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Analytics Articles for Business, Supply Chain and HR

Analytics Articles: Human Resources, Supply Chain, Diversity and Business Analysis

Numerical Insights publishes articles on a variety of topics including business analytics, data analysis, data visualizations tools, improving business results, supply chain analytics, HR Analytics, strategic workforce planning, and improving profitability. We aim to make our articles informative and educational.

 

How to Create A Diversity Strategy

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Within certain countries, the topic of diversity has become a strategic focus. Whether it's issues like gender pay equity or proper representation within a company's workforce, making good decisions regarding diversity goals and strategies has become key. Of course, by "good decisions," I am referencing the data-driven kind.

Like all new initiatives, there comes a time when a company has invested so much money that it starts to wonder when it will see a return. This is where the rubber meets the road and HR has to prove the value of its efforts. Diversity initiatives are approaching this point, especially in companies that struggle to achieve their diversify goals and are seeing very slow progress in enhancing their workforce composition. Human Resources and People Analytics teams seem to reach this point after about two years, especially in industries that are currently under financial pressure.

Selecting Random Diversity Goals

If you’re struggling with your diversity goals, consider this. Are you using data to focus your recruitment efforts or are you just hoping you will meet your diversity goal by telling managers and recruiters to hire more women? I’m seeing too many companies that have a multi-year diversity target but aren’t actively changing their actions to meet that target.

Secondly, I’m seeing diversity goals that have been set to a randomly chosen value. Often the value was selected by an executive and the employees below that level are left with the task of somehow achieving that diversity target.

How to Select A Diversity Goal

The U.S. population is estimated to be about 50.8% female, according to the 2013 census data. Would it be realistic to set a diversity target for each company to be 50.8% female? Definitely not. The reality of the situation is that the supply of people into each job role is not fed by sources that are all 50.8% female, so a deeper assessment is needed. An obvious example you can think of is technology companies and product companies where the workforce is dominated by various types of engineers and IT experts. These roles are predominately male because the supply of these experts in the market and graduating from universities is predominately male.

I work with several tech and engineering companies that are trying to determine a realistic goal for "how female" their workforce can become. Let's use Chemical Engineers as one job role for example. If I use my customized labour market dashboard, which contains labour statistics and college/university graduate data, I can see that the graduating classes of 2016 for Chemical Engineering degrees produced 9,105 grads with 32.2% of those being female. Companies can examine their current state of diversity in their chemical engineering roles and look at supply data like the one I've used here, to set a realistic diversity goal and a realistic time frame in which to reach that goal. This, of course, needs to be coupled with a study of what I call the "flow of diversity" in and out of the company and at various levels and job categories.

As an aside, once you have that goal, the graduate data is an excellent source of finding which schools are producing the type of graduates you need so you can increase your chances of getting the hires you need most.

Using Data to Achieve Your Diversity Targets

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Let’s use the activity of hiring new graduates in an attempt to reach your diversity goals as an example of how to use data to focus your efforts.

  • Are you still recruiting from the same schools that you chose 10 years ago?

  • Do you have any idea how many females are in the graduating class for each school?

  • Do you know where can you find female software engineers? Female mechanical engineers? Male nurses?

  • What is the ethnic diversity of all universities offering a certain degree?

  • Do you know where to find students with a certain degree that you need for the future of your company?

Looking at this type of data can help pinpoint the actions you can take to meet your diversity goals. Here at Numerical Insights, we have generated custom workforce and academic reports and fully interactive workforce dashboards to better assist those that are responsible for strategic recruiting of personnel.

After Hiring/Onboarding, Track the Terminations and Retention of Diverse Hires

Once you hire diverse candidates, what happens to them? I’ll use gender for this example since it’s the most common diversity category tracked by many large companies.

  • Do women leave the company faster than men?

  • Do they leave even faster at higher management levels?

Segmenting your workforce to look at your employee data will help you zoom into the areas of concern within your organizations. There’s no return on your efforts if you try to solve every retention problem throughout your company. Use analytics to focus your efforts on what will yield the most value. Creating focus will drive specific actions.

