For release 10:00 a.m. (EST), Tuesday, January 10, 2012 USDL-12-0026
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
County Employment and Wages
Second Quarter 2011
From June 2010 to June 2011, employment increased in 215 of the 322 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. Ottawa, Mich., posted
the largest increase, with a gain of 4.7 percent over the year, compared with
national job growth of 0.9 percent. Within Ottawa, the largest employment increase
occurred in manufacturing, which gained 2,514 jobs over the year (9.0 percent). San
Joaquin, Calif., experienced the largest over-the-year decrease in employment among
the largest counties in the U.S. with a loss of 4.0 percent.
The U.S. average weekly wage increased over the year by 3.0 percent to $891 in the
second quarter of 2011. Among the large counties in the U.S., Williamson, Texas, had
the largest over-the-year increase in average weekly wages in the second quarter of
2011 with a gain of 18.0 percent. Within Williamson, a total wage increase of $195.2
million (39.2 percent) in the trade, transportation, and utilities industry had the
largest impact on the county’s over-the-year increase in average weekly wages. Champaign,
Ill., experienced the largest decline in average weekly wages with a loss of 3.6
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 June 2011 employment, June 2010-11 employment
increase, and June 2010-11 percent increase in employment
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Employment in large counties
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June 2011 employment | Increase in employment, | Percent increase in employment,
(thousands) | June 2010-11 | June 2010-11
| (thousands) |
--------------------------------------------------------------------------------------------------------
| |
United States 130,469.9| United States 1,131.6| United States 0.9
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| |
Los Angeles, Calif. 3,899.6| Harris, Texas 48.4| Ottawa, Mich. 4.7
Cook, Ill. 2,397.5| New York, N.Y. 43.6| Montgomery, Texas 4.1
New York, N.Y. 2,334.1| Cook, Ill. 28.1| Utah, Utah 4.0
Harris, Texas 2,043.2| Maricopa, Ariz. 28.0| Washington, Pa. 3.9
Maricopa, Ariz. 1,593.3| Dallas, Texas 26.5| Webb, Texas 3.9
Dallas, Texas 1,438.3| Los Angeles, Calif. 24.7| Elkhart, Ind. 3.8
Orange, Calif. 1,379.2| King, Wash. 22.9| Weld, Colo. 3.5
San Diego, Calif. 1,249.3| Miami-Dade, Fla. 20.8| Oakland, Mich. 3.3
King, Wash. 1,145.6| Oakland, Mich. 20.3| Travis, Texas 3.3
Miami-Dade, Fla. 953.4| Hennepin, Minn. 20.1| Saginaw, Mich. 3.2
| | Washington, Ore. 3.2
| |
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Large County Employment
In June 2011, national employment, as measured by the QCEW program, was 130.5
million, up by 0.9 percent or 1.1 million workers, from June 2010. The 322 U.S.
counties with 75,000 or more employees accounted for 70.5 percent of total U.S.
employment and 76.0 percent of total wages. These 322 counties had a net job growth
of 802,400 over the year, accounting for 70.9 percent of the overall U.S. employment
increase.
Ottawa, Mich., had the largest percentage increase in employment among the largest
U.S. counties (4.7 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 174,600, or
15.4 percent of the overall employment increase for the U.S.
Employment declined in 89 of the large counties from June 2010 to June 2011. San
Joaquin, Calif., had the largest over-the-year percentage decrease in employment (-
4.0 percent). Within San Joaquin, natural resources and mining was the largest
contributor to the decrease in employment with a loss of 5,268 jobs (-17.8 percent).
Yakima, Wash., had the second largest employment decrease, followed by Montgomery,
Ala., and Marion, Ore., both tied for the third largest decline, and Monterey,
Calif. (See table 1.)
Table B. Large counties ranked by second quarter 2011 average weekly wages, second quarter 2010-11
increase in average weekly wages, and second 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
second quarter 2011 | wage, second quarter 2010-11 | weekly wage, second
| | quarter 2010-11
--------------------------------------------------------------------------------------------------------
| |
United States $891| United States $26| United States 3.0
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| |
Santa Clara, Calif. $1,743| Williamson, Texas $159| Williamson, Texas 18.0
New York, N.Y. 1,645| Santa Clara, Calif. 137| Middlesex, Mass. 10.2
Arlington, Va. 1,553| Middlesex, Mass. 128| Harford, Md. 8.8
Washington, D.C. 1,541| San Mateo, Calif. 81| Santa Clara, Calif. 8.5
Fairfield, Conn. 1,469| San Francisco, Calif. 79| Butler, Pa. 7.5
San Francisco, Calif. 1,435| Fairfield, Conn. 76| Douglas, Colo. 7.4
Fairfax, Va. 1,421| Harford, Md. 72| New Castle, Del. 6.9
San Mateo, Calif. 1,403| New Castle, Del. 68| San Mateo, Calif. 6.1
Middlesex, Mass. 1,385| Douglas, Colo. 67| San Francisco, Calif. 5.8
Suffolk, Mass. 1,382| Arlington, Va. 65| Erie, Pa. 5.8
| | Dane, Wis. 5.8
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased by 3.0 percent over the year in the
second quarter of 2011. Among the 322 largest counties, 307 had over-the-year
increases in average weekly wages. Williamson, Texas, had the largest wage gain
among the largest U.S. counties (18.0 percent).
