For release 10:00 a.m. (EDT), Thursday, April 1, 2010 USDL-10-0393
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov *
www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
County Employment and Wages
Third Quarter 2009
From September 2008 to September 2009, employment declined in 329 of
the 334 largest U.S. counties according to preliminary data, the U.S.
Bureau of Labor Statistics reported today. Elkhart County, Ind.,
located about 100 miles east of Chicago, posted the largest
percentage decline, with a loss of 14.5 percent over the year,
compared with a national job decrease of 5.3 percent. Two-thirds of
the employment decline in Elkhart occurred in manufacturing, which
lost 10,868 jobs over the year (-21.6 percent). Yakima County, Wash.,
experienced the largest over-the-year percentage increase in
employment among the largest counties in the U.S., with a gain of 1.7
percent.
The U.S. average weekly wage fell over the year by 0.1 percent in the
third quarter of 2009. This is the first time there has been an over-
the-year average weekly wage decline for three consecutive quarters,
and this decline is one of only five declines dating back to 1978,
when these quarterly data were first comparable. (See Technical
Note.) Employment and wage losses in the relatively high paid
financial activities and manufacturing supersectors contributed
significantly to the over-the-year decline in the U.S. average weekly
wages for third quarter 2009. Average weekly wages fell 2.3 percent
in financial activities and 0.2 percent in manufacturing. Among the
large counties in the U.S., Rutherford, Tenn., had the largest over-
the-year decrease in average weekly wages in the third quarter of
2009, with a loss of 13.2 percent. Within Rutherford, manufacturing
had the largest over-the-year decline in average weekly wages with a
loss of 27.9 percent. Bell, Texas, experienced the largest growth in
average weekly wages with a gain of 6.6 percent.
Table A. Top 10 large counties ranked by September 2009 employment, September 2008-09 employment
decrease, and September 2008-09 percent decrease in employment
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Employment in large counties
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September 2009 employment | Decrease in employment, | Percent decrease in employment,
(thousands) | September 2008-09 | September 2008-09
| (thousands) |
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| |
United States 128,088.7| United States -7,109.1| United States -5.3
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| |
Los Angeles, Calif. 3,858.5| Los Angeles, Calif. -278.0| Elkhart, Ind. -14.5
Cook, Ill. 2,364.2| Maricopa, Ariz. -155.0| Trumbull, Ohio -11.0
New York, N.Y. 2,240.3| Cook, Ill. -140.1| Clark, Nev. -10.6
Harris, Texas 1,979.6| Orange, Calif. -126.4| Catawba, N.C. -10.4
Maricopa, Ariz. 1,605.7| New York, N.Y. -125.1| Macomb, Mich. -10.3
Dallas, Texas 1,405.1| Clark, Nev. -95.4| Collier, Fla. -10.0
Orange, Calif. 1,340.7| San Diego, Calif. -88.3| Oakland, Mich. -9.8
San Diego, Calif. 1,229.1| Dallas, Texas -76.9| Washoe, Nev. -9.6
King, Wash. 1,122.7| Santa Clara, Calif. -76.3| Marion, Fla. -9.5
Miami-Dade, Fla. 935.1| King, Wash. -72.9| Winnebago, Ill. -9.3
| |
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Of the 334 largest counties in the United States (as measured by 2008
annual average employment), 147 had over-the-year percentage declines
in employment greater than or equal to the national average (-5.3
percent) in September 2009; 182 large counties experienced smaller
declines than the national average, while 2 counties experienced
employment gains. The percent change in average weekly wages was
equal to or lower than the national average (-0.1 percent) in 131 of
the largest U.S. counties and was above the national average in 198
counties.
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 9.1 million employer reports cover 128.1 million full- and
part-time workers.
Large County Employment
In September 2009, national employment, as measured by the QCEW
program, was 128.1 million, down by 5.3 percent from September 2008.
The 334 U.S. counties with 75,000 or more employees accounted for
71.1 percent of total U.S. employment and 76.6 percent of total
wages. These 334 counties had a net job decline of 5,262,400 over the
year, accounting for 74.0 percent of the overall U.S. employment
decrease.
Employment declined in 329 counties from September 2008 to September
2009. The largest percentage decline in employment was in Elkhart,
Ind. (-14.5 percent). Trumbull, Ohio, had the next largest percentage
decline (-11.0 percent), followed by the counties of Clark, Nev. (-
10.6 percent), Catawba, N.C. (-10.4 percent), and Macomb, Mich. (-
10.3 percent). The largest decline in employment levels occurred in
Los Angeles, Calif. (-278,000), followed by the counties of Maricopa,
Ariz. (-155,000), Cook, Ill. (-140,100), Orange, Calif. (-126,400),
and New York, N.Y. (-125,100). (See table A.) Combined employment
losses in these five counties over the year totaled 824,600 or 11.6
percent of the employment decline for the U.S. as a whole.
Table B. Top 10 large counties ranked by third quarter 2009 average weekly wages, third quarter 2008-09
decrease in average weekly wages, and third quarter 2008-09 percent decrease in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Decrease in average weekly | Percent decrease in average
third quarter 2009 | wage, third quarter 2008-09 | weekly wage, third
| | quarter 2008-09
--------------------------------------------------------------------------------------------------------
| |
United States $840| United States -$1| United States -0.1
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| |
Santa Clara, Calif. $1,506| Rutherford, Tenn. -$111| Rutherford, Tenn. -13.2
New York, N.Y. 1,500| San Mateo, Calif. -67| Trumbull, Ohio -8.3
Washington, D.C. 1,450| Trumbull, Ohio -59| Olmsted, Minn. -5.8
Arlington, Va. 1,413| Hennepin, Minn. -56| Santa Cruz, Calif. -5.5
Fairfax, Va. 1,321| Olmsted, Minn. -55| Lake, Ind. -5.2
San Francisco, Calif. 1,309| New York, N.Y. -53| Hennepin, Minn. -5.1
San Mateo, Calif. 1,306| Santa Cruz, Calif. -44| San Mateo, Calif. -4.9
Suffolk, Mass. 1,306| San Francisco, Calif. -43| Lorain, Ohio -4.1
Fairfield, Conn. 1,268| Fairfield, Conn. -43| Williamson, Tenn. -4.1
Somerset, N.J. 1,244| Lake, Ind. -40| New York, N.Y. -3.4
| |
| |
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Employment rose in two of the large counties from September 2008 to
September 2009. Yakima, Wash., had the largest over-the-year
percentage increase in employment (1.7 percent) among the largest
counties in the U.S. Bronx, N.Y., had the other employment increase
(0.2 percent).
Large County Average Weekly Wages
Average weekly wages for the nation fell 0.1 percent over the year in
the third quarter of 2009. This is the third consecutive over-the-
year decline in average weekly wages and one of only five declines
dating back to 1978. Among the 334 largest counties, 131 had over-
the-year decreases in average weekly wages in the third quarter. The
largest wage loss occurred in Rutherford, Tenn., with a decline of
13.2 percent from the third quarter of 2008. Trumbull, Ohio, had the
second largest decline (-8.3 percent), followed by the counties of
Olmsted, Minn. (-5.8 percent), Santa Cruz, Calif. (-5.5 percent), and
Lake, Ind. (-5.2 percent). (See table B.)
Of the 334 largest counties, 189 experienced growth in average weekly
wages. Bell, Texas, led the nation in growth in average weekly wages
with an increase of 6.6 percent from the third quarter of 2008.
Within Bell County, large wage gains occurred in federal government
where average weekly wages grew 18.1 percent over the year. Harford,
Md., had the second largest overall increase (6.2 percent), followed
by the counties of Cumberland, N.C. (6.1 percent), Madison, Ala. (5.8
percent), and Arlington, Va. (4.8 percent).
