1. A Study of the Disparity in Wages and Benefits Between Men and Women in Wyoming: Update 2018
Presented to the Joint Labor, Health, and Social Service Committee of the Wyoming Legislature in Casper, Wyoming, October 4, 2018, by Tony Glover, Manager, and Patrick Harris, Senior Economist, Research & Planning, Wyoming Department of Workforce Services

2. Research & Planning http://doe.state.wy.us/LMI
OUR ORGANIZATION:
R&P is an exclusively statistical entity within DWS.
WHAT WE DO:
R&P collects, analyzes, and publishes timely and
accurate labor market information (LMI)
meeting established statistical standards.
OUR CUSTOMERS:
LMI makes the labor market more efficient by
providing the public and the public’s
representatives with the basis for
informed decision making.
Research & Planning is a statistical entity within the Department of Workforce Services that collects, analyzes and publishes objective data and research.

3. About this Project
A Study of the Disparity in Wages Between Men and Women in Wyoming: Update 2018
House Bill 0209 (2017)
Update to A Study of the Disparity in Wages Between Men and Women in Wyoming, published in 2003
The Department of Workforce Services was directed to update a study from 2003 on the Gender Wage Gap.
This study recreates some of the analysis from 2003 report on larger scale utilizing several administrative databases.

4. About the Data
About the data

5. Data Sources The current research utilizes administrative data from
About the Data
About
The Data
Data Sources
The current research utilizes administrative data from
DWS,
WDH,
WCCC,
UW, and the
DOT.
Many of data sources spanned 20 or more years of micro data on Wyoming’s labor force.
We also used data from the Census Bureau American Community Survey and the Bureau of Labor Statistics. Many of data sources spanned 20 or more years of micro data on Wyoming’s labor force.
Although the data were extensive thay are incomplete as you will see in the next few slides.

6. Figure: Total Persons Working in Wyoming by Gender, 2016
About the Data
About
The Data
Gender
Figure: Total Persons Working in Wyoming by Gender, 2016
The majority of demographic data were collected utilizing driver’s license.
The remainder of the demographic data was added by abstracting data from UI Claims and other administrative databases.
This leaves 19,236 or 7.1% with an unknown gender.

7. Figure: Total Persons Working in Wyoming by Age, 2016
About the Data
About
The Data
Age
Figure: Total Persons Working in Wyoming by Age, 2016
This is a breakdown by age groups

8. Wyoming Driver’s License
About the Data
About
The Data
Wyoming Driver’s License
Figure: Number of Years with a WY Driver’s License for Persons
Working in Wyoming, 2016
More than half of the persons working in Wyoming in 2016 have been attached to Wyoming (via drivers license) more than 10 years.
There are also 40,088 that are non-resident workers.

9. Lifetime Primary Industry of Employment
About the Data
About
The Data
Lifetime Primary Industry of Employment
Figure: Number of Persons Working in Wyoming in 2016 by Primary Lifetime
Industry of Employment
This image shows the primary industry (industry paying the most wages) over the lifetime of the individuals by gender.

10. Lifetime Primary Industry of Employment
About the Data
About
The Data
Lifetime Primary Industry of Employment
Figure: Number of Persons Working in Wyoming in 2016 by Primary Lifetime
Industry of Employment
This is first slide that shows a separation in labor force attachment between men and women.
Men tend to work in goods producing industries such as
mining,
construction and
manufacturing,
as well as, the services industries of
Wholesale Trade,
Transportation, and
Utilities
which are primarily industries that support the goods producing sector.

11. Lifetime Primary Industry of Employment
About the Data
About
The Data
Lifetime Primary Industry of Employment
Figure: Number of Persons Working in Wyoming in 2016 by Primary Lifetime
Industry of Employment
Women tend to be found in the services industries of Educational Service,
Healthcare Services, and
Leisure and Hospitality.

