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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Replacing Human Judgment With AI: Danger Lurks

An organization is considering the adoption of a particular type of incentive plan to increase the motivation of its employees to extend their best efforts and to focus that effort on meeting organizational objectives. Using a data scientist the HR Director requests that research be performed that will predict the likelihood of success with a plan that was effective in another division. Using machine learning tools the data scientist examines the results experienced by the other division to conclude that the same plan will be effective if used in other divisions.

The HR Director is very familiar with the benchmarking principle that in order for results produced in one context to be likely to have the same results in another context the two contexts must be substantially similar. This principle is based on concepts from research that an internally valid study must also be externally valid (generalizable) in order for it to produce the same results in another context. A recent book argued that extrinsic rewards reduced intrinsic rewards based on a lab study that involved people throwing tennis balls at targets for short periods for insignificant rewards. When one compares that context with one where people work to support themselves over a career, often doing very unpleasant things, it seems absurd to claim the two contexts are even remotely similar.  Yet a machine learning algorithm may well make that error because its predictions lack any understanding of human nature. A Facebook algorithm was recently accused of gender discrimination because it sent ads for jobs in STEM fields much more often to men then to women. However, it was found that the decision was made on economic factors (it was more costly to reach working women), rather than on a judgment as to whether women were considered to be capable of doing jobs in the STEM arena. Regrettably on the surface it appeared that the results supported the latter assumption.

Machine learning tools could be incapable of predicting the success of an incentive plan considering the impact of differences between the two divisions mentioned earlier. For example, if one division had a hierarchical culture while the other allowed employees to take the initiative to deal with issues this might result in different levels of motivation to excel. Recognition by employees in the top-down control culture that they were not empowered to impact results can certainly depress initiative. That would certainly limit the motivational impact of the plan in one type of culture. So the differences in the cultural contexts might make comparisons across the divisions inappropriate. When working in the Middle East I had an HR executive ask me if U.S. style incentive plans would be effective if employees believed results were out of their control and that it would be irreverent of them to assume they could have an impact on outcomes that were determined by a higher power. Lacking an answer based on experience I had to apply behavioral science principles and suggest the cultural difference would certainly impact individual decisions about taking the initiative. A manager might be able to convince employees that their talent was a gift and that it was their responsibility to use the gift but the different cultural orientation certainly changed the game.

Melanie Mitchell, a researcher at the Santa Fe Institute, observed in a recent New York Times article that AI systems lack an understanding of situations and the meaning of the differences across situations. Again, what works in one context needs to be understood before projections can be made about what results would be in another context. Her long experience with AI systems has made it clear to her that even minute differences in contextual characteristics can have a major impact on outcomes. Someone hacking into a system and making changes that are so minor that they would not be detectable by humans could dramatically reduce the effectiveness of the algorithms. And if the target is a control systems for a city’s power network this could be too important to overlook. The fact that machine learning algorithms have been trained in specific contexts makes them vulnerable to erratic results when contextual details change.

Back to the HR Director deciding whether to adopt the incentive plan from one division in another. The division that had used the plan had included all employees as participants. But the division executive considering adopting wishes to carefully select plan participants based on their ability to significantly impact results (perhaps including only managerial and professional employees). This decision would be driven by human judgment, applying the principle that those lacking the latitude to vary their behavior and control their results would not be affected by participation. The algorithm based on the division that had used the plan may in this case be a bad choice to project the success in the other division. Interestingly the algorithm might find the new application to be a good choice because it would cost less (smaller participant payroll), when in fact the trimming of participants may result in strong negative reactions by those excluded. The lower cost would be detectable by the algorithm but it would be incapable of detecting the probable angst felt by non-participants, which is attributable to human characteristics.

Conclusion

Mitchell points out in the NYT article that humans can generalize what we know, form abstract concepts and make analogies. So the HR Director would have to consider whether the exclusion of some employees from the plan would result in negative outcomes that would be significant. Current AI technology cannot help with that judgment, lacking the “fuzzy logic” we use to combine black and white in a way that produces the best shade of grey. Human judgment is certainly not infallible and cognitive bias is always leading us to make less than optimal decisions. But in complex contexts where multiple factors impact systems someone has to come up with the best choice among imperfect solutions. AI is certainly gaining in its ability to help us extend our human reach, as well as to enable us to turn work that is fully defined and requiring speed, precision and accuracy over to systems that will almost certainly be better at doing that kind of work. Our biggest mistake would be to assume AI will surpass human cognition in all types of endeavor. Organizations are not likely to let AI make decisions about selecting the next CEO without significant human intervention.