The national average weekly wage in the third quarter of 2009 was
$840. Average weekly wages were higher than the national average in
112 of the 334 largest U.S. counties. Santa Clara, Calif., held the
top position among the highest-paid large counties with an average
weekly wage of $1,506. New York, N.Y., was second with an average
weekly wage of $1,500, followed by Washington, D.C. ($1,450),
Arlington, Va. ($1,413), and Fairfax, Va. ($1,321). There were 222
counties with an average weekly wage below the national average in
the third quarter of 2009. The lowest average weekly wage was
reported in Horry, S.C. ($534), followed by the counties of Cameron,
Texas ($553), Hidalgo, Texas ($564), Webb, Texas ($574), and Yakima,
Wash. ($584). (See table 1.)
Average weekly wages are affected not only by changes in total wages
but also by employment changes in high- and low-paying industries.
(See Technical Note.) The 0.1-percent over-the-year decrease in
average weekly wages for the nation was partially due to large
employment declines in high-paying industries such as manufacturing.
(See table 2.)
Ten Largest U.S. Counties
All of the 10 largest counties (based on 2008 annual average
employment levels) experienced over-the-year percent declines in
employment in September 2009. Maricopa, Ariz., experienced the
largest decline in employment among the 10 largest counties with an
8.8 percent decrease. Within Maricopa, every private industry group
except education and health services experienced an employment
decline, with construction experiencing the largest decline (-32.2
percent). (See table 2.) Orange, Calif., had the next largest decline
in employment, 8.6 percent, followed by San Diego, Calif., and Los
Angeles, Calif. (-6.7 percent each). Harris, Texas, experienced the
smallest decline in employment (-3.4 percent) among the 10 largest
counties. Dallas, Texas (-5.2 percent), and New York, N.Y. (-5.3
percent), had the second and third smallest employment losses
respectively.
Eight of the 10 largest U.S. counties saw an over-the-year decrease
in average weekly wages. New York, N.Y., experienced the largest
decline in average weekly wages among the 10 largest counties with a
decrease of 3.4 percent. Within New York County, financial activities
sustained the largest total wage loss (-$2.3 billion) over the year.
Average weekly wages for this supersector fell by 7.3 percent. New
York’s average weekly wage loss was followed by Cook, Ill. (-1.4
percent), and Dallas, Texas (-1.1 percent). King, Wash., and
Maricopa, Ariz., had the only wage increases among the 10 largest
counties, with increases of 1.4 percent and 0.4 percent respectively.
Largest County by State
Table 3 shows September 2009 employment and the 2009 third quarter
average weekly wage in the largest county in each state, which is
based on 2008 annual average employment levels. The employment levels
in the counties in table 3 in September 2009 ranged from 3.9 million
in Los Angeles County, Calif., to 43,500 in Laramie County, Wyo. The
highest average weekly wage of these counties was in New York, N.Y.
($1,500), while the lowest average weekly wage was in Yellowstone,
Mont. ($691).
For More Information
The tables included in this release contain data for the nation and
for the 334 U.S. counties with annual average employment levels of
75,000 or more in 2008. September 2009 employment and 2009 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. Data for the third quarter of
2009 will be available later at http://www.bls.gov/cew/. 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 2009 is
scheduled to be released on Wednesday, July 7, 2010.
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
release are based on the 2007 North American Industry Classification System. Data
for 2009 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment le-
vels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are pro-
vided, 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 prelimi-
nary annual average of employment for the previous year. The 335 counties presented
in this release were derived using 2008 preliminary annual averages of employment.
For 2009 data, two counties have been added to the publication tables: Johnson,
Iowa, and Gregg, Texas. These counties will be included in all 2009 quarterly re-
leases. Two counties, Boone, Ky., and St. Tammany, La., which were published in the
2008 releases, will be excluded from this and future 2009 releases because their
2008 annual average employment levels were less than 75,000. 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 6.8 |
| ments in first | million private-sec-|
| quarter of 2009 | 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 | | |
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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 sub-
mitted 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 multiple 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. QCEW employment and wage data are derived
from microdata summaries of 9.1 million employer reports of employment and wages
submitted by states to the BLS in 2008. These reports are based on place of employ-
ment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state
since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became ef-
fective, expanding coverage to include most State and local government employees.
In 2008, UI and UCFE programs covered workers in 134.8 million jobs. The estimated
129.4 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.5 percent of civilian wage and salary employment. Covered workers
received $6.142 trillion in pay, representing 93.8 percent of the wage and salary
component of personal income and 42.5 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 re-
lease.
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
employees of covered firms are reported, including production and sales workers,
corporation officials, executives, supervisory personnel, and clerical workers.
Workers 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 av-
erages 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
gratuities, and, in some states, employer contributions to certain deferred compen-
sation plans such as 401(k) plans and stock options. Over-the-year comparisons of
average weekly wages may reflect fluctuations in average monthly employment and/or
total 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 week-
ly 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 pe-
riods 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 oc-
cur 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
comparisons can be pronounced in federal government due to the uniform nature of
federal payroll processing. This pattern may exist in private sector pay; however,
because there are more pay period types (weekly, biweekly, semimonthly, monthly) it
is less pronounced. The effect is most visible in counties with large concentra-
tions of federal employment.
In order to ensure the highest possible quality of data, states verify with employ-
ers and update, if necessary, the industry, location, and ownership classification
of all establishments on a 4-year cycle. Changes in establishment classification
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 indi-
vidual 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 adjusted version of the final 2008 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 un-
adjusted 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 re-
lease.
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. In-
cluded in these adjustments are administrative changes involving the classification
of establishments that were previously reported in the unknown or statewide county
or unknown industry categories. Beginning with the first quarter of 2008, adjusted
data 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.
