Technical information:(202) 691-6567 USDL 09-0362
http://www.bls.gov/cew/
For release: 10:00 A.M. EDT
Media contact: 691-5902 Wednesday, April 8, 2009
COUNTY EMPLOYMENT AND WAGES: THIRD QUARTER 2008
From September 2007 to September 2008, employment declined in more
than half of the largest U.S. counties, according to preliminary data
released today by the Bureau of Labor Statistics of the U.S.
Department of Labor. Elkhart County, Ind., located about 100 miles
east of Chicago, posted the largest percentage decline, with a loss
of 10.8 percent over the year, compared with a national job decrease
of 0.8 percent. Manufacturing sustained the largest employment losses
in Elkhart. Yakima, Wash., in the south-central part of the State,
experienced the largest over-the-year percentage increase in
employment among the largest counties in the U.S., with a gain of 3.2
percent, led by growth in agriculture.
Rutherford County, Tenn., within the metropolitan Nashville area,
had the largest over-the-year gain in average weekly wages in the
third quarter of 2008, with an increase of 17.3 percent coming
largely from manufacturing. The U.S. average weekly wage rose by 2.8
percent over the same time span.
Of the 334 largest counties in the United States (as measured by
2007 annual average employment) 139 had over-the-year percentage
change in employment below the national average (-0.8 percent) in
September 2008; 178 large counties experienced changes above the
national average. The percent change in average weekly wages was
higher than the national average (2.8 percent) in 155 of the largest
U.S. counties but was below the national average in 168 counties.
Table A. Top 10 large counties ranked by September 2008 employment, September 2007-08 employment
decrease, and September 2007-08 percent decrease in employment
--------------------------------------------------------------------------------------------------------
Employment in large counties
--------------------------------------------------------------------------------------------------------
September 2008 employment | Decrease in employment, | Percent decrease in employment,
(thousands) | September 2007-08 | September 2007-08
| (thousands) |
--------------------------------------------------------------------------------------------------------
| |
United States 135,173.8| United States -1,056.1| United States -0.8
--------------------------------------------------------------------------------------------------------
| |
Los Angeles, Calif. 4,141.1| Maricopa, Ariz. -67.1| Elkhart, Ind. -10.8
Cook, Ill. 2,504.2| Los Angeles, Calif. -61.5| Lee, Fla. -8.1
New York, N.Y. 2,363.8| Orange, Calif. -42.2| Collier, Fla. -7.4
Harris, Texas 2,047.2| Riverside, Calif. -35.5| Sarasota, Fla. -7.1
Maricopa, Ariz. 1,761.0| Miami-Dade, Fla. -33.1| Marion, Fla. -6.4
Dallas, Texas 1,489.1| Cook, Ill. -33.0| Volusia, Fla. -5.9
Orange, Calif. 1,469.5| Wayne, Mich. -31.2| Seminole, Fla. -5.8
San Diego, Calif. 1,318.0| Hillsborough, Fla. -31.1| Macomb, Mich. -5.8
King, Wash. 1,198.7| Broward, Fla. -31.0| Riverside, Calif. -5.6
Miami-Dade, Fla. 993.1| San Bernardino, Calif. -25.1| Washoe, Nev. -5.4
| Palm Beach, Fla. -25.1|
| |
| |
--------------------------------------------------------------------------------------------------------
The employment and average weekly wage data by county are compiled
under the Quarterly Census of Employment and Wages (QCEW) program,
also known as the ES-202 program. The data are derived from reports
submitted by every employer subject to unemployment insurance (UI)
laws. The 9.2 million employer reports cover 135.2 million full- and
part-time workers.
Large County Employment
In September 2008, national employment, as measured by the QCEW
program, was 135.2 million, down by 0.8 percent from September 2007.
The 334 U.S. counties with 75,000 or more employees accounted for
71.2 percent of total U.S. employment and 76.8 percent of total
wages. These 334 counties had a net job decline of 891,159 over the
year, accounting for 84.4 percent of the overall U.S. employment
decrease.
Employment declined in 207 counties from September 2007 to
September 2008. The largest percentage decline in employment was in
Elkhart, Ind. (-10.8 percent). Lee, Fla., had the next largest
percentage decline (-8.1 percent), followed by the counties of
Collier, Fla. (-7.4 percent), Sarasota, Fla. (-7.1 percent), and
Marion, Fla. (-6.4 percent). The largest decline in employment
levels occurred in Maricopa, Ariz. (-67,100), followed by the
counties of Los Angeles, Calif. (-61,500), Orange, Calif. (-42,200),
Riverside, Calif. (-35,500), and Miami-Dade, Fla. (-33,100). (See
table A.) Combined employment losses in these five counties over the
year totaled 239,400, or 23 percent of the employment decline for the
U.S. as a whole.
Employment rose in 109 of the large counties from September 2007 to
September 2008. Yakima County, Wash., had the largest over-the-year
percentage increase in employment (3.2 percent). Potter, Texas, had
the next largest increase, 3.1 percent, followed by the counties of
Montgomery, Texas (3.0 percent), Douglas, Colo. (2.9 percent), and
Cass, N.D. (2.6 percent). The largest gains in the level of
employment from September 2007 to September 2008 were recorded in the
counties of Harris, Texas (26,500), King, Wash. (17,100), New York,
N.Y. (14,800), Travis, Texas (9,400), and Washington, D.C. (9,300).
Table B. Top 10 large counties ranked by third quarter 2008 average weekly wages, third quarter 2007-08
growth in average weekly wages, and third quarter 2007-08 percent growth in average weekly wages
--------------------------------------------------------------------------------------------------------
Average weekly wage in large counties
--------------------------------------------------------------------------------------------------------
Average weekly wage, | Growth in average weekly | Percent growth in average
third quarter 2008 | wage, third quarter 2007-08 | weekly wage, third
| | quarter 2007-08
--------------------------------------------------------------------------------------------------------
| |
United States $841| United States $23| United States 2.8
--------------------------------------------------------------------------------------------------------
| |
New York, N.Y. $1,552| Rutherford, Tenn. $124| Rutherford, Tenn. 17.3
Santa Clara, Calif. 1,530| Suffolk, N.Y. 77| Yolo, Calif. 9.7
Washington, D.C. 1,391| Yolo, Calif. 73| Madison, Ill. 9.2
San Mateo, Calif. 1,374| San Francisco, Calif. 65| Suffolk, N.Y. 8.6
San Francisco, Calif. 1,350| Lake, Ill. 63| Calcasieu, La. 7.8
Arlington, Va. 1,348| Solano, Calif. 61| Solano, Calif. 7.7
Suffolk, Mass. 1,321| Madison, Ill. 61| Santa Cruz, Calif. 7.5
Fairfield, Conn. 1,310| Wyandotte, Kan. 58| Wyandotte, Kan. 7.5
Fairfax, Va. 1,295| Santa Cruz, Calif. 56| Polk, Fla. 7.0
Somerset, N.J. 1,233| Hennepin, Minn. 56| Benton, Ark. 6.7
| | Lafayette, La. 6.7
| |
--------------------------------------------------------------------------------------------------------
Large County Average Weekly Wages
The national average weekly wage in the third quarter of 2008 was
$841. Average weekly wages were higher than the national average in
108 of the largest 334 U.S. counties. New York, N.Y., held the top
position among the highest-paid large counties with an average weekly
wage of $1,552. Santa Clara, Calif., was second with an average
weekly wage of $1,530, followed by Washington, D.C. ($1,391), San
Mateo, Calif. ($1,374), and San Francisco, Calif. ($1,350). (See
table B.) Over the year, the national average weekly wage rose by 2.8
percent. Among the largest counties, Rutherford, Tenn., led the
nation in growth in average weekly wages with an increase of 17.3
percent from the third quarter of 2007. Yolo, Calif., was second with
growth of 9.7 percent, followed by the counties of Madison, Ill. (9.2
percent), Suffolk, N.Y. (8.6 percent), and Calcasieu, La. (7.8
percent).
Average weekly wages are affected by the number of high-paying and
low-paying jobs in an industry. The 2.8 percent over-the-year gain in
average weekly wages for the nation is partially due to large
employment declines in the construction and manufacturing industries,
which posted the largest over-the-year percent declines in September
employment. (See table 2.) Average weekly wages for construction
workers increased 5.1 percent as employment fell by more than 6
percent. Construction and manufacturing lost 518,400 and 499,200
jobs, respectively, over the year in September. Employment declines
exceeded 3 percent in manufacturing as average weekly wages for these
workers grew by 1.9 percent. (See Technical Note.)
There were 226 counties with an average weekly wage below the
national average in the third quarter of 2008. The lowest average
weekly wage was reported in Horry, S.C. ($537), followed by the
counties of Cameron, Texas ($538), Hidalgo, Texas ($549), Webb, Texas
($559), and Yakima, Wash. ($580). (See table 1.) Twenty-one large
counties experienced over-the-year declines in average weekly wages.