Of the 322 largest counties, 11 experienced declines in average weekly wages.
Champaign, Ill., had the largest wage decline with a loss of 3.6 percent over the
year. A $55.3 million total wage loss (-29.3 percent) within education and health services
contributed significantly to the county’s overall average weekly wage decline.
Benton, Ark., had the second largest decline in average weekly wages among the
counties, followed by Rutherford, Tenn., New York, N.Y., and Elkhart, Ind. (See
table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties experienced over-the-year percent increases in
employment in June 2011. Harris, Texas, experienced the largest gain in employment
(2.4 percent). Within Harris, professional and business services had the largest
over-the-year increase among all private industry groups with a gain of 16,936
workers (5.3 percent). San Diego, Calif., had the smallest increase in employment
among the 10 largest counties. (See table 2.)
Nine of the 10 largest U.S. counties had an over-the-year increase in average weekly
wages. Harris, Texas, experienced the largest increase in average weekly wages with
a gain of 5.0 percent. Within Harris, the largest impact on the county’s average
weekly wage growth occurred in natural resources and mining, largely due to
significant total wage gains over the year ($522.2 million or 20.0 percent). New
York, N.Y., had the only average weekly wage decrease.
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. June 2011
employment and 2011 second 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 second 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 third quarter 2011 is scheduled to be
released on Wednesday, March 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- | 440,000 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,
second quarter 2011(2)
Employment Average weekly wage(4)
Establishments,
County(3) second quarter Percent Ranking Percent Ranking
2011 June change, by Second change, by
(thousands) 2011 June percent quarter second percent
(thousands) 2010-11(5) change 2011 quarter change
2010-11(5)
United States(6)......... 9,084.2 130,469.9 0.9 - $891 3.0 -
Jefferson, AL............ 17.7 332.4 0.1 204 883 2.3 185
Madison, AL.............. 8.8 178.8 -1.3 290 997 3.3 101
Mobile, AL............... 9.8 166.4 -1.9 303 777 4.0 65
Montgomery, AL........... 6.3 128.0 -2.8 311 779 2.6 153
Tuscaloosa, AL........... 4.3 82.6 1.6 74 778 4.9 30
Anchorage Borough, AK.... 8.3 152.5 1.5 82 992 2.2 196
Maricopa, AZ............. 94.9 1,593.3 1.8 66 878 2.2 196
Pima, AZ................. 19.0 338.1 -0.7 267 794 4.3 50
Benton, AR............... 5.4 94.6 (7) - 816 -2.7 318
Pulaski, AR.............. 15.1 242.8 -1.4 293 817 3.9 72
Washington, AR........... 5.5 91.4 (7) - 735 0.8 291
Alameda, CA.............. 55.8 637.6 0.4 178 1,172 2.0 218
Contra Costa, CA......... 29.9 316.5 -1.3 290 1,099 3.9 72
Fresno, CA............... 30.6 338.0 -1.9 303 709 1.7 243
Kern, CA................. 17.9 282.6 0.9 134 792 2.7 148
Los Angeles, CA.......... 434.5 3,899.6 0.6 166 993 2.2 196
Marin, CA................ 11.7 103.1 1.0 123 1,112 5.6 14
Monterey, CA............. 12.9 182.5 -2.4 310 754 1.3 265
Orange, CA............... 103.5 1,379.2 0.8 145 999 3.2 109
Placer, CA............... 10.7 126.2 0.2 196 875 3.4 96
Riverside, CA............ 49.6 562.2 -0.9 276 743 1.9 232