Comparisons may not be valid for any time period other than the one featured in a
release 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 common 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 de-
tailed industry on establishments, employment, and wages for the nation and all
states. The 2008 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 2009 version of this news release. Tables and additional
content from the 2008 Employment and Wages Annual Bulletin are now available online
at http://www.bls.gov/cew/cewbultn08.htm. These tables present final 2008 annual
averages. The tables are included on the CD which accompanies the hardcopy version
of the Annual Bulletin. Employment and Wages Annual Averages, 2008 will be available
for sale as a chartbook from the United States Government Printing Office, Superin-
tendent 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 2009(2)
Employment Average weekly wage(4)
Establishments,
County(3) third quarter Percent Ranking Percent Ranking
2009 September change, by Average change, by
(thousands) 2009 September percent weekly third percent
(thousands) 2008-09(5) change wage quarter change
2008-09(5)
United States(6)......... 9,066.0 128,088.7 -5.3 - $840 -0.1 -
Jefferson, AL............ 18.3 333.2 -7.0 267 861 -0.5 230
Madison, AL.............. 8.8 178.2 -2.8 39 967 5.8 4
Mobile, AL............... 9.8 163.7 -6.8 263 744 4.2 9
Montgomery, AL........... 6.4 129.6 -6.1 226 743 2.2 55
Shelby, AL............... 4.9 70.2 -7.4 287 792 -1.7 290
Tuscaloosa, AL........... 4.3 82.1 -6.2 235 730 0.1 177
Anchorage Borough, AK.... 8.1 150.4 -1.2 10 942 2.1 60
Maricopa, AZ............. 98.8 1,605.7 -8.8 318 838 0.4 159
Pima, AZ................. 20.3 349.0 -6.1 226 753 0.9 118
Benton, AR............... 5.5 91.2 -4.2 108 755 -1.0 260
Pulaski, AR.............. 15.0 241.7 -4.2 108 789 2.6 33
Washington, AR........... 5.6 89.1 -3.4 68 687 1.0 113
Alameda, CA.............. 52.9 628.5 -8.3 308 1,101 -1.4 280
Butte, CA................ 7.9 72.0 -6.2 235 668 1.4 96
Contra Costa, CA......... 29.5 316.7 -6.7 256 1,027 -0.8 248
Fresno, CA............... 30.3 345.8 -7.4 287 672 2.1 60
Kern, CA................. 17.9 275.4 -6.2 235 730 -1.9 295
Los Angeles, CA.......... 422.5 3,858.5 -6.7 256 942 -0.8 248
Marin, CA................ 11.6 100.2 -8.3 308 1,021 -0.6 237
Monterey, CA............. 12.6 177.4 -4.1 100 740 -1.2 273
Orange, CA............... 99.8 1,340.7 -8.6 314 948 -0.7 244
Placer, CA............... 10.7 123.2 -9.1 321 832 0.8 124
Riverside, CA............ 47.1 549.0 -8.7 317 711 -0.4 219
Sacramento, CA........... 53.5 590.8 -5.5 194 943 -0.9 252
San Bernardino, CA....... 49.5 594.0 -7.9 302 748 1.1 109
San Diego, CA............ 96.5 1,229.1 -6.7 256 918 -0.4 219
San Francisco, CA........ 51.5 544.0 -5.3 185 1,309 -3.2 315
San Joaquin, CA.......... 17.6 209.5 -7.3 281 745 0.1 177
San Luis Obispo, CA...... 9.5 98.0 -6.5 253 722 1.0 113
San Mateo, CA............ 23.5 318.0 -7.3 281 1,306 -4.9 323
Santa Barbara, CA........ 14.2 179.4 -5.9 219 796 1.5 89
Santa Clara, CA.......... 60.1 835.1 -8.4 312 1,506 -1.6 285
Santa Cruz, CA........... 9.0 97.6 -4.0 94 754 -5.5 326
Solano, CA............... 10.0 120.5 -5.2 176 855 0.1 177
Sonoma, CA............... 18.5 177.1 -8.2 304 821 -0.4 219
Stanislaus, CA........... 14.9 166.0 -6.3 240 737 2.2 55
Tulare, CA............... 9.5 149.1 -6.3 240 604 0.8 124
Ventura, CA.............. 23.4 292.4 -7.4 287 865 -0.2 207
Yolo, CA................. 5.9 99.1 -4.8 148 838 1.0 113
Adams, CO................ 9.2 149.8 -5.8 211 788 -0.1 199
Arapahoe, CO............. 19.4 271.1 -4.3 116 990 -1.2 273
Boulder, CO.............. 13.0 152.4 -4.7 140 976 -3.1 312
Denver, CO............... 25.6 419.3 -7.0 267 1,040 0.0 190
Douglas, CO.............. 9.6 89.7 -5.4 189 904 4.4 8
El Paso, CO.............. 17.3 233.4 -4.8 148 798 2.4 45
Jefferson, CO............ 18.4 203.3 -4.5 128 880 0.8 124
Larimer, CO.............. 10.3 127.3 -4.9 162 772 0.3 163
Weld, CO................. 6.0 78.6 -7.0 267 716 -1.6 285
Fairfield, CT............ 32.9 398.9 -4.8 148 1,268 -3.3 318
Hartford, CT............. 25.5 484.1 -4.7 140 1,020 0.8 124
New Haven, CT............ 22.5 347.5 -4.5 128 912 0.1 177
New London, CT........... 7.0 126.6 -4.4 121 870 0.8 124
New Castle, DE........... 18.0 263.6 -5.8 211 985 0.6 143
Washington, DC........... 34.4 682.6 -1.1 9 1,450 4.2 9
Alachua, FL.............. 6.6 115.7 -5.5 194 744 2.9 22
Brevard, FL.............. 14.6 187.2 -5.6 200 808 2.0 65
Broward, FL.............. 62.7 673.9 -7.3 281 797 -0.1 199
Collier, FL.............. 11.8 104.6 -10.0 326 739 -1.1 266
Duval, FL................ 26.7 430.1 -6.0 224 810 1.1 109
Escambia, FL............. 7.9 118.9 -5.0 166 684 2.5 38
Hillsborough, FL......... 37.0 560.0 -7.3 281 830 2.6 33
Lake, FL................. 7.3 78.8 -6.7 256 591 -1.7 290
Lee, FL.................. 18.7 186.1 -8.4 312 703 -0.3 213
Leon, FL................. 8.1 138.6 -3.2 61 747 0.0 190
Manatee, FL.............. 9.1 104.7 -6.9 266 659 0.2 170
Marion, FL............... 8.1 89.3 -9.5 323 607 0.7 136
Miami-Dade, FL........... 84.1 935.1 -5.8 211 839 -0.2 207
Okaloosa, FL............. 6.0 76.4 -3.1 50 701 1.7 77
Orange, FL............... 35.1 638.4 -6.8 263 759 -0.5 230
Palm Beach, FL........... 49.0 480.8 -7.5 293 810 -0.4 219
Pasco, FL................ 9.8 94.6 -4.6 135 590 -0.7 244
Pinellas, FL............. 30.8 387.4 -6.7 256 738 0.3 163
Polk, FL................. 12.4 186.2 -6.3 240 680 0.1 177
Sarasota, FL............. 14.7 130.7 -7.7 298 707 -0.4 219
Seminole, FL............. 14.1 155.8 -8.9 319 696 -2.2 297
Volusia, FL.............. 13.6 149.3 -7.3 281 618 0.8 124
Bibb, GA................. 4.7 80.2 -5.9 219 682 1.9 67
Chatham, GA.............. 7.7 127.0 -6.3 240 732 1.0 113
Clayton, GA.............. 4.4 107.6 -4.0 94 786 -0.3 213
Cobb, GA................. 20.7 293.0 -7.2 277 898 -1.1 266
De Kalb, GA.............. 17.6 275.6 -5.9 219 897 1.6 82
Fulton, GA............... 39.4 693.5 -6.7 256 1,087 0.6 143
Gwinnett, GA............. 23.8 292.8 -7.8 301 832 -1.3 278
Muscogee, GA............. 4.7 91.1 -4.5 128 700 3.7 14