Clayton, Ga., had the largest decrease (-14.6 percent), followed by
the counties of Santa Clara, Calif. and Duval, Fla. (-3.4 percent
each), Gwinnett, Ga. (-3.1 percent), and Rock Island, Ill. (-2.6
percent).
Ten Largest U.S. Counties
Six of the 10 largest counties (based on 2007 annual average
employment levels) experienced over-the-year percent declines in
employment in September 2008. Maricopa, Ariz., experienced the
largest decline in employment among the 10 largest counties with a
3.7 percent decrease. Within Maricopa, eight industry groups
experienced employment declines, with construction experiencing the
largest decline, -21.8 percent. Miami-Dade, Fla., had the next
largest decline in employment, -3.2 percent, followed by Orange,
Calif. (-2.8 percent). (See table 2.) King, Wash., experienced the
largest percent gain in employment (1.4 percent) among the 10 largest
counties. Within King County, the largest gains in employment were in
information (5.9 percent) and education and health services (5.2
percent). Harris, Texas, had the next largest increase in employment,
1.3 percent, followed by New York, N.Y. (0.6 percent).
Each of the 10 largest U.S. counties saw an over-the-year increase
in average weekly wages. San Diego, Calif., had the fastest growth in
wages among the 10 largest counties, with a gain of 3.8 percent.
Within San Diego County, average weekly wages increased the most in
the information industry (30.4 percent). Los Angeles, Calif., was
second in wage growth with a gain of 3.1 percent, followed by Orange,
Calif., and Harris, Texas (3.0 percent each). The smallest wage gain
occurred in New York, N.Y. (0.5 percent), followed by Maricopa, Ariz.
(1.8 percent), and Miami-Dade, Fla. (2.2 percent).
Largest County by State
Table 3 shows September 2008 employment and the 2008 third quarter
average weekly wage in the largest county in each state, which is
based on 2007 annual average employment levels. (This table includes
one county--Laramie, Wyo.--that had an employment level below 75,000
in 2007.) The employment levels in the counties in table 3 in
September 2008 ranged from approximately 4.14 million in Los Angeles
County, Calif., to 44,200 in Laramie County, Wyo. The highest average
weekly wage of these counties was in New York, N.Y. ($1,552), while
the lowest average weekly wage was in Yellowstone, Mont. ($688).
For More Information
The tables included in this release contain data for the nation and
for the 334 counties with annual average employment levels of 75,000
or more in 2007. September 2008 employment and 2008 third-quarter
average weekly wages for all states are provided in table 4 of this
release.
For additional information about the quarterly employment and wages
data, please read the Technical Note. Final data for all states,
metropolitan statistical areas, counties, and the nation through the
fourth quarter of 2007 are available on the BLS Web site at
http://www.bls.gov/cew/. Preliminary data for first and second
quarter 2008 also are available on the site. Updated data for first
and second quarter 2008 and preliminary data for third quarter 2008
will be available later in April online. Additional information about
the QCEW data may be obtained by calling (202) 691-6567.
Several BLS regional offices are issuing QCEW news releases
targeted to local data users. For links to these releases, see
http://www.bls.gov/cew/cewregional.htm.
____________________________________________________
The County Employment and Wages release for fourth quarter 2008 is
scheduled to be released on Tuesday, July 21, 2009.
Technical Note
These data are the product of a federal-state cooperative program, the Quarterly
Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The
data are derived from summaries of employment and total pay of workers covered by
state and federal unemployment insurance (UI) legislation and provided by State
Workforce Agencies (SWAs). The summaries are a result of the administration of state
unemployment insurance programs that require most employers to pay quarterly taxes
based on the employment and wages of workers covered by UI. QCEW data in this re-
lease are based on the 2007 North American Industry Classification System. Data for
2008 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment lev-
els of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided,
but not used in calculating U.S. averages, rankings, or in the analysis in the text.
Each year, these large counties are selected on the basis of the preliminary annual
average of employment for the previous year. The 335 counties presented in this re-
lease were derived using 2007 preliminary annual averages of employment. For 2008
data, six counties have been added to the publication tables: Shelby, Ala., Boone,
Ky., St. Tammany, La., Yellowstone, Mont., Warren, Ohio, and Potter, Texas. These
counties will be included in all 2008 quarterly releases. The counties in table 2
are selected and sorted each year based on the annual average employment from the
preceding year.
The preliminary QCEW data presented in this release may differ from data released
by the individual states. These potential differences result from the states' con-
tinuing receipt of UI data over time and ongoing review and editing. The individual
states determine their data release timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for
any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED),
and Current Employment Statistics (CES)--makes use of the quarterly UI employment
reports in producing data; however, each measure has a somewhat different universe
coverage, estimation procedure, and publication product.
Differences in coverage and estimation methods can result in somewhat different
measures of employment change over time. It is important to understand program dif-
ferences and the intended uses of the program products. (See table.) Additional in-
formation on each program can be obtained from the program Web sites shown in the
table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
---------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 400,000 establish-
| submitted by 9.1 | ministrative records| ments
| million establish- | submitted by 7.1 |
| ments | million private-sec-|
| | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -7 months after the| -8 months after the | -Usually first Friday
| end of each quar- | end of each quarter| of following month
| ter | |
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and annu-
| new quarter of UI | dinal database and | ally realigns (bench-
| data | directly summarizes | marks) sample esti-
| | gross job gains and | mates to first quar-
| | losses | ter UI levels
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
---------------------------------------------------------------------------------
Coverage
Employment and wage data for workers covered by state UI laws are compiled from
quarterly contribution reports submitted to the SWAs by employers. For federal ci-
vilian workers covered by the Unemployment Compensation for Federal Employees (UCFE)
program, employment and wage data are compiled from quarterly reports submitted by
four major federal payroll processing centers on behalf of all federal agencies,
with the exception of a few agencies which still report directly to the individual
SWA. In addition to the quarterly contribution reports, employers who operate multi-
ple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry
of each of their establishments. The employment and wage data included in this re-
lease are derived from microdata summaries of 9.1 million employer reports of em-
ployment and wages submitted by states to the BLS. These reports are based on place
of employment rather than place of residence.
UI and UCFE coverage is broad and basically comparable from state to state. In
2007, UI and UCFE programs covered workers in 135.4 million jobs. The estimated
130.3 million workers in these jobs (after adjustment for multiple jobholders) rep-
resented 96.2 percent of civilian wage and salary employment. Covered workers re-
ceived $6.018 trillion in pay, representing 94.6 percent of the wage and salary com-
ponent of personal income and 43.6 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural
workers on small farms, all members of the Armed Forces, elected officials in most
states, most employees of railroads, some domestic workers, most student workers at
schools, and employees of certain small nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on
the employment and wages reported by employers covered under the UI program. Cover-
age changes may affect the over-the-year comparisons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received
pay for the pay period including the 12th of the month. With few exceptions, all em-
ployees of covered firms are reported, including production and sales workers, cor-
poration officials, executives, supervisory personnel, and clerical workers. Work-
ers on paid vacations and part-time workers also are included.
Average weekly wage values are calculated by dividing quarterly total wages by the
average of the three monthly employment levels (all employees, as described above)
and dividing the result by 13, for the 13 weeks in the quarter. These calculations
are made using unrounded employment and wage values. The average wage values that
can be calculated using rounded data from the BLS database may differ from the aver-
ages reported. Included in the quarterly wage data are non-wage cash payments such
as bonuses, the cash value of meals and lodging when supplied, tips and other gra-
tuities, and, in some states, employer contributions to certain deferred compensa-
tion plans such as 401(k) plans and stock options. Over-the-year comparisons of av-
erage weekly wages may reflect fluctuations in average monthly employment and/or to-
tal quarterly wages between the current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers
as well as the number of individuals in high-paying and low-paying occupations and
the incidence of pay periods within a quarter. For instance, the average weekly wage
of the work force could increase significantly when there is a large decline in the
number of employees that had been receiving below-average wages. Wages may include
payments to workers not present in the employment counts because they did not work
during the pay period including the 12th of the month. When comparing average weekly
wage levels between industries, states, or quarters, these factors should be taken
into consideration.