We Are All Biased: Acknowledging That Can Help Moderate The Impact

The work of Kahneman & Tversky on cognitive bias led to a Nobel Prize. It also provided us with a window into the inner workings of our mental processes. Whatever the cause our biases influence how we view things and make decisions. Knowing we are biased can help us pay attention to tendencies that may lower the quality of our decisions.

We:

  • Think others agree with us more than they actually do
  • Accept data/hypotheses that agree with what we believe/want to be so more readily… resist contradictory evidence
  • Accept too readily conclusions based on inadequate samples

“four employees complained about this policy last week… angst is rampant”

  • Rely on literature that reports only successes and not on failures
  • Believe that intuitive conclusions are valid
  • Overuse similarity as a simplifying heuristic

“candidate is great (thinks/looks like me)”

  • Assign weight to evidence based on its availability

“recency bias; familiarity bias”

  • Do not adequately consider regression to the mean

“why did the best performers last year seem to do worse this year?”

“giving a QB a huge raise because of a great year may prove to be foolish”

  • Assume quantitative data has higher validity than subjective data
  • See puppies and other shapes in clouds… this is System 1 thinking (intuitive/automatic/emotional); realizing that it is silly is System 2 thinking, which takes considerable effort
  • Think the world makes much more sense than it does
  • Sometimes answer a simpler question when we have no answer to a complex question – System 1 is puzzled and System 2 goes to work
  • Can learn to apply System 2 control over impulsive conclusions reached by System 1 with training but emotional intensity of issue makes it harder
  • May use repetition of anything, even falsehoods, to make something seem more feasible
  • Are overly prone to assume causation when we see correlation
  • Believe we are too intelligent to be prone to bias… believe that rationality is used and overrides impulse
  • Know less about how we think than we think we do
  • Are primed to make associations by recent/repeated experience
  • Falsely believe that a “hot hand” in sports or in gambling exists
  • Believe a lot of experience makes us wiser – a lot of experience is generally good but we need to organize the experience to make it relevant/useful

Conclusion

Clear thinking requires hard work… and overcoming what comes easily.

Overcoming Resistance To Sound Evidence-Based Recommendations

We all have made recommendations based on sound evidence, only to find the decision-maker did not share our belief that it was the right thing to do. The human tendency is to push harder, emphasizing the evidence that supports the recommendation. But the behavioral scientist Kurt Lewin found that decreasing resistance was more effective than increasing force.

When decision-makers are considering actions that involve significant change barriers are common. People must believe that the benefits to them outweigh the costs to them if they are to consider change. Resistance is apt to be very strong if the decision-maker must reverse a prior decision (s)he made. And if an admission that the prior decision was wrong is required the intensity of the resistance escalates. As evidence-based management becomes more widespread it is likely that improved analytics will increasingly show prior decisions were less than optimal. Many of those prior decisions were made using experience, intuition and judgment, since tools like workforce analytics had not yet been sufficiently developed or accepted as another valid form of evidence. As technology develops at light speed decision-makers are often overwhelmed by the volume of new forms of evidence and they may be ill-equipped to understand the data science tools that are used to generate it. Consequently, they may feel their decision making competence is being challenged by things they do not understand.

Practitioners can be intimidated by a specialist in another discipline using “tech speak” since they do not speak the language or understand the underlying technology. Understanding how financial analysts, IT specialists or data scientists have formulated their recommendations helps immensely when one is deciding whether or not to accept them. That is not to say that one must be able to perform a multiple regression analysis on a data base to discover what factors impact something. If I am told that education, experience and performance appraisal ratings strongly predict someone’s pay I may accept that because it is reasonable. If I am told gender and race are strong predictors of pay it can alert me to something about which I should be concerned. After all, acknowledging that gender or racial bias is a problem in the organization is both difficult and distasteful.