Richmond, GA............. 4.8 97.3 -4.4 121 746 1.9 67
Honolulu, HI............. 25.1 428.1 -4.0 94 818 2.4 45
Ada, ID.................. 14.6 193.3 -8.3 308 754 1.2 106
Champaign, IL............ 4.2 88.7 -4.8 148 745 2.3 53
Cook, IL................. 142.0 2,364.2 -5.6 200 975 -1.4 280
Du Page, IL.............. 36.2 547.3 -6.8 263 964 -2.7 307
Kane, IL................. 12.9 192.5 -8.2 304 759 -0.5 230
Lake, IL................. 21.2 316.8 -6.2 235 1,001 -3.3 318
McHenry, IL.............. 8.5 95.9 -7.7 298 705 -3.0 311
McLean, IL............... 3.7 84.0 -3.8 83 834 2.2 55
Madison, IL.............. 6.0 92.5 -4.3 116 720 0.1 177
Peoria, IL............... 4.7 97.6 -8.3 308 803 -0.9 252
Rock Island, IL.......... 3.5 74.3 -7.1 272 859 3.9 11
St. Clair, IL............ 5.5 93.6 -4.9 162 711 2.4 45
Sangamon, IL............. 5.3 125.9 -3.1 50 880 3.7 14
Will, IL................. 14.2 189.6 -5.8 211 746 -1.2 273
Winnebago, IL............ 6.9 123.5 -9.3 322 738 0.1 177
Allen, IN................ 9.0 171.3 -6.1 226 701 -0.3 213
Elkhart, IN.............. 4.9 95.6 -14.5 331 682 2.4 45
Hamilton, IN............. 7.9 108.4 -6.3 240 790 -2.2 297
Lake, IN................. 10.3 184.3 -6.2 235 731 -5.2 325
Marion, IN............... 23.9 546.7 -5.1 169 856 0.2 170
St. Joseph, IN........... 6.1 114.7 -7.0 267 709 0.1 177
Tippecanoe, IN........... 3.3 72.1 -7.1 272 740 2.2 55
Vanderburgh, IN.......... 4.8 103.4 -4.8 148 702 0.0 190
Johnson, IA.............. 3.5 74.9 -1.6 15 793 0.5 148
Linn, IA................. 6.3 123.0 -2.9 41 802 -2.8 308
Polk, IA................. 14.8 267.5 -3.3 64 831 0.0 190
Scott, IA................ 5.3 85.2 -5.7 205 686 -1.3 278
Johnson, KS.............. 20.9 299.4 -5.6 200 858 -0.9 252
Sedgwick, KS............. 12.5 241.5 -6.1 226 755 -0.9 252
Shawnee, KS.............. 4.9 92.9 -3.6 76 724 1.5 89
Wyandotte, KS............ 3.2 78.8 -2.7 35 812 -2.2 297
Fayette, KY.............. 9.2 171.0 -3.1 50 766 1.6 82
Jefferson, KY............ 21.7 409.1 -3.8 83 812 1.2 106
Caddo, LA................ 7.4 120.2 -4.0 94 708 -1.7 290
Calcasieu, LA............ 4.9 83.0 -3.6 76 726 -2.3 302
East Baton Rouge, LA..... 14.6 259.3 -0.8 6 815 2.6 33
Jefferson, LA............ 14.1 192.7 -1.2 10 787 1.4 96
Lafayette, LA............ 9.0 128.5 -4.7 140 802 -2.8 308
Orleans, LA.............. 10.7 166.3 -1.3 14 925 0.8 124
Cumberland, ME........... 12.2 167.8 -3.9 89 773 0.8 124
Anne Arundel, MD......... 14.3 227.3 -2.9 41 927 3.5 17
Baltimore, MD............ 21.2 360.6 -4.1 100 873 1.6 82
Frederick, MD............ 5.9 91.7 -3.7 78 844 2.7 27
Harford, MD.............. 5.6 81.1 -2.7 35 834 6.2 2
Howard, MD............... 8.7 143.9 -3.7 78 1,021 3.9 11
Montgomery, MD........... 32.3 443.0 -3.1 50 1,144 2.1 60
Prince Georges, MD....... 15.6 300.8 -4.1 100 951 1.9 67
Baltimore City, MD....... 13.7 324.3 -4.2 108 981 -0.2 207
Barnstable, MA........... 9.0 94.4 -2.6 29 706 -0.3 213
Bristol, MA.............. 15.4 206.7 -5.2 176 753 0.4 159
Essex, MA................ 20.8 291.9 -3.3 64 889 0.0 190
Hampden, MA.............. 14.7 192.8 -3.7 78 799 1.8 70
Middlesex, MA............ 47.3 791.7 -4.3 116 1,194 -0.3 213
Norfolk, MA.............. 23.4 310.9 -4.4 121 968 -0.1 199
Plymouth, MA............. 13.6 171.5 -3.4 68 789 0.5 148
Suffolk, MA.............. 21.9 571.3 -3.9 89 1,306 -1.1 266
Worcester, MA............ 20.6 306.2 -4.8 148 854 -0.5 230
Genesee, MI.............. 7.6 127.7 -6.1 226 719 -2.3 302
Ingham, MI............... 6.7 149.4 -6.4 249 820 1.5 89
Kalamazoo, MI............ 5.5 108.0 -5.8 211 768 -2.2 297
Kent, MI................. 14.2 304.3 -7.6 295 768 1.3 103
Macomb, MI............... 17.4 269.4 -10.3 327 854 0.1 177
Oakland, MI.............. 38.4 607.3 -9.8 325 937 -3.1 312
Ottawa, MI............... 5.7 102.8 -7.6 295 685 -3.2 315
Saginaw, MI.............. 4.3 80.3 -4.8 148 704 0.6 143
Washtenaw, MI............ 8.0 180.4 -4.2 108 958 1.5 89
Wayne, MI................ 31.6 660.0 -8.6 314 918 -2.5 306
Anoka, MN................ 7.6 106.5 -7.4 287 764 -0.8 248
Dakota, MN............... 10.3 166.6 -4.8 148 798 -0.4 219
Hennepin, MN............. 42.3 794.9 -5.5 194 1,047 -5.1 324
Olmsted, MN.............. 3.5 87.5 -3.5 73 894 -5.8 327
Ramsey, MN............... 14.8 317.2 -5.7 205 920 -1.4 280
St. Louis, MN............ 5.8 92.0 -6.3 240 690 -1.0 260
Stearns, MN.............. 4.4 77.6 -5.6 200 703 2.9 22
Harrison, MS............. 4.6 84.1 -2.9 41 652 -1.8 293
Hinds, MS................ 6.3 124.1 -2.3 25 760 2.4 45
Boone, MO................ 4.5 80.7 -3.1 50 691 4.7 6
Clay, MO................. 5.0 86.6 -5.7 205 785 2.7 27
Greene, MO............... 8.1 148.8 -5.2 176 666 2.0 65
Jackson, MO.............. 18.4 353.1 -4.4 121 858 0.7 136
St. Charles, MO.......... 8.2 118.0 -4.8 148 679 -2.4 304
St. Louis, MO............ 32.2 568.8 -5.7 205 893 0.2 170
St. Louis City, MO....... 8.6 219.5 (7) - 904 (7) -
Yellowstone, MT.......... 5.9 76.6 -2.6 29 691 0.1 177
Douglas, NE.............. 15.8 311.0 -2.9 41 795 -3.2 315
Lancaster, NE............ 8.2 154.5 -2.9 41 698 1.6 82
Clark, NV................ 49.7 808.7 -10.6 329 804 -1.0 260
Washoe, NV............... 14.4 188.2 -9.6 324 800 0.5 148
Hillsborough, NH......... 12.1 186.2 -5.3 185 933 0.8 124
Rockingham, NH........... 10.8 132.7 -4.3 116 792 -0.6 237
Atlantic, NJ............. 6.9 137.8 -6.3 240 737 -0.5 230
Bergen, NJ............... 34.0 425.1 -4.7 140 1,035 0.3 163
Burlington, NJ........... 11.3 194.5 -3.1 50 904 0.7 136
Camden, NJ............... 12.9 196.8 -4.8 148 845 -0.6 237
Essex, NJ................ 21.1 340.6 -4.2 108 1,059 1.7 77
Gloucester, NJ........... 6.3 99.1 -5.8 211 763 -0.4 219
Hudson, NJ............... 13.9 229.9 -3.8 83 1,170 0.0 190
Mercer, NJ............... 11.1 222.9 -3.5 73 1,071 0.5 148
Middlesex, NJ............ 21.8 377.8 -5.5 194 1,019 -0.9 252
Monmouth, NJ............. 20.5 246.6 -4.2 108 889 0.1 177
Morris, NJ............... 17.9 270.1 -5.1 169 1,203 0.4 159
Ocean, NJ................ 12.3 149.9 -2.2 24 699 1.5 89
Passaic, NJ.............. 12.4 167.0 -4.7 140 890 1.8 70
Somerset, NJ............. 10.1 165.3 -4.9 162 1,244 0.1 177
Union, NJ................ 14.8 217.6 -5.1 169 1,036 0.2 170