Federal government pay levels are subject to periodic, sometimes large, fluctua-
tions due to a calendar effect that consists of some quarters having more pay peri-
ods than others. Most federal employees are paid on a biweekly pay schedule. As a
result of this schedule, in some quarters, federal wages contain payments for six
pay periods, while in other quarters their wages include payments for seven pay pe-
riods. Over-the-year comparisons of average weekly wages may reflect this calendar
effect. Higher growth in average weekly wages may be attributed, in part, to a com-
parison of quarterly wages for the current year, which include seven pay periods,
with year-ago wages that reflect only six pay periods. An opposite effect will occur
when wages in the current period, which contain six pay periods, are compared with
year-ago wages that include seven pay periods. The effect on over-the-year pay com-
parisons can be pronounced in federal government due to the uniform nature of fed-
eral payroll processing. This pattern may exist in private sector pay; however, be-
cause there are more pay period types (weekly, biweekly, semimonthly, monthly) it is
less pronounced. The effect is most visible in counties with large concentrations of
federal employment.
In order to ensure the highest possible quality of data, states verify with em-
ployers and update, if necessary, the industry, location, and ownership classifica-
tion of all establishments on a 4-year cycle. Changes in establishment classifica-
tion codes resulting from this process are introduced with the data reported for the
first quarter of the year. Changes resulting from improved employer reporting also
are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of in-
dividual establishment records and reflect the number of establishments that exist
in a county or industry at a point in time. Establishments can move in or out of a
county or industry for a number of reasons--some reflecting economic events, others
reflecting administrative changes. For example, economic change would come from a
firm relocating into the county; administrative change would come from a company
correcting its county designation.
The over-the-year changes of employment and wages presented in this release have
been adjusted to account for most of the administrative corrections made to the un-
derlying establishment reports. This is done by modifying the prior-year levels used
to calculate the over-the-year changes. Percent changes are calculated using an ad-
justed version of the final 2007 quarterly data as the base data. The adjusted
prior-year levels used to calculate the over-the-year percent change in employment
and wages are not published. These adjusted prior-year levels do not match the unad-
justed data maintained on the BLS Web site. Over-the-year change calculations based
on data from the Web site, or from data published in prior BLS news releases, may
differ substantially from the over-the-year changes presented in this news release.
The adjusted data used to calculate the over-the-year change measures presented in
this release account for most of the administrative changes--those occurring when
employers update the industry, location, and ownership information of their estab-
lishments. The most common adjustments for administrative change are the result of
updated information about the county location of individual establishments. Included
in these adjustments are administrative changes involving the classification of es-
tablishments that were previously reported in the unknown or statewide county or un-
known industry categories. Beginning with the first quarter of 2008, adjusted data
will also account for administrative changes caused by multi-unit employers who
start reporting for each individual establishment rather than as a single entity.
The adjusted data used to calculate the over-the-year change measures presented in
any County Employment and Wages news release are valid for comparisons between the
starting and ending points (a 12-month period) used in that particular release. Com-
parisons may not be valid for any time period other than the one featured in a re-
lease even if the changes were calculated using adjusted data.
County definitions are assigned according to Federal Information Processing Stan-
dards Publications (FIPS PUBS) as issued by the National Institute of Standards and
Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of
the Information Technology Management Reform Act of 1996 and the Computer Security
Act of 1987, Public Law 104-106. Areas shown as counties include those designated as
independent cities in some jurisdictions and, in Alaska, those designated as census
areas where counties have not been created. County data also are presented for the
New England states for comparative purposes even though townships are the more com-
mon designation used in New England (and New Jersey). The regions referred to in
this release are defined as census regions.
Additional statistics and other information
An annual bulletin, Employment and Wages, features comprehensive information by
detailed industry on establishments, employment, and wages for the nation and all
states. The 2007 edition of this bulletin contains selected data produced by Busi-
ness Employment Dynamics (BED) on job gains and losses, as well as selected data
from the first quarter 2008 version of this news release. Tables and additional con-
tent from the 2007 Employment and Wages Annual Bulletin are now available online at
http://www.bls.gov/cew/cewbultn07.htm. These tables present final 2007 annual aver-
ages. The tables will also be included on the CD which accompanies the hardcopy
version of the Annual Bulletin. Employment and Wages Annual Averages, 2007 is ex-
pected to be available for sale as a chartbook by the end of the second quarter of
2009 from the United States Government Printing Office, Superintendent of Documents,
P.O. Box 371954, Pittsburgh, PA 15250, telephone (866) 512-1800, outside Washington,
D.C. Within Washington, D.C., the telephone number is (202) 512-1800. The fax number
is (202) 512-2104.
News releases on quarterly measures of gross job flows also are available upon re-
quest from the Division of Administrative Statistics and Labor Turnover (Business
Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail:
BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals
upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-
877-8339.
Table 1. Covered(1) establishments, employment, and wages in the 335 largest counties,
