For release 10:00 a.m. (EDT), Wednesday, March 28, 2012 USDL-12-0549
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 2011
From September 2010 to September 2011, employment increased in 271 of
the 322 largest U.S. counties, the U.S. Bureau of Labor Statistics
reported today. Williamson, Tenn., posted the largest increase, with
a gain of 5.4 percent over the year, compared with national job
growth of 1.6 percent. Within Williamson, the largest employment
increase occurred in professional and business services, which gained
1,743 jobs over the year (9.0 percent). Frederick, Md., experienced
the largest over-the-year decrease in employment among the largest
counties in the U.S. with a loss of 2.6 percent.
The U.S. average weekly wage increased over the year by 5.3 percent
to $916 in the third quarter of 2011. Among the large counties in the
U.S., Lake, Ohio, had the largest over-the-year increase in average
weekly wages with a gain of 17.1 percent. Within Lake, a total wage
increase of $124.7 million (48.5 percent) in the manufacturing
industry had the largest impact on the county’s over-the-year
increase in average weekly wages. A third quarter acquisition in this
industry resulted in large payouts, which may include bonuses and
stock options. Clay, Mo., experienced the largest decline in average
weekly wages with a loss of 2.3 percent over the year. County
employment and wage data are compiled under the Quarterly Census of
Employment and Wages (QCEW) program.
Table A. Large counties ranked by September 2011 employment, September 2010-11 employment
increase, and September 2010-11 percent increase in employment
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Employment in large counties
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September 2011 employment | Increase in employment, | Percent increase in employment,
(thousands) | September 2010-11 | September 2010-11
| (thousands) |
--------------------------------------------------------------------------------------------------------
| |
United States 130,524.7| United States 2,040.9| United States 1.6
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| |
Los Angeles, Calif. 3,872.5| Harris, Texas 62.3| Williamson, Tenn. 5.4
Cook, Ill. 2,402.7| New York, N.Y. 60.6| Weld, Colo. 4.8
New York, N.Y. 2,332.5| Cook, Ill. 48.5| Montgomery, Texas 4.8
Harris, Texas 2,054.1| Maricopa, Ariz. 46.0| Utah, Utah 4.5
Maricopa, Ariz. 1,641.4| Dallas, Texas 37.9| Washington, Pa. 4.4
Dallas, Texas 1,448.7| King, Wash. 31.7| Webb, Texas 4.4
Orange, Calif. 1,372.4| Los Angeles, Calif. 31.1| Loudoun, Va. 4.4
San Diego, Calif. 1,252.4| Hennepin, Minn. 28.2| Kern, Calif. 4.2
King, Wash. 1,150.7| Miami-Dade, Fla. 27.6| Fort Bend, Texas 4.2
Miami-Dade, Fla. 970.3| Santa Clara, Calif. 26.4| San Francisco, Calif. 4.1
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Large County Employment
In September 2011, national employment, as measured by the QCEW
program, was 130.5 million, up by 1.6 percent or 2.0 million workers,
from September 2010. The 322 U.S. counties with 75,000 or more
employees accounted for 70.5 percent of total U.S. employment and
75.9 percent of total wages. These 322 counties had a net job growth
of 1.5 million over the year, accounting for 71.5 percent of the
overall U.S. employment increase.
Williamson, Tenn., had the largest percentage increase in employment
among the largest U.S. counties (5.4 percent). The five counties with
the largest increases in employment level were Harris, Texas; New
York, N.Y.; Cook, Ill.; Maricopa, Ariz.; and Dallas, Texas. These
counties had a combined over-the-year gain of 255,300, or 12.5
percent of the overall employment increase for the U.S.
Employment declined in 39 of the large counties from September 2010
to September 2011. Frederick, Md., had the largest over-the-year
percentage decrease in employment (-2.6 percent). Within Frederick,
financial activities was the largest contributor to the decrease in
employment with a loss of 2,168 jobs (-27.2 percent). Broome, N.Y.,
had the second largest employment decrease, followed by Monmouth,
N.J., Mobile, Ala., and Montgomery, Ala. (See table 1.)
Table B. Large counties ranked by third quarter 2011 average weekly wages, third quarter 2010-11
increase in average weekly wages, and third quarter 2010-11 percent increase in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Increase in average weekly | Percent increase in average
third quarter 2011 | wage, third quarter 2010-11 | weekly wage, third
| | quarter 2010-11
--------------------------------------------------------------------------------------------------------
| |
United States $916| United States $46| United States 5.3
--------------------------------------------------------------------------------------------------------
| |
Santa Clara, Calif. $1,857| Santa Clara, Calif. $195| Lake, Ohio 17.1
New York, N.Y. 1,647| Lake, Ohio 123| Santa Clara, Calif. 11.7
Arlington, Va. 1,550| Mercer, N.J. 97| Oklahoma, Okla. 11.5
Washington, D.C. 1,527| Durham, N.C. 96| Williamson, Texas 10.2
San Francisco, Calif. 1,457| Fairfield, Conn. 93| Sacramento, Calif. 9.8
Fairfax, Va. 1,440| Oklahoma, Okla. 93| Yolo, Calif. 9.7
Fairfield, Conn. 1,432| Sacramento, Calif. 91| St. Louis, Minn. 9.5
San Mateo, Calif. 1,426| King, Wash. 90| York, Pa. 9.3
Suffolk, Mass. 1,419| Williamson, Texas 86| Tulsa, Okla. 9.0
Somerset, N.J. 1,338| San Francisco, Calif. 84| Kitsap, Wash. 9.0
| Yolo, Calif. 84|
| Lake, Ill. 84|
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Large County Average Weekly Wages
Average weekly wages for the nation increased by 5.3 percent over the
year in the third quarter of 2011. Among the 322 largest counties,
315 had over-the-year increases in average weekly wages. Lake, Ohio,
had the largest wage gain among the largest U.S. counties (17.1
percent).
Of the 322 largest counties, 3 experienced declines in average weekly
wages. Clay, Mo., had the largest wage decline with a loss of 2.3
percent over the year due to a 23.8 percent decline (-$49.7 million)
in manufacturing wages. In the third quarter of 2010, an acquisition
in manufacturing had boosted wages. Alachua, Fla., and Leon, Fla.,
had the second and third largest declines in average weekly wages.
