The Gender Pay Gap: How Large & Caused By What?

There has been a lot of attention paid to the gender pay gap recently. If there is a gap caused by gender-based discrimination it should be remedied. The law of the land and a sense of fairness dictate that this should be done. Many who are skeptical about the magnitude of a gap believe numbers in the press are overstated. Large numbers increase readership so the journalist’s temptation to magnify differences is understandable. But it is unprofessional to artificially magnify something in order to get noticed, particularly on sensitive subjects. Some of the more responsible analyses of the issue use statistically sound measurement techniques but determining how large any gap is and what causes it are tricky things to pin down. Before a pay gap can be addressed its causes and its magnitude need to correctly determined.

Calculating the gap can be done in many ways. Many of the articles published about the issue use an organization-wide average to calculate the relationship between the two genders. Regulatory agencies often use this approach when determining whether an organization is guilty of discrimination. Other studies aggregate the data nationally, by industry, by occupation and even globally.

Scenario 1:

An organization with 100 employees has an even gender distribution. The male CEO has a total direct compensation of $1,000,000 and five other male senior executives average $ 250,000 each, while the remaining 44 males and the 50 female employees each have an average pay of $ 50,000. When calculated on an organization-wide basis there is a significant gender pay gap between the average pay for all men and all women. Is there gender pay discrimination? Is it of the illegal type? Does the difference raise any concerns? What additional information would be necessary to decide on any action?

Subjecting the aggregate results to further analysis might reveal that a glass ceiling exists, which results in the highest ranking (and highest paid) employees being male. Gender-based discrimination in employee selection and career management is as illegal and unethical as pay discrimination between similarly situated employees but it has different remedies and should be correctly labeled. And regulators should do more in-depth analysis and pressure organizations to fix the real problems.

Differences in pay across genders can also be caused by different distributions across occupations. My PhD thesis was on the determinants of occupational worth. The relative value organizations place on jobs is supposedly based on their relative economic value to the organization. For example, a senior executive will have a direct and significant impact on the organization’s performance while a clerical person will have a much smaller impact. Job evaluation systems have been used for a century to enable organizations to determine the relative internal value of jobs and occupations. U.S. labor law (Equal Pay Act, Title VII of the Civil Rights Act) specifies that a system for establishing the relative value of jobs must consider the skill, effort, responsibility and working conditions required in the jobs. The gender, age and race of incumbents should have no impact. But in the U.S. there historically have been significant differences in the gender mix for many occupations. And if discrimination is caused by undervaluing jobs that are filled by women is that as odious as direct discrimination based on someone’s gender? If the concentration of genders in occupations is due to personal choice there is no basis for blaming illegal discrimination that is a result of such concentration. But when artificial obstacles exist that deny equally competent women or minorities access to certain jobs that is discrimination. And if the undervaluing of female dominated jobs is merely another way to discriminate there needs to be some remedy.

Some occupations have historically enjoyed higher pay than others because of the existence of influential blue collar unions. Blue collar jobs have historically tended to be paid more than white collar jobs, even though the latter might require more education and judgment. As the impact of unions has declined their impact has become less of a factor but historical patterns tend to persist. Early job evaluation plans were designed for manufacturing environments and placed heavier weights on factors such as physical effort, physical skill, adverse working conditions and hazards. When these plans were used to evaluate both blue and white collar jobs their design favored blue collar jobs. As gender discrimination became a political concern in the 1960s laws such as Title VII of the Civil Rights Act were passed to minimize this discrimination. Many job evaluation plans were revised so that heavier weights were placed on education, mental skill and mental effort. Few plans had been designed in such a way as to appropriately value both blue and white collar jobs and how much revision was appropriate to the weighting of the factors determining value was difficult to determine. A National Academy of Science study was done in the 1980s measuring the impact of job evaluation on gender-based pay discrimination. Principles were articulated that enabled those using plans to ensure their design was balanced, mostly ensuring that the factors gave credit for all types of skill (mental and physical), effort (mental and physical), and responsibility (influencing and managing). In some states (and in Canada) pay equity regulations were established that were aimed at discrimination, but it is unclear how much they have resulted in less discrimination.