Sacramento, CA........... 53.9 577.3 -2.1 307 1,004 2.7 148
San Bernardino, CA....... 51.0 596.8 -0.6 264 774 1.8 238
San Diego, CA............ 99.6 1,249.3 0.4 178 982 4.7 39
San Francisco, CA........ 55.0 556.9 2.7 24 1,435 5.8 9
San Joaquin, CA.......... 17.5 209.2 -4.0 314 767 1.7 243
San Luis Obispo, CA...... 9.6 102.1 -0.9 276 764 4.7 39
San Mateo, CA............ 24.3 325.0 1.0 123 1,403 6.1 8
Santa Barbara, CA........ 14.4 184.7 -0.1 226 840 2.9 131
Santa Clara, CA.......... 62.6 869.1 2.3 33 1,743 8.5 4
Santa Cruz, CA........... 9.1 96.9 -1.7 302 806 4.7 39
Solano, CA............... 10.1 120.9 0.1 204 902 3.4 96
Sonoma, CA............... 18.9 176.9 -0.4 248 856 4.9 30
Stanislaus, CA........... 15.1 162.9 -1.6 298 752 0.8 291
Tulare, CA............... 9.4 151.6 -0.5 258 617 1.8 238
Ventura, CA.............. 24.0 303.3 0.6 166 934 3.8 76
Yolo, CA................. 6.1 89.8 -0.7 267 896 3.3 101
Adams, CO................ 8.8 156.1 0.0 216 814 3.0 125
Arapahoe, CO............. 18.8 280.6 2.3 33 1,023 3.4 96
Boulder, CO.............. 12.8 157.2 2.0 48 1,027 2.1 209
Denver, CO............... 25.3 425.0 2.0 48 1,072 4.1 59
Douglas, CO.............. 9.4 92.9 0.9 134 972 7.4 6
El Paso, CO.............. 16.8 238.1 1.4 90 820 2.6 153
Jefferson, CO............ 17.7 208.5 1.5 82 915 4.2 52
Larimer, CO.............. 10.0 131.4 1.4 90 758 2.0 218
Weld, CO................. 5.8 82.1 3.5 7 740 4.2 52
Fairfield, CT............ 32.5 406.3 0.7 156 1,469 5.5 17
Hartford, CT............. 25.3 492.3 0.8 145 1,095 3.6 84
New Haven, CT............ 22.2 352.7 0.7 156 947 2.2 196
New London, CT........... 6.9 126.5 -0.1 226 903 1.0 288
New Castle, DE........... 17.4 264.0 -0.2 237 1,051 6.9 7
Washington, DC........... 35.5 711.3 1.4 90 1,541 2.4 181
Alachua, FL.............. 6.5 114.7 -0.4 248 777 5.1 22
Brevard, FL.............. 14.4 187.8 -0.8 271 852 2.3 185
Broward, FL.............. 62.1 681.6 0.0 216 837 2.6 153
Collier, FL.............. 11.6 107.3 2.1 43 796 1.1 280
Duval, FL................ 26.6 435.1 0.7 156 845 1.6 248
Escambia, FL............. 7.8 119.2 0.2 196 726 4.5 44
Hillsborough, FL......... 37.1 562.7 0.5 174 860 2.6 153
Lake, FL................. 7.1 75.4 1.5 82 619 0.8 291
Lee, FL.................. 18.3 190.3 1.6 74 732 0.8 291
Leon, FL................. 8.2 135.9 -0.3 244 767 4.5 44
Manatee, FL.............. 9.1 98.6 -0.7 267 718 2.9 131
Marion, FL............... 7.9 88.9 -0.4 248 656 1.5 256
Miami-Dade, FL........... 86.3 953.4 2.2 37 876 3.2 109
Orange, FL............... 35.4 650.9 1.8 66 792 2.1 209
Palm Beach, FL........... 48.8 485.7 0.6 166 874 1.4 261
Pasco, FL................ 9.9 91.3 2.0 48 666 -0.3 310
Pinellas, FL............. 30.3 379.0 -0.1 226 798 3.6 84
Polk, FL................. 12.4 183.0 -1.1 286 696 3.6 84
Sarasota, FL............. 14.2 131.0 1.1 114 750 2.5 168
Seminole, FL............. 13.7 152.6 -1.5 296 760 3.0 125
Volusia, FL.............. 13.2 145.8 -0.1 226 656 1.2 272
Bibb, GA................. 4.6 79.1 -0.1 226 688 1.3 265
Chatham, GA.............. 7.5 129.7 1.0 123 753 1.2 272
Clayton, GA.............. 4.2 101.7 0.1 204 793 3.1 120
Cobb, GA................. 20.4 288.9 0.6 166 918 1.9 232
De Kalb, GA.............. 17.2 274.3 0.4 178 926 3.5 90
Fulton, GA............... 39.3 712.7 1.2 104 1,155 2.8 142
Gwinnett, GA............. 23.2 300.8 2.3 33 862 1.3 265
Muscogee, GA............. 4.6 93.6 (7) - 714 (7) -