Orleans, La., and Richmond, N.Y., were tied for the smallest over-
the-year increase in average weekly wages. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties experienced over-the-year percent
increases in employment in September 2011. Harris, Texas, experienced
the largest gain in employment (3.1 percent). Within Harris,
professional and business services had the largest over-the-year
level increase among all private industry groups with a gain of
19,560 workers (6.1 percent). Los Angeles, Calif., had the smallest
percent increase in employment among the 10 largest counties. (See
table 2.)
All of the 10 largest U.S. counties had an over-the-year increase in
average weekly wages. San Diego, Calif., experienced the largest
increase in average weekly wages with a gain of 7.5 percent, largely
due to significant total wage gains over the year in professional and
business services ($261.6 million or 7.8 percent). Miami-Dade, Fla.,
had the smallest average weekly wage increase.
For More Information
The tables included in this release contain data for the nation and
for the 322 U.S. counties with annual average employment levels of
75,000 or more in 2010. September 2011 employment and 2011 third
quarter average weekly wages for all states are provided in table 3
of this release.
The employment and wage data by county are compiled under the 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 130.5 million full-
and part-time workers. For additional information about the quarterly
employment and wages data, please read the Technical Note. Data for
the third quarter of 2011 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 2011 is
scheduled to be released on Thursday, June 28, 2012.
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 2012 North American Industry Classification System. Data
for 2011 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 323 counties presented
in this release were derived using 2010 preliminary annual averages of employment.
For 2011 data, four counties, Okaloosa, Fla., Rock Island, Ill., St. Tammany, La.,
and Potter, Texas, which were published in the 2010 releases, will be excluded from
this and future 2011 releases because their 2010 annual average employment levels
were less than 75,000. No counties have been added to the publication tables.
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- | 486,000 establish-
| submitted by 9.1 | ministrative records| ments
| million establish- | submitted by 6.7 |
| ments in first | million private-sec-|
| quarter of 2011 | 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 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 9.0 million employer reports of employment and wages
submitted by states to the BLS in 2010. 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 2010, UI and UCFE programs covered workers in 127.8 million jobs. The estimated
123.2 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.3 percent of civilian wage and salary employment. Covered workers
received $5.976 trillion in pay, representing 93.3 percent of the wage and salary
component of personal income and 41.1 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 2010 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
Employment and Wages Annual Averages Online features comprehensive information by
detailed industry on establishments, employment, and wages for the nation and all
states. The 2010 edition of this publication, which was published in November 2011,
contains selected data produced by Business Employment Dynamics (BED) on job gains
and losses, as well as selected data from the first quarter 2011 version of this news
release. Tables and additional content from Employment and Wages Annual Averages 2010
are now available online at http://www.bls.gov/cew/cewbultn10.htm. The 2011 edition of
Employment and Wages Annual Averages Online will be available later in 2012.
News releases on quarterly measures of gross job flows also are available upon request
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 323 largest counties,
third quarter 2011(2)
Employment Average weekly wage(4)
Establishments,
County(3) third quarter Percent Ranking Percent Ranking
2011 September change, by Third change, by
(thousands) 2011 September percent quarter third percent
(thousands) 2010-11(5) change 2011 quarter change
2010-11(5)
United States(6)......... 9,135.8 130,524.7 1.6 - $916 5.3 -
Jefferson, AL............ 17.7 332.9 1.3 151 921 4.4 197
Madison, AL.............. 8.8 178.3 0.0 272 1,035 3.2 267
Mobile, AL............... 9.8 166.1 -1.1 313 836 8.2 18
Montgomery, AL........... 6.3 127.1 -0.9 312 812 4.2 214
Tuscaloosa, AL........... 4.3 84.2 1.1 167 804 3.1 274
Anchorage Borough, AK.... 8.2 155.5 1.8 109 1,017 4.3 204
Maricopa, AZ............. 95.6 1,641.4 2.9 42 901 4.8 164
Pima, AZ................. 19.0 343.2 (7) - 799 (7) -
Benton, AR............... 5.5 94.7 (7) - 866 3.6 249
Pulaski, AR.............. 15.2 244.7 0.3 249 842 6.2 69
Washington, AR........... 5.5 91.5 (7) - 743 (7) -
Alameda, CA.............. 56.7 640.9 1.6 128 1,217 5.3 125