12. Now a few things we don’t know.
About the Data
About
The Data
Availability of Education Data
Figure: Availability of Education Data for Persons Working in Wyoming, 2016
Now a few things we don’t know.
For example, while R&P has administrative data for Wyoming’s Community Colleges and the University of Wyoming from 2006 to present.
Individuals that received education at an earlier time or from another state are part of the 65.3% of the workforce for which we have no education.

13. Availability of Hours Worked
About the Data
About
The Data
Availability of Hours Worked
Figure: Persons Working in Wyoming in 2016 with Hours Attached
to Wage Records
Another data element with limited information is hours worked.
R&P is missing hours worked for 35.9% of the 2016 labor force.
Hours is a voluntary field attached to Unemployment Insurance Wages Records.
Data are submitted for approximately 74% of all submissions, after quality assurance standards are applied we have hours for 64.1% of the records.
The importance of hours are to establish a rate of pay which is more appropriate to analyze than the total wages paid in the year.

14. Full-Time Equivalency
About the Data
About
The Data
Full-Time Equivalency
Figure: Persons Working in Wyoming in 2016 by Gender and Lifetime
Percent of Full-Time Equivalency (FTE)
Hours allowed R&P to determine Full Time Equivalency utilization as seen in the distribution above.
FTE was determined as the number of hours worked as ratio to 2080 potential hours worked in a year.

15. Full-Time Equivalency
About the Data
About
The Data
Full-Time Equivalency
Figure: Persons Working in Wyoming in 2016 by Gender and Lifetime
Percent of Full-Time Equivalency (FTE)
Men appeared more often than women working at an FTE level greater than 100%.

16. Full-Time Equivalency
About the Data
About
The Data
Full-Time Equivalency
Figure: Persons Working in Wyoming in 2016 by Gender and Lifetime
Percent of Full-Time Equivalency (FTE)
Women more often appeared in the range of 26 to 100%

17. Availability of Occupation
About the Data
About
The Data
Availability of Occupation
Figure: Availability of Occupation Data for Persons Working in Wyoming, 2016
Another critical piece of missing information is the Occupation of the individual.
R&P was only able to identify occupations on 23.2% of workers in 2016.
The primary sources for occupations were Department of Education Staffing files, State Employee Auditor files, and R&P’s Job Skills Survey.
Healthcare Licensing databases were utilized to infer occupation based on licensing status.

18. Availability of Occupation by Gender
About the Data
About
The Data
Availability of Occupation by Gender
Figure: Availability of Occupation Data for Persons Working in Wyoming
by Gender, 2016
A result of the occupations inferred from healthcare licensing boards created the unequal distribution of occupations between men and women, as shown in this figure.

19. States with Enhanced Wage Records
About the Data
About
The Data
States with Enhanced Wage Records
Issue related to the availability of hours, to create pay rates have been addressed in Minnesota, Nebraska, Oregon, Rhode Island, Washington and Louisiana by adding hours to their Wage Records.
Shortcomings related to Occupations were addressed by Alaska, Louisiana, and Nebraska by adding occupation to their Wage Records.

20. Key Findings

21. $0.68 on the dollar (Chapter 1) Full-time, year-round work in 2016
Key Findings: Wage Gap
Key Finding
Wyoming’s gender wage gap varies depending on the data source used and the limitations placed upon the data.
Examples
$0.68 on the dollar (Chapter 1)
Full-time, year-round work in 2016
Source: American Community Survey 5-year estimates
$0.74 on the dollar (Chapter 2)
Worked at least two consecutive quarters in 2016
Source: Wage Records, Research & Planning, WY DWS
$0.86 on the dollar (Chapter 3)
Worked at least two consecutive quarters from
Occupation data available
In this publication, there may be discrepancies in the wage gap based on the data sources used.

22. Key Findings: Wage Gap
Figure 2.1: Cents Women Earned on a Man’s Dollar by Selected Characteristics,
2016 … see page 17
Here we see that depending on certain characteristics, the wage gap differs.
Source: Custom extract from Wage Records and other Research & Planning administrative databases.