Richmond, GA............. 4.6 97.4 0.6 166 761 3.3 101
Honolulu, HI............. 24.4 433.5 0.9 134 830 2.9 131
Ada, ID.................. 14.0 195.4 0.8 145 775 2.6 153
Champaign, IL............ 4.2 87.1 -2.2 309 757 -3.6 319
Cook, IL................. 145.8 2,397.5 1.2 104 1,037 2.6 153
Du Page, IL.............. 36.6 568.4 2.4 29 1,031 4.4 47
Kane, IL................. 13.2 193.4 -0.9 276 798 2.8 142
Lake, IL................. 21.7 321.2 -0.2 237 1,141 4.0 65
McHenry, IL.............. 8.6 94.9 -0.3 244 747 2.5 168
McLean, IL............... 3.8 86.0 -0.4 248 864 1.2 272
Madison, IL.............. 6.0 96.1 1.6 74 733 1.4 261
Peoria, IL............... 4.7 102.0 1.3 97 843 5.1 22
St. Clair, IL............ 5.5 98.6 1.1 114 793 4.1 59
Sangamon, IL............. 5.3 132.0 1.3 97 917 2.9 131
Will, IL................. 14.7 202.1 0.5 174 798 1.9 232
Winnebago, IL............ 6.8 126.5 1.0 123 749 2.6 153
Allen, IN................ 9.0 175.2 1.6 74 744 1.6 248
Elkhart, IN.............. 4.9 105.5 3.8 6 728 -1.0 315
Hamilton, IN............. 8.3 113.2 3.1 12 821 0.4 301
Lake, IN................. 10.4 186.4 1.2 104 807 4.1 59
Marion, IN............... 23.7 546.8 0.3 188 892 2.5 168
St. Joseph, IN........... 6.0 115.1 0.2 196 736 1.5 256
Vanderburgh, IN.......... 4.8 105.9 0.9 134 738 1.1 280
Linn, IA................. 6.2 127.4 1.6 74 838 0.8 291
Polk, IA................. 14.5 268.3 0.1 204 872 3.2 109
Scott, IA................ 5.2 87.6 1.7 70 706 2.5 168
Johnson, KS.............. 21.4 303.4 1.6 74 907 2.1 209
Sedgwick, KS............. 12.6 238.1 -1.3 290 815 2.9 131
Shawnee, KS.............. 4.9 94.3 -1.9 303 780 4.1 59
Wyandotte, KS............ 3.3 82.0 1.2 104 853 2.4 181
Fayette, KY.............. 9.5 175.0 (7) - 822 2.9 131
Jefferson, KY............ 22.4 418.2 1.0 123 880 3.3 101
Caddo, LA................ 7.5 121.0 -1.0 284 762 2.3 185
Calcasieu, LA............ 4.9 83.1 0.1 204 753 4.9 30
East Baton Rouge, LA..... 14.6 248.4 -0.9 276 827 2.9 131
Jefferson, LA............ 13.9 192.9 -1.5 296 825 2.6 153
Lafayette, LA............ 9.0 132.5 0.8 145 852 3.9 72
Orleans, LA.............. 11.1 171.3 0.9 134 937 2.3 185
Cumberland, ME........... 12.4 171.0 1.1 114 798 2.4 181
Anne Arundel, MD......... 14.5 233.4 1.5 82 960 1.8 238
Baltimore, MD............ 21.1 363.3 -0.4 248 906 1.1 280
Frederick, MD............ 6.0 92.1 -1.4 293 861 1.1 280
Harford, MD.............. 5.6 84.8 2.8 22 890 8.8 3
Howard, MD............... 9.0 153.7 1.5 82 1,080 4.9 30
Montgomery, MD........... 32.9 453.0 1.1 114 1,213 3.3 101
Prince Georges, MD....... 15.7 301.7 -0.4 248 981 2.1 209
Baltimore City, MD....... 13.8 329.0 -0.2 237 1,034 3.3 101
Barnstable, MA........... 9.4 97.8 0.9 134 754 2.2 196
Bristol, MA.............. 16.7 212.9 0.9 134 837 5.0 27
Essex, MA................ 22.1 304.0 1.1 114 976 5.6 14
Hampden, MA.............. 15.7 197.7 0.7 156 814 4.4 47
Middlesex, MA............ 50.1 814.7 0.4 178 1,385 10.2 2
Norfolk, MA.............. 24.9 319.2 0.4 178 1,047 2.5 168
Plymouth, MA............. 14.5 174.7 0.1 204 875 3.2 109
Suffolk, MA.............. 23.6 585.2 1.8 66 1,382 3.7 80
Worcester, MA............ 22.0 316.8 0.9 134 908 2.6 153
Genesee, MI.............. 7.2 129.7 1.4 90 735 1.2 272
Ingham, MI............... 6.3 153.6 -0.5 258 854 0.0 308
Kalamazoo, MI............ 5.3 107.5 -0.4 248 804 2.0 218
Kent, MI................. 13.6 320.3 3.1 12 785 1.7 243