Bernalillo, NM........... 17.6 318.0 -5.4 189 776 1.7 77
Albany, NY............... 10.0 220.9 -3.3 64 905 2.6 33
Bronx, NY................ 16.4 230.1 0.2 2 850 1.8 70
Broome, NY............... 4.5 92.4 -3.1 50 697 0.0 190
Dutchess, NY............. 8.2 111.1 -3.8 83 885 3.0 21
Erie, NY................. 23.6 449.0 -3.2 61 739 0.5 148
Kings, NY................ 48.0 477.8 -0.3 4 737 -0.1 199
Monroe, NY............... 18.0 366.8 -3.9 89 813 -0.4 219
Nassau, NY............... 52.4 580.9 -3.1 50 922 0.8 124
New York, NY............. 118.4 2,240.3 -5.3 185 1,500 -3.4 320
Oneida, NY............... 5.3 108.2 -1.2 10 681 1.3 103
Onondaga, NY............. 12.8 243.8 -4.5 128 791 (7) -
Orange, NY............... 10.0 128.6 -2.7 35 729 2.5 38
Queens, NY............... 44.2 493.7 -3.3 64 843 1.0 113
Richmond, NY............. 8.8 91.8 -1.8 16 761 -1.2 273
Rockland, NY............. 9.8 111.5 -4.1 100 891 -1.5 283
Saratoga, NY............. 5.4 74.4 -2.4 26 719 1.4 96
Suffolk, NY.............. 50.4 602.1 -4.1 100 955 -1.6 285
Westchester, NY.......... 36.1 400.2 -4.7 140 1,058 (7) -
Buncombe, NC............. 7.8 109.6 -5.1 169 669 0.9 118
Catawba, NC.............. 4.4 76.2 -10.4 328 640 0.5 148
Cumberland, NC........... 6.2 118.4 -1.9 18 692 6.1 3
Durham, NC............... 7.0 178.8 -3.7 78 1,148 3.2 19
Forsyth, NC.............. 9.0 175.8 -5.4 189 769 0.3 163
Guilford, NC............. 14.3 256.5 -7.4 287 753 -0.8 248
Mecklenburg, NC.......... 32.2 534.6 -6.5 253 950 -0.9 252
New Hanover, NC.......... 7.3 96.8 -6.7 256 710 2.5 38
Wake, NC................. 28.3 428.7 -5.4 189 837 0.5 148
Cass, ND................. 5.9 99.2 -2.0 19 734 1.4 96
Butler, OH............... 7.4 136.6 -7.2 277 746 1.1 109
Cuyahoga, OH............. 36.9 687.4 -6.1 226 851 -0.2 207
Franklin, OH............. 29.5 646.3 -4.4 121 851 0.0 190
Hamilton, OH............. 23.6 489.7 -4.8 148 926 -1.1 266
Lake, OH................. 6.6 92.6 -7.7 298 696 1.8 70
Lorain, OH............... 6.2 92.1 -7.1 272 681 -4.1 321
Lucas, OH................ 10.6 199.6 -5.7 205 741 0.5 148
Mahoning, OH............. 6.3 97.5 -4.8 148 615 0.0 190
Montgomery, OH........... 12.6 241.8 -7.2 277 763 -2.8 308
Stark, OH................ 8.9 148.7 -7.6 295 650 -0.6 237
Summit, OH............... 14.8 254.2 -7.2 277 758 0.5 148
Trumbull, OH............. 4.7 67.9 -11.0 330 655 -8.3 328
Warren, OH............... 4.2 73.8 -3.9 89 714 -0.4 219
Oklahoma, OK............. 23.9 408.9 -4.4 121 799 1.7 77
Tulsa, OK................ 19.7 329.7 -6.4 249 772 0.7 136
Clackamas, OR............ 12.6 138.3 -8.9 319 784 0.9 118
Jackson, OR.............. 6.5 76.9 -7.9 302 645 1.6 82
Lane, OR................. 10.9 135.0 -8.2 304 672 -1.8 293
Marion, OR............... 9.3 137.6 -5.1 169 690 2.5 38
Multnomah, OR............ 28.2 420.9 -6.6 255 863 0.6 143
Washington, OR........... 16.2 229.7 -7.5 293 975 -1.2 273
Allegheny, PA............ 35.1 666.5 -2.9 41 881 -0.5 230
Berks, PA................ 9.0 160.3 -4.8 148 762 -1.0 260
Bucks, PA................ 19.8 247.9 -5.3 185 824 0.7 136
Butler, PA............... 4.8 79.1 -2.6 29 736 -1.6 285
Chester, PA.............. 15.1 232.9 -4.7 140 1,024 -0.5 230
Cumberland, PA........... 6.0 119.7 -5.2 176 785 1.7 77
Dauphin, PA.............. 7.4 178.1 -2.9 41 820 -0.1 199
Delaware, PA............. 13.5 202.2 -4.2 108 882 0.2 170
Erie, PA................. 7.5 121.1 -5.9 219 676 -0.6 237
Lackawanna, PA........... 5.9 97.7 -3.7 78 659 1.4 96
Lancaster, PA............ 12.5 217.7 -5.2 176 733 1.8 70
Lehigh, PA............... 8.7 170.4 -4.5 128 849 2.4 45
Luzerne, PA.............. 7.8 138.0 -4.1 100 666 0.3 163
Montgomery, PA........... 27.4 462.8 -4.6 135 1,018 0.8 124
Northampton, PA.......... 6.5 97.1 -2.8 39 746 0.4 159
Philadelphia, PA......... 31.4 616.9 -3.1 50 1,020 0.1 177
Washington, PA........... 5.4 78.1 -4.0 94 752 1.3 103
Westmoreland, PA......... 9.4 130.5 -5.1 169 682 -0.9 252
York, PA................. 9.0 168.4 -5.7 205 749 1.2 106
Kent, RI................. 5.6 72.9 -7.3 281 736 0.7 136
Providence, RI........... 17.8 266.0 -5.6 200 825 2.5 38
Charleston, SC........... 12.0 199.2 -6.3 240 741 2.5 38
Greenville, SC........... 12.4 221.3 -7.4 287 727 -0.4 219
Horry, SC................ 8.0 109.4 -7.0 267 534 0.2 170
Lexington, SC............ 5.6 91.7 -6.4 249 647 -1.1 266
Richland, SC............. 9.2 204.5 -5.1 169 768 2.7 27
Spartanburg, SC.......... 6.1 110.7 -7.1 272 723 -1.5 283
Minnehaha, SD............ 6.5 113.2 -2.6 29 723 1.1 109
Davidson, TN............. 18.3 416.6 -4.5 128 858 -0.2 207
Hamilton, TN............. 8.5 176.0 -8.6 314 743 3.2 19
Knox, TN................. 11.0 216.3 -5.2 176 715 -0.1 199
Rutherford, TN........... 4.3 92.6 -5.8 211 730 -13.2 329
Shelby, TN............... 19.6 468.0 -6.3 240 852 -0.6 237
Williamson, TN........... 6.0 84.2 -4.6 135 879 -4.1 321
Bell, TX................. 4.6 102.6 -0.7 5 696 6.6 1
Bexar, TX................ 33.0 709.3 -3.0 49 752 2.7 27
Brazoria, TX............. 4.7 82.7 -4.2 108 780 -2.0 296
Brazos, TX............... 3.9 86.0 (7) - 653 (7) -
Cameron, TX.............. 6.4 121.7 -1.0 8 553 2.8 24
Collin, TX............... 17.4 277.9 (7) - 976 -3.1 312
Dallas, TX............... 67.7 1,405.1 -5.2 176 1,012 -1.1 266
Denton, TX............... 10.7 164.8 -3.4 68 746 -0.1 199
El Paso, TX.............. 13.5 264.4 -2.5 27 619 2.8 24
Fort Bend, TX............ 8.6 127.9 -1.8 16 863 -0.7 244
Galveston, TX............ 5.2 92.5 -2.6 29 805 0.2 170
Gregg, TX................ 4.0 71.0 -4.9 162 717 -2.2 297
Harris, TX............... 98.2 1,979.6 -3.4 68 1,044 -0.6 237
Hidalgo, TX.............. 10.6 213.5 -0.8 6 564 2.7 27
Jefferson, TX............ 5.9 118.2 -5.0 166 841 2.6 33
Lubbock, TX.............. 6.8 122.5 -2.0 19 643 0.3 163
McLennan, TX............. 4.8 101.3 -2.1 21 692 1.8 70
Montgomery, TX........... 8.4 124.9 -1.2 10 768 -2.4 304
Nueces, TX............... 8.0 149.6 -3.4 68 721 -1.0 260
Potter, TX............... 3.8 74.1 -2.1 21 721 1.5 89
Smith, TX................ 5.3 90.7 -4.0 94 738 (7) -
Tarrant, TX.............. 37.2 740.9 -3.8 83 839 -0.1 199
Travis, TX............... 29.4 555.4 -4.3 116 932 0.8 124
Webb, TX................. 4.7 83.8 -5.2 176 574 2.5 38