Contra Costa, CA......... 30.3 315.8 -0.5 301 1,105 5.8 90
Fresno, CA............... 31.1 346.3 -0.1 278 724 5.7 98
Kern, CA................. 18.1 293.3 4.2 8 809 8.2 18
Los Angeles, CA.......... 440.1 3,872.5 0.8 204 1,026 5.2 130
Marin, CA................ 11.8 103.6 2.7 54 1,077 5.8 90
Monterey, CA............. 13.1 181.4 0.4 236 789 4.9 158
Orange, CA............... 104.4 1,372.4 1.7 121 1,036 6.0 78
Placer, CA............... 10.9 127.0 2.1 83 901 6.3 65
Riverside, CA............ 50.3 553.5 0.8 204 757 5.0 147
Sacramento, CA........... 54.5 576.9 0.5 226 1,021 9.8 5
San Bernardino, CA....... 51.8 593.9 0.3 249 795 5.4 118
San Diego, CA............ 100.7 1,252.4 1.2 158 1,014 7.5 30
San Francisco, CA........ 55.8 566.9 4.1 10 1,457 6.1 75
San Joaquin, CA.......... 17.7 206.2 0.4 236 799 5.0 147
San Luis Obispo, CA...... 9.7 103.3 2.0 92 756 5.0 147
San Mateo, CA............ 24.6 327.8 2.4 69 1,426 6.2 69
Santa Barbara, CA........ 14.6 183.0 2.1 83 879 5.9 86
Santa Clara, CA.......... 63.4 873.1 3.1 34 1,857 11.7 2
Santa Cruz, CA........... 9.2 95.7 -0.1 278 840 5.5 109
Solano, CA............... 10.1 119.9 0.4 236 917 5.5 109
Sonoma, CA............... 19.1 178.8 0.4 236 884 5.0 147
Stanislaus, CA........... 15.3 166.7 0.1 268 784 5.8 90
Tulare, CA............... 9.5 150.7 3.7 13 634 2.6 290
Ventura, CA.............. 24.2 295.9 0.9 193 940 6.0 78
Yolo, CA................. 6.1 96.6 -0.1 278 949 9.7 6
Adams, CO................ 9.0 156.6 1.9 100 861 4.2 214
Arapahoe, CO............. 19.0 280.5 3.4 24 1,085 6.2 69
Boulder, CO.............. 13.1 157.8 3.2 30 1,070 3.1 274
Denver, CO............... 25.6 426.2 2.0 92 1,125 7.6 29
Douglas, CO.............. 9.6 92.5 2.8 48 972 5.2 130
El Paso, CO.............. 16.9 237.2 1.8 109 855 3.6 249
Jefferson, CO............ 17.9 207.5 2.6 60 944 5.1 139
Larimer, CO.............. 10.1 131.4 2.2 77 826 5.5 109
Weld, CO................. 5.8 83.4 4.8 2 800 6.0 78
Fairfield, CT............ 32.5 406.2 1.4 143 1,432 6.9 44
Hartford, CT............. 25.4 491.1 1.1 167 1,093 2.7 288
New Haven, CT............ 22.3 351.3 0.5 226 973 3.6 249
New London, CT........... 6.9 124.5 -0.8 309 929 3.7 242
New Castle, DE........... 17.4 266.3 0.8 204 1,060 4.8 164
Washington, DC........... 36.3 708.1 2.1 83 1,527 3.9 230
Alachua, FL.............. 6.5 115.8 0.3 249 761 -0.8 317
Brevard, FL.............. 14.4 186.2 -0.3 293 893 6.6 52
Broward, FL.............. 62.4 683.4 0.9 193 861 4.4 197
Collier, FL.............. 11.5 110.1 3.7 13 787 3.8 235
Duval, FL................ 26.8 436.7 0.6 216 869 4.6 184
Escambia, FL............. 7.8 119.6 0.2 261 730 5.2 130
Hillsborough, FL......... 37.1 569.7 1.7 121 885 5.1 139
Lake, FL................. 7.2 78.4 1.4 143 638 3.2 267
Lee, FL.................. 18.4 194.1 3.4 24 737 3.9 230
Leon, FL................. 8.2 137.4 -0.5 301 759 -0.1 316
Manatee, FL.............. 9.2 99.7 2.2 77 721 4.8 164
Marion, FL............... 7.9 88.5 -0.2 288 634 4.3 204
Miami-Dade, FL........... 86.8 970.3 2.9 42 880 3.3 262
Orange, FL............... 35.6 662.0 2.9 42 811 3.8 235
Palm Beach, FL........... 49.0 487.5 1.9 100 876 4.4 197
Pasco, FL................ 9.9 97.3 0.4 236 636 4.3 204
Pinellas, FL............. 30.4 376.0 -0.2 288 805 5.8 90
Polk, FL................. 12.4 186.0 -0.2 288 712 1.9 308
Sarasota, FL............. 14.2 132.3 2.3 72 745 3.8 235
Seminole, FL............. 13.7 153.9 0.0 272 753 5.6 101
Volusia, FL.............. 13.2 149.1 0.3 249 650 2.0 306
Bibb, GA................. 4.6 79.6 0.5 226 735 5.9 86
Chatham, GA.............. 7.6 129.7 1.0 177 786 5.8 90
Clayton, GA.............. 4.2 100.3 -0.1 278 825 2.9 280
Cobb, GA................. 20.7 290.5 2.2 77 935 2.5 293
De Kalb, GA.............. 17.5 273.8 1.3 151 958 4.8 164
Fulton, GA............... 40.2 716.6 1.3 151 1,206 7.3 36
Gwinnett, GA............. 23.6 301.7 2.4 69 919 8.0 24
Muscogee, GA............. 4.7 93.5 1.0 177 732 3.1 274
Richmond, GA............. 4.6 97.4 1.4 143 801 4.7 177
Honolulu, HI............. 24.4 435.7 1.2 158 871 4.6 184
Ada, ID.................. 13.9 198.3 2.5 65 799 3.1 274
Champaign, IL............ 4.2 87.8 -0.5 301 804 4.6 184
Cook, IL................. 146.8 2,402.7 2.1 83 1,047 4.0 224
Du Page, IL.............. 36.8 564.8 2.7 54 1,054 4.4 197
Kane, IL................. 13.3 194.0 1.2 158 829 5.6 101
Lake, IL................. 21.8 317.3 0.7 212 1,143 7.9 26
McHenry, IL.............. 8.6 93.6 -0.8 309 785 6.9 44
McLean, IL............... 3.8 85.7 0.0 272 907 4.5 192
Madison, IL.............. 6.0 95.8 1.0 177 772 4.9 158
Peoria, IL............... 4.7 101.7 0.8 204 877 4.7 177
St. Clair, IL............ 5.5 98.3 -0.4 296 819 5.8 90
Sangamon, IL............. 5.3 130.8 0.6 216 941 3.3 262
Will, IL................. 14.9 201.5 1.3 151 814 5.2 130
Winnebago, IL............ 6.8 125.2 0.4 236 799 5.4 118
Allen, IN................ 9.0 175.1 1.4 143 766 4.2 214
Elkhart, IN.............. 4.8 104.8 3.2 30 737 3.2 267
Hamilton, IN............. 8.3 113.3 2.9 42 869 5.0 147
Lake, IN................. 10.3 188.0 2.9 42 844 6.2 69
Marion, IN............... 23.7 555.7 1.9 100 943 6.9 44
St. Joseph, IN........... 6.0 117.1 0.3 249 757 5.3 125