Macomb, MI............... 16.6 288.8 2.8 22 880 1.6 248
Oakland, MI.............. 36.6 640.6 3.3 8 989 4.0 65
Ottawa, MI............... 5.5 106.8 4.7 1 728 2.2 196
Saginaw, MI.............. 4.1 82.6 3.2 10 723 0.1 306
Washtenaw, MI............ 7.9 187.8 2.0 48 939 3.6 84
Wayne, MI................ 30.6 675.4 1.3 97 961 1.9 232
Anoka, MN................ 7.0 108.5 0.0 216 859 4.0 65
Dakota, MN............... 9.5 171.8 0.6 166 892 4.1 59
Hennepin, MN............. 42.8 832.7 2.5 28 1,116 3.3 101
Olmsted, MN.............. 3.3 89.0 0.4 178 1,015 2.7 148
Ramsey, MN............... 13.7 318.3 0.5 174 993 3.8 76
St. Louis, MN............ 5.5 95.6 1.0 123 749 3.2 109
Stearns, MN.............. 4.2 79.8 2.6 26 699 2.9 131
Harrison, MS............. 4.5 83.8 0.7 156 669 0.6 298
Hinds, MS................ 6.0 121.9 -1.1 286 777 2.0 218
Boone, MO................ 4.5 83.8 1.9 56 697 1.8 238
Clay, MO................. 5.0 91.2 1.6 74 826 2.2 196
Greene, MO............... 8.0 148.7 0.9 134 680 1.2 272
Jackson, MO.............. 18.2 342.2 -0.5 258 887 1.3 265
St. Charles, MO.......... 8.1 125.2 2.1 43 710 0.3 304
St. Louis, MO............ 31.8 569.8 0.0 216 924 1.3 265
St. Louis City, MO....... 8.9 212.4 -0.9 276 975 5.1 22
Yellowstone, MT.......... 5.9 77.4 1.4 90 733 2.5 168
Douglas, NE.............. 15.9 313.5 0.0 216 814 2.0 218
Lancaster, NE............ 8.1 154.7 0.3 188 722 2.6 153
Clark, NV................ 47.2 805.3 0.2 196 806 2.5 168
Washoe, NV............... 13.6 185.0 0.0 216 808 1.3 265
Hillsborough, NH......... 11.8 187.6 0.8 145 986 2.6 153
Rockingham, NH........... 10.5 136.1 -0.1 226 853 -0.8 313
Atlantic, NJ............. 6.8 140.3 -2.0 306 781 1.4 261
Bergen, NJ............... 33.4 430.8 0.0 216 1,085 3.1 120
Burlington, NJ........... 11.1 194.4 -1.6 298 947 2.6 153
Camden, NJ............... 12.4 195.9 -1.1 286 892 1.6 248
Essex, NJ................ 20.8 336.5 -1.2 289 1,130 4.1 59
Gloucester, NJ........... 6.2 98.5 -1.4 293 798 -0.3 310
Hudson, NJ............... 13.8 230.7 0.3 188 1,227 2.0 218
Mercer, NJ............... 11.1 229.9 -0.5 258 1,182 4.2 52
Middlesex, NJ............ 21.8 378.5 -0.5 258 1,095 3.0 125
Monmouth, NJ............. 20.1 250.5 -1.6 298 924 2.3 185
Morris, NJ............... 17.4 273.0 -1.0 284 1,257 2.0 218
Ocean, NJ................ 12.2 155.3 -0.2 237 734 2.2 196
Passaic, NJ.............. 12.2 172.8 0.3 188 924 1.0 288
Somerset, NJ............. 10.1 172.1 0.2 196 1,304 1.7 243
Union, NJ................ 14.5 220.9 0.1 204 1,119 0.6 298
Bernalillo, NM........... 17.6 312.3 -0.8 271 781 0.1 306
Albany, NY............... 10.0 218.8 -0.9 276 931 2.1 209
Bronx, NY................ 17.0 236.0 -0.9 276 876 3.7 80
Broome, NY............... 4.5 91.8 -0.8 271 722 1.1 280
Dutchess, NY............. 8.2 111.6 -0.6 264 946 3.2 109
Erie, NY................. 23.7 457.5 0.7 156 782 1.7 243
Kings, NY................ 51.2 508.4 1.8 66 743 0.4 301
Monroe, NY............... 18.1 377.6 1.0 123 852 0.2 305
Nassau, NY............... 52.6 600.0 0.9 134 1,034 2.0 218
New York, NY............. 121.6 2,334.1 1.9 56 1,645 -1.1 316
Oneida, NY............... 5.3 107.8 -2.1 307 731 4.0 65
Onondaga, NY............. 12.7 244.2 0.1 204 826 1.1 280
Orange, NY............... 10.0 133.1 0.2 196 811 3.2 109
Queens, NY............... 46.0 504.5 1.2 104 845 0.8 291
Richmond, NY............. 9.0 92.6 0.2 196 774 0.5 300
Rockland, NY............. 9.9 116.4 1.1 114 997 4.5 44