It is not necessary to be able to do the analysis in order to use the information it generates. But when data analytics produce surprising results which don’t align with common sense or professional knowledge it is likely that the user may be reluctant to act on the evidence without being certain it is valid. And, as said earlier, if the evidence calls into question strongly held beliefs or shows a prior decision to be flawed the level of reluctance escalates even more. So how does a practitioner acquire the knowledge and skill that is necessary to integrate data science into the decision-making process?

If two parties do not share a common language it is like the time I tried to find insect repellent before going into the jungle in a city where no one spoke English. When I found someone who was multi-lingual to act as a translator the problem was solved. But finding someone who is competent in both data science and the discipline of the decision-maker is a big challenge today. This historically has been a challenge when IT people have dealt with neophytes. It also has been common for academics and practitioners to have trouble communicating. The IT challenge was dealt with by creating Systems Analysts, who knew enough about both what was needed by the user and what it would take for Programmers to meet that need. So the intermediary approach is a feasible way to resolve the communication problem. But this still requires finding a multi-lingual party.

Teams are widely accepted as an effective way to resolve issues, assuming the needed skills and knowledge are possessed by someone on the team. So replacing a sequential process by a concurrent process can improve the creation and utilization of data science efforts. Having data scientists do their technical work and then presenting the results to practitioners can result in ineffective communication… the translation challenge. But if both parties work together from the start it is possible for the practitioner to convey what is needed to the data scientist and for the data scientist to inform the user of the alternative approaches to performing data analysis and how each would be likely to address the needs. If two parties are separated by a canyon building a bridge is the way to bring them together. But in order to fully integrate the two parties it is necessary for each to go halfway across the bridge and to conduct an open dialogue in the middle.

This will require practitioners to acquire at least a basic understanding of the scientific method and of how the analytical tools work. It will also require a data scientist to learn more about the issues faced by the practitioner and the type of analysis that will help resolve dilemmas. For example, if the stakes are high the degree of certainty produced by the analysis may need to be high as well. But if the practitioner is seeking an approach that will improve the ability to predict something (i.e., an employee with critical skills leaving the organization) the data scientist might be able to produce an adequate result with data that is of lower quality. Knowing whether approximation or precision is required helps to guide the approach to analysis. As artificial intelligence and machine learning techniques evolve it is increasingly possible to use qualitative data and even unstructured data to produce an adequate result when a precise answer is not required. By approaching the problem with continuous dialogue the two parties can both become more efficient and effective because they share a common understanding of what is needed and how needs will be met.

Practitioners have a wealth of options for developing a basic understanding of data science. Online courses are available and books are being released continuously, both containing the type of knowledge needed by the practitioner. It is somewhat more difficult for the data scientist, since much of the knowledge about practitioner issues is accessible only by hands-on experience. But by using a continuous dialogue between team members this bridge can be crossed. It is of course necessary to convince both parties that they need to add to their reservoir of knowledge but continued frustration in attempting to communicate across languages without a translator should make it clear that this is the way to ensure that the best evidence is used to make decisions and that the decision makers do the best job of using it.

Cultures That Are Hospitable To Data Science

Workforce analytics are all the rage. AI and machine learning are expected to transform the way decisionsabout managing people are made. But not all cultures are open to the potential contributions data science can make. Much has been promised in terms of new tools that can better inform decisions yet the actual realization of these benefits has not yet lived up to its potential. And it is being found that organizational culture can be a potent impediment.

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The obstacles that appear to slow down adoption of data science are:

 

  1. Data scientists often do not understand the needs of practitioners, the culture of the organization and the way in which decisions are made
  2. Practitioners do not have the background to understand how the data scientists do their work and how their findings can be applied
  3. Data scientists speak a different language, that is difficult for practitioners to understand… and they are sometimes impatient with the lack of knowledge practitioners have about the scientific method
  4. Practitioners are often suspicious of the data being used, the mysterious black box analytical tools and the scientific methodology that is being used
  5. Decision-makers are over-confident in the quality of their intuition and judgment and view it as being superior to quantitative analysis

Most professionals know that if they present evidence that contradicts what is currently being done or conflicts with the beliefs held by decision-makers there will be resistance. Cognitive bias is the villain here… we are by our nature over-confident and we welcome confirming evidence more readily. If analysis produces evidence that past decisions are flawed it puts the person that made the decision in a tough spot. Acknowledging mistakes is hard for everyone but if resources have been expended as a result of a bad decision there are more consequences than just wounded pride. And when intermediaries who use the findings of data scientists to make recommendations to executive management they must decide how much to convey and how strongly. It is hard to enter into contentious discussions when one is out-ranked and/or when the advisor knows the decision-maker holds onto beliefs tenaciously.