Vanderburgh, IN.......... 4.8 106.9 1.7 121 742 3.9 230
Linn, IA................. 6.2 125.8 1.1 167 884 6.5 56
Polk, IA................. 14.5 268.7 1.8 109 912 6.4 59
Scott, IA................ 5.1 87.7 2.6 60 754 4.7 177
Johnson, KS.............. 21.7 303.7 2.5 65 935 4.6 184
Sedgwick, KS............. 12.6 238.2 0.2 261 825 5.6 101
Shawnee, KS.............. 4.9 95.1 0.9 193 788 8.5 14
Wyandotte, KS............ 3.3 82.8 3.0 39 871 5.3 125
Fayette, KY.............. 9.4 176.5 (7) - 833 6.5 56
Jefferson, KY............ 22.1 417.6 1.6 128 887 5.1 139
Caddo, LA................ 7.4 121.0 0.3 249 772 3.3 262
Calcasieu, LA............ 4.8 82.5 1.0 177 801 5.5 109
East Baton Rouge, LA..... 14.4 255.0 1.2 158 854 3.6 249
Jefferson, LA............ 13.7 191.6 -0.6 306 874 5.2 130
Lafayette, LA............ 9.0 134.5 2.8 48 910 7.1 42
Orleans, LA.............. 10.9 173.8 3.0 39 931 1.4 314
Cumberland, ME........... 12.5 171.5 1.9 100 813 2.8 284
Anne Arundel, MD......... 14.4 231.9 2.0 92 999 6.3 65
Baltimore, MD............ 20.9 359.1 0.3 249 957 6.0 78
Frederick, MD............ 6.0 90.6 -2.6 316 895 2.3 302
Harford, MD.............. 5.5 85.1 3.7 13 915 3.5 258
Howard, MD............... 9.0 153.2 3.1 34 1,128 7.3 36
Montgomery, MD........... 32.6 451.2 1.0 177 1,245 4.5 192
Prince Georges, MD....... 15.5 301.9 0.5 226 1,001 5.6 101
Baltimore City, MD....... 13.7 329.9 1.1 167 1,074 7.2 40
Barnstable, MA........... 9.4 94.4 -0.8 309 757 5.1 139
Bristol, MA.............. 17.0 212.2 1.5 134 823 5.1 139
Essex, MA................ 22.4 302.2 2.0 92 959 2.8 284
Hampden, MA.............. 15.9 198.5 1.9 100 841 4.6 184
Middlesex, MA............ 50.9 814.8 1.5 134 1,325 2.5 293
Norfolk, MA.............. 25.1 319.1 1.4 143 1,058 5.4 118
Plymouth, MA............. 14.7 173.6 0.4 236 842 4.3 204
Suffolk, MA.............. 24.2 590.2 3.1 34 1,419 5.7 98
Worcester, MA............ 22.2 316.2 1.8 109 928 2.3 302
Genesee, MI.............. 7.2 129.2 1.7 121 774 5.0 147
Ingham, MI............... 6.3 154.8 0.3 249 860 1.5 311
Kalamazoo, MI............ 5.3 108.2 0.2 261 850 6.6 52
Kent, MI................. 13.6 323.7 3.0 39 817 4.7 177
Macomb, MI............... 16.7 285.9 2.5 65 926 4.3 204
Oakland, MI.............. 36.8 641.4 3.6 17 1,013 4.9 158
Ottawa, MI............... 5.5 108.2 3.5 21 750 4.2 214
Saginaw, MI.............. 4.1 83.4 3.6 17 760 1.5 311
Washtenaw, MI............ 7.9 190.6 1.4 143 994 3.4 259
Wayne, MI................ 30.6 678.7 1.8 109 1,005 5.1 139
Anoka, MN................ 7.1 108.7 2.3 72 872 4.7 177
Dakota, MN............... 9.7 170.0 1.8 109 885 8.1 22
Hennepin, MN............. 43.8 835.5 3.5 21 1,125 3.1 274
Olmsted, MN.............. 3.4 89.1 2.1 83 949 3.2 267
Ramsey, MN............... 13.9 323.4 2.2 77 1,024 6.1 75
St. Louis, MN............ 5.6 94.8 1.1 167 785 9.5 7
Stearns, MN.............. 4.3 80.0 2.3 72 750 3.7 242
Harrison, MS............. 4.5 82.7 0.6 216 687 3.6 249
Hinds, MS................ 6.0 122.4 0.6 216 799 4.0 224
Boone, MO................ 4.5 84.4 2.4 69 733 4.3 204
Clay, MO................. 5.0 90.2 0.9 193 844 -2.3 318
Greene, MO............... 8.0 149.4 1.6 128 714 4.4 197
Jackson, MO.............. 18.3 340.4 -0.4 296 925 6.0 78
St. Charles, MO.......... 8.2 124.4 1.5 134 731 4.4 197
St. Louis, MO............ 31.9 564.3 0.4 236 970 6.4 59
St. Louis City, MO....... 9.0 219.1 0.6 216 1,013 7.3 36
Yellowstone, MT.......... 6.0 77.5 2.1 83 768 7.7 27
Douglas, NE.............. 16.0 311.7 0.2 261 865 6.0 78
Lancaster, NE............ 8.2 154.9 0.8 204 746 5.4 118
Clark, NV................ 47.7 804.3 1.4 143 833 2.8 284
Washoe, NV............... 13.6 186.5 0.5 226 849 4.8 164
Hillsborough, NH......... 11.9 187.1 0.7 212 999 5.6 101
Rockingham, NH........... 10.5 135.6 1.0 177 889 8.0 24
Atlantic, NJ............. 6.7 135.7 -0.4 296 785 2.6 290
Bergen, NJ............... 33.2 426.8 1.0 177 1,083 2.6 290
Burlington, NJ........... 11.0 191.1 0.1 268 971 2.9 280
Camden, NJ............... 12.3 192.3 0.0 272 903 3.2 267
Essex, NJ................ 20.6 335.0 0.5 226 1,138 4.8 164
Gloucester, NJ........... 6.2 96.7 -0.5 301 815 3.3 262
Hudson, NJ............... 13.7 230.4 1.0 177 1,283 3.7 242
Mercer, NJ............... 11.1 226.3 0.3 249 1,206 8.7 12
Middlesex, NJ............ 21.7 377.9 0.9 193 1,104 4.8 164
Monmouth, NJ............. 20.1 241.5 -1.4 314 929 2.4 297
Morris, NJ............... 17.3 268.3 -0.1 278 1,292 4.3 204
Ocean, NJ................ 12.2 149.7 0.6 216 736 3.1 274
Passaic, NJ.............. 12.2 170.0 0.6 216 916 2.5 293
Somerset, NJ............. 10.0 168.8 1.7 121 1,338 3.6 249
Union, NJ................ 14.5 218.2 0.4 236 1,142 5.9 86
Bernalillo, NM........... 17.6 312.3 0.2 261 832 4.3 204
Albany, NY............... 10.0 218.2 0.2 261 968 2.5 293
Bronx, NY................ 17.0 234.9 0.3 249 889 (7) -
Broome, NY............... 4.5 89.9 -1.6 315 738 2.8 284
Dutchess, NY............. 8.2 110.9 0.3 249 925 (7) -
Erie, NY................. 23.8 457.7 1.3 151 813 5.4 118
Kings, NY................ 51.7 507.7 3.3 26 764 1.5 311
Monroe, NY............... 18.2 374.6 1.6 128 887 3.9 230
Nassau, NY............... 52.7 588.9 1.5 134 987 2.1 304