Suffolk, NY.............. 50.7 631.3 0.7 156 980 1.2 272
Westchester, NY.......... 36.1 412.8 1.0 123 1,205 3.1 120
Buncombe, NC............. 7.8 111.2 0.8 145 672 -0.1 309
Catawba, NC.............. 4.4 78.5 1.4 90 677 1.2 272
Cumberland, NC........... 6.3 120.5 1.7 70 748 4.8 35
Durham, NC............... 7.2 176.4 1.9 56 1,196 5.7 12
Forsyth, NC.............. 8.9 171.6 0.4 178 815 1.6 248
Guilford, NC............. 14.1 261.6 1.9 56 780 1.4 261
Mecklenburg, NC.......... 32.1 547.8 3.0 16 993 1.1 280
New Hanover, NC.......... 7.2 98.8 (7) - 741 (7) -
Wake, NC................. 28.9 449.2 (7) - 896 1.0 288
Cass, ND................. 6.0 103.1 2.7 24 769 4.3 50
Butler, OH............... 7.4 138.3 -0.1 226 783 2.5 168
Cuyahoga, OH............. 35.8 691.3 0.0 216 898 1.9 232
Franklin, OH............. 29.5 657.0 1.5 82 864 2.0 218
Hamilton, OH............. 23.3 488.4 0.3 188 959 4.0 65
Lake, OH................. 6.5 95.5 0.8 145 758 5.3 19
Lorain, OH............... 6.1 94.1 0.4 178 731 4.6 43
Lucas, OH................ 10.3 199.6 0.2 196 767 3.5 90
Mahoning, OH............. 6.0 97.0 0.6 166 649 3.5 90
Montgomery, OH........... 12.2 243.8 0.6 166 785 1.6 248
Stark, OH................ 8.7 151.7 1.5 82 689 3.9 72
Summit, OH............... 14.3 255.9 0.4 178 790 2.3 185
Oklahoma, OK............. 24.3 420.1 2.1 43 832 5.3 19
Tulsa, OK................ 20.2 328.6 0.3 188 816 4.2 52
Clackamas, OR............ 12.6 138.9 0.1 204 837 4.9 30
Jackson, OR.............. 6.5 75.6 -0.8 271 683 2.1 209
Lane, OR................. 10.7 138.5 0.8 145 704 2.8 142
Marion, OR............... 9.3 132.5 -2.8 311 725 4.0 65
Multnomah, OR............ 29.0 432.2 1.9 56 923 4.2 52
Washington, OR........... 16.2 244.7 3.2 10 1,033 3.8 76
Allegheny, PA............ 35.2 686.8 1.1 114 948 3.2 109
Berks, PA................ 9.0 165.4 1.6 74 808 2.5 168
Bucks, PA................ 19.6 253.2 -0.4 248 863 2.9 131
Butler, PA............... 4.9 83.6 3.1 12 827 7.5 5
Chester, PA.............. 15.0 240.2 1.3 97 1,163 2.6 153
Cumberland, PA........... 6.1 123.1 1.9 56 835 3.6 84
Dauphin, PA.............. 7.4 179.7 0.0 216 882 3.4 96
Delaware, PA............. 13.7 208.1 0.9 134 944 2.8 142
Erie, PA................. 7.7 126.2 2.4 29 710 5.8 9
Lackawanna, PA........... 5.8 98.3 0.0 216 682 1.6 248
Lancaster, PA............ 12.5 220.2 -0.2 237 741 2.3 185
Lehigh, PA............... 8.6 176.9 1.9 56 866 5.1 22
Luzerne, PA.............. 7.7 139.9 1.2 104 697 2.7 148
Montgomery, PA........... 27.1 464.7 -0.4 248 1,081 1.3 265
Northampton, PA.......... 6.5 101.0 2.3 33 778 2.2 196
Philadelphia, PA......... 34.3 630.3 0.1 204 1,031 2.3 185
Washington, PA........... 5.6 84.8 3.9 4 820 5.5 17
Westmoreland, PA......... 9.4 134.4 -0.1 226 726 4.8 35
York, PA................. 9.1 171.4 1.0 123 791 2.5 168
Providence, RI........... 17.2 269.3 -0.1 226 898 4.8 35
Charleston, SC........... 11.7 212.0 2.4 29 781 2.1 209
Greenville, SC........... 12.1 230.9 2.2 37 788 3.7 80
Horry, SC................ 7.5 117.3 2.1 43 526 -0.8 313
Lexington, SC............ 5.6 94.8 -0.2 237 662 2.2 196
Richland, SC............. 8.9 201.5 -0.5 258 779 2.2 196
Spartanburg, SC.......... 5.8 110.4 0.7 156 781 2.4 181
Minnehaha, SD............ 6.5 115.3 1.0 123 739 4.8 35
Davidson, TN............. 18.0 423.4 1.4 90 892 2.1 209
Hamilton, TN............. 8.4 183.7 2.0 48 783 3.3 101
Knox, TN................. 10.7 218.4 1.2 104 763 3.7 80