When the gender pay gap issue heated up in the 70s and 80s there was consideration of the concept of “comparable worth.” That is, if two jobs were considered to be comparable in “value” they should be paid the same. U.S. law only required that “substantially equal” jobs should receive equal pay, but some politicians were amenable to including a “substantially equivalent worth” test. A committee of representatives from ASPA (now SHRM) and ACA (now World at Work) on which I served studied the issue of determining comparable value. We concluded that attempting to legislate this at a macro level would create clashes of subjective opinions about relative worth that could never be settled. As stated earlier, each organization is likely to view relative value differently based on whether occupations were central to their primary business and critical to their performance. I once asked representatives of Arthur Anderson and Anderson Consulting whether Accountants or IT Professionals were more valuable – and they responded with different answers. Accountants are generally considered to be more critical to accounting firms while IT professionals are viewed as more critical to IT firms.

Scenario 2:

Assume faculty members at a university are paid differently based on their field. STEM (Science; Technology; Engineering; Mathematics) field incumbents are paid 40% more than those teaching in the Liberal Arts fields.  A large percentage of STEM field faculty members are males and a large percentage of Liberal Arts faculty are females. Is this gender based discrimination? What factors should be considered when deciding?

This type of difference is often the result of supply and demand in the labor market, or at least that is the defense offered when relationships are challenged. There has been a shortage of STEM graduates in the U.S. for some time, causing demand to exceed supply. And short-sighted policies that restrict the use of H1-B visas exacerbate the shortage. As we learned in Economics 101 that will drive the market price up. So even when males and females are doing the same general type of work (teaching) the result of market forces and different gender mixes between fields can produce what looks like gender pay discrimination if these causal factors are not recognized. The same holds true within fields… Nuclear Engineers command a higher price than Civil Engineers, even though their education is similar. On the other hand, their relative value may change if a surplus of Nuclear Engineers manifests while Civil Engineers become scarce. One of the defenses for paying faculty differently might be the fact that there is a business necessity to respond to competitive pay levels in the labor markets.

Scenario 3:

Two Accountants were hired three years ago out of school with BS degrees. One is a male with a current salary of $70,000 and the other a female with a salary of $60,000. Is gender-based pay discrimination a problem? How would this be determined?

A sound pay administration system will determine pay actions based on employee competence and performance and if policies are administered equitably employee pay levels should be defensible. So if the two Accountants had received similar performance ratings and were equal in competence any difference in their pay rates should be explored. But there should also be attention paid to new hire and new entrant pay rate determination. If gender differences impact starting rates fair equivalent administration of pay adjustments going forward will not correct the differences. The problem was born at the point of hire. Research suggests females are often less aggressive in negotiating pay rates and this can result in start rate differences across genders. This can be a major concern in many public sector organizations using automatic step-based pay progression systems, since start rate differences are perpetuated. On the other hand, forcing everyone to start at the same step when entering a job may result in unfair practice when there are large differences in qualifications across incumbents. Recent legislation and supporting regulations have been implemented in the U.S. to preclude asking about past pay levels, which could moderate the perpetuation of past discrimination in determining hiring rates.

Unwarranted pay discrimination based on personal characteristics has detrimental effects, in addition to being wrong. If equally qualified incumbents of a job with equal performance are paid differently based solely on gender, race, age and other prohibited bases there is a clear violation of the law. And there are consequences. If I am a member of a gender or race that is discriminated against I will view my employer’s pay management system as unfair and inappropriate. This will lead to diminished satisfaction and engagement, which will tend to have a detrimental impact on my contribution. This makes discrimination something every organization should attempt to prevent, even if fairness and ethical behavior are not considered… it is just good business. Well managed organizations should have policies and systems in place that control pay actions, ensuring pay is determined by the value of the job and the contribution of the individual. What someone looks like has nothing to do with what constitutes fair rewards.