Cultures that require everyone to tell the truth, rather than what is politically correct, encourage advisers to present all evidence, even when it is contradictory. As long as the adviser behaves respectfully it may be permissible for the unpolished truth to be presented and for the adviser to aggressively fight for what is believed to be correct. But that is not to say it should be done in a way that publicly exposes flawed decisions without offering some face-saving options. For example, it can be pointed out that the prior decision may well have been correct, given all the facts known at that time. The discovery of new evidence can be used to change everyone’s understanding of the context within which a decision will be made. However, in some national cultures it will still be difficult or unacceptable to show that a higher ranking person’s decisions were wrong (i.e., in high power distance cultures). It may be necessary to fabricate the appearance that this is a new decision, unconnected to the prior one. Face-saving is a very important consideration in many cultures and globalization has made multinational organizations realize that it must be respected. But considering the feelings of someone about to be shown wrong is a good strategy anywhere. The Golden Rule learned by Westerners during their socialization advised them to “Treat others as you would like to be treated” but the Global Golden Rule is “Treat others as they would like to be treated.”

Unfortunately inadequate attention is paid to culture when technological advancements are considered for adoption. Hiring a data scientist into a hostile culture is a sure formula for failure. So someone should consider what has to be changed in order for the organization to reap the benefits of technology. And rarely is the job title VP of Culture found on organization charts. Changing the culture seems to be in no one’s job description, which means it is hard to recognize when culture needs to be changed and who must take the lead. HR is a function that seems to be ideally suited for this role, but the entire C-suite must be on board as well. HR must be given the mandate and must have the knowledge needed to operate on the culture.

Conclusion

Data science is racing ahead and offers the possibility of dramatically improving decision-making. Evidence-based management is being embraced by more organizations. A key requirement for it to succeed is not only the generation of high quality evidence but the willingness to accept and apply the evidence. An appropriate culture is to decision science utilization as pure water is to fish… necessary for survival.

The State Of Workforce Management… And Those Responsible For It

It was pointed out to me recently I’ve written 100 Linked In posts. That calls for an assessment of whether anything of value has been offered or if it just involved filling the digital space.

At least no trees had to be sacrificed.

I have decided to create this post by using some of the themes covered over the last two years, but focusing on what those responsible for workforce management should be competent to do well and what they should actually be doing.