Rutherford, TN........... 4.3 96.5 1.5 82 788 -2.2 317
Shelby, TN............... 18.8 465.4 0.7 156 917 2.8 142
Williamson, TN........... 6.1 91.7 2.2 37 968 1.6 248
Bell, TX................. 4.8 107.7 2.0 48 733 2.2 196
Bexar, TX................ 34.1 733.9 0.8 145 798 3.2 109
Brazoria, TX............. 4.8 88.8 2.0 48 869 4.7 39
Brazos, TX............... 3.9 84.1 -1.6 298 678 2.9 131
Cameron, TX.............. 6.4 127.2 1.1 114 572 1.8 238
Collin, TX............... 18.2 296.1 3.1 12 1,039 3.8 76
Dallas, TX............... 68.0 1,438.3 1.9 56 1,055 2.0 218
Denton, TX............... 11.1 180.0 3.0 16 782 3.4 96
El Paso, TX.............. 13.8 273.0 0.3 188 648 2.5 168
Fort Bend, TX............ 9.3 137.0 2.2 37 880 2.9 131
Galveston, TX............ 5.4 96.8 1.7 70 816 5.6 14
Harris, TX............... 101.2 2,043.2 2.4 29 1,120 5.0 27
Hidalgo, TX.............. 11.1 225.2 1.9 56 571 1.2 272
Jefferson, TX............ 5.9 121.6 2.1 43 881 5.1 22
Lubbock, TX.............. 7.0 125.0 1.7 70 684 1.9 232
McLennan, TX............. 4.8 101.2 -0.6 264 719 2.0 218
Montgomery, TX........... 8.7 133.7 4.1 2 837 5.3 19
Nueces, TX............... 7.9 154.7 1.2 104 763 4.2 52
Smith, TX................ 5.5 92.9 0.3 188 761 2.3 185
Tarrant, TX.............. 37.7 764.2 2.2 37 899 3.2 109
Travis, TX............... 30.6 584.4 3.3 8 974 3.5 90
Webb, TX................. 4.8 88.9 3.9 4 616 4.2 52
Williamson, TX........... 7.6 129.7 1.3 97 1,040 18.0 1
Davis, UT................ 7.1 106.8 (7) - 729 2.0 218
Salt Lake, UT............ 36.6 569.3 1.9 56 833 3.0 125
Utah, UT................. 12.6 171.0 4.0 3 714 5.0 27
Weber, UT................ 5.4 89.1 -0.8 271 671 1.5 256
Chittenden, VT........... 6.0 95.8 2.9 19 894 3.0 125
Arlington, VA............ 8.2 169.7 2.2 37 1,553 4.4 47
Chesterfield, VA......... 7.6 114.9 -0.9 276 800 0.4 301
Fairfax, VA.............. 34.3 585.2 1.3 97 1,421 2.2 196
Henrico, VA.............. 9.7 173.1 -0.1 226 887 1.5 256
Loudoun, VA.............. 9.7 138.8 2.9 19 1,051 -0.3 310
Prince William, VA....... 7.7 110.3 3.0 16 804 1.1 280
Alexandria City, VA...... 6.1 94.9 (7) - 1,258 (7) -
Chesapeake City, VA...... 5.6 95.6 0.4 178 713 2.0 218
Newport News City, VA.... 3.8 96.4 0.5 174 839 3.5 90
Norfolk City, VA......... 5.7 138.7 1.2 104 879 0.8 291
Richmond City, VA........ 7.1 150.0 1.3 97 982 2.3 185
Virginia Beach City, VA.. 11.2 168.2 -0.2 237 718 3.2 109
Benton, WA............... 5.5 84.0 -0.7 267 963 5.7 12
Clark, WA................ 13.1 129.6 1.2 104 808 2.8 142
King, WA................. 81.2 1,145.6 2.0 48 1,134 3.0 125
Kitsap, WA............... 6.6 82.0 0.1 204 863 2.5 168
Pierce, WA............... 21.1 261.9 -0.3 244 823 2.6 153
Snohomish, WA............ 18.7 248.2 2.9 19 952 3.5 90
Spokane, WA.............. 15.5 199.9 0.1 204 755 3.1 120
Thurston, WA............. 7.3 97.3 -0.3 244 827 2.7 148
Whatcom, WA.............. 6.8 80.0 0.8 145 749 3.6 84
Yakima, WA............... 8.6 102.5 -3.8 313 610 2.0 218
Kanawha, WV.............. 6.0 105.6 -0.4 248 797 3.1 120
Brown, WI................ 6.6 148.1 1.1 114 758 2.3 185
Dane, WI................. 14.1 300.3 0.7 156 877 5.8 9
Milwaukee, WI............ 22.5 471.9 0.8 145 878 1.5 256
Outagamie, WI............ 5.0 102.4 1.0 123 743 2.6 153
Waukesha, WI............. 12.7 227.7 2.6 26 869 2.5 168
Winnebago, WI............ 3.7 90.3 -0.1 226 814 2.1 209