New York, NY............. 122.0 2,332.5 2.7 54 1,647 4.6 184
Oneida, NY............... 5.3 106.2 -0.5 301 726 1.7 309
Onondaga, NY............. 12.8 241.9 0.5 226 845 3.9 230
Orange, NY............... 9.9 131.1 0.4 236 776 3.6 249
Queens, NY............... 46.2 509.0 2.7 54 866 2.4 297
Richmond, NY............. 9.0 91.7 1.1 167 804 1.4 314
Rockland, NY............. 10.0 114.0 2.0 92 975 5.0 147
Suffolk, NY.............. 50.7 617.2 0.9 193 1,022 2.7 288
Westchester, NY.......... 36.1 406.2 1.7 121 1,146 3.4 259
Buncombe, NC............. 7.9 111.7 0.5 226 714 4.1 220
Catawba, NC.............. 4.4 77.8 1.0 177 698 4.0 224
Cumberland, NC........... 6.3 119.1 1.6 128 764 5.2 130
Durham, NC............... 7.3 179.7 1.1 167 1,261 8.2 18
Forsyth, NC.............. 9.0 171.5 0.9 193 851 7.2 40
Guilford, NC............. 14.2 262.6 1.5 134 814 4.1 220
Mecklenburg, NC.......... 32.5 553.5 3.3 26 1,046 8.1 22
New Hanover, NC.......... 7.3 97.0 0.6 216 763 3.8 235
Wake, NC................. 29.4 443.1 2.2 77 894 3.8 235
Cass, ND................. 6.0 104.2 3.9 12 823 8.4 15
Butler, OH............... 7.4 140.2 1.0 177 810 3.4 259
Cuyahoga, OH............. 36.0 692.8 1.0 177 928 5.6 101
Franklin, OH............. 29.7 663.8 2.8 48 930 4.5 192
Hamilton, OH............. 23.4 486.2 0.9 193 1,010 5.2 130
Lake, OH................. 6.5 94.9 1.2 158 842 17.1 1
Lorain, OH............... 6.1 93.0 -0.1 278 773 8.3 16
Lucas, OH................ 10.3 200.2 -0.1 278 795 3.7 242
Mahoning, OH............. 6.1 98.4 1.4 143 685 8.6 13
Montgomery, OH........... 12.3 242.1 0.7 212 814 4.0 224
Stark, OH................ 8.7 152.0 1.7 121 719 6.4 59
Summit, OH............... 14.4 256.0 1.3 151 826 6.4 59
Oklahoma, OK............. 24.3 422.5 2.1 83 903 11.5 3
Tulsa, OK................ 20.2 329.7 1.5 134 871 9.0 9
Clackamas, OR............ 12.6 138.7 0.9 193 841 5.0 147
Jackson, OR.............. 6.6 77.4 1.1 167 686 5.2 130
Lane, OR................. 10.8 136.4 1.0 177 717 5.6 101
Marion, OR............... 9.3 135.8 -0.6 306 717 3.8 235
Multnomah, OR............ 29.2 433.1 2.3 72 937 5.2 130
Washington, OR........... 16.2 244.2 2.6 60 1,119 7.4 32
Allegheny, PA............ 35.1 678.8 1.0 177 978 6.8 48
Berks, PA................ 8.9 163.7 1.2 158 837 5.7 98
Bucks, PA................ 19.5 248.0 -0.2 288 880 4.9 158
Butler, PA............... 4.9 83.1 3.5 21 846 6.5 56
Chester, PA.............. 15.0 235.8 0.5 226 1,134 6.6 52
Cumberland, PA........... 6.0 122.1 1.5 134 859 6.2 69
Dauphin, PA.............. 7.5 174.3 -0.7 308 913 8.2 18
Delaware, PA............. 13.5 205.9 0.4 236 968 4.8 164
Erie, PA................. 7.6 125.7 1.8 109 772 7.5 30
Lackawanna, PA........... 5.8 97.0 -0.2 288 713 4.9 158
Lancaster, PA............ 12.5 218.1 -0.1 278 776 4.3 204
Lehigh, PA............... 8.5 175.4 1.9 100 898 3.2 267
Luzerne, PA.............. 7.7 138.7 0.5 226 731 4.9 158
Montgomery, PA........... 27.0 458.4 0.1 268 1,108 4.7 177
Northampton, PA.......... 6.4 100.4 2.2 77 810 3.7 242
Philadelphia, PA......... 34.2 626.2 -0.3 293 1,114 5.8 90
Washington, PA........... 5.5 85.0 4.4 5 867 7.0 43
Westmoreland, PA......... 9.3 131.7 -0.4 296 768 6.7 50
York, PA................. 9.1 171.1 0.8 204 854 9.3 8
Providence, RI........... 17.3 269.8 0.3 249 913 6.0 78
Charleston, SC........... 11.8 211.5 3.3 26 805 5.5 109
Greenville, SC........... 12.1 231.5 3.2 30 806 6.1 75
Horry, SC................ 7.6 110.4 1.0 177 561 3.7 242
Lexington, SC............ 5.6 94.2 0.2 261 700 4.6 184
Richland, SC............. 8.9 201.1 -0.1 278 813 4.1 220
Spartanburg, SC.......... 5.8 112.7 1.8 109 784 4.8 164
Minnehaha, SD............ 6.5 114.1 1.2 158 776 2.0 306
Davidson, TN............. 18.0 425.9 2.5 65 945 6.4 59
Hamilton, TN............. 8.4 184.6 1.9 100 815 4.8 164
Knox, TN................. 10.7 221.1 1.9 100 786 5.1 139
Rutherford, TN........... 4.3 97.1 1.8 109 803 4.6 184
Shelby, TN............... 18.8 466.9 1.6 128 948 4.8 164
Williamson, TN........... 6.1 93.0 5.4 1 948 4.4 197
Bell, TX................. 4.8 107.4 1.8 109 758 1.7 309
Bexar, TX................ 34.4 734.7 2.0 92 823 5.8 90
Brazoria, TX............. 4.9 90.2 3.7 13 899 7.4 32
Brazos, TX............... 4.0 86.1 (7) - 719 8.3 16
Cameron, TX.............. 6.4 125.7 1.1 167 591 5.5 109
Collin, TX............... 18.6 296.6 3.6 17 1,038 4.2 214
Dallas, TX............... 68.5 1,448.7 2.7 54 1,102 6.4 59
Denton, TX............... 11.2 179.5 2.9 42 814 6.7 50
El Paso, TX.............. 13.8 274.9 1.2 158 671 5.5 109
Fort Bend, TX............ 9.4 137.1 4.2 8 926 5.1 139
Galveston, TX............ 5.4 95.2 1.9 100 844 7.7 27
Harris, TX............... 101.9 2,054.1 3.1 34 1,156 6.3 65
Hidalgo, TX.............. 11.2 222.3 2.1 83 602 4.5 192
Jefferson, TX............ 5.9 123.6 3.6 17 919 5.0 147
Lubbock, TX.............. 7.0 123.8 1.0 177 703 5.9 86
McLennan, TX............. 4.9 101.0 0.4 236 756 4.0 224
Montgomery, TX........... 8.8 135.1 4.8 2 869 6.2 69
Nueces, TX............... 7.9 153.4 0.8 204 797 6.8 48
Smith, TX................ 5.6 92.1 0.4 236 788 2.9 280
Tarrant, TX.............. 38.0 766.3 3.1 34 917 3.6 249