Williamson, TX........... 7.3 118.8 -2.5 27 792 -1.1 266
Davis, UT................ 7.2 101.0 -2.9 41 678 2.1 60
Salt Lake, UT............ 37.5 560.7 -5.0 166 800 0.5 148
Utah, UT................. 12.8 165.2 -6.0 224 663 -0.3 213
Weber, UT................ 5.7 89.2 -6.1 226 652 2.4 45
Chittenden, VT........... 6.0 91.9 -3.9 89 851 1.6 82
Arlington, VA............ 8.0 157.9 -0.1 3 1,413 4.8 5
Chesterfield, VA......... 7.6 113.1 -4.7 140 779 0.5 148
Fairfax, VA.............. 34.2 568.2 -2.7 35 1,321 1.8 70
Henrico, VA.............. 9.7 167.5 -6.1 226 849 -0.7 244
Loudoun, VA.............. 9.2 128.7 -3.5 73 1,013 0.9 118
Prince William, VA....... 7.4 102.0 -2.1 21 789 2.1 60
Alexandria City, VA...... 6.2 97.5 -2.6 29 1,212 4.6 7
Chesapeake City, VA...... 5.7 93.7 -5.8 211 696 2.4 45
Newport News City, VA.... 4.0 95.4 -3.8 83 788 2.3 53
Norfolk City, VA......... 5.9 137.3 -4.1 100 826 1.6 82
Richmond City, VA........ 7.3 149.4 -4.6 135 952 0.7 136
Virginia Beach City, VA.. 11.5 165.6 -4.6 135 666 1.4 96
Clark, WA................ 12.9 127.8 -5.2 176 778 0.3 163
King, WA................. 79.4 1,122.7 -6.1 226 1,177 1.4 96
Kitsap, WA............... 6.7 81.5 -3.1 50 794 3.8 13
Pierce, WA............... 21.4 263.5 -5.4 189 804 3.7 14
Snohomish, WA............ 18.4 240.5 -6.4 249 887 3.5 17
Spokane, WA.............. 15.9 200.9 -4.8 148 720 2.7 27
Thurston, WA............. 7.2 97.3 -4.1 100 810 2.8 24
Whatcom, WA.............. 7.0 78.5 -5.5 194 693 2.2 55
Yakima, WA............... 8.6 112.7 1.7 1 584 0.9 118
Kanawha, WV.............. 6.0 105.2 -3.2 61 750 1.5 89
Brown, WI................ 6.6 142.6 -4.4 121 746 -0.9 252
Dane, WI................. 13.9 291.6 -4.5 128 822 0.6 143
Milwaukee, WI............ 20.9 470.7 -5.9 219 834 -0.2 207
Outagamie, WI............ 5.0 100.2 -5.5 194 711 -1.0 260
Racine, WI............... 4.1 70.4 -8.2 304 742 -1.6 285
Waukesha, WI............. 12.9 218.9 -7.1 272 834 -0.4 219
Winnebago, WI............ 3.7 87.3 -3.1 50 776 0.9 118
San Juan, PR............. 12.3 266.2 -5.7 (8) 594 4.0 (8)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 334 U.S. counties comprise 71.1 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 2009(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
County by NAICS supersector 2009 Percent Percent
(thousands) September change, Average change,
2009 September weekly third
(thousands) 2008-09(4) wage quarter
2008-09(4)
United States(5)............................. 9,066.0 128,088.7 -5.3 $840 -0.1
Private industry........................... 8,771.6 106,481.7 -6.2 828 -0.6
Natural resources and mining............. 126.3 1,866.9 -7.1 836 -5.1
Construction............................. 837.9 5,957.4 -17.9 934 1.3
Manufacturing............................ 351.9 11,624.2 -12.9 1,005 -0.2
Trade, transportation, and utilities..... 1,892.5 24,412.0 -6.0 711 -1.0
Information.............................. 145.9 2,767.5 -7.0 1,317 -1.3
Financial activities..................... 839.2 7,507.9 -5.2 1,180 -2.3
Professional and business services....... 1,531.0 16,316.4 -8.1 1,060 1.4
Education and health services............ 871.0 18,290.1 1.6 819 2.0
Leisure and hospitality.................. 741.7 13,180.4 -2.9 357 -0.3
Other services........................... 1,241.5 4,339.9 -3.3 543 -0.2
Government................................. 294.4 21,607.1 -0.4 907 2.4
Los Angeles, CA.............................. 422.5 3,858.5 -6.7 942 -0.8
Private industry........................... 418.6 3,306.5 -7.5 914 -1.0
Natural resources and mining............. 0.5 10.3 -12.3 1,296 5.1
Construction............................. 13.6 112.8 -21.8 1,023 3.1
Manufacturing............................ 14.0 380.4 -12.7 1,025 2.8
Trade, transportation, and utilities..... 52.6 728.1 -8.0 763 -1.0
Information.............................. 8.7 194.3 -9.7 1,598 1.7
Financial activities..................... 23.2 216.6 -7.2 1,341 -9.6
Professional and business services....... 42.4 517.8 -11.1 1,124 1.5
Education and health services............ 28.4 493.0 0.8 910 2.6
Leisure and hospitality.................. 27.3 382.6 -5.2 528 -2.0
Other services........................... 200.1 262.2 1.6 417 -4.8
Government................................. 3.9 552.0 -1.9 1,113 -0.6
Cook, IL..................................... 142.0 2,364.2 -5.6 975 -1.4
Private industry........................... 140.6 2,058.2 -6.3 964 -2.2
Natural resources and mining............. 0.1 1.1 -6.5 973 -2.2
Construction............................. 12.3 76.5 -17.2 1,271 -0.9
Manufacturing............................ 6.9 198.0 -12.0 1,004 0.1
Trade, transportation, and utilities..... 27.5 430.9 -7.0 764 -2.7
Information.............................. 2.6 52.2 (6) 1,363 -12.2
Financial activities..................... 15.4 192.2 -6.4 1,525 -1.6
Professional and business services....... 29.3 393.4 -9.4 1,241 -0.9
Education and health services............ 14.4 386.5 1.5 864 -0.6
Leisure and hospitality.................. 12.1 230.2 -2.8 443 -0.7
Other services........................... 14.9 92.8 -4.3 723 2.3
Government................................. 1.4 306.1 -0.9 1,051 4.2
New York, NY................................. 118.4 2,240.3 -5.3 1,500 -3.4
Private industry........................... 118.1 1,799.5 -6.3 1,609 -3.9
Natural resources and mining............. 0.0 0.1 -15.9 1,948 7.4
Construction............................. 2.3 32.4 -16.0 1,551 1.4
Manufacturing............................ 2.7 28.2 -19.4 1,195 1.3
Trade, transportation, and utilities..... 21.2 227.1 -9.0 1,107 -1.9
Information.............................. 4.4 125.5 -7.8 1,907 -3.6
Financial activities..................... 18.8 344.3 -8.3 2,762 -7.3
Professional and business services....... 24.9 450.7 -8.5 1,793 -0.3
Education and health services............ 8.8 284.4 0.9 1,089 2.5
Leisure and hospitality.................. 11.8 214.6 -2.8 733 -2.0
Other services........................... 18.2 85.4 -4.2 935 1.7
Government................................. 0.3 440.8 -0.7 1,058 3.0
Harris, TX................................... 98.2 1,979.6 -3.4 1,044 -0.6