third quarter 2008(2)
Employment Average weekly wage(4)
Establishments,
County(3) third quarter Percent Ranking Percent Ranking
2008 September change, by Average change, by
(thousands) 2008 September percent weekly third percent
(thousands) 2007-08(5) change wage quarter change
2007-08(5)
United States(6)......... 9,150.8 135,173.8 -0.8 - $841 2.8 -
Jefferson, AL............ 19.0 357.9 -1.6 233 863 3.1 123
Madison, AL.............. 9.0 183.1 2.2 13 913 2.0 221
Mobile, AL............... 10.1 175.4 0.5 69 715 2.9 148
Montgomery, AL........... 6.7 138.5 0.0 110 725 4.8 33
Shelby, AL............... 5.0 75.7 -0.6 162 806 0.4 297
Tuscaloosa, AL........... 4.5 87.2 0.1 101 730 4.4 43
Anchorage Borough, AK.... 8.3 152.0 2.0 15 922 3.1 123
Maricopa, AZ............. 103.0 1,761.0 -3.7 300 836 1.8 234
Pima, AZ................. 21.3 370.5 -1.5 226 747 2.2 201
Benton, AR............... 5.6 95.9 -0.8 179 760 6.7 10
Pulaski, AR.............. 15.1 252.2 0.3 84 765 2.0 221
Washington, AR........... 5.8 92.1 -1.2 205 679 2.3 192
Alameda, CA.............. 54.3 684.0 -1.5 226 1,115 3.6 76
Butte, CA................ 8.0 77.0 -1.8 250 660 3.3 103
Contra Costa, CA......... 30.1 339.9 -2.0 258 1,034 3.1 123
Fresno, CA............... 30.5 372.4 -0.4 145 658 2.2 201
Kern, CA................. 18.3 297.4 0.7 59 737 2.9 148
Los Angeles, CA.......... 428.8 4,141.1 -1.5 226 951 3.1 123
Marin, CA................ 12.1 109.5 0.3 84 1,029 0.7 287
Monterey, CA............. 12.9 182.8 -0.1 121 747 1.5 255
Orange, CA............... 102.5 1,469.5 -2.8 283 955 3.0 139
Placer, CA............... 11.0 135.0 -3.8 303 815 -0.1 308
Riverside, CA............ 47.1 598.5 -5.6 319 716 2.1 211
Sacramento, CA........... 54.4 623.6 -2.5 272 952 5.1 24
San Bernardino, CA....... 49.1 642.8 -3.8 303 740 2.2 201
San Diego, CA............ 99.6 1,318.0 -1.2 205 921 3.8 66
San Francisco, CA........ 52.3 575.4 0.8 50 1,350 5.1 24
San Joaquin, CA.......... 18.0 224.5 -3.1 290 744 4.1 52
San Luis Obispo, CA...... 9.9 105.3 -2.0 258 714 3.9 62
San Mateo, CA............ 24.2 343.8 0.1 101 1,374 3.5 82
Santa Barbara, CA........ 14.4 189.8 0.0 110 788 1.2 264
Santa Clara, CA.......... 60.7 910.5 0.5 69 1,530 -3.4 326
Santa Cruz, CA........... 9.1 102.1 -1.7 245 798 7.5 7
Solano, CA............... 10.2 126.4 -2.5 272 853 7.7 6
Sonoma, CA............... 18.9 193.0 -2.4 271 828 2.1 211
Stanislaus, CA........... 14.9 177.1 -1.6 233 723 4.0 57
Tulare, CA............... 9.6 154.1 0.8 50 606 3.4 94
Ventura, CA.............. 23.7 314.3 -1.6 233 858 2.4 183
Yolo, CA................. 5.9 104.0 -0.6 162 829 9.7 2
Adams, CO................ 9.4 155.8 1.0 39 792 3.1 123
Arapahoe, CO............. 19.6 282.9 -0.3 141 1,002 4.6 40
Boulder, CO.............. 13.1 162.3 0.9 45 1,020 3.1 123
Denver, CO............... 26.0 453.3 0.5 69 1,031 3.6 76
Douglas, CO.............. 9.7 94.2 2.9 4 864 3.1 123
El Paso, CO.............. 17.6 245.3 -1.6 233 780 2.1 211
Jefferson, CO............ 18.9 212.4 0.2 97 883 5.2 22
Larimer, CO.............. 10.5 133.6 0.1 101 771 2.4 183
Weld, CO................. 6.1 84.4 0.0 110 731 0.6 290
Fairfield, CT............ 33.1 418.8 -0.5 153 1,310 0.5 293
Hartford, CT............. 25.6 506.7 0.3 84 1,012 0.9 275
New Haven, CT............ 22.7 364.0 -1.1 200 909 2.9 148
New London, CT........... 7.0 131.8 0.4 76 864 1.1 266
New Castle, DE........... 18.5 278.0 -1.5 226 981 2.6 174
Washington, DC........... 33.8 688.2 1.4 24 1,391 1.0 270
Alachua, FL.............. 6.8 123.2 -0.6 162 723 2.0 221
Brevard, FL.............. 15.0 196.9 -4.2 308 793 3.5 82
Broward, FL.............. 65.9 728.6 -4.1 307 792 2.2 201
Collier, FL.............. 12.5 116.5 -7.4 325 749 (7) -
Duval, FL................ 27.6 456.0 -3.4 295 797 -3.4 326
Escambia, FL............. 8.2 125.0 -4.9 315 667 2.9 148
Hillsborough, FL......... 38.1 604.0 -4.9 315 807 3.5 82
Lake, FL................. 7.4 83.6 -4.5 312 606 1.7 244
Lee, FL.................. 20.2 201.1 -8.1 326 706 1.0 270
Leon, FL................. 8.3 142.3 -2.6 277 750 4.2 46
Manatee, FL.............. 9.4 109.6 -1.8 250 663 0.8 281
Marion, FL............... 8.7 98.9 -6.4 323 606 2.5 176
Miami-Dade, FL........... 87.8 993.1 -3.2 291 842 2.2 201
Okaloosa, FL............. 6.2 78.4 -4.3 310 688 1.8 234
Orange, FL............... 36.4 680.9 -2.5 272 764 1.3 260
Palm Beach, FL........... 51.5 519.2 -4.6 313 811 0.9 275
Pasco, FL................ 10.3 99.4 -2.8 283 595 1.9 230
Pinellas, FL............. 31.9 414.8 -4.4 311 737 3.4 94
Polk, FL................. 12.9 197.1 -3.3 293 699 7.0 9
Sarasota, FL............. 15.4 143.5 -7.1 324 709 1.0 270
Seminole, FL............. 14.9 171.0 -5.8 320 712 0.8 281
Volusia, FL.............. 14.2 159.4 -5.9 322 615 2.8 156
Bibb, GA................. 4.7 84.8 0.8 50 669 2.0 221
Chatham, GA.............. 7.6 134.6 -2.6 277 728 3.4 94
Clayton, GA.............. 4.4 111.8 -2.6 277 787 -14.6 328
Cobb, GA................. 20.7 310.3 -2.8 283 906 3.2 110
De Kalb, GA.............. 16.7 293.9 -1.9 255 888 1.6 250
Fulton, GA............... 39.1 741.7 -1.0 192 1,078 1.9 230
Gwinnett, GA............. 23.4 315.1 -3.6 298 842 -3.1 325
Muscogee, GA............. 4.8 94.5 -2.7 282 676 -2.0 321
Richmond, GA............. 4.8 101.1 0.2 97 733 3.2 110
Honolulu, HI............. 24.7 444.6 -1.6 233 800 1.8 234
Ada, ID.................. 15.0 210.4 -1.5 226 746 -0.5 310
Champaign, IL............ 4.1 93.2 0.7 59 728 3.3 103
Cook, IL................. 140.4 2,504.2 -1.3 212 988 2.8 156
Du Page, IL.............. 36.0 590.9 -1.9 255 990 0.9 275
Kane, IL................. 12.8 208.2 -2.9 286 765 3.1 123
Lake, IL................. 21.1 335.8 -1.2 205 1,037 6.5 13
McHenry, IL.............. 8.5 103.6 -1.3 212 729 1.8 234
McLean, IL............... 3.7 86.9 0.6 64 818 4.3 44
Madison, IL.............. 6.0 96.6 0.3 84 723 9.2 3
Peoria, IL............... 4.8 105.7 1.0 39 806 4.0 57
Rock Island, IL.......... 3.5 79.9 0.4 76 823 -2.6 324
St. Clair, IL............ 5.5 98.4 1.2 30 694 2.8 156
Sangamon, IL............. 5.2 129.4 -0.5 153 850 3.8 66
Will, IL................. 13.8 199.0 1.0 39 751 3.2 110
Winnebago, IL............ 7.0 136.1 -1.4 221 739 3.5 82
Allen, IN................ 9.1 183.1 -1.0 192 702 1.6 250
Elkhart, IN.............. 5.0 112.3 -10.8 327 667 -2.2 322
Hamilton, IN............. 7.7 113.6 1.0 39 809 0.5 293
Lake, IN................. 10.3 195.9 0.1 101 771 5.0 28
Marion, IN............... 24.2 580.5 -0.7 174 852 2.7 161
St. Joseph, IN........... 6.1 123.3 -2.0 258 715 5.0 28
Tippecanoe, IN........... 3.3 77.3 -0.3 141 725 2.7 161
Vanderburgh, IN.......... 4.8 108.2 0.6 64 702 3.8 66
Linn, IA................. 6.3 126.4 1.9 17 826 4.7 36