Travis, TX............... 31.0 583.3 3.3 26 1,010 5.4 118
Webb, TX................. 4.8 89.2 4.4 5 629 5.5 109
Williamson, TX........... 7.7 129.1 2.6 60 933 10.2 4
Davis, UT................ 7.2 106.6 (7) - 744 6.0 78
Salt Lake, UT............ 37.0 574.7 2.8 48 868 5.6 101
Utah, UT................. 12.8 172.8 4.5 4 715 4.2 214
Weber, UT................ 5.5 89.2 0.8 204 686 2.1 304
Chittenden, VT........... 5.9 96.6 2.6 60 892 2.4 297
Arlington, VA............ 8.3 167.7 1.8 109 1,550 2.4 297
Chesterfield, VA......... 7.6 113.0 0.0 272 829 3.6 249
Fairfax, VA.............. 34.4 584.9 2.0 92 1,440 5.0 147
Henrico, VA.............. 9.8 173.4 2.7 54 912 3.8 235
Loudoun, VA.............. 9.7 137.8 4.4 5 1,114 7.4 32
Prince William, VA....... 7.7 108.2 3.2 30 845 5.4 118
Alexandria City, VA...... 6.2 95.6 1.3 151 1,268 2.4 297
Chesapeake City, VA...... 5.6 95.0 -0.1 278 736 5.3 125
Newport News City, VA.... 3.8 97.3 2.8 48 876 8.8 11
Norfolk City, VA......... 5.6 138.8 1.5 134 904 6.9 44
Richmond City, VA........ 7.1 150.3 2.0 92 1,011 5.5 109
Virginia Beach City, VA.. 11.2 163.0 -0.3 293 725 4.8 164
Benton, WA............... 5.6 82.9 1.1 167 991 2.9 280
Clark, WA................ 13.5 129.1 1.5 134 836 4.1 220
King, WA................. 83.3 1,150.7 2.8 48 1,323 7.3 36
Kitsap, WA............... 6.8 81.2 0.6 216 894 9.0 9
Pierce, WA............... 21.7 263.5 0.0 272 841 3.2 267
Snohomish, WA............ 19.2 250.7 4.0 11 987 5.3 125
Spokane, WA.............. 15.9 199.4 0.7 212 782 6.3 65
Thurston, WA............. 7.5 96.4 0.1 268 849 4.7 177
Whatcom, WA.............. 7.0 80.7 2.3 72 756 4.0 224
Yakima, WA............... 8.8 110.8 -0.4 296 619 3.3 262
Kanawha, WV.............. 6.0 105.7 1.0 177 804 3.7 242
Brown, WI................ 6.6 145.6 0.6 216 823 6.6 52
Dane, WI................. 14.1 301.3 2.1 83 880 5.0 147
Milwaukee, WI............ 22.6 473.0 0.9 193 917 7.4 32
Outagamie, WI............ 5.0 101.6 0.9 193 770 4.5 192
Waukesha, WI............. 12.7 225.4 1.8 109 901 4.8 164
Winnebago, WI............ 3.7 90.5 1.2 158 825 4.3 204
San Juan, PR............. 11.9 257.7 0.4 (8) 604 0.2 (8)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 322 U.S. counties comprise 70.5 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 2011(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
County by NAICS supersector 2011 Percent Percent
(thousands) September change, Third change,
2011 September quarter third
(thousands) 2010-11(4) 2011 quarter
2010-11(4)
United States(5)............................. 9,135.8 130,524.7 1.6 $916 5.3
Private industry........................... 8,838.2 109,309.0 2.1 907 5.3
Natural resources and mining............. 128.4 2,023.6 5.0 988 11.6
Construction............................. 768.5 5,725.3 0.5 991 4.8
Manufacturing............................ 338.3 11,806.5 1.9 1,127 4.9
Trade, transportation, and utilities..... 1,880.5 24,834.9 1.8 779 5.0
Information.............................. 143.4 2,674.8 -1.0 1,522 7.3
Financial activities..................... 809.1 7,435.0 0.7 1,323 7.1
Professional and business services....... 1,569.8 17,513.3 3.7 1,149 5.1
Education and health services............ 913.3 19,080.1 2.2 882 4.8
Leisure and hospitality.................. 760.4 13,605.1 2.2 389 5.1
Other services........................... 1,330.5 4,410.3 1.4 587 4.4
Government................................. 297.6 21,215.7 -1.0 965 5.2
Los Angeles, CA.............................. 440.1 3,872.5 0.8 1,026 5.2
Private industry........................... 434.4 3,347.1 1.2 1,000 5.3
Natural resources and mining............. 0.4 9.5 -6.2 2,296 22.0
Construction............................. 12.3 107.5 3.5 1,049 4.0
Manufacturing............................ 12.8 366.4 -1.6 1,108 3.1
Trade, transportation, and utilities..... 50.6 743.1 1.6 828 6.2
Information.............................. 8.3 190.0 -4.0 1,720 3.6
Financial activities..................... 21.9 208.9 -0.3 1,549 7.0
Professional and business services....... 41.2 546.6 2.8 1,227 6.6
Education and health services............ 29.1 516.0 2.3 984 5.4
Leisure and hospitality.................. 26.9 402.5 2.8 577 5.9
Other services........................... 209.2 239.5 -3.3 474 1.7
Government................................. 5.7 525.4 -1.5 1,191 5.7
Cook, IL..................................... 146.8 2,402.7 2.1 1,047 4.0
Private industry........................... 145.4 2,104.8 2.4 1,038 3.8
Natural resources and mining............. 0.1 1.0 2.6 1,001 -2.5
Construction............................. 12.4 67.7 1.5 1,291 5.1
Manufacturing............................ 6.6 194.4 0.0 1,093 2.5
Trade, transportation, and utilities..... 28.6 438.6 2.0 831 5.9
Information.............................. 2.6 51.9 0.0 1,549 6.7
Financial activities..................... 15.5 185.6 -1.0 1,741 5.2
Professional and business services....... 30.9 423.9 4.8 1,298 3.4
Education and health services............ 15.3 403.4 2.7 926 2.7
Leisure and hospitality.................. 12.9 240.7 3.6 482 4.1
Other services........................... 16.1 95.0 2.6 779 2.2
