measuring eeo compliance

Measuring Compliance

presented by

Stephanie R. Thomas, Ph.D.Director, Equal Employment Advisory and

Litigation Support DivisionMCG


presented by



Good afternoon, and welcome to the first installment of vidEEO. My name is Stephanie Thomas. Today we’ll be talking about measuring compliance.


presented by



Before we get started, I’d just like to say that I am an economic and statistical consultant, and not a lawyer. The information I’ll be presenting should not be construed as legal advice.

EEO Policy Statement

I’m going to assume that you‘re all familiar with the basic idea of an EEO policy. Typically, the policy will begin with something like this:

EEO Policy Statement “It is the policy of the Employer to provide equal employment

opportunities to all qualified persons, and to recruit, hire, train, promote, and compensate persons in all jobs without regard to race, color, religion, sex, national origin, disability, or sexual orientation.”

“It is the policy of the employer to provide equal employment opportunities to all qualified persons, and to recruit, hire, train, promote, and compensate persons in all jobs without regard torace, color, religion, sex, national origin, disability, or sexual orientation.”

EEO Policy Statement• Typically contains language about measuring the success of

your EEO program:

The EEO policy typically includes some language about measuring the success of your EEO program.


your EEO program:– Evaluating EEO progress and developing alternative approaches where

necessary

Commonly, you’ll see statements about evaluating EEO progress and developing alternative approaches where necessary,



necessary– Designing and implementing audit and reporting systems to allow

continual monitoring of EEO progress

designing and implementing audit and reporting systems to allow continual monitoring of EEO progress, and




continual monitoring of EEO progress– Periodically audit training programs and hiring and promotion patterns

so that any impediments to achieving the organization’s goals and timetables are removed

periodic audits of training programs and hiring and promotion patterns so that any impediments to achieving the organization’s goals and timetables are removed.




continual monitoring of EEO progress– Periodically audit training programs and hiring and promotion patterns

so that any impediments to achieving the organization’s goals and timetables are removed

Statements about periodic auditing and identification of impediments are important to the EEO policy, but they’re pretty abstract.

How do we measure ourEEO “success”?

How do we measure our EEO success?


Number of complaints brought to HR?

Is it the number of complaints brought to HR?


Number of complaints brought to HR?The amount of time spent on EEO issues by the legal department?

The amount of time spent on EEO issues by the legal department?



Number of lawsuits filed against company?

The number of lawsuits filed against the company?


Total expenditures on settlements, mediations and conciliations of EEO claims?



Our total expenditures on settlements, mediations and conciliations of EEO claims?





These are all “reactionary” strategies

All of these strategies are “reactionary” – they’re essentially waiting for a problem to occur, and then measuring the problem.





Waiting until you’ve paid out thousands – or even hundreds of thousands – of dollars on settlements, mediations and conciliations is not the way to go.

These are all “reactionary” strategies

Quantifying – and Communicating – Success Can Be Difficult

Part of the reason we adopt reactionary strategies is because quantifying EEO success is hard. And communicating it is even harder.


• Difficult to analyze and understand the data

It’s difficult to analyze and understand the data. Your analysis is only as good as your data. And if you’re not collecting and maintaining the right data in the right way, you can’t do an analysis.



Some of the analyses rely on external benchmarks. It’s not always easy to identify the appropriate benchmark, and if you use the wrong benchmark, your analysis is useless.



It’s hard to understand the data and the results of the analysis. What does it really mean if males are underutilized in your administrative support job group?


• Difficult to analyze and understand the data• Difficult to communicate what the data reveal

It’s also hard to communicate to others what the analyses show. If you’re the one performing the analyses, you probably have a pretty good understanding of what’s going on, because you are familiar with the data



and the analysis method, and you know what you’re looking for. For someone not familiar with the analyses and the concepts, it’s hard to understand what the analyses are showing, and it’s difficult to explain it to them.



