March 2006
Montana Economy
at a Glance
7%
6%
5%
4%
3%
Unemployment Rate
Seasonally Adjusted
i MT
US
F*XL
U.S.
4.7%
^
Montana
3.4%
2% 1 1 1 r n i r 1 1 ii r i i Mi â– m â– n â– i n i r i i i in r iu i iuHi r ini 1 1 i nnnvmnnn n r 1 1 â– n i r n â– r n
1999 2000 2001 2002 2003 2004 2005 2006
Montana saw a record low unemployment rate of 3.4% in March
2006. This 0.3% over-the-month decrease marks the
the rate's history. The U.S. rate also fell, dropping 0.1
lowest point in
% to 4.7%.
COUNTY UNEMPLOYMENT RATES
Not Seasonally Adjusted
UNITED STATES
MONTANA
Beaverhead
Big Horn
Blaine
Broadwater
Carbon
Carter
Cascade
Chouteau
Custer
Daniels
Dawson
Deer Lodge
Fallon
Fergus
Flathead
Gallatin
Garfield
Glacier
Golden Valley
Granite
Hill
Jefferson
Judith Basin
Lake
Lewis & Clark
Liberty
Lincoln
March
2006
4.7%
3.4%
4.4%
8.5%
4.7%
3.8%
3.6%
4.8%
4.2%
3.5%
4.3%
3.5%
3.8%
6.1%
2.5%
5.4%
5.0%
2.8%
5.1%
7.7%
3.5%
5.5%
4.9%
4.5%
3.8%
5.8%
3.9%
4.0%
9.6%
March
2005
5.4%
5.1%
4.6%
11.1%
6.2%
4.9%
4.2%
4.3%
4.9%
4.4%
4.9%
4.4%
4.7%
6.9%
3.5%
6.8%
6.3%
3.3%
6.0%
9.7%
6.4%
7.3%
5.6%
4.7%
5.7%
6.6%
4.6%
6.4%
11.7%
McCone
Madison
Meagher
Mineral
Missoula
Musselshell
Park
Petroleum
Phillips
Pondera
Powder River
Powell
Prairie
Ravalli
Richland
Roosevelt
Rosebud
Sanders
Sheridan
Silver Bow
Stillwater
Sweet Grass
Teton
Toole
Treasure
Valley
Wheatland
Wibaux
Yellowstone
March
March
2006
2005
3.9%
5.1%
4.1%
4.4%
4.7%
5.0%
6.7%
7.6%
3.9%
4.6%
4.6%
6.7%
4.4%
5.2%
6.0%
7.2%
5.6%
5.7%
5.0%
6.1%
5.2%
5.0%
6.5%
7.7%
6.8%
6.5%
5.0%
5.9%
4.1%
5.1%
6.5%
7.6%
6.4%
7.1%
6.2%
8.1%
4.2%
4.7%
4.6%
5.2%
3.2%
3.8%
1.8%
2.6%
3.7%
4.8%
3.3%
4.0%
5.3%
6.0%
4.5%
5.5%
4.0%
5.1%
4.4%
4.3%
3.4%
3.9%
Nonfarm Employment
January 2001 - March 2006
370,000
360,000 \ 1 1 i 1 1 i 1 1 i i j i i 1 1 i i ii i i 1 1 i 1 1 1 1 1 1 i 1 1 i i ii 1 1 1> i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 â– r m â– r
2001 2002 2003 2004 2005 2006
Montana's seasonally- adjusted nonagricultural payroll employment
gained 3,200 jobs (0.8%) over the month for March 2006. The
largest increases were in Construction, which was up by 900 jobs
(3.1%); Trade, Transportation, and Utilities, up by 800 jobs (0.9%);
and Professional and Business Services, up by 600 jobs (1.7%).
EMPLOYMENT BY INDUSTRY
Industry Employment
(in thousands)
March
2006
Feb.