Government................................. 1.4 297.9 -0.4 1,114 5.1
New York, NY................................. 122.0 2,332.5 2.7 1,647 4.6
Private industry........................... 121.7 1,897.5 3.5 1,766 4.5
Natural resources and mining............. 0.0 0.1 8.5 1,530 -17.5
Construction............................. 2.2 31.2 1.9 1,644 1.0
Manufacturing............................ 2.4 26.0 -0.7 1,214 -0.9
Trade, transportation, and utilities..... 21.0 242.6 3.5 1,183 4.3
Information.............................. 4.3 138.0 4.0 2,108 3.9
Financial activities..................... 19.1 356.4 2.1 3,096 6.5
Professional and business services....... 25.5 471.8 3.9 1,982 4.3
Education and health services............ 9.3 296.5 2.3 1,208 5.1
Leisure and hospitality.................. 12.8 238.2 5.1 770 2.1
Other services........................... 18.9 89.7 3.2 1,008 -1.9
Government................................. 0.3 435.0 -0.9 1,125 2.6
Harris, TX................................... 101.9 2,054.1 3.1 1,156 6.3
Private industry........................... 101.4 1,800.8 3.9 1,173 6.9
Natural resources and mining............. 1.6 83.2 10.6 3,015 12.0
Construction............................. 6.5 134.8 -0.3 1,134 7.8
Manufacturing............................ 4.5 179.3 6.5 1,427 5.5
Trade, transportation, and utilities..... 22.9 428.5 3.1 1,028 6.1
Information.............................. 1.3 28.1 0.0 1,343 3.4
Financial activities..................... 10.5 113.1 1.7 1,412 9.2
Professional and business services....... 20.2 341.2 6.1 1,361 3.7
Education and health services............ 11.5 244.8 2.4 952 5.8
Leisure and hospitality.................. 8.3 185.0 4.3 415 4.0
Other services........................... 13.6 62.0 3.3 661 7.1
Government................................. 0.6 253.4 -2.3 1,037 2.1
Maricopa, AZ................................. 95.6 1,641.4 2.9 901 4.8
Private industry........................... 94.9 1,428.6 3.5 895 5.0
Natural resources and mining............. 0.5 6.7 7.1 880 10.3
Construction............................. 8.5 83.3 3.2 941 5.5
Manufacturing............................ 3.2 109.1 1.3 1,332 6.6
Trade, transportation, and utilities..... 22.1 335.3 3.4 846 6.3
Information.............................. 1.6 27.0 2.1 1,172 5.7
Financial activities..................... 11.1 137.0 4.8 1,090 5.9
Professional and business services....... 22.7 267.5 3.1 937 4.3
Education and health services............ 10.6 242.8 3.7 942 2.2
Leisure and hospitality.................. 7.3 172.1 4.1 435 6.4
Other services........................... 6.7 47.0 3.7 609 6.3
Government................................. 0.7 212.9 -0.8 947 3.4
Dallas, TX................................... 68.5 1,448.7 2.7 1,102 6.4
Private industry........................... 68.0 1,284.3 3.4 1,107 6.3
Natural resources and mining............. 0.6 8.6 -0.4 3,396 12.5
Construction............................. 3.9 68.7 0.8 1,031 9.1
Manufacturing............................ 2.8 115.1 1.1 1,251 5.2
Trade, transportation, and utilities..... 14.9 288.2 3.2 1,025 6.8
Information.............................. 1.6 45.3 -0.4 1,661 10.3
Financial activities..................... 8.5 140.2 2.7 1,429 7.5
Professional and business services....... 15.1 275.5 5.2 1,230 4.3
Education and health services............ 7.3 169.5 3.6 1,013 5.0
Leisure and hospitality.................. 5.7 131.9 4.6 510 10.6
Other services........................... 7.2 40.5 6.2 680 6.1
Government................................. 0.5 164.5 -2.6 1,062 5.8
Orange, CA................................... 104.4 1,372.4 1.7 1,036 6.0
Private industry........................... 103.0 1,240.2 2.0 1,022 5.6
Natural resources and mining............. 0.2 3.4 -4.7 727 10.8
Construction............................. 6.1 71.2 3.2 1,131 4.2
Manufacturing............................ 4.9 154.0 2.0 1,308 7.9
Trade, transportation, and utilities..... 15.9 244.4 0.1 970 4.8
Information.............................. 1.2 23.2 -2.2 1,554 6.2
Financial activities..................... 9.5 105.3 1.3 1,525 11.0
Professional and business services....... 18.4 247.3 1.7 1,146 4.5
Education and health services............ 10.4 158.7 2.9 968 3.1
Leisure and hospitality.................. 7.1 178.0 3.5 440 2.3
Other services........................... 22.0 48.6 0.0 564 5.2
Government................................. 1.4 132.2 -1.0 1,173 11.0
San Diego, CA................................ 100.7 1,252.4 1.2 1,014 7.5
Private industry........................... 99.3 1,035.5 1.5 973 6.1
Natural resources and mining............. 0.7 10.0 -9.4 615 7.9
Construction............................. 6.0 55.8 0.8 1,090 4.5
Manufacturing............................ 2.9 93.4 0.5 1,391 5.6
Trade, transportation, and utilities..... 13.4 200.4 1.6 784 5.4
Information.............................. 1.2 24.2 -2.4 1,617 2.2
Financial activities..................... 8.4 67.5 1.6 1,173 4.5
Professional and business services....... 16.0 212.6 0.3 1,311 7.4
Education and health services............ 8.5 148.3 3.1 988 8.7
Leisure and hospitality.................. 7.0 159.7 1.4 451 6.4