Scenario 4:

A department with ten females and ten males does a comparison of pay rates and finds that the males are being paid 15% more than the females on average. The typical length of service is similar across genders. However, the average performance ratings for males have been higher than those of females consistently over the last six years. Is there gender-based pay discrimination? How could this be determined? What might be causing the pay differences?

The majority of organizations claim to use merit (performance-dependent) pay administration systems. The policies regulating pay actions typically base an individual’s pay action on performance, as well as competence in the job and where the individual is being paid within the established pay range. One of the greatest challenges associated with performance-based pay is to ensure that those rating performance have equivalent standards and that their ratings are based on job-related results and behaviors. If personal bias influences ratings this can be a form of discrimination and should be eliminated. But changing someone’s biases is a tall order.

The last decade has been technologically turbulent. New tools have come on the scene, such as AI, machine learning and evidence-based decision-making. When new technology emerges there is almost always a shortage of people with the skills to effectively utilize the new technology. That often leads to very large spikes in competitive pay levels. For example, workforce analytics is all the rage now and data scientists who can do the work command a premium price. If organizations employ people with these skills they will almost certainly create large differences between current employees and the new folks in the neighborhood. Although the other employees may view the magnitude of difference in pay as unreasonable an organization needs to meet competitive pay levels. But when even one person with hot skills is employed it creates a new “referent other” for all employees (referent other in equity theory is the person people select as a reference point to determine if their pay is reasonable). And if the people with the high priced skills are generally male (I have not found data on the gender mix for data scientists) it can exacerbate numeric gaps between gender pay levels. Over the last few decades more females have entered STEM (science, technology, engineering and mathematics) fields but statistics show the majority of incumbents to be male. This is a difficult difference to attack, since the choice of field is almost always voluntary.

Another trend is the increased use of non-employees to perform work. There are a number of reasons organizations are utilizing outsiders. One is the lessening availability of skilled people who are interested in an employment relationship. Those who work on gigs may find it allows them to focus on the specific skills that pay a premium… if they were to be employed they may have to do other things that are of less value. Another reason is that the supply of skills can be global if the work can be performed virtually, which more of it can. This avoids the necessity of relocating and also enables people to extract high pay while living in areas with reasonable living costs and avoiding long commutes. A final reason for using contractors and freelancers is that they do not enter the organization and remain hired hands. This might lessen the focus of employees on the gap between their pay and those working contractually. And it may even lessen the gender pay gap if male contractor pay does not enter into the calculations.

Conclusion

What is quantitatively measurable may not tell the story as to what lies behind pay differences. Governmental attempts to find illegal pay discrimination often result in findings that are not warranted by the facts. Some of their evaluations create statistical anomalies that do not represent true discrimination. The regulators also tend to label all differences as pay discrimination, even though they might be attributable to bias in selection or career management… or even people voluntarily choosing occupations that command lower pay. Statistical correlation does not constitute causation. If some of the conditions mentioned here exist they may produce results that look like pay discrimination, but that may be something entirely different. It is prudent for every organization to not only run statistical tests required by law but to explore much more deeply to determine the causal relationships. If differences cannot be attributed to anything other than personal characteristics a fix is in order. Waiting until an accusation has been made puts the organization on the defensive and is unprofessional, despite the fact that many attorneys recommend that discovery be averted. This argument is facetious… the facts are the facts and it is certainly easier to deal with them if the organization has defined the reality and explored in advance how it can be dealt with.

The subject of pay discrimination creates strong emotional responses. And people act on their perceptions, which become their reality, even when their perceptions are wrong. It is therefore important to measure the magnitude of pay differences accurately and to attribute it to the correct causes. If gender pay differences are due to occupational clustering caused by unwarranted barriers then the focus should be on alleviating those barriers. If the differences are due to personal biases this necessitates a different path to correction. If numeric differences are caused by factors that are not related to discrimination analysis should be sophisticated enough to avoid mislabeling differences. Working on the wrong problem is unlikely to produce an effective solution. And accusing organizations of things they are not guilty of will not result in a rational reaction. So let’s find the illegal and unwarranted discrimination and devise strategies to remedy it.

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