For those that do these analyses day in and day out, trying to explain to someone what underutilization is can be kind of like trying to explain how two plus two equals four. You just know it, and it’s really hard to explain to someone who doesn’t get it.


• Difficult to analyze and understand the data• Difficult to communicate what the data reveal• Difficult to educate managers in the legal

requirements, technical content and statistical analysis of EEO data

It’s really hard to educate your manager and supervisor population about this stuff. To really understand it, they need to know about the legal requirements, the technical details, and the statistics.


• Difficult to analyze and understand the data• Difficult to communicate what the data reveal• Difficult to educate managers in the legal

requirements, technical content and statistical analysis of EEO data

Training them is not always successful. Some times they’re too busy with other issues, sometimes they just don’t care, and sometimes they simply don’t get it, no matter how you explain it.


• Managers and supervisors can be overwhelmed with presentations and data

They can also easily get overwhelmed with presentations and with data. If they’re overwhelmed, they’re not going to focus, and they won’t understand.



• Even after presentations and discussions, many still have little idea of how they’re doing

And even after our reports and presentations, they still may have no idea about how they’re doing in terms of compliance.



• Even after presentations and discussions, many still have little idea of how they’re doing

It’s our responsibility to present the information in such a way that it’s easily digestible and people will get it. What we need is a bottom line solution.

Looking for a Bottom Line Solution

• Relatively easy to calculate from available data

We need something that’s relatively easy to calculate from available data.If the data is really hard or really expensive to collect, it doesn’t matter how great our solution is, we’re not going to be able to implement it.


• Relatively easy to calculate from available data• Easy to explain

Our bottom line solution should be easy to explain. We shouldn’t have to rely on jargon or abstract concepts when we explain the metric to others.


• Relatively easy to calculate from available data• Easy to explain• Intuitive

We want the solution to be intuitive- people with no experience with EEO analyses and metrics should be able to get it. We want it to have the intiuitivness of “two plus two equals four”. People get it right away.


• Relatively easy to calculate from available data• Easy to explain• Intuitive• Bonus = can be displayed visually

And as an added bonus – it’s really nice if the information can be presented visually. The old saying “a picture is worth a thousand words” is really true here.



We’re so used to getting information visually – charts, graphs, the sexy new infographics as they’re called – it helps a lot to have a visual representation to communicate your point.



So what’s the bottom line solution? There’s a lot of different ones, and you may even have one that you regularly use that works really well for you and your organization.



But for those that don’t have a bottom line solution, or would like to consider alternatives, today I’m going to talk about something called the SMG Index.

The SMG Index

• Developed by Microsoft• The SMG Index acronym comes from the three

employees who collaborated on its development: – Jonathan Stutz, PHR, senior diversity consultant;– Randy Massengale, SPHR, senior diversity

manager; – Andrea Gordon, human resource director.

The SMG Index was developed by Microsoft. It’s called SMG because of the three people who created it. Jonathan Stutz, Randy Massengale, and Andrea Gordon.

The SMG Index

• Provides a single, separate numerical index for each protected group

The SMG Index can be calculated for each protected group. So you can have one index number for women, one for African Americans, one for Hispanics, one for Asians, and so forth.

The SMG Index


• Allows comparisons of organizational groups’ performance relative to goals or to other groups

The index lets you compare a group’s performance to its goals. You can also compare one group’s performance to another group’s.

The SMG Index



• Can be used as benchmark, measuring the success or failure of specific programs

You can also use the index as a benchmark for measuring the success of a specific program. For example, let’s say you launched an outreach campaign to attract more minority applicants.

The SMG Index




You can use the metric to assess whether your campaign was successful. You would calculate the index for the time period immediately before the campaign, and then calculate the index for the time period right after the campaign ends.

The SMG Index




You would look at how the index value changed, and if the index improves, then you could conclude that your campaign was successful.