Private industry........................... 97.6 1,724.8 -4.1 1,048 -1.2
Natural resources and mining............. 1.5 81.3 (6) 2,579 -0.6
Construction............................. 6.7 138.7 -11.9 1,033 2.5
Manufacturing............................ 4.6 169.4 -10.7 1,278 0.3
Trade, transportation, and utilities..... 22.3 411.9 -3.7 897 -2.1
Information.............................. 1.4 30.0 -5.5 1,217 -5.1
Financial activities..................... 10.4 114.4 -4.2 1,212 -5.1
Professional and business services....... 19.6 310.9 -7.3 1,245 0.8
Education and health services............ 10.6 231.0 5.6 871 0.8
Leisure and hospitality.................. 7.8 177.4 1.7 389 1.3
Other services........................... 12.2 58.8 -1.2 608 0.0
Government................................. 0.5 254.8 (6) 1,013 (6)
Maricopa, AZ................................. 98.8 1,605.7 -8.8 838 0.4
Private industry........................... 98.2 1,387.1 -9.7 826 0.1
Natural resources and mining............. 0.5 7.4 -8.5 716 -14.6
Construction............................. 10.0 88.8 -32.2 869 -1.1
Manufacturing............................ 3.4 107.5 -13.9 1,133 -0.4
Trade, transportation, and utilities..... 22.4 333.8 -7.6 767 -0.3
Information.............................. 1.5 27.6 -8.1 1,077 -1.1
Financial activities..................... 12.1 133.7 -5.8 997 -0.7
Professional and business services....... 22.0 258.8 -12.1 893 3.7
Education and health services............ 10.3 217.4 0.2 917 1.6
Leisure and hospitality.................. 7.1 164.5 -7.0 398 0.5
Other services........................... 7.1 46.6 -6.2 559 -4.9
Government................................. 0.7 218.6 -3.0 921 0.7
Dallas, TX................................... 67.7 1,405.1 -5.2 1,012 -1.1
Private industry........................... 67.2 1,237.9 -5.8 1,015 -1.7
Natural resources and mining............. 0.6 8.3 -0.1 2,857 -41.2
Construction............................. 4.2 72.0 -15.4 940 2.4
Manufacturing............................ 3.0 119.0 -11.6 1,150 (6)
Trade, transportation, and utilities..... 14.9 283.1 -6.1 941 -1.2
Information.............................. 1.6 44.9 -6.2 1,436 -0.8
Financial activities..................... 8.7 137.2 (6) 1,295 (6)
Professional and business services....... 14.7 249.5 -9.3 1,158 0.7
Education and health services............ 6.8 159.4 (6) 941 1.0
Leisure and hospitality.................. 5.4 126.4 (6) 454 (6)
Other services........................... 6.8 37.5 -3.9 635 1.3
Government................................. 0.5 167.2 -0.7 990 (6)
Orange, CA................................... 99.8 1,340.7 -8.6 948 -0.7
Private industry........................... 98.4 1,204.3 -9.1 936 -1.2
Natural resources and mining............. 0.2 4.0 -6.6 647 -6.8
Construction............................. 6.7 70.4 -22.9 1,099 0.6
Manufacturing............................ 5.2 150.1 -13.4 1,145 1.4
Trade, transportation, and utilities..... 16.7 245.4 -9.0 875 0.1
Information.............................. 1.3 26.7 -8.8 1,350 -13.8
Financial activities..................... 10.2 104.1 (6) 1,290 -4.7
Professional and business services....... 19.0 234.7 -12.0 1,087 0.5
Education and health services............ 10.2 148.1 -1.7 931 4.1
Leisure and hospitality.................. 7.1 170.8 -4.0 413 -1.0
Other services........................... 19.3 47.2 -4.3 525 -3.1
Government................................. 1.4 136.4 -4.0 1,058 2.8
San Diego, CA................................ 96.5 1,229.1 -6.7 918 -0.4
Private industry........................... 95.2 1,014.7 -7.7 890 -1.7
Natural resources and mining............. 0.7 10.3 -9.5 557 -1.9
Construction............................. 6.7 58.6 -22.9 1,032 4.6
Manufacturing............................ 3.1 93.0 -10.4 1,229 (6)
Trade, transportation, and utilities..... 13.9 196.7 -8.3 732 (6)
Information.............................. 1.2 36.2 -6.7 1,824 -18.8
Financial activities..................... 9.0 69.4 -6.5 1,069 -2.4
Professional and business services....... 16.2 193.5 -10.1 1,137 0.3
Education and health services............ 8.3 141.6 2.6 887 1.5
Leisure and hospitality.................. 6.9 156.3 -6.3 407 -2.9
Other services........................... 26.8 56.7 -3.1 488 0.4
Government................................. 1.3 214.5 -1.6 1,052 3.5
King, WA..................................... 79.4 1,122.7 -6.1 1,177 1.4
Private industry........................... 78.8 968.6 -7.1 1,191 1.2
Natural resources and mining............. 0.4 3.0 -5.7 1,042 -19.7
Construction............................. 6.5 53.8 -24.8 1,123 3.7
Manufacturing............................ 2.4 100.5 -10.2 1,321 4.7
Trade, transportation, and utilities..... 15.0 205.6 -6.1 920 -0.1
Information.............................. 1.8 79.1 -2.2 3,385 0.7
Financial activities..................... 6.8 67.7 -8.3 1,307 -4.7
Professional and business services....... 14.1 171.8 -11.2 1,257 (6)
Education and health services............ 6.8 130.0 3.3 896 3.5
Leisure and hospitality.................. 6.4 109.6 -5.2 455 (6)
Other services........................... 18.6 47.5 0.4 600 -0.3
Government................................. 0.5 154.1 0.8 1,093 3.6
Miami-Dade, FL............................... 84.1 935.1 -5.8 839 -0.2
Private industry........................... 83.8 789.2 -6.3 803 0.0
Natural resources and mining............. 0.5 6.9 -6.4 485 -1.0
Construction............................. 5.7 34.2 -22.3 862 1.8
Manufacturing............................ 2.6 36.1 -16.9 763 2.6
Trade, transportation, and utilities..... 23.1 231.7 -6.9 742 -0.3
Information.............................. 1.5 17.2 -9.1 1,208 -1.5
Financial activities..................... 9.6 61.5 -8.2 1,147 -1.5
Professional and business services....... 17.6 119.7 -8.6 1,020 1.3
Education and health services............ 9.6 147.7 2.6 826 0.6
Leisure and hospitality.................. 6.1 100.1 -1.4 474 0.6
Other services........................... 7.5 34.0 -6.7 539 2.1
Government................................. 0.4 146.0 -3.1 1,041 -1.6
(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 2009(2)