Polk, IA................. 14.9 276.3 0.6 64 831 3.5 82
Scott, IA................ 5.3 90.1 0.9 45 697 2.3 192
Johnson, KS.............. 20.6 318.1 0.1 101 867 4.0 57
Sedgwick, KS............. 12.2 258.0 -0.2 132 763 3.8 66
Shawnee, KS.............. 4.8 96.8 1.1 37 710 3.5 82
Wyandotte, KS............ 3.2 81.7 -0.2 132 830 7.5 7
Boone, KY................ 3.5 75.2 1.7 20 724 -1.4 316
Fayette, KY.............. 9.1 176.2 (7) - 754 2.7 161
Jefferson, KY............ 22.3 426.4 -2.3 269 799 1.1 266
Caddo, LA................ 7.4 125.3 -0.7 174 717 5.8 15
Calcasieu, LA............ 4.9 85.8 -0.5 153 750 7.8 5
East Baton Rouge, LA..... 14.4 261.4 -0.1 121 790 6.6 12
Jefferson, LA............ 14.0 195.0 -1.1 200 777 3.3 103
Lafayette, LA............ 8.8 135.0 -0.2 132 826 6.7 10
Orleans, LA.............. 10.6 170.7 (7) - 901 1.5 255
St. Tammany, LA.......... 7.2 74.0 -0.6 162 699 4.2 46
Cumberland, ME........... 12.0 174.0 -0.1 121 768 3.4 94
Anne Arundel, MD......... 14.5 234.0 -0.4 145 891 1.8 234
Baltimore, MD............ 21.5 373.0 -1.3 212 858 3.1 123
Frederick, MD............ 6.0 94.3 -1.7 245 819 2.4 183
Harford, MD.............. 5.6 83.5 (7) - 785 (7) -
Howard, MD............... 8.7 148.4 (7) - 979 3.4 94
Montgomery, MD........... 32.8 459.0 -0.4 145 1,122 2.9 148
Prince Georges, MD....... 15.7 312.7 -1.4 221 933 3.6 76
Baltimore City, MD....... 13.9 340.8 -1.0 192 988 5.2 22
Barnstable, MA........... 9.2 96.9 -1.6 233 709 3.1 123
Bristol, MA.............. 15.4 216.6 -2.0 258 751 3.9 62
Essex, MA................ 20.9 301.4 -0.2 132 888 0.9 275
Hampden, MA.............. 14.4 200.2 -0.4 145 785 3.2 110
Middlesex, MA............ 47.6 825.1 0.8 50 1,200 1.8 234
Norfolk, MA.............. 23.7 327.0 0.4 76 971 0.7 287
Plymouth, MA............. 13.7 177.3 -1.3 212 786 3.4 94
Suffolk, MA.............. 21.7 591.8 0.4 76 1,321 2.2 201
Worcester, MA............ 20.6 320.8 -0.6 162 859 3.4 94
Genesee, MI.............. 7.9 135.5 -5.1 317 738 0.3 302
Ingham, MI............... 6.8 159.5 -2.1 264 806 3.2 110
Kalamazoo, MI............ 5.6 113.4 -2.3 269 784 6.4 14
Kent, MI................. 14.3 329.8 -3.5 296 757 3.0 139
Macomb, MI............... 17.7 298.8 -5.8 320 853 -2.4 323
Oakland, MI.............. 39.2 671.0 -3.5 296 966 0.9 275
Ottawa, MI............... 5.7 109.8 -2.6 277 730 3.0 139
Saginaw, MI.............. 4.3 83.7 -3.7 300 703 0.1 306
Washtenaw, MI............ 8.1 187.3 -2.5 272 944 -0.9 313
Wayne, MI................ 32.1 717.9 -4.2 308 942 1.4 259
Anoka, MN................ 7.8 114.8 -1.6 233 769 0.3 302
Dakota, MN............... 10.6 175.1 -1.2 205 801 3.4 94
Hennepin, MN............. 42.2 840.7 -0.8 179 1,102 5.4 20
Olmsted, MN.............. 3.5 90.6 -1.0 192 949 5.1 24
Ramsey, MN............... 15.2 335.2 -0.2 132 933 3.7 73
St. Louis, MN............ 5.9 98.3 0.4 76 696 3.9 62
Stearns, MN.............. 4.5 83.0 0.3 84 679 3.2 110
Harrison, MS............. 4.6 86.1 -2.0 258 664 3.4 94
Hinds, MS................ 6.4 126.6 -0.6 162 745 4.3 44
Boone, MO................ 4.6 83.5 0.0 110 660 3.6 76
Clay, MO................. 5.1 90.2 -1.3 212 765 -1.7 319
Greene, MO............... 8.2 156.2 -1.6 233 653 2.5 176
Jackson, MO.............. 18.7 370.0 -0.1 121 851 3.0 139
St. Charles, MO.......... 8.2 123.2 -2.1 264 695 0.4 297
St. Louis, MO............ 32.9 605.6 -1.0 192 890 1.8 234
St. Louis City, MO....... 8.5 237.9 1.6 21 937 5.5 18
Yellowstone, MT.......... 5.8 78.5 0.7 59 688 2.4 183
Douglas, NE.............. 16.0 321.4 0.9 45 820 4.9 31
Lancaster, NE............ 8.1 158.2 0.3 84 687 3.2 110
Clark, NV................ 50.9 903.7 -2.0 258 812 2.0 221
Washoe, NV............... 14.6 208.5 -5.4 318 796 2.3 192
Hillsborough, NH......... 12.4 196.5 -0.6 162 924 2.7 161
Rockingham, NH........... 11.0 138.6 -1.8 250 796 1.7 244
Atlantic, NJ............. 7.1 147.0 0.0 110 740 2.2 201
Bergen, NJ............... 35.0 445.7 -1.3 212 1,031 2.3 192
Burlington, NJ........... 11.6 198.9 -2.6 277 890 1.8 234
Camden, NJ............... 13.3 207.7 -0.5 153 858 2.8 156
Essex, NJ................ 21.7 357.2 -0.5 153 1,038 1.8 234
Gloucester, NJ........... 6.4 103.9 0.4 76 763 2.7 161
Hudson, NJ............... 14.2 236.2 -0.8 179 1,162 4.1 52
Mercer, NJ............... 11.4 229.5 0.3 84 1,063 3.2 110
Middlesex, NJ............ 22.3 399.0 -2.1 264 1,033 4.1 52
Monmouth, NJ............. 21.1 257.2 -0.6 162 888 1.3 260
Morris, NJ............... 18.4 285.6 -0.7 174 1,178 2.7 161
Ocean, NJ................ 12.7 152.8 -0.9 189 689 1.6 250
Passaic, NJ.............. 12.9 174.5 -1.4 221 873 2.5 176
Somerset, NJ............. 10.4 172.9 -0.8 179 1,233 2.6 174
Union, NJ................ 15.3 232.1 -1.1 200 1,057 0.4 297
Bernalillo, NM........... 17.6 335.6 0.2 97 763 3.8 66
Albany, NY............... 10.0 227.7 0.0 110 878 5.3 21
Bronx, NY................ 16.0 227.5 2.3 9 836 (7) -
Broome, NY............... 4.5 95.3 -0.6 162 696 4.8 33
Dutchess, NY............. 8.4 115.4 -1.3 212 860 1.7 244
Erie, NY................. 23.7 463.8 1.2 30 736 3.1 123
Kings, NY................ 46.4 478.2 1.4 24 735 2.1 211
Monroe, NY............... 18.1 381.1 0.3 84 817 1.5 255
Nassau, NY............... 52.7 601.7 -0.5 153 915 0.2 305
New York, NY............. 118.9 2,363.8 0.6 64 1,552 0.5 293
Oneida, NY............... 5.3 109.7 -0.2 132 671 3.2 110
Onondaga, NY............. 12.8 254.3 -0.3 141 774 2.2 201
Orange, NY............... 10.0 132.0 -0.2 132 711 3.3 103
Queens, NY............... 43.7 506.9 1.2 30 836 3.0 139
Richmond, NY............. 8.8 93.3 0.8 50 769 2.5 176
Rockland, NY............. 9.9 116.0 -0.1 121 918 5.8 15
Saratoga, NY............. 5.4 76.2 -0.8 179 708 1.7 244
Suffolk, NY.............. 50.7 626.3 -0.4 145 969 8.6 4
Westchester, NY.......... 36.6 420.7 -0.1 121 1,086 1.6 250
Buncombe, NC............. 8.2 115.8 -1.4 221 666 3.1 123
Catawba, NC.............. 4.7 85.2 -3.6 298 637 0.5 293
Cumberland, NC........... 6.3 120.8 2.3 9 654 0.3 302
Durham, NC............... 7.1 184.3 -0.8 179 1,115 0.9 275
Forsyth, NC.............. 9.3 184.1 -1.0 192 764 1.2 264
Guilford, NC............. 14.8 278.9 -1.1 200 758 5.0 28
Mecklenburg, NC.......... 33.3 570.0 0.0 110 956 3.5 82
New Hanover, NC.......... 7.5 103.3 -3.3 293 696 3.1 123
Wake, NC................. 28.9 453.6 0.7 59 836 2.7 161
Cass, ND................. 5.9 101.1 2.6 5 723 5.1 24
Butler, OH............... 7.5 147.3 -1.2 205 743 -1.6 317
Cuyahoga, OH............. 38.1 732.3 -1.7 245 853 2.4 183
Franklin, OH............. 30.3 678.7 -0.8 179 851 2.3 192
Hamilton, OH............. 24.3 515.4 -0.6 162 933 4.9 31
Lake, OH................. 6.8 100.5 -0.7 174 685 2.7 161
