  Compensation professionals know if you want reliable data you get it from employers who have access to sophisticated computer data systems that provide exactly what is being analyzed.

Dear Cafe reader ... you are a compensation professional.  You have done more compensation surveys than you can count.  If you were analyzing this data and reporting it to your employer or clients, could you say with confidence that your analysis represents the compensation for the position that is being reported?  Would you stake your credibility, or even your job, on the data you have received from the US Census?

The Kicker

Now that you have decided for yourself how valid any of this compensation data is, get ready for the real kicker.  On page 7 of the US General Accountability Office (GAO) report, "Women in Management: Female Managers' Representation, Characteristics, and Pay," which analyzed and presented the US Census data, the GAO director says:

Our analysis is descriptive in nature and neither confirms nor refutes the presence of discriminatory practices.  Some of the unexplained differences to pay seen here could be explained by factors for which we lacked data or are difficult to measure, such as level of management responsibility, field of study, years of experience, or discriminatory practices, all of which are cited in the research literature as affecting earnings.

Well how about that!!!  The GAO says the US Census data does not prove discrimination!  Yet politicians want to use this data to justify leveraging huge punitive damages against employers through the proposed Paycheck Fairness Act!

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So how do you feel about the US Census data as a source for compensation data?  Is this the type of evidence we should accept as rationale for enacting unlimited punitive pay damages?

Do you feel that our professional associations - WorldatWork, Society for Human Resource Management, etc. - need to address this issue when the Paycheck Fairness Act is debated in November?

 Guest blogger Paul Weatherhead is a Pay Program Manager for the US Postal Service.  His primary pay program is their award winning Pay-For-Performance program, but he also is involved with other pay and benefit policy issues at the USPS.  Paul actively participates in the WorldatWork professional association as an instructor, author, speaker, reviewer, and blogger.  In his spare time he teaches human resource management courses for the University of South Carolina.  He can be reached at [email protected]

Comments

[Stephanie R. Thomas, Ph.D.]

Really enjoyed your guest post! I took a closer look at the GAO report you referenced. The report describes the comparison of male and female managers' earnings; that comparison adjusted for age, hours worked beyond full time, race and ethnicity, veteran status, education, industry sector, citizenship, marital status and the presence of children in the household.

The inclusion of age in the model may introduce gender bias. While age is commonly used as a proxy for prior work experience, it’s flawed and does not accurately reflect prior experience. It fails to consider that women typically experience more absences from the labor force than men, due to childbirth and child rearing.

The inclusion of age in a compensation model can introduce an artificial gender bias, which gives the appearance of gender discrimination.

Combined with the issues you raised, one is led to question the relevance of the report. Much of the discussion on gender pay equity is based on flawed analysis or misinterpretation of the analysis results. The GAO report is just one more example of "bad math" leading to "bad policy".

[E. James (Jim) Brennan]

Stephanie: Would not the factor measuring the "presence of children in the household" be a control or permit cross-correlation with your "age" argument? Knowing if there were children, one could test to see if that factor were either a positive or negative predictor of pay or if it had any effect on the age measure as a proxy for experience. The absence of comment about "children" is interesting, suggesting that it did not correlate with age or pay in any significant degree.

Perhaps the hypothesis that child-bearing reduces the work time sufficiently to justify a pay penalty (the "mommy track") could be easily proven or disproven here. Whether any such resulting negative differential (if it exists due to that factor) is legal, substantial, or desirable from either an economic or sociological standpoint is another question.

Regardless, Census data is poor justification for public policy decisions, but it's used for that all the time.

[Paul Weatherhead]

Stephanie & Jim -- Thank you for sharing your reactions to the blog. From reading your own writings on compensation issues, I greatly value your expertise and insights.

Stephanie -- Thanks for the generous feedback. Its an honor to hear from someone who has such a commanding knowledge of economic and litigation issues.

Jim -- I could see the value of such a special analysis of "cross-correlation". But such extra effort for the special analysis would only be valid if the foundation of the compensation analysis were valid, which it sounds like we agree -- it is not.

[Stephanie R. Thomas, Ph.D.]

Jim,

The presence (or absence) of children in the household certainly could be used to examine the "mommy track" issue you raised. But it doesn't really get at the artificial gender bias introduced by age. To see how age as a proxy for experience is problematic, consider the following example.

A 35-year old male employee and a 35-year old female employee have identical educational backgrounds. Both entered the labor force at age 22, right after college. The male employee has thirteen years of prior experience, while the female employee has eight years of prior experience because she left the labor force after giving birth and did not return until her child began elementary school.

Because of the five year difference in experience, the female employee earns $2,500 per year less than the male employee. And for sake of argument, let’s assume that we know with 100% certainty that the difference in earnings is attributable to a difference in experience and nothing else. Using age as a proxy for experience does not, and in fact cannot, account for the situation described above. It assumes that both individuals have thirteen years of experience. If we compare the compensation of the male and female in the above example and control for gender and age, the model will tell us that the $2,500 difference is attributable to gender. More specifically, we might infer that the $2,500 difference is attributable to gender discrimination. That’s all the model can tell us – we’ve made an assumption that age captures all the relevant information about prior experience, but it doesn’t.

My next Cafe post will look more closely at labor market intermittency and the gender wage differential. Thanks for the inspiration!