Employment Average weekly
wage(4)
Establishments,
third quarter
County(3) 2009 Percent Percent
(thousands) September change, Average change,
2009 September weekly third
(thousands) 2008-09(5) wage quarter
2008-09(5)
United States(6)......... 9,066.0 128,088.7 -5.3 $840 -0.1
Jefferson, AL............ 18.3 333.2 -7.0 861 -0.5
Anchorage Borough, AK.... 8.1 150.4 -1.2 942 2.1
Maricopa, AZ............. 98.8 1,605.7 -8.8 838 0.4
Pulaski, AR.............. 15.0 241.7 -4.2 789 2.6
Los Angeles, CA.......... 422.5 3,858.5 -6.7 942 -0.8
Denver, CO............... 25.6 419.3 -7.0 1,040 0.0
Hartford, CT............. 25.5 484.1 -4.7 1,020 0.8
New Castle, DE........... 18.0 263.6 -5.8 985 0.6
Washington, DC........... 34.4 682.6 -1.1 1,450 4.2
Miami-Dade, FL........... 84.1 935.1 -5.8 839 -0.2
Fulton, GA............... 39.4 693.5 -6.7 1,087 0.6
Honolulu, HI............. 25.1 428.1 -4.0 818 2.4
Ada, ID.................. 14.6 193.3 -8.3 754 1.2
Cook, IL................. 142.0 2,364.2 -5.6 975 -1.4
Marion, IN............... 23.9 546.7 -5.1 856 0.2
Polk, IA................. 14.8 267.5 -3.3 831 0.0
Johnson, KS.............. 20.9 299.4 -5.6 858 -0.9
Jefferson, KY............ 21.7 409.1 -3.8 812 1.2
East Baton Rouge, LA..... 14.6 259.3 -0.8 815 2.6
Cumberland, ME........... 12.2 167.8 -3.9 773 0.8
Montgomery, MD........... 32.3 443.0 -3.1 1,144 2.1
Middlesex, MA............ 47.3 791.7 -4.3 1,194 -0.3
Wayne, MI................ 31.6 660.0 -8.6 918 -2.5
Hennepin, MN............. 42.3 794.9 -5.5 1,047 -5.1
Hinds, MS................ 6.3 124.1 -2.3 760 2.4
St. Louis, MO............ 32.2 568.8 -5.7 893 0.2
Yellowstone, MT.......... 5.9 76.6 -2.6 691 0.1
Douglas, NE.............. 15.8 311.0 -2.9 795 -3.2
Clark, NV................ 49.7 808.7 -10.6 804 -1.0
Hillsborough, NH......... 12.1 186.2 -5.3 933 0.8
Bergen, NJ............... 34.0 425.1 -4.7 1,035 0.3
Bernalillo, NM........... 17.6 318.0 -5.4 776 1.7
New York, NY............. 118.4 2,240.3 -5.3 1,500 -3.4
Mecklenburg, NC.......... 32.2 534.6 -6.5 950 -0.9
Cass, ND................. 5.9 99.2 -2.0 734 1.4
Cuyahoga, OH............. 36.9 687.4 -6.1 851 -0.2
Oklahoma, OK............. 23.9 408.9 -4.4 799 1.7
Multnomah, OR............ 28.2 420.9 -6.6 863 0.6
Allegheny, PA............ 35.1 666.5 -2.9 881 -0.5
Providence, RI........... 17.8 266.0 -5.6 825 2.5
Greenville, SC........... 12.4 221.3 -7.4 727 -0.4
Minnehaha, SD............ 6.5 113.2 -2.6 723 1.1
Shelby, TN............... 19.6 468.0 -6.3 852 -0.6
Harris, TX............... 98.2 1,979.6 -3.4 1,044 -0.6
Salt Lake, UT............ 37.5 560.7 -5.0 800 0.5
Chittenden, VT........... 6.0 91.9 -3.9 851 1.6
Fairfax, VA.............. 34.2 568.2 -2.7 1,321 1.8
King, WA................. 79.4 1,122.7 -6.1 1,177 1.4
Kanawha, WV.............. 6.0 105.2 -3.2 750 1.5
Milwaukee, WI............ 20.9 470.7 -5.9 834 -0.2
Laramie, WY.............. 3.2 43.5 -2.0 739 2.8
San Juan, PR............. 12.3 266.2 -5.7 594 4.0
St. Thomas, VI........... 1.8 22.5 -4.4 679 4.8
(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 2009(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
State 2009 Percent Percent
(thousands) September change, Average change,
2009 September weekly third
(thousands) 2008-09 wage quarter
2008-09
United States(4)......... 9,066.0 128,088.7 -5.3 $840 -0.1
Alabama.................. 117.7 1,814.8 -6.3 744 1.8
Alaska................... 21.3 329.3 -0.9 887 1.6
Arizona.................. 155.0 2,365.2 -8.0 800 0.3
Arkansas................. 86.1 1,137.0 -3.9 658 1.1
California............... 1,354.4 14,494.0 -6.6 950 -0.9
Colorado................. 176.3 2,188.1 -5.8 876 -0.1
Connecticut.............. 112.5 1,611.1 -4.8 1,024 -0.9
Delaware................. 29.0 401.9 -5.0 881 0.5
District of Columbia..... 34.4 682.6 -1.1 1,450 4.2
Florida.................. 598.2 7,047.8 -6.5 759 0.4
Georgia.................. 271.5 3,757.9 -6.4 800 0.8
Hawaii................... 39.6 585.1 -4.8 788 1.9
Idaho.................... 56.2 624.3 -6.2 646 0.5
Illinois................. 374.8 5,539.8 -5.7 880 -1.2
Indiana.................. 159.7 2,715.4 -6.3 714 -0.6
Iowa..................... 94.6 1,444.1 -3.8 695 -0.1
Kansas................... 88.3 1,310.1 -4.3 706 -0.6
Kentucky................. 107.3 1,714.3 -4.4 706 1.9
Louisiana................ 125.8 1,832.7 -2.4 761 0.3
Maine.................... 50.1 592.0 -3.4 688 0.7
Maryland................. 162.0 2,458.1 -3.4 941 2.4
Massachusetts............ 213.9 3,140.7 -4.0 1,022 -0.2
Michigan................. 255.1 3,785.6 -7.6 809 -1.3
Minnesota................ 169.9 2,561.2 -5.1 836 -3.0
Mississippi.............. 70.6 1,076.9 -4.7 635 0.8
Missouri................. 174.3 2,610.3 -4.6 744 0.7
Montana.................. 42.7 428.7 -3.9 637 1.3
Nebraska................. 60.0 901.1 -2.7 689 -0.7
Nevada................... 75.4 1,126.2 -10.1 805 -0.5
New Hampshire............ 49.0 607.6 -4.3 831 1.1
New Jersey............... 268.7 3,782.9 -4.2 995 0.4
New Mexico............... 54.3 793.7 -5.0 722 1.3
New York................. 587.7 8,325.5 -3.6 1,012 -1.7
North Carolina........... 250.8 3,810.7 -6.3 745 0.7
North Dakota............. 25.9 354.9 -0.6 680 2.3
Ohio..................... 289.4 4,925.5 -6.2 764 -0.5
Oklahoma................. 101.3 1,484.5 -5.1 700 0.1
Oregon................... 131.1 1,613.8 -6.9 768 0.3
Pennsylvania............. 342.3 5,448.2 -4.1 826 0.5
Rhode Island............. 35.4 451.3 -5.2 793 1.9
South Carolina........... 114.0 1,752.7 -6.5 688 0.9
South Dakota............. 31.0 392.1 -2.3 633 1.6
Tennessee................ 141.2 2,561.4 -6.0 745 -0.3
Texas.................... 565.4 10,050.2 -3.8 845 -0.5
Utah..................... 85.5 1,162.5 -5.4 719 0.3
Vermont.................. 24.7 292.5 -4.0 734 1.8
Virginia................. 232.0 3,530.7 -3.9 897 2.2
Washington............... 228.6 2,862.3 -4.7 916 1.3
West Virginia............ 48.5 691.2 -3.6 673 1.7
Wisconsin................ 157.4 2,639.7 -5.6 726 -0.4
Wyoming.................. 25.2 278.6 -5.4 756 -3.2
Puerto Rico.............. 52.3 936.9 -5.9 494 3.8
Virgin Islands........... 3.5 42.4 -5.7 724 2.4
(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