Lorain, OH............... 6.3 99.1 -1.4 221 710 1.3 260
Lucas, OH................ 10.8 212.2 -3.8 303 737 0.4 297
Mahoning, OH............. 6.5 103.0 -1.9 255 616 3.5 82
Montgomery, OH........... 13.0 261.1 -3.0 288 787 4.5 42
Stark, OH................ 9.1 160.9 -1.6 233 658 2.3 192
Summit, OH............... 15.1 273.9 -0.4 145 756 2.3 192
Trumbull, OH............. 4.7 76.3 -1.8 250 713 2.9 148
Warren, OH............... 4.3 76.9 -1.7 245 719 3.3 103
Oklahoma, OK............. 24.0 427.1 1.2 30 784 4.7 36
Tulsa, OK................ 19.4 351.8 1.0 39 767 3.0 139
Clackamas, OR............ 12.8 151.7 0.3 84 772 0.8 281
Jackson, OR.............. 6.7 83.7 -2.9 286 634 1.1 266
Lane, OR................. 11.0 147.1 -3.0 288 684 3.5 82
Marion, OR............... 9.5 145.1 0.6 64 673 2.0 221
Multnomah, OR............ 28.2 451.7 0.4 76 858 2.1 211
Washington, OR........... 16.2 248.1 -1.6 233 985 1.9 230
Allegheny, PA............ 35.4 686.8 -0.1 121 886 2.7 161
Berks, PA................ 9.3 168.3 -0.2 132 770 0.8 281
Bucks, PA................ 20.2 260.8 -1.8 250 819 3.9 62
Butler, PA............... 4.9 81.1 1.1 37 747 4.2 46
Chester, PA.............. 15.2 244.2 0.9 45 1,024 -1.9 320
Cumberland, PA........... 6.0 125.8 -0.9 189 774 1.7 244
Dauphin, PA.............. 7.4 183.1 0.1 101 820 2.1 211
Delaware, PA............. 13.7 210.7 0.1 101 878 3.7 73
Erie, PA................. 7.4 128.5 -0.5 153 680 3.3 103
Lackawanna, PA........... 5.9 101.2 -1.0 192 651 3.5 82
Lancaster, PA............ 12.5 229.4 -0.8 179 720 2.7 161
Lehigh, PA............... 8.8 178.9 -0.2 132 829 -0.6 312
Luzerne, PA.............. 7.9 143.6 0.0 110 663 1.8 234
Montgomery, PA........... 27.6 487.7 0.3 84 1,012 1.5 255
Northampton, PA.......... 6.5 99.0 -1.6 233 743 3.2 110
Philadelphia, PA......... 31.0 634.8 0.7 59 1,021 4.6 40
Washington, PA........... 5.4 81.3 2.3 9 739 2.4 183
Westmoreland, PA......... 9.5 137.6 0.3 84 684 4.0 57
York, PA................. 9.2 178.7 0.2 97 741 1.9 230
Kent, RI................. 5.7 78.5 -4.7 314 732 0.7 287
Providence, RI........... 18.1 281.9 -2.2 268 805 3.5 82
Charleston, SC........... 12.7 212.6 -0.6 162 723 3.1 123
Greenville, SC........... 12.9 241.1 0.8 50 728 2.2 201
Horry, SC................ 8.5 116.9 -3.8 303 537 0.4 297
Lexington, SC............ 5.8 98.4 -0.6 162 652 2.2 201
Richland, SC............. 9.6 215.2 -1.1 200 749 2.7 161
Spartanburg, SC.......... 6.2 119.2 -3.2 291 734 4.0 57
Minnehaha, SD............ 6.4 116.7 1.4 24 717 3.2 110
Davidson, TN............. 18.7 437.4 -2.1 264 861 2.4 183
Hamilton, TN............. 8.6 193.4 -0.5 153 718 1.0 270
Knox, TN................. 11.3 230.1 0.0 110 711 2.0 221
Rutherford, TN........... 4.4 97.7 -3.7 300 840 17.3 1
Shelby, TN............... 20.0 500.6 -1.7 245 855 0.6 290
Williamson, TN........... 6.0 87.1 -0.4 145 915 5.8 15
Bell, TX................. 4.6 103.0 1.9 17 663 2.5 176
Bexar, TX................ 32.5 729.1 1.2 30 734 2.1 211
Brazoria, TX............. 4.6 86.0 0.1 101 800 0.8 281
Brazos, TX............... 3.8 85.2 1.0 39 646 3.2 110
Cameron, TX.............. 6.4 122.4 -0.1 121 538 4.1 52
Collin, TX............... 17.2 294.8 2.0 15 997 1.1 266
Dallas, TX............... 68.2 1,489.1 0.5 69 1,025 2.4 183
Denton, TX............... 10.6 168.8 1.4 24 738 3.1 123
El Paso, TX.............. 13.5 271.4 1.2 30 601 0.8 281
Fort Bend, TX............ 8.4 128.9 2.5 6 865 2.1 211
Galveston, TX............ 5.2 95.7 0.4 76 803 3.5 82
Harris, TX............... 97.3 2,047.2 1.3 29 1,050 3.0 139
Hidalgo, TX.............. 10.6 214.8 1.2 30 549 3.8 66
Jefferson, TX............ 5.9 123.3 -1.3 212 820 3.8 66
Lubbock, TX.............. 6.8 124.7 1.9 17 641 4.2 46
McLennan, TX............. 4.9 103.2 (7) - 685 4.1 52
Montgomery, TX........... 8.2 125.8 3.0 3 785 5.5 18
Nueces, TX............... 8.1 155.0 2.5 6 728 2.5 176
Potter, TX............... 3.8 77.2 3.1 2 729 (7) -
Smith, TX................ 5.3 94.4 2.4 8 743 4.2 46
Tarrant, TX.............. 37.4 769.5 0.8 50 843 1.0 270
Travis, TX............... 29.0 581.5 1.6 21 924 1.3 260
Webb, TX................. 4.8 88.5 -0.4 145 559 2.0 221
Williamson, TX........... 7.2 120.8 1.6 21 800 3.1 123
Davis, UT................ 7.3 103.5 -1.5 226 659 -1.1 314
Salt Lake, UT............ 38.5 591.7 0.5 69 796 3.0 139
Utah, UT................. 13.1 175.8 -1.3 212 665 3.3 103
Weber, UT................ 5.7 94.4 -0.8 179 637 3.6 76
Chittenden, VT........... 6.0 95.6 -0.3 141 838 3.2 110
Arlington, VA............ 7.8 156.2 0.8 50 1,348 -1.3 315
Chesterfield, VA......... 7.6 118.9 -2.5 272 774 3.6 76
Fairfax, VA.............. 34.0 587.0 0.3 84 1,295 4.2 46
Henrico, VA.............. 9.7 177.2 -1.5 226 852 2.3 192
Loudoun, VA.............. 9.1 133.3 2.3 9 1,006 -0.3 309
Prince William, VA....... 7.3 103.3 -0.8 179 775 2.8 156
Alexandria City, VA...... 6.2 100.9 0.9 45 1,160 2.4 183
Chesapeake City, VA...... 5.8 99.3 -1.6 233 678 2.3 192
Newport News City, VA.... 4.0 98.6 -1.2 205 769 2.1 211
Norfolk City, VA......... 5.9 144.1 0.0 110 815 -1.6 317
Richmond City, VA........ 7.5 159.0 (7) - 954 (7) -
Virginia Beach City, VA.. 11.7 174.7 -0.9 189 656 2.0 221
Clark, WA................ 12.5 134.4 0.5 69 777 3.7 73
King, WA................. 78.5 1,198.7 1.4 24 1,162 2.9 148
Kitsap, WA............... 6.8 83.8 -0.1 121 766 -0.5 310
Pierce, WA............... 21.1 278.4 -0.1 121 774 2.7 161
Snohomish, WA............ 18.2 256.0 0.3 84 856 1.7 244
Spokane, WA.............. 15.6 211.5 0.1 101 700 2.9 148
Thurston, WA............. 7.1 102.0 2.1 14 786 0.6 290
Whatcom, WA.............. 7.0 83.3 0.8 50 679 3.0 139
Yakima, WA............... 8.3 111.2 3.2 1 580 2.1 211
Kanawha, WV.............. 6.1 108.5 -0.5 153 738 4.8 33
Brown, WI................ 6.8 148.7 -1.0 192 754 4.7 36
Dane, WI................. 14.3 304.1 (7) - 823 (7) -
Milwaukee, WI............ 21.5 498.3 -0.1 121 839 4.7 36
Outagamie, WI............ 5.1 104.2 0.0 110 719 1.6 250
Racine, WI............... 4.2 76.4 -0.7 174 756 2.7 161
Waukesha, WI............. 13.4 233.7 -1.2 205 836 2.5 176
Winnebago, WI............ 3.8 91.1 0.5 69 768 0.0 307
San Juan, PR............. 13.1 283.5 -1.2 (8) 569 6.0 (8)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 334 U.S. counties comprise 71.2 percent of the total covered workers
in the U.S.
(2) Data are preliminary.
(3) Includes areas not officially designated as counties. See Technical Note.
(4) Average weekly wages were calculated using unrounded data.
(5) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(6) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(7) Data do not meet BLS or State agency disclosure standards.