23. Key Findings: Age and Births
(i)(A) Data and Analysis According to County
The gender wage gap varied by county.
Figure: Gender Wage Gap by Selected County of Employment in Wyoming,
2016 … see page 18 …
Depends somewhat on the primary industry in the county that explains the gap and also population and demographics.
Source: Custom extract from Wage Records and other Research & Planning administrative databases.

24. Key Findings: Age and Births
(i)(B) Data and Analysis According to Occupation
Data on occupations are limited
Occupation data not collected with wage records
Occupation data available to R&P:
Department of Education
Wyoming state licensing boards
State auditor’s file
New Hires Job Skills Survey
Source: Custom extract from Wage Records and other Research & Planning administrative databases.

25. Key Findings: Age and Births
(i)(B) Data and Analysis According to Occupation
Gender wage gap was narrower in many occupations in the educational services industry.
Key Finding
Figure 2.6: Gender Wage Gap for Selected Occupations in Educational
Services in Wyoming, … see page 25
This is just an example of how occupation plays a role within a certain industry.
Source: Custom extract from Wage Records and other Research & Planning administrative databases.

26. Key Findings: Age and Births
(i)(B) Data and Analysis According to Occupation
… see page 36
Source: Custom extract from Wage Records and other Research & Planning administrative databases.

27. Key Findings: Age and Births
(i)(C) Comparative State Data with Other State and Federal Info.
Wyoming had one of the widest gender wage gaps in the country.
Figure: Women’s Earnings Compared to Men’s (Cents on the Dollar) for Wyoming and Other Selected States, 2016
… see page 41
Full-time, year-round workers.
Source: American Community Survey 5-Year Estimates. .

28. Key Findings: Age and Births
(i)(C) Comparative State Data with Other State and Federal Info.
Wyoming had a large proportion of women working in lower paying occupations.
Key Finding
Figure: Women as a % of Total Employment by Region and Selected Occupation, 2016
… see page 47
Full-time, year-round workers.
Source: American Community Survey 5-Year Estimates. .

29. Key Findings: Age and Births
(i)(C) Comparative State Data with Other State and Federal Info.
Wyoming had a small proportion of women working in some higher paying occupations.
Key Finding
Figure: Women as a Percent of Total Employment in Production Occupations by Region, 2016
… see page 47
Women tend to dominate the service occupations such as cashiers and healthcare providers.
Full-time, year-round workers.
Source: American Community Survey 5-Year Estimates. .

30. Key Findings: Hours Worked
(ii) The Causes of Any Wage and Benefit Disparities
Hours worked and industry of employment had the greatest influence on Wyoming’s gender wage gap.
Key Finding
Table: Percent Working in Wyoming in 2016 by Gender
… see page 12
Men = 195,740
Women = 182,751
Worked in Past 12 Months
87.1%
78.5%
Hours Worked = 35+
86.0%
69.2%
Hours Worked = <35
14.0%
30.8%
Did Not Work in Past 12 Months
12.9%
21.5%
Worked Full-Time, Year-Round
61.5%
43.8%
Average Hours Worked per Week
44.0
36.0
Source: U.S. Census Bureau, 2016 American Community Survey 5-Year Estimates.

31. Key Findings: Age and Births
(ii) The Causes of Any Wage and Benefit Disparities
Hours worked and industry of employment had the greatest influence on Wyoming’s gender wage gap.
Key Finding
Total Number of Persons Working in Wyoming by Gender, Industry, and Average Hourly Wage, 2016
… see page 20
We highlighted mining for women because there tend to be fewer women working in mining but for about the same rate of pay. We don’t know, through wage records, what occupations women work.

32. Chapter 1: Introduction
Primary data source: American Community Survey 5-Year Estimates

33. 1 Highlights from the Chapter
Chapter 1: Introduction
Chapter 1
Highlights from the Chapter
Wyoming women made $0.60 to $0.68 for each $1 paid to men,
National average: $0.80 per $1
Wyoming ranked 51st in the nation, ahead of only Louisiana ($0.66 per $1)
Anti-discrimination laws
Possible solutions
Possible solutions – encouraging young women to enter STEM fields early on in their education.
Encourage women to enter different industries such as mining and construction.