So what point am I try to make? I’m seeing quite a few companies missing their diversity targets because they fail to use the data to prioritize the actions they should take. Hoping to hit your diversity target is not a strategy. Like many HR strategic initiatives, eventually someone will ask you to prove the value of it.

What Factors of Diversity Are Being Analyzed Today?

Several diversity topics are being examined. Gender is the most obvious and we have discussed that one in this article, but companies within the U.S. are also looking at veteran/non-veteran equity and proper representation across ethnicity.

Government involvement in diversity initiatives varied by country. Within a few European countries, we see mandated quotas of female representation on boards and executive level jobs. Most companies are now setting diversity goals for their entire workforce since that's the pipeline that eventually feeds the higher management positions.

The Difference Between the Gender Pay Gap and Pay Equity

The news headlines after UK companies reported their “gender pay gap” were highly sensationalized. Here are a few examples. 

  • How Can We Fix the Music Industry’s Shocking Gender Pay Gap? – Huffington Post UK

  • American Companies such as Goldman Sachs Among UK’s Worst Gender Pay Gap Offenders – New York Daily News

  • Science’s Vast Gender Pay Gap Revealed in UK Wage Data – nature.com

  • Britain Aims to Close Gender Pay Gap with Transparency and Shame – New York Times

What the media failed to understand is that the gender pay gap measure that UK companies had to report was not a measure of pay equity.

Gender Pay Gap vs. Pay  Equity

Gender pay reporting legislation by Government Equalities Office (GEO) in the UK requires employers with 250 or more employees to publish statutory calculations every year showing how large the pay gap is between their male and female employees. 

Equal pay deals with the pay differences between men and women who carry out the same jobs, similar jobs or work of equal value. That’s not what the gender pay gap measures. 

The gender pay gap is defined as the difference in the average pay between men and women. Averages tell us very little in the analytics world as there are so many factors that impact an average and lead the public to draw incorrect conclusions. Specifically, companies had to report the following for men and women: 

  • The mean pay

  • The median pay

  • The mean bonus pay

  • The median bonus pay

  • The proportion of males receiving a bonus payment

  • The proportion of females receiving a bonus payment

  • The proportion of males and females in each quartile pay band

How the Definition of Gender Pay Gap Made Excellent Companies Look Bad 

Mathematically, we are dealing with averages and medians. If you’re not a math person, the median is just the “middle value” of a set of values. Let’s see how mandating these values can make excellent companies look horrible. 

Reporting averages does not take into account factors such as job specialization and gender variation among specializations. For example, a company that is highly engineering or science-based will likely have a much greater percentage of men in its workforce. Certain STEM-based professions (Science, Technology, Engineering and Math) are naturally male-dominated because the graduating classes are male-dominated. That’s the hiring pool, so through no fault of any company, these STEM companies will be “more male” in the specialized jobs. 

As a personal example, when I attended the University of Waterloo to study applied math and mechanical engineering, most of my math classes were 10 men for every woman. When I walked into my first engineering class, it was 200 men and me. So, since we know that these types of specializations are chosen by men more often than women, we can’t penalize STEM-based companies for choices that men and women made years before joining anyone’s workforce. 

As another example, it is not unusual in STEM-based companies for there to be a higher percentage of men in the upper levels. Since the UK mandated the reporting of the average value, having more men in the upper levels instantly puts a company in an unfavorable light by the larger impact they have over the average. 

Having a larger percentage of men in the upper levels is not necessarily an indication that the company has a bias against women. For example, I have examined the fairness of promotions and the “flow of women” up the organizational levels for several STEM-based companies. These companies have gone to great lengths to try to balance the gender in the management levels. However, sometimes what the data shows is that while the company has made these upper-level jobs available for anyone to apply to, women are choosing not to apply to positions in the management ranks. For example, if the pool of people that could apply was 50% female, we saw that the candidate pool of those that chose to apply was only 30% female. There are multiple reasons why this happens which have been proven by corporate data and scientific studies.

So, by the math of the defined average that must be reported, merely having more men in the upper levels of your company will skew your average and widen your gender pay gap. But remember, this has nothing to do with pay equity. Pay equity is ensuring that men and women at the same levels and performing similar jobs are being paid the same.