(8) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties,
third quarter 2008(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
County by NAICS supersector 2008 Percent Percent
(thousands) September change, Average change,
2008 September weekly third
(thousands) 2007-08(4) wage quarter
2007-08(4)
United States(5)............................. 9,150.8 135,173.8 -0.8 $841 2.8
Private industry........................... 8,857.7 113,499.1 -1.1 833 2.8
Natural resources and mining............. 126.2 2,003.6 3.6 880 7.3
Construction............................. 889.2 7,255.4 -6.7 922 5.1
Manufacturing............................ 361.0 13,345.0 -3.6 1,006 1.9
Trade, transportation, and utilities..... 1,927.8 25,953.1 -1.3 719 1.7
Information.............................. 146.3 2,973.8 -2.0 1,335 4.9
Financial activities..................... 866.3 7,919.9 -2.5 1,207 0.8
Professional and business services....... 1,528.7 17,752.2 -1.4 1,045 4.6
Education and health services............ 851.2 17,996.4 2.7 803 3.6
Leisure and hospitality.................. 739.3 13,568.1 0.0 358 2.9
Other services........................... 1,205.9 4,482.9 0.9 544 2.4
Government................................. 293.1 21,674.7 1.0 886 3.0
Los Angeles, CA.............................. 428.8 4,141.1 -1.5 951 3.1
Private industry........................... 424.8 3,581.8 -1.4 923 2.7
Natural resources and mining............. 0.5 11.7 -2.8 1,232 9.3
Construction............................. 14.0 145.0 -9.5 994 5.2
Manufacturing............................ 14.6 432.3 -3.4 1,009 4.6
Trade, transportation, and utilities..... 53.7 792.1 -2.1 775 2.1
Information.............................. 8.7 214.8 (6) 1,551 (6)
Financial activities..................... 24.1 233.8 -5.4 1,482 0.1
Professional and business services....... 42.5 583.7 (6) 1,104 (6)
Education and health services............ 28.0 488.8 1.7 888 4.5
Leisure and hospitality.................. 27.0 401.6 -0.2 536 3.3
Other services........................... 195.2 259.5 4.2 439 0.5
Government................................. 4.0 559.3 (6) 1,132 5.8
Cook, IL..................................... 140.4 2,504.2 -1.3 988 2.8
Private industry........................... 139.0 2,195.4 -1.5 986 2.8
Natural resources and mining............. 0.1 1.3 -3.6 960 -9.3
Construction............................. 12.4 92.9 -5.9 1,284 5.9
Manufacturing............................ 7.0 226.3 -4.1 1,002 2.5
Trade, transportation, and utilities..... 27.6 460.4 -2.3 788 1.8
Information.............................. 2.5 56.5 -1.5 1,557 10.2
Financial activities..................... 15.7 206.3 -3.2 1,538 -0.8
Professional and business services....... 28.9 434.2 -2.1 1,248 5.3
Education and health services............ 13.9 378.9 2.9 873 3.3
Leisure and hospitality.................. 11.7 237.8 -1.3 443 3.3
Other services........................... 14.5 96.6 1.5 707 2.2
Government................................. 1.4 308.8 0.0 1,009 2.9
New York, NY................................. 118.9 2,363.8 0.6 1,552 0.5
Private industry........................... 118.6 1,919.7 0.7 1,673 0.4
Natural resources and mining............. 0.0 0.2 -8.9 1,820 14.0
Construction............................. 2.4 37.8 4.1 1,535 5.4
Manufacturing............................ 3.0 35.4 -5.8 1,183 -2.6
Trade, transportation, and utilities..... 22.1 248.9 0.4 1,127 0.4
Information.............................. 4.6 135.9 0.0 1,982 4.2
Financial activities..................... 19.1 372.9 -2.1 2,985 -2.2
Professional and business services....... 25.6 491.8 1.4 1,799 2.3
Education and health services............ 8.8 283.4 0.6 1,059 4.7
Leisure and hospitality.................. 11.7 218.9 3.9 748 3.2
Other services........................... 18.0 89.1 2.1 919 4.1
Government................................. 0.3 444.1 0.1 1,027 1.4
Harris, TX................................... 97.3 2,047.2 1.3 1,050 3.0
Private industry........................... 96.7 1,796.9 1.1 1,061 2.9
Natural resources and mining............. 1.6 84.8 7.9 2,585 (6)
Construction............................. 6.7 157.2 (6) 1,005 (6)
Manufacturing............................ 4.6 187.3 2.8 1,272 -1.1
Trade, transportation, and utilities..... 22.4 428.3 1.0 919 2.1
Information.............................. 1.4 31.9 -2.4 1,285 2.1
Financial activities..................... 10.6 118.2 (6) 1,287 2.6
Professional and business services....... 19.4 336.5 (6) 1,233 4.8
Education and health services............ 10.3 218.7 1.6 865 4.3
Leisure and hospitality.................. 7.5 174.2 -1.2 385 5.2
Other services........................... 11.7 58.5 0.2 598 1.2
Government................................. 0.5 250.3 2.7 973 5.1
Maricopa, AZ................................. 103.0 1,761.0 -3.7 836 1.8
Private industry........................... 102.3 1,535.7 -4.5 825 1.9
Natural resources and mining............. 0.5 8.5 0.9 840 16.5
Construction............................. 11.0 130.8 -21.8 878 5.1
Manufacturing............................ 3.6 125.0 -5.6 1,137 2.1
Trade, transportation, and utilities..... 22.8 361.4 -3.9 770 -0.3
Information.............................. 1.7 29.8 -2.0 1,083 5.5
Financial activities..................... 12.9 142.4 -4.0 1,004 -1.8
Professional and business services....... 22.9 293.9 -6.4 863 4.2
Education and health services............ 10.1 216.2 7.8 906 2.7
Leisure and hospitality.................. 7.4 176.8 -1.7 394 1.8
Other services........................... 7.3 49.2 -2.3 584 3.4
Government................................. 0.7 225.3 2.3 915 0.9
Orange, CA................................... 102.5 1,469.5 -2.8 955 3.0
Private industry........................... 101.1 1,327.1 -3.0 947 2.4
Natural resources and mining............. 0.2 4.5 -10.7 681 7.1
Construction............................. 6.9 90.0 -13.4 1,094 6.0
Manufacturing............................ 5.3 171.4 -3.2 1,133 3.5
Trade, transportation, and utilities..... 17.3 270.0 -4.0 880 1.7
Information.............................. 1.3 29.4 -1.2 1,552 15.6
Financial activities..................... 10.8 112.3 -9.0 1,346 -1.0
Professional and business services....... 19.0 266.8 -4.2 1,071 4.5
Education and health services............ 10.0 148.9 3.9 899 3.7
Leisure and hospitality.................. 7.1 177.8 1.3 420 2.2
Other services........................... 17.5 49.4 2.6 551 -1.6
Government................................. 1.4 142.3 -1.2 1,033 9.2
Dallas, TX................................... 68.2 1,489.1 0.5 1,025 2.4
Private industry........................... 67.6 1,321.8 0.3 1,034 2.3
Natural resources and mining............. 0.6 8.3 14.7 4,831 61.8
Construction............................. 4.4 84.7 0.3 922 2.6
Manufacturing............................ 3.1 132.9 -4.0 1,148 -1.0
Trade, transportation, and utilities..... 15.1 304.7 0.1 953 0.3
Information.............................. 1.7 47.6 -3.2 1,445 5.8
Financial activities..................... 8.9 143.9 0.4 1,311 -3.7
Professional and business services....... 14.8 279.1 0.7 1,153 2.6
Education and health services............ 6.7 150.7 3.1 938 4.1
Leisure and hospitality.................. 5.4 129.7 1.5 461 4.5
Other services........................... 6.5 39.1 -0.5 634 4.1
Government................................. 0.5 167.3 2.0 952 3.6
San Diego, CA................................ 99.6 1,318.0 -1.2 921 3.8
Private industry........................... 98.3 1,099.8 -1.5 904 4.1
Natural resources and mining............. 0.8 11.4 -3.6 564 1.6
Construction............................. 7.1 76.2 -12.9 988 4.2
Manufacturing............................ 3.1 102.1 -0.4 1,198 3.3
Trade, transportation, and utilities..... 14.2 214.5 -3.2 733 -0.8
Information.............................. 1.3 39.1 3.6 2,244 30.4
Financial activities..................... 9.6 75.2 -5.2 1,090 -2.2
Professional and business services....... 16.2 215.9 -2.2 1,131 4.6
Education and health services............ 8.1 135.5 3.8 869 4.3
Leisure and hospitality.................. 6.9 165.8 0.0 419 2.9
Other services........................... 26.1 58.2 1.6 489 1.5
Government................................. 1.3 218.2 0.4 1,014 2.7
King, WA..................................... 78.5 1,198.7 1.4 1,162 2.9
Private industry........................... 78.0 1,045.7 1.3 1,176 2.7
Natural resources and mining............. 0.4 3.2 0.8 1,288 12.1
Construction............................. 6.9 72.3 -2.9 1,083 4.9
Manufacturing............................ 2.5 112.0 -0.8 1,259 0.6
Trade, transportation, and utilities..... 15.2 220.2 0.3 921 3.5
Information.............................. 1.8 80.9 5.9 3,364 8.3
Financial activities..................... 7.1 74.6 -0.9 1,368 6.0
Professional and business services....... 13.9 193.2 1.3 1,243 -6.3
Education and health services............ 6.6 126.5 5.2 863 3.0
Leisure and hospitality.................. 6.2 115.7 1.9 447 0.9
Other services........................... 17.5 47.2 4.2 601 4.7
Government................................. 0.5 153.0 2.1 1,064 4.9
Miami-Dade, FL............................... 87.8 993.1 -3.2 842 2.2
Private industry........................... 87.5 842.7 -3.5 805 1.5
Natural resources and mining............. 0.5 7.7 -9.6 474 -2.3
Construction............................. 6.6 44.2 -20.3 844 2.9
Manufacturing............................ 2.6 42.8 -10.2 745 3.5
Trade, transportation, and utilities..... 23.5 248.8 -2.1 746 -0.4
Information.............................. 1.5 19.0 -7.5 1,227 2.8
Financial activities..................... 10.4 68.0 -5.6 1,156 0.3
Professional and business services....... 18.1 129.8 -4.4 1,011 4.6
Education and health services............ 9.4 144.2 2.8 822 1.7
Leisure and hospitality.................. 6.0 100.6 -2.0 481 4.3
Other services........................... 7.6 35.9 -0.5 523 1.4
Government................................. 0.4 150.4 -1.4 1,058 4.9
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
(2) Data are preliminary.
(3) Average weekly wages were calculated using unrounded data.
(4) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(5) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(6) Data do not meet BLS or State agency disclosure standards.