  • First, they should possess business acumen. 👔 In order to serve an organization it is necessary to address the issues it faces and to contribute to mission fulfillment. If one does not understand the business of the organization this is not possible. Practitioners in the Human Resource function must know what the organization needs and be able to help meet those needs. But so must every manager, supervisor and lead person, and each must recognize their responsibility for workforce management and to invest the needed effort on making their people effective.
  • Second, they must base recommendations/decisions on an analysis of all relevant evidence. 📊Historically this meant applying the skills and knowledge gained through education and experience, supplemented by an appropriate dose of intuition and creativity. Of late the emphasis is on the use of workforce analytics. Certainly the use of artificial intelligence and machine learning can augment human judgment… but it cannot replace it. Applying analytical tools to databases can disclose relationships, such as correlations. But these correlations may be statistical artifacts… sufficiently tortured data will confess to anything. And the need to innovate in today’s competitive world may limit what analytics can do. Trying to predict the future based on data that is limited to what was and what is a high risk endeavor. The effective integration of technology and people has been a theme since socio-technical systems thinking came on the scene decades ago… and it has become even more critical with the advent of new technology.
  • Third, they must understand cognitive bias and its impact on the perceptions of employees. 👨‍💼 Research in Behavioral Economics and Neuroscience have disclosed almost 100 types of bias that are within us all. Though bias removal cannot be done surgically managers can recognize what impact biases are likely to have and can create a dialogue with employees that may lessen the unwarranted employee dissatisfaction biases cause. For example, we all think we are better than we are. This impacts employee perceptions about what their contributions are and what their rewards should be… and their perceptions are their reality.
  • Fourth, they must understand the impact of cultural diversity in workforce management. 🙏Even organizations operating in one U.S. location will be likely to have employees who originated somewhere else in the world or be the product of another cultural heritage. Cultural diversity will result in different values and beliefs, which will impact what employees believe to be appropriate. The widespread focus on individual performance in the West may be seen as wrongheaded by people from a more collectivist culture. Hierarchical, top-down management may be viewed as inappropriate by those who are more egalitarian in their beliefs. Managers must recognize cultural differences when they exist, they must respect that people have a right to hold different beliefs, and they must reconcile the issues caused by cultural differences.
  • And fifth (not going to do ten), they must understand: 1. The scientific method, 2. What makes research sound and relevant, and 3. Statistical and mathematical tools relevant to workforce analytics. ⚙️This may require going back to school, but there are a wealth of online training options that can be used to augment knowledge and skills. An example of how critical this knowledge is would be the recent proliferation in the pop literature of claims that extrinsic rewards destroy intrinsic rewards. These claims are based on lab studies, such as a popular one involving throwing tennis balls at targets. Even though a lab research study is well designed and passes the internal validity test it may not be generalizable to the field (have external validity). That means the context within which the study was done must be consistent with the context within which one tries to apply the research results to. This attempt to generalize lab findings to the field is a failure and results in wrong conclusions…there is extensive field research that contradicts the lab result. Without the necessary knowledge to evaluate the validity of research practitioners may draw erroneous conclusions and make poor decisions about workforce management. The fact that so many read material espousing flawed research is evidence that there is inadequate competence in this area.

One additional thought on the debates that arise relative to sound workforce management. Most of them should not be debates at all. We live in a Quantum Physics/Fuzzy Logic kind of world today, where “both – and” replaces “either –or.” The current proliferation of articles debating whether year-end performance appraisals or continuous measurement and feedback are necessary could all be combined into one that says you have to have both. That would save lots of tree and free up a lot of digital space.

This author believes an organization’s people are its most important asset. Workforce management therefore is one of the most critical responsibilities given to those who manage people. The quality of managers given that responsibility and the adequacy of investment in their training will have a profound impact on the organization’s performance and its viability in the future. Training may be expensive but cost out the alternatives.

Time To Rethink Pay Structure Design?

In 2006 a research-intensive organization designs new pay structures for its employees. The Scientists & Engineers represent about 2/3 of the workforce, mostly data scientists and software engineers. The organization creates career ladders with four levels in each job family (Associate, S/E, Senior S/E and Principal S/E). A comprehensive review of prevailing practice and competitive pay levels produces four grades for each family and pay ranges with a midpoint to midpoint progression of 20% and ranges that are 50% wide. The framework is set for the administration of a merit pay system.

For the next five years prevailing pay rates in the relevant markets are flat, due to a significant economic downturn. Surveys show that pay- rates are going up at 2-3% per year and that structures are being increased by 1 – 2%. The organization was able to afford a budget of about 3% each year and believed it was tracking the market levels with that level of spending. And the pay ranges were increased by 1% each year. Since the organization was a government contractor it was obligated to pay no more than market average and diverging from that created a breach of contract. But an in-depth analysis of the surveys by one of the data scientists discovers that the methodology used by the surveying organizations is flawed and statistical anomalies have been created because of this. The surveys reporting on budgets failed to realize that when the economic picture darkened a number of organizations stopped reporting their data. After all, if you have frozen pay and/or reduced pay or staff levels why would you bother to report in the survey, so you could pay for data you would not be acting on? The surveys of pay levels suffered the same deficiency and they showed a continued increase in competitive pay levels when in fact that result was attributable to sample change and market levels had not actually moved.

Now the organization faces two problems. 

  1. One is that their spending level has outpaced the true market movement and since they were at market previously they were now ahead of the real market average.
  2. The second problem is that employee pay rates are still significantly below midpoint. Since their pay policy included a clause that stated that satisfactory performers should reach their range midpoint in about 5 years they clearly had not fulfilled that commitment. Employees began to complain that they must be paid well below competitive market rates because of this. So the reality was that the organization had run ahead of the market but was perceived to be below market. How could this happen if it had supposedly followed the market?