Why the US Chose not to Mandate Reporting of Pay

The USA was heading down the path of mandating the reporting of pay on a company’s annual EEO-1 form. The deadline for reporting was March 2018. In August 2017, the OMB (Office of Management and Budget) halted this requirement. Here is a brief quote from the official memo. 

“Among other things, OMB is concerned that some aspects of the revised collection of information lack practical utility, are unnecessarily burdensome, and do not adequately address privacy and confidentiality issues.” 

Lacking practical utility means that the testimony provided to the EEOC from a corporate Vice President of Diversity of Inclusion proved that the proposed reporting requirement would not accurately report on what the EEOC was trying to measure. In short, measuring the pay gap would not be an indicator of pay equity. Reporting the proposed values then became an unnecessary burden since it didn’t serve the purpose that was originally intended when the reporting requirement was first conceived. 

Scientific Studies and Corporate Data Draw the Same Conclusions

For several years, I have been working with large, global companies analyzing topics related to gender. Specifically, many companies are concerned with providing equity between men and women.

  • Are we promoting men and women in equal proportions?

  • Are we hiring fairly across gender?

  • Are we paying men and women equal pay for the same job role?

In recent times, this topic has received great focus in the news. Unfortunately, there is often a large difference between facts and opinions. Real company data show results that are very much in line with studies that have been conducted by academic professionals for the last 30 years. Here are a few real results.

Women are primarily choosing to stay out of technical professions such as those relating to science, technology, engineering and math. Since these professions tend to command a higher salary on the job market, their own career choice is contributing to the gender pay gap. The exception to these choices seems to be in fields such as biotechnology and environmental sciences which can be viewed as more “caring sciences.” To be clear, within these sentences I am stating the facts regarding career choices and the impact these choices have on female pay levels. Why these choices are made is an entirely separate topic.

There is also scientific evidence that women are less likely to desire promotions or leadership positions. While this is not the case for all women, many women view leadership positions as being in conflict with their desire for family and time with their family.

Confidence levels between the genders has also been extensively studied. Studies testing male and female confidence levels pertaining to entrepreneurship show a higher self-efficacy level in men. When applying for jobs, women tend to interpret job requirements as absolute. They are less likely than men to apply for a job unless they have almost 100% of the listed skills.

Scientific studies also show that men tend to have a higher level of competitiveness than women. In an experiment where the first task given to men and women was easy and test subjects could pick the difficulty level of the subsequent task, men were 50% more likely to select a harder task.

More than 31 scientific studies are summarized in an eBook called,  Gender Gaps in the Workplace: Facts and Science. This eBook shows that it is the choices that women make about career and family that affect the gender pay gap the most, and yet, society blames the companies for which they work.

Having analyzed a great deal of gender data for large companies and tested the fairness of promotions, performance scores and hiring, the results I see are consistent with the scientific studies presented. The gender pay gap is driven primarily by career choices (made for a variety of reasons) and aspiration levels for management and leadership positions.

Armed with this knowledge, companies can focus on gender diversity initiatives that will be more effective. For example, knowing that most women assume that entering management means that long hours and travel are required, which may interfere too much with their dedication to family life, companies can explicitly alter job descriptions to point our management positions that do not require extensive travel and long hours. An another example, knowing that men and women respond differently to job descriptions that have aggressive vocabulary vs. more “socially friendly” words, altering how the job description duties are phrased can chance the number of women that are willing to apply.

Knowledge is power… and the careful application of that knowledge to diversity initiatives can drive greater changes in your organization.



Author's Notes:

  • I received a question from a reader asking me whether companies have a role in solving societal problems. My reply was as follows: "Voluntarily, they do contribute to closing the gap. I work with several that do. However, the issue starts long before men and women enter the workforce."

  • I received multiple requests from both companies and academia for additional information on this topic, so the following eBook was created and released on Amazon. 

Tracey Smith is an internationally recognized business author, speaker and analytics consultant. She is one of the most highly respected voices when it comes to business analytics and HR analytics. She is the author of multiple business books and hundreds of articles in a variety of publications. Tracey has worked with and advised organizations, both well-known and little-known, on how to use data analytics to impact the bottom line. If you would like to talk to Tracey about consulting work or speaking engagements, please use the Contact Us form.