Other services........................... 28.8 58.3 (6) 580 7.0
Government................................. 1.4 216.8 -0.4 1,220 13.9
King, WA..................................... 83.3 1,150.7 2.8 1,323 7.3
Private industry........................... 82.8 997.8 3.4 1,346 7.9
Natural resources and mining............. 0.4 2.9 8.3 1,286 8.9
Construction............................. 5.8 48.9 -0.5 1,181 3.9
Manufacturing............................ 2.3 101.6 4.5 1,504 3.3
Trade, transportation, and utilities..... 14.9 210.6 3.7 1,018 5.2
Information.............................. 1.8 80.8 2.3 4,177 15.1
Financial activities..................... 6.3 64.8 -1.4 1,376 5.1
Professional and business services....... 14.3 184.9 4.8 1,435 7.9
Education and health services............ 7.3 135.6 4.0 993 6.9
Leisure and hospitality.................. 6.5 114.6 4.9 484 6.1
Other services........................... 23.2 53.2 2.5 603 4.3
Government................................. 0.6 152.9 -0.9 1,171 2.9
Miami-Dade, FL............................... 86.8 970.3 2.9 880 3.3
Private industry........................... 86.5 829.2 3.7 858 4.9
Natural resources and mining............. 0.5 7.3 5.0 534 9.9
Construction............................. 5.0 30.1 -4.5 882 5.0
Manufacturing............................ 2.6 35.9 1.2 877 10.0
Trade, transportation, and utilities..... 24.9 248.0 4.7 796 5.4
Information.............................. 1.4 17.1 -1.0 1,387 6.9
Financial activities..................... 9.0 62.2 3.8 1,292 5.9
Professional and business services....... 18.1 126.5 4.6 1,036 3.9
Education and health services............ 9.7 154.6 2.5 900 4.7
Leisure and hospitality.................. 6.6 110.7 4.9 515 3.8
Other services........................... 7.8 36.0 4.5 568 2.7
Government................................. 0.4 141.1 -1.4 1,009 -3.4
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
(2) Data are preliminary. Counties selected are based on 2010 annual average employment.
(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 by state,
third quarter 2011(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
State 2011 Percent Percent
(thousands) September change, Third change,
2011 September quarter third
(thousands) 2010-11 2011 quarter
2010-11
United States(4)......... 9,135.8 130,524.7 1.6 $916 5.3
Alabama.................. 116.6 1,823.2 0.5 803 3.7
Alaska................... 21.7 341.5 2.1 963 4.0
Arizona.................. 146.0 2,391.6 2.1 860 4.9
Arkansas................. 86.2 1,151.0 0.4 715 4.5
California............... 1,394.4 14,686.3 1.5 1,051 6.8
Colorado................. 171.8 2,234.4 2.3 948 5.6
Connecticut.............. 110.7 1,626.5 0.9 1,118 4.7
Delaware................. 28.2 406.1 0.2 949 5.4
District of Columbia..... 36.3 708.1 2.1 1,527 3.9
Florida.................. 597.4 7,167.5 1.7 812 4.2
Georgia.................. 267.3 3,799.6 1.3 867 5.3
Hawaii................... 38.3 593.6 1.2 836 4.0
Idaho.................... 54.1 623.8 1.1 697 4.7
Illinois................. 386.7 5,629.1 1.6 958 4.6
Indiana.................. 159.5 2,797.5 2.1 785 5.8
Iowa..................... 93.3 1,466.9 1.6 760 5.6
Kansas................... 88.5 1,311.7 1.1 772 5.6
Kentucky................. 108.3 1,757.4 1.7 764 4.8
Louisiana................ 125.2 1,852.3 0.9 821 3.9
Maine.................... 49.4 595.6 0.9 734 2.9
Maryland................. 163.0 2,497.6 1.1 1,023 5.9
Massachusetts............ 231.3 3,227.8 1.8 1,114 4.1
Michigan................. 243.1 3,920.5 2.4 876 4.4
Minnesota................ 167.9 2,642.8 2.5 916 4.8
Mississippi.............. 69.2 1,081.3 0.1 681 4.4
Missouri................. 175.1 2,610.3 0.6 804 5.2
Montana.................. 42.2 433.9 1.3 687 6.2
Nebraska................. 60.8 905.0 0.5 747 5.7
Nevada................... 72.0 1,122.0 1.3 845 3.8
New Hampshire............ 48.5 613.2 0.7 903 5.6
New Jersey............... 263.5 3,774.1 0.6 1,069 4.3
New Mexico............... 55.1 788.7 0.4 779 4.7
New York................. 599.6 8,511.6 1.7 1,099 4.0
North Carolina........... 255.9 3,863.6 1.3 809 5.3
North Dakota............. 27.6 390.8 6.7 820 12.9
Ohio..................... 288.7 5,015.3 1.4 834 5.6
Oklahoma................. 102.7 1,518.5 1.8 785 8.3
Oregon................... 131.8 1,645.0 1.4 835 5.7
Pennsylvania............. 345.7 5,550.9 0.9 912 6.2
Rhode Island............. 35.0 456.8 0.3 871 5.3
South Carolina........... 111.9 1,789.9 1.4 746 4.8
South Dakota............. 31.2 398.9 1.3 684 3.6
Tennessee................ 139.1 2,631.4 2.1 819 5.3
Texas.................... 583.2 10,480.4 2.7 931 6.2
Utah..................... 84.2 1,192.9 2.9 779 5.1
Vermont.................. 24.2 297.0 0.8 778 3.3
Virginia................. 234.2 3,602.5 1.6 974 4.7
Washington............... 235.4 2,905.4 1.7 1,011 6.1
West Virginia............ 48.8 710.8 1.6 742 5.8
Wisconsin................ 159.7 2,697.9 1.4 792 5.6
Wyoming.................. 25.3 284.0 1.6 832 5.1
Puerto Rico.............. 50.8 910.3 -0.2 506 1.2
Virgin Islands........... 3.6 42.7 -1.5 718 -5.2
(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