2006
Net
Change
Percent
Change
Total Non-Agricultural
428.3
425.1
0.8%
Natural Resources & Mining
8.1
8.2
-0.1
-1.2%
Construction
29.8
28.9
0.9
3.1%
Manufacturing
19.3
19.3
0.0
0.0%
Trade, Transportation, Utilities
89.0
88.2
0.8
0.9%
Information*
7.7
7.7
0.0
0.0%
Financial Activities
21.6
21.6
0.0
0.0%
Professional & Business Services
35.3
34.7
0.6
1.7%
Education & Health Services*
57.8
57.5
0.5%
Leisure& Hospitality
56.2
55.9
0.3
0.5%
Other Services*
16.5
16.4
0.1
0.6%
Total Government
87.7
87.3
0.4
0.5%
*These series ore not seasonally adjusted
Research & Analysis Bureau
Montana Department of Labor & Industry
Ph: (406) 444-2430 or (800) 541-3904 Fax: (406) 444-2638
www.ourfactsyourfuture.org
Montana Economy at a Glance
An Analysis of the Gender Wage Gap
in the State Government Workforce
By Brad Eldredge, Ph.D. and Tyler Turner
For many years, both academics and the popular press
have focused from time to time on the gap in average pay
between male and female workers. At the same time, em-
ployers and governments have increasingly sought to en-
act legislation and internal policies designed to shrink this
gap and encourage equal pay for equal work. Recently, the
Interagency Committee for Change By Women (ICCW)
asked the Research and Analysis Bureau to investigate dif-
ferences in male and female pay within state government.
This article provides a summary of the results.
Data for this study came from the Montana Department
of Administration and included wage records for all em-
ployees in state government. Besides wages, the data set
included other employee characteristics that might affect
wages, such as job tenure, pay grade, pay plan, job title,
race, marital status, gender, age, full or part time status,
and union affiliation. The richness of the data allowed us to
isolate the effects of gender on wages while controlling for
the other variables listed in Table 1 .
To isolate the effect of each individual
variable on wages, a statistical technique
called regression analysis was used. Re-
gression analysis is powerful, in that it
allows a researcher to mathematically
hold constant all variables but one, in
order to see what the effect of that one
variable is on wages. Think of regression
as a tool that permits us to compare two
workers, Joe and Jane, who are identi-
cal in terms of all the control variables
except for their gender. Any difference
in Joe and Jane's salary results from ei-
ther their gender or the fact that there is
an important missing variable excluded
from the data set.
Table 1: Variables Used
in the Regression Analysis
One variable absent from the data set
was educational attainment. While it is \
well known that educational attainment
affects wages, and it would be preferable to include educa-
tional attainment in our model, we do not feel that the lack
of this variable drastically affects the results. We draw this
conclusion because occupational title, which we included
Dependent Variable:
. Wage
Control Variables:
• Gender
• Race
• Marital Status
• Job Title
• Pay Grade
• Pay Plan
• Tenure
• Union Affiliation
• Full Time or Part
Time Status
in the model, will in many cases correlate with educational
attainment. For example, lawyers need a certain level of
education to practice. While there is a risk that some indi-
viduals are under-employed given their educational back-
ground, we believe that most individuals will be employed
in job titles that reflect their education.
We ran a regression analysis using 3,900 wage records.
Because of data requirements, we excluded workers who
held more than one job and included only those occupations
with at least ten men and ten women. Economists, for
example, are excluded because there are 9 men and only
1 woman with this job title in state government. We also
exclude employees of the state's university system and
elected officials.
Overall, the average female salary in state government
was about $6,900 less than the average male salary. This
equates to the average woman making about 83% of the
average man's salary. Most of this gap disappears after tak-
ing into account the variables in Table
1. After controlling for these variables,
the regression results showed that the re-
maining unexplained gap between male
and female pay was $1,010.
Referring back to the example of Joe
and Jane, if Joe and Jane both had av-
erage values for the control variables in
Table 1, then Jane would earn about
97% of what Joe earns. More specifi-
cally, the model estimates that if Joe and
Jane both had the average state govern-
ment tenure (10.4 years) worked in the
same "average" occupation, belonged to
the same union, and in all other respects
were the same in terms of the control
variables in table one, then Jane would
make $35,480 which is 97% of Joe's
/ $36,490 annual salary.
Those interested in shrinking the gender pay gap should
find it heartening that most of the gap does not appear to
result from women earning less while doing the same job
and having the same characteristics as men.