A 40 year analysis that I did recently shows that on average pay levels have moved about 1% per year faster than cost of living and about 1.5% per year more than average pay structure movement (see a prior posting). This means that an employee newly hired or just entering a job who starts at or near the minimum is going to take about 18 years to reach range midpoint. This is going to create discontent, since being fully competent in a job but being paid well below the market rate contradicts how pay levels should be distributed in pay structures.

Perhaps the realities of the last decade (and likely near future) makes the traditional pay structure design obsolete. There is greater pressure on organizations to motivate the most capable employees to contribute their best. Yet research has shown that when an outstanding performance rating results in a pay increase that is only 2-3% more than someone rated as satisfactory it is unlikely that they will view that difference as warranting the extra effort and as being equitable. And being below range midpoint for very long is likely to be viewed by exceptional performers as a failure on the part of the organization to appropriately reward their contribution.

The most common range width is 50%. So is a 50% range too wide for the current context? Should ranges be narrowed to 25% so those entering a job at the range minimum can be brought up to the midpoint in a more reasonable time? One approach is to have narrower ranges for jobs with shorter learning curves and with limited ability to contribute much more than satisfactory performance. And many organizations do that. But is 50% still workable even for professional and managerial jobs, or should they be truncated as well? The “broad-banding” fad that seems to break out every 7-10 years has pretty much withered again… 100-200% ranges are useless when it comes to administration, since they no longer serve as control parameters. When I bring the possibility of narrower ranges up I typically get resistance because there is a perception that a lot of headroom above competitive market levels is necessary to accommodate long service people. But how many jobs allow incumbents to contribute value at a level that warrants paying more than 20% over market? I get considerable resistance when executive jobs are being considered, but there has been a trend to use variable pay much more, to provide upside income opportunity that is warranted when results are stellar. Tech jobs have really created a challenge. When Bill Gates said his top programmer was at least ten times as productive as the second rank does that mean pay levels have to reflect this?

That would challenge organizations in two ways:

  1. justifying that relationship
  2. finding the money to fund it.

It is surprising that many tech firms have not turned to variable pay more extensively, since in theory extraordinary contributions could be compensated with one-time awards, avoiding rapid compounding of fixed cost payroll.

The existence of wide ranges when time-based step progression is used can produce excessive pay levels… and pay levels related to longevity rather than performance. This is still common practice in the public sector, although steps are disappearing at many entities. And since inflated base pay rates inflate generous pension benefits it results in indefensible benefits cost escalation. There is less pressure on costs in the not-for-profit arena and it is difficult to convince city councils, county boards and state legislatures to begin to exercise the necessary level of restraint, particularly near elections. If steps are to continue for some jobs it seems reasonable to limit pay ranges to no more than 20% in width.

Each organization should seriously question whether their current pay structures and pay administration programs are effective and appropriate in this era. Although it is difficult to predict the next 10-20 years most Economists would advise that organizations anticipate wide economic cycles with patterns that have not been seen in the past. In 2018 there is little evidence of increasing pressure on pay levels even while unemployment is at the lowest level for many years. Continuing to operate with pay structures and practices that seem to be out of synch with the environment seems a chancy strategy.

If I were running a start-up or emerging organization I would put tight constraints on base pay levels and I would look to well-designed incentive programs to motivate the desired results and to control the growth of fixed costs, such as base payroll. When downturns in fortunes materialize it is far easier to retain critical talent and avoid dysfunctional downsizing when you are not faced with cutting base pay to achieve immediate cost reductions. Every base pay adjustment is a career annuity – employees do not accept your request for a refund when revenues drop. They after all base their standard of living on base pay and look at reductions as a breach of contract – emotional even if not legal. Replacing a portion of base pay increases with variable pay awards is not a simple task however. An earlier post addressed a test of readiness for using variable pay. And there may be employee resistance. But an “all base pay” direct compensation package has all of the issues just discussed. And aligning rewards with organizational performance in addition to individual performance may create a shared destiny mindset, which should convince employees they are a part of a larger organization and the funds for rewards has to be generated somehow.