The SMG Index

• “Original” SMG Index based on existing data points within standard required EEO reports

The original SMG index was calculated using the data available within the standard required EEO reports. A separate index was calculated for each protected group in each of the 10 job categories.

The SMG Index


The data used come from three different reports- the utilization analysis, the promotions analysis, and the terminations analysis.

The SMG Index


• Can be modified to focus on different job groups, functional lines of business, department, etc.

As I mentioned, the original used the ten EEO job categories. But there’s no reason it couldn’t be modified to look at different job groupings, or by functional lines of business, department, location, or other characteristic.

The SMG Index



The important thing to keep in mind here is that the grouping needs to make sense for the question you’re looking at. If your grouping doesn’t make sense, your analysis won’t either.

The SMG Index



So, for this presentation, we’re going to stick with the original calculation and use the 10 EEO job categories.

Sample EEO-1 Report

Male Female WhiteBlack or African

American

Native Hawaiian or Other Pacific

Asian

American Indian or Alaskan Native

Two or more races

WhiteBlack or African

American


Asian


Two or more races

A B C D E F G H I J K L M NExecutive/Senior Level Offi cials and Managers

1 0 2 1 0 2 0 0 2 0 0 1 1 0 10

First/Mid-Level Offi cials and Managers

0 1 9 1 0 1 0 0 5 2 0 1 0 0 20

Professionals 15 12 57 28 2 14 0 0 22 6 0 9 0 0 165Technicians 11 0 41 11 0 7 0 0 3 2 0 0 0 0 75Sales Workers 7 4 37 19 0 2 0 0 13 7 0 1 0 0 90Administrative Support Workers 0 6 0 1 0 0 0 0 20 10 0 3 0 0 40

Craft Workers 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Operatives 1 0 2 3 0 2 0 0 1 1 0 0 0 0 10

Laborers and Helpers 5 0 5 5 0 0 0 0 0 0 0 0 0 0 15

Service Workers 3 1 1 2 0 0 0 0 1 2 0 0 0 0 10Total 43 24 154 71 2 28 0 0 67 30 0 15 1 0 435

Hispanic or LatinoMale FemaleJob Categories

Total

Not-Hispanic or Latino

Race/Ethnicity

Number of Employees(Report employees in only one category)

Here’s a sample EEO-1 report. I’m going to assume that you’re all familiar with the basics of this report. We have the ten job categories listed down the left hand side.

Sample EEO-1 Report


American


Asian


Two or more races


American


Asian


Two or more races


1 0 2 1 0 2 0 0 2 0 0 1 1 0 10


0 1 9 1 0 1 0 0 5 2 0 1 0 0 20






Total


Race/Ethnicity


And for each job category, we have headcounts by race and gender. So, for example, in the Executive and Senior Level Officials and Managers, we have one Hispanic male and no Hispanic females.

Sample EEO-1 Report


American


Asian


Two or more races


American


Asian


Two or more races


1 0 2 1 0 2 0 0 2 0 0 1 1 0 10


0 1 9 1 0 1 0 0 5 2 0 1 0 0 20






Total


Race/Ethnicity


We have two white males, one African American male, no native Hawaiian or other pacific males, two Asian males, and no males that are American Indian or Alaskan Native or two or more races.

Sample EEO-1 Report


American


Asian


Two or more races


American


Asian


Two or more races


1 0 2 1 0 2 0 0 2 0 0 1 1 0 10


0 1 9 1 0 1 0 0 5 2 0 1 0 0 20






Total


Race/Ethnicity


We have two white females, no African American or native Hawaiian females, one Asian female, one American Indian or Alaskan Native female, and no females of two or more races.

Sample EEO-1 Report


American


Asian


Two or more races


American


Asian


Two or more races


1 0 2 1 0 2 0 0 2 0 0 1 1 0 10


0 1 9 1 0 1 0 0 5 2 0 1 0 0 20






Total


Race/Ethnicity


These headcounts are filled in for each of the ten EEO job categories. Using these headcounts, and some kind of availability estimate, we can perform a utilization analysis.