Table 3. Covered(1) establishments, employment, and wages in the largest county by
state, third quarter 2008(2)
Employment Average weekly
wage(4)
Establishments,
third quarter
County(3) 2008 Percent Percent
(thousands) September change, Average change,
2008 September weekly third
(thousands) 2007-08(5) wage quarter
2007-08(5)
United States(6)......... 9,150.8 135,173.8 -0.8 $841 2.8
Jefferson, AL............ 19.0 357.9 -1.6 863 3.1
Anchorage Borough, AK.... 8.3 152.0 2.0 922 3.1
Maricopa, AZ............. 103.0 1,761.0 -3.7 836 1.8
Pulaski, AR.............. 15.1 252.2 0.3 765 2.0
Los Angeles, CA.......... 428.8 4,141.1 -1.5 951 3.1
Denver, CO............... 26.0 453.3 0.5 1,031 3.6
Hartford, CT............. 25.6 506.7 0.3 1,012 0.9
New Castle, DE........... 18.5 278.0 -1.5 981 2.6
Washington, DC........... 33.8 688.2 1.4 1,391 1.0
Miami-Dade, FL........... 87.8 993.1 -3.2 842 2.2
Fulton, GA............... 39.1 741.7 -1.0 1,078 1.9
Honolulu, HI............. 24.7 444.6 -1.6 800 1.8
Ada, ID.................. 15.0 210.4 -1.5 746 -0.5
Cook, IL................. 140.4 2,504.2 -1.3 988 2.8
Marion, IN............... 24.2 580.5 -0.7 852 2.7
Polk, IA................. 14.9 276.3 0.6 831 3.5
Johnson, KS.............. 20.6 318.1 0.1 867 4.0
Jefferson, KY............ 22.3 426.4 -2.3 799 1.1
East Baton Rouge, LA..... 14.4 261.4 -0.1 790 6.6
Cumberland, ME........... 12.0 174.0 -0.1 768 3.4
Montgomery, MD........... 32.8 459.0 -0.4 1,122 2.9
Middlesex, MA............ 47.6 825.1 0.8 1,200 1.8
Wayne, MI................ 32.1 717.9 -4.2 942 1.4
Hennepin, MN............. 42.2 840.7 -0.8 1,102 5.4
Hinds, MS................ 6.4 126.6 -0.6 745 4.3
St. Louis, MO............ 32.9 605.6 -1.0 890 1.8
Yellowstone, MT.......... 5.8 78.5 0.7 688 2.4
Douglas, NE.............. 16.0 321.4 0.9 820 4.9
Clark, NV................ 50.9 903.7 -2.0 812 2.0
Hillsborough, NH......... 12.4 196.5 -0.6 924 2.7
Bergen, NJ............... 35.0 445.7 -1.3 1,031 2.3
Bernalillo, NM........... 17.6 335.6 0.2 763 3.8
New York, NY............. 118.9 2,363.8 0.6 1,552 0.5
Mecklenburg, NC.......... 33.3 570.0 0.0 956 3.5
Cass, ND................. 5.9 101.1 2.6 723 5.1
Cuyahoga, OH............. 38.1 732.3 -1.7 853 2.4
Oklahoma, OK............. 24.0 427.1 1.2 784 4.7
Multnomah, OR............ 28.2 451.7 0.4 858 2.1
Allegheny, PA............ 35.4 686.8 -0.1 886 2.7
Providence, RI........... 18.1 281.9 -2.2 805 3.5
Greenville, SC........... 12.9 241.1 0.8 728 2.2
Minnehaha, SD............ 6.4 116.7 1.4 717 3.2
Shelby, TN............... 20.0 500.6 -1.7 855 0.6
Harris, TX............... 97.3 2,047.2 1.3 1,050 3.0
Salt Lake, UT............ 38.5 591.7 0.5 796 3.0
Chittenden, VT........... 6.0 95.6 -0.3 838 3.2
Fairfax, VA.............. 34.0 587.0 0.3 1,295 4.2
King, WA................. 78.5 1,198.7 1.4 1,162 2.9
Kanawha, WV.............. 6.1 108.5 -0.5 738 4.8
Milwaukee, WI............ 21.5 498.3 -0.1 839 4.7
Laramie, WY.............. 3.2 44.2 0.9 718 3.9
San Juan, PR............. 13.1 283.5 -1.2 569 6.0
St. Thomas, VI........... 1.8 23.6 1.4 651 2.2
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs.
(2) Data are preliminary.
(3) Includes areas not officially designated as counties. See Technical Note.
(4) Average weekly wages were calculated using unrounded data.
(5) Percent changes were computed from quarterly employment and pay data adjusted
for noneconomic county reclassifications. See Technical Note.
(6) Totals for the United States do not include data for Puerto Rico or the Virgin
Islands.
Table 4. Covered(1) establishments, employment, and wages by state,
third quarter 2008(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
State 2008 Percent Percent
(thousands) September change, Average change,
2008 September weekly third
(thousands) 2007-08 wage quarter
2007-08
United States(4)......... 9,150.8 135,173.8 -0.8 $841 2.8
Alabama.................. 121.8 1,936.4 -1.2 730 3.3
Alaska................... 21.6 332.1 1.4 872 3.7
Arizona.................. 164.1 2,570.1 -3.0 798 2.0
Arkansas................. 86.1 1,185.0 -0.1 649 3.0
California............... 1,344.6 15,527.1 -1.4 959 2.9
Colorado................. 180.4 2,322.7 0.4 877 3.8
Connecticut.............. 113.5 1,692.5 -0.3 1,032 1.0
Delaware................. 29.5 420.6 -1.1 879 2.1
District of Columbia..... 33.8 688.2 1.4 1,391 1.0
Florida.................. 625.2 7,546.4 -4.1 756 2.2
Georgia.................. 276.6 4,018.6 -1.6 794 1.5
Hawaii................... 39.1 613.0 -2.1 774 1.8
Idaho.................... 57.0 665.7 -1.4 643 1.3
Illinois................. 369.7 5,872.8 -0.7 891 2.9
Indiana.................. 160.5 2,897.6 -1.4 718 2.3
Iowa..................... 94.6 1,499.0 0.2 696 4.2
Kansas................... 86.7 1,368.9 0.0 711 4.6
Kentucky................. 110.4 1,795.3 -1.0 692 2.4
Louisiana................ 124.1 1,877.4 -0.2 756 5.6
Maine.................... 50.7 610.8 -0.6 683 3.5
Maryland................. 163.9 2,543.4 -0.8 920 3.1
Massachusetts............ 213.9 3,265.7 0.0 1,025 2.3
Michigan................. 259.0 4,093.9 -3.0 820 1.5
Minnesota................ 171.6 2,699.6 -0.5 862 4.7
Mississippi.............. 70.8 1,128.3 -1.3 631 4.0
Missouri................. 175.4 2,736.1 -0.4 739 2.8
Montana.................. 43.3 446.4 0.1 628 3.1
Nebraska................. 60.0 925.7 0.2 694 4.2
Nevada................... 77.5 1,253.0 -2.7 809 2.1
New Hampshire............ 49.8 634.6 -0.5 822 2.8
New Jersey............... 277.8 3,952.9 -0.7 990 2.5
New Mexico............... 54.7 835.2 0.7 712 3.5
New York................. 586.1 8,633.8 0.5 1,030 2.2
North Carolina........... 259.4 4,064.2 -1.0 741 3.1
North Dakota............. 25.8 357.0 2.8 665 6.9
Ohio..................... 295.5 5,251.1 -1.5 766 2.8
Oklahoma................. 100.9 1,562.8 1.2 698 4.5
Oregon................... 132.5 1,734.1 -1.0 766 2.1
Pennsylvania............. 343.5 5,679.0 0.0 822 2.5
Rhode Island............. 35.9 476.0 -2.0 778 2.5
South Carolina........... 119.6 1,874.6 -1.5 683 2.9
South Dakota............. 30.6 401.3 1.0 623 4.2
Tennessee................ 143.5 2,730.4 -1.5 745 2.8
Texas.................... 563.6 10,438.3 1.4 850 2.9
Utah..................... 87.3 1,229.3 -0.1 717 2.9
Vermont.................. 25.1 304.2 -0.5 722 3.3
Virginia................. 232.7 3,676.1 -0.3 877 2.3
Washington............... 225.5 3,007.5 1.0 903 3.0
West Virginia............ 48.9 716.4 0.6 661 5.9
Wisconsin................ 161.6 2,788.7 -0.6 730 3.4
Wyoming.................. 25.2 294.0 3.3 781 6.4
Puerto Rico.............. 55.6 992.8 -1.6 477 5.5
Virgin Islands........... 3.5 44.9 -0.9 709 4.3
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs.
(2) Data are preliminary.
(3) Average weekly wages were calculated using unrounded data.
(4) Totals for the United States do not include data for Puerto Rico or the
Virgin Islands.
United States Department of Labor