34. Chapter 2: Factors that Influence the Gender Wage Gap
Primary data source: Wyoming administrative databases

35. 2 Highlights from the Chapter The gender wage gap:
Chapter 2: Factors that Influence the Gender Wage Gap
Chapter 2
Highlights from the Chapter
The gender wage gap:
varied by county and industry
widened with age
was narrower among individuals with a postsecondary degree
widened with the number of births
Wyoming’s two most populous counties – Laramie & Natrona – women were paid .80 and .82 cents on the dollar compared to men. Teton (.93) is the narrowest while Lincoln is the widest (.61.
Where mining is the dominate industry, such as Sweetwater and Sublette, the wage gap was higher, .59 and .62 respectively.
Younger women tend to make the most relative to men at .96 at ages As the population ages, the gap widens and those women 55 or older are at .70 relative to men. The increase in the gap tends to accelerate at around the ages of – Prime birth years.
The gap was narrowest when women and men earned a bachelor’s or master’s degree. Interestingly, it also narrowed when a post-secondary certificate was attained. – Most likely LPN or CNA
Women who have one or multiple children are also less likely to make the same as men. Women who have zero births earn .75 while those with more than two earn .61.
Employer tenure and firm size also play a role. If a woman worked for a smaller company (less than 50) she tended to earn less.
Further, during economic downturns (for which we’ve had two in the past 10 years), the gap tends to narrow. This is partly due to men in certain industries losing their jobs (e.g. mining).

36. Chapter 3: Occupations and Hourly Wages
Primary data source: Wyoming administrative databases

37. 3 Highlights from the Chapter
Chapter 3: Occupations and Hourly Wages
Chapter 3
Highlights from the Chapter
Women made more than men in 5 occupations:
Office & administrative support workers
Lifeguards, ski patrol, & other recreational protective service workers
Combined food preparation & serving workers
Men made more than women in 76 occupations:
General & operations managers
Accountants & auditors
Physician assistants
No difference in 147 occupations:
Human resource managers
Educational, vocational, & school counselors
Cashiers

38. Chapter 4: Regional Occupational Comparison
Primary data source: American Community Survey 5-Year Estimates

39. 4 Highlights from the Chapter
Chapter 4: Regional Occupational Comparison
Chapter 4
Highlights from the Chapter
Staffing patterns in Wyoming were often different from other regions and the national average.
The gender wage gap varied by occupation and region.
The proportion of women working varied by occupation and region.
As stated in Chapter 1, the US as a whole, the gap was .80 while in Wyoming it was .68 ranking at 51 throughout the nation according to the American Community Survey.
Utah - .70
Montana - .73
North Dakota - .74
Nebraska – .77
Texas - .79
Colorado - .81
Management occupations and construction and extraction occupations were the highest percentage of full-time employment in Wyoming (11.4%). Office and administrative support occupations were also high across the five states and the two MSAs ranging from 10.7% in Wyoming to 15.7% in Salt Lake City MSA.
The only instances of women earning more than men were in the Denver-Aurora MSA (1.01) and in community and social service occupations in North Dakota (1.10).
In Wyoming, women made up 92.3% of persons working in healthcare support occupations (doesn’t include nurses) compared to 85.7% nationally and 78.0% in Salt Lake City MSA. Women earned .56 on the dollar compared to men in Wyoming in this occupation while in Nebraska, they earned .86 on the dollar. Even though relatively small, Wyoming had the highest percentage of women working in Construction and Extraction occupations (5%).