AdditionalAvailability Females

Job Category Total Female % Female Estimate Difference Needed

Executive/Senior Level Offi cials and Managers 10 4 40.0% 39.0% -1.0% -

First/Mid-Level Offi cials and Managers 20 9 45.0% 39.0% -6.0% -

Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0

Administrative Support Workers 40 39 97.5% 85.0% -12.5% -

Craft Workers 0 0 N/A N/A N/A N/A

Operatives 10 2 20.0% 15.0% -5.0% -

Laborers and Helpers 15 0 0.0% 10.0% 10.0% 1.5

Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

Here we see an example utilization report. I’m not going to get into the details of constructing availability estimates. That’s a topic that could fill up a whole separate presentation.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

The important thing here is that by comparing our availability estimate to the percentage of protected group members in each job category, we can come up with a shortfall estimate.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

For example, looking at the Executive and Senior Level Officials and Managers, we have a total of ten employees. Four, or forty percent, are female.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

Our female availability for this category is 39 percent. We calculate the difference between the 39 percent availability and the 40 percent actual, which is negative one percent.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

The difference here is negative because our actual percentage is bigger than our availability. So we have more women in this group than we would have expected, based on the availability.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

So we go through and repeat this calculation for each of our groupings, making sure to record the sign of the difference. The positives and negatives are important here.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

Then, for each group that has a positive difference, where the availability is bigger than the actual female percentage, we calculate the number of additional females we would need to get our actual percentage to match the availability percentage.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

We do this by multiplying the percentage difference by the total number of people in the job category.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

So for the Professionals category, there’s a 0.3 percent difference between actual and available. We multiply this 0.3 percent by the total number of people in the group, which is 165.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

When we multiply 0.3 percent by 165, we get 0.5. This means we would need to have one half of an additional female in the Professionals group for actual utilization and availability to be equal.





Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

Utilization Report

We go through and repeat the calculation for each of the job categories. But remember, we only do the calculations for those groups with a female shortfall. After we finish up the calculations for the groupings in the utilization analysis, we move on to promotions.

AdditionalFemales

Job Category Total Female % Female Expected Difference Needed

Executive/Senior Level Offi cials and Managers 0 0 N/A 40.0% N/A -


Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

We’re going to look at the same groupings as we had in the utilization analysis. It’s important that the groupings used are consistent throughout the analysis.

AdditionalFemales




Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

The calculation is basically the same as in the utilization section. For each group, we compare the actual female promotions, in terms of a percentage of total promotions, to the expected percentage.

AdditionalFemales




Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

So let’s look at the first and mid-level officials and managers. There were two promotions, one of which was female. So females were 50 percent of all promotions.

AdditionalFemales




Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

Among the eligible pool for promotions, females were 45 percent. The difference here is negative 5 percent. This means we promoted more females in this group than expected. Since the difference is negative, which is favorable to females, we stop here and move on to the next grouping.

AdditionalFemales




Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

Looking at the professionals, we see that there were 17 promotions, 4 of which were female. Females were 23.5 percent of all promotions in the professionals grouping. Among the eligible pool for promotions, females were 29.7 percent.

AdditionalFemales




Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

The difference here is 6.2. We multiply the 6.2 percent by the total number of promotions, 17, to calculate the number of additional female promotions within the professionals grouping we would need in order for the actual and expected percentages to be equal.

AdditionalFemales




Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

In this case, we would need one additional female promotion.We then move to the next grouping and repeat the calculations. We do this calculation for all of the groupings that have a positive difference. Remember, if the difference is negative, the outcome is favorable to females.

AdditionalFemales




Professionals 17 4 23.5% 29.7% 6.2% 1.0

Technicians 8 0 0.0% 6.7% 6.7% 0.5

Sales Workers 19 5 26.3% 27.8% 1.5% 0.3



Operatives 22 4 18.2% 20.0% 1.8% 0.4


Service Workers 2 1 50.0% 40.0% -10.0% -

# of Promotions

Promotions

After we finish up the calculations for the groupings in the promotions analysis, we move on to terminations.

SurplusFemale

Job Category Total Female % Female Expected Difference Terminations

Executive/Senior Level Offi cials and Managers 0 0 N/A 40.0% - -


Professionals 3 1 33.3% 29.7% 3.6% 0.1

Technicians 1 1 100.0% 6.7% 93.3% 0.9

Sales Workers 4 1 25.0% 27.8% -2.8% -

Administrative Support Workers 2 2 100.0% 97.5% 2.5% 0.1

Craft Workers 0 0 N/A N/A - -

Operatives 1 0 0.0% 20.0% -20.0% -

Laborers and Helpers 0 0 N/A 0.0% - -

Service Workers 0 1 N/A 40.0% - -

# of Terminations

Terminations

We’re going to look at the same groupings as in the utilization and promotion analyses. For each group, we compare the actual female terminations, in terms of a percentage of total terminations, to the expected percentage.

SurplusFemale




Professionals 3 1 33.3% 29.7% 3.6% 0.1

Technicians 1 1 100.0% 6.7% 93.3% 0.9

Sales Workers 4 1 25.0% 27.8% -2.8% -



Operatives 1 0 0.0% 20.0% -20.0% -



# of Terminations

Terminations

Let’s go right to the Professionals group and look at the terminations here. There were three terminations, one of which was female. So the female percentage is 33.3 percent.

SurplusFemale




Professionals 3 1 33.3% 29.7% 3.6% 0.1

Technicians 1 1 100.0% 6.7% 93.3% 0.9

Sales Workers 4 1 25.0% 27.8% -2.8% -



Operatives 1 0 0.0% 20.0% -20.0% -



# of Terminations

Terminations

Among those eligible for termination, females were 29.7 percent. So we terminated more females in this group than expected. The difference here is 3.6%.

SurplusFemale




Professionals 3 1 33.3% 29.7% 3.6% 0.1

Technicians 1 1 100.0% 6.7% 93.3% 0.9

Sales Workers 4 1 25.0% 27.8% -2.8% -



Operatives 1 0 0.0% 20.0% -20.0% -



# of Terminations

Terminations

The thing to keep in mind with terminations is that it’s a negative employment outcome. So rather than looking for a shortfall like we did in the utilization and promotion analyses, herewe’re going to be looking for a surplus of females.

SurplusFemale




Professionals 3 1 33.3% 29.7% 3.6% 0.1

Technicians 1 1 100.0% 6.7% 93.3% 0.9

Sales Workers 4 1 25.0% 27.8% -2.8% -



Operatives 1 0 0.0% 20.0% -20.0% -



# of Terminations

Terminations

So rather than subtracting the actual from expected, here we subtract the expected from the actual. The only difference is in the sign. So if the actual female percentage is bigger than the expected percentage, the difference is positive. If actual is smaller than expected, the difference is negative.

SurplusFemale




Professionals 3 1 33.3% 29.7% 3.6% 0.1

Technicians 1 1 100.0% 6.7% 93.3% 0.9

Sales Workers 4 1 25.0% 27.8% -2.8% -



Operatives 1 0 0.0% 20.0% -20.0% -



# of Terminations

Terminations

We then take our 3.6 percent difference and multiply it by the total number of terminations, 3, to get our surplus estimate. And in this case, we have a surplus of 0.1 female terminations.

SurplusFemale




Professionals 3 1 33.3% 29.7% 3.6% 0.1

Technicians 1 1 100.0% 6.7% 93.3% 0.9

Sales Workers 4 1 25.0% 27.8% -2.8% -



Operatives 1 0 0.0% 20.0% -20.0% -



# of Terminations

Terminations

Just like before, we repeat this calculation for all of the groupings, only calculating the surplus where the difference is positive. Once we finish up with our surplus female terminations calculations, we’re ready to calculate the overall index.