40. Chapter 5: Predicting Gender Using Logistic Regression
Primary data source: Wyoming administrative databases

41. 5 Highlights from the Chapter 3 models
Chapter 5: Predicting Gender Using Logistic Regression
Chapter 5
Highlights from the Chapter
3 models
Workers’ interaction with the labor force
Wage was purposely excluded
Accurately predicted gender 69.6% to 79.6% of the time
2008: Accurately predicted gender 72.2% of the time
Model 1 – Was correct 69.6% of the time, meaning primary industry does play a significant role in terms of predicting gender. – Almost all industries had a significant impact on the odds of a particular individual being female. 4.2 times more likely to be female in retail trade. Construction, less so. The number of quarters worked was also a predictor of a participant being female.
Model 2 – Primary industry, number of total employers, percent quarters worked, and percentage of full-time equivalents (FTEs). This model was correct 71.7%. All independent variables were statistically significant.
Model 3 – Primary industry, number of employers since 1992, percentage of quarters worked, percentage of full-time equivalences worked to date, and occupation. Using occupation limits the number of observations. This model was correct 79.6% of the time.

42. Chapter 6: Breaking Down the Gender Wage Gap
Primary data source: Wyoming administrative databases
- Decomposition analysis

43. 6 Highlights from the Chapter
Chapter 6: Breaking Down the Gender Wage Gap
Chapter 6
Highlights from the Chapter
Women made $0.72 for every dollar made by men
Leaving a $0.28 wage gap
Able to explain $0.15 of the wage gap
Two main variables:
Hours worked: $0.09
Industry: $0.12
$0.13 could not be accounted for
Log wages due to skewness.
Regression analysis with log wages as the dependent variable.
Using variables such as gender, age, marital status, number of children, hours worked, education and occupation among just a few, we were able to explain nearly $.15 of the $.28 wage gap.
The remaining $.13 was due to random error in the model or we didn’t capture certain variables (e.g., occupations). There were instances when women did better than men hence the discrepancy between the .15 and the .21.

44. Chapter 7: Analysis of Benefits in Wyoming
Primary data sources:
- Wyoming New Hires Job Skills Survey
- American Community Survey 5-year estimates

45. 7 Highlights from the Chapter New Hires
Chapter 7: Analysis of Benefits in Wyoming
Chapter 7
Highlights from the Chapter
New Hires
Health insurance, retirement plan, paid time off
Greater proportion of men offered benefits than women
ACS 5-Year Estimates
Ages 16-65
Slightly greater proportion of women (84.9%) covered by some insurance than men (82.1%)
Medicaid: greater proportion of women (9.4%) than men (6.6%)
Men were offered a heath insurance plan 33.6% of the time while women were offered 24.7%.
Similarly, women were offered a retirement plan 21.9% while men 25.5%.
Overall, 29.6% of all employees were offered health insurance.
68.9% of men who worked in heath care and technical were offered health insurance compared to 55.9%.
However, of those women working in mathematical occupations, 72.5% were receiving a retirement plan versus men 67.7%.

46. Chapter 8: Benefits of Reducing Wage Disparities
Primary data source: Wyoming administrative databases
- IMPLAN modeling software

47. 8 Highlights from the Chapter Scenario:
Chapter 8: Benefits of Reducing Wage Disparities
Chapter 8
Highlights from the Chapter
Scenario:
Hourly wage of women was increased to the hourly wage of men
Total number of hours worked unchanged
Results
Increased labor income of $153 million
Additional 604 jobs
$22.2 million in additional labor income
Over $80 million in output to the Wyoming economy
Note: Scenario is not entirely realistic
Profits could decrease with increased labor costs
Men worked a total of million hours while women worked million hours in 2016.
IMPLAN software provides:
Direct Impacts: impacts as a result of the actual project such as higher of a general contractor.
Indirect Impacts: economic impacts as a result of business to business spending when projects do occur.
Induced Impacts: economic impacts as a result of household spending changes because of a project.
Increase in tax revenue.

48. Possible Solutions and Actions Other States Have Taken Please see page 7 of your publication.

49. Questions? Tony Glover, Manager Tony.glover@wyo.gov
Research & Planning
Wyoming Department
of Workforce Services
PO Box 2760
246 S. Center St.
Casper, WY (307)