Total

Job Category Employees Utilization Promotion Termination

Executive/Senior Level Offi cials and Managers 10 0.0 0.0 0.0 0.00

First/Mid-Level Offi cials and Managers 20 0.0 0.0 0.0 0.00

Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03

Administrative Support Workers 40 0.0 0.0 0.1 0.00

Craft Workers 0 N/A N/A N/A N/A

Operatives 10 0.0 0.4 0.0 0.04

Laborers and Helpers 15 1.5 0.0 0.0 0.10

Service Workers 10 1.0 0.0 0.0 0.10

Additional FemalesSMG Index

The SMG Index

Again, we’re going to use the same groupings – consistency is critical. And what we’re going to do is record our utilization and promotion shortfalls and termination surpluses in the chart.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

Now we’re ready to actually calculate the SMG index. The calculation is really simple. We add up the utilization shortfall, promotion shortfall, and termination surplus, and then divide by the number of all employees in that grouping.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

So the first grouping that has anything to calculate is professionals. We add the 0.5 utilization shortfall, the 1.0 promotion shortfall and the 0.1 terminations surplus together.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

This is equal to 1.6. We then divide the 1.6 by the total number of professional employees, 165. And we get 0.01. This is the SMG index for the professionals grouping.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

We go through each of our groupings and do the same calculations, and when we’re done, we have an SMG index for each of the groupings.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

The next question is what does the SMG index tell us, and how to we interpret it.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

The ideal value for the index is zero. The closer we are to zero, the better our performance on utilization, promotion and termination for the given protected group.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

In a perfect situation, our utilization and promotion shortfalls and termination surpluses would be zero. And when we divide zero by anything, we get zero. So the perfect value is zero.

Total




Professionals 165 0.5 1.0 0.1 0.01

Technicians 75 1.0 0.5 0.9 0.03

Sales Workers 90 2.0 0.3 0.0 0.03



Operatives 10 0.0 0.4 0.0 0.04




The SMG Index

By looking at how far away from zero we are, we can get an idea of how well we’re performing, and how one group compares to another.

It’s not what you say,but how you say it

There’s one final point I’d like to make. It’s not what you say, but how you say it. I’m going to show you two representations of the same information.

Which Do You Prefer?

Which do you prefer?

Which Do You Prefer?Additional

Availability Females




Professionals 165 49 29.7% 30.0% 0.3% 0.5

Technicians 75 5 6.7% 8.0% 1.3% 1.0

Sales Workers 90 25 27.8% 30.0% 2.2% 2.0



Operatives 10 2 20.0% 15.0% -5.0% -


Service Workers 10 4 40.0% 50.0% 10.0% 1.0

# of Employees

This?


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

Executive/Senior Level Officials and ManagersFirst/Mid-Level Officials and ManagersProfessionalsTechniciansSales WorkersAdministrative Support WorkersOperativesLaborers and HelpersService Workers

Percent Female in Group

Or this? They both present the same information. But for me, and probably for you too, you’d rather see the information visually.


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%



In this display, we’re showing the percent female in each of our 10 groupings along the horizontal axis, and the SMG index value along the vertical axis. The size of the spheres represent the number of total employees in the group.


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%



We can see right away which groups have the most employees, which have the most females, and how they all compare in terms of the index.


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%



Your visual display doesn’t have to be as complex as this one. Even the simplest of charts conveys a lot of information easily.


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Measuring compliance can be hard, and explaining it to managers and supervisors is even harder. The key is to adopt a bottom line solution that relies on proactive, rather than reactive, information and to present that information in an intuitive way.


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If you can do this, you’ll be successful at measuring compliance.


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measuring eeo compliance

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