San Juan, PR............. 11.9 258.5 -0.8 (8) 596 0.7 (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,
second quarter 2011(2)
Employment Average weekly
wage(3)
Establishments,
second quarter
County by NAICS supersector 2011 Percent Percent
(thousands) June change, Second change,
2011 June quarter second
(thousands) 2010-11(4) 2011 quarter
2010-11(4)
United States(5)............................. 9,084.2 130,469.9 0.9 $891 3.0
Private industry........................... 8,786.9 109,010.2 1.6 874 2.9
Natural resources and mining............. 127.7 1,966.3 1.2 964 9.4
Construction............................. 769.8 5,625.6 -0.7 936 2.7
Manufacturing............................ 338.5 11,760.7 1.8 1,094 2.9
Trade, transportation, and utilities..... 1,875.6 24,807.3 1.3 752 2.7
Information.............................. 142.7 2,685.6 -1.3 1,398 5.7
Financial activities..................... 808.9 7,433.9 0.0 1,287 2.3
Professional and business services....... 1,558.4 17,325.3 3.1 1,134 4.1
Education and health services............ 907.4 18,921.8 1.8 835 2.1
Leisure and hospitality.................. 755.3 13,816.3 2.1 365 2.0
Other services........................... 1,309.3 4,456.3 1.2 566 2.4
Government................................. 297.3 21,459.7 -2.7 973 3.3
Los Angeles, CA.............................. 434.5 3,899.6 0.6 993 2.2
Private industry........................... 428.8 3,334.4 1.3 961 2.6
Natural resources and mining............. 0.5 9.7 -7.2 1,210 9.2
Construction............................. 12.4 104.3 -1.0 1,011 2.1
Manufacturing............................ 12.9 368.8 -1.7 1,089 3.5
Trade, transportation, and utilities..... 50.5 737.9 1.1 803 3.1
Information.............................. 8.2 190.8 -0.4 1,696 1.9
Financial activities..................... 21.9 209.4 -0.6 1,452 3.7
Professional and business services....... 40.8 539.9 2.1 1,193 2.6
Education and health services............ 28.8 510.6 1.5 938 3.6
Leisure and hospitality.................. 26.7 398.0 1.9 546 3.8
Other services........................... 205.0 243.4 1.7 437 -4.0
Government................................. 5.7 565.3 -3.2 1,178 0.7
Cook, IL..................................... 145.8 2,397.5 1.2 1,037 2.6
Private industry........................... 144.4 2,094.5 1.9 1,020 2.6
Natural resources and mining............. 0.1 0.9 3.1 896 -5.9
Construction............................. 12.3 66.2 -0.2 1,227 2.4
Manufacturing............................ 6.6 195.2 0.9 1,087 3.9
Trade, transportation, and utilities..... 28.4 438.1 1.7 817 4.1
Information.............................. 2.6 52.1 -0.3 1,494 4.4
Financial activities..................... 15.5 186.5 -1.4 1,794 4.2
Professional and business services....... 30.7 415.3 3.5 1,301 2.8
Education and health services............ 15.3 400.5 2.3 863 0.2
Leisure and hospitality.................. 12.8 241.3 3.5 462 2.9
Other services........................... 16.0 96.4 2.0 757 2.6
Government................................. 1.4 303.0 -3.6 1,152 3.0
New York, NY................................. 121.6 2,334.1 1.9 1,645 -1.1
Private industry........................... 121.3 1,898.3 3.2 1,767 -2.1
Natural resources and mining............. 0.0 0.1 8.5 1,789 -6.0
Construction............................. 2.2 30.6 1.4 1,614 4.8
Manufacturing............................ 2.5 26.1 0.7 1,278 4.0
Trade, transportation, and utilities..... 21.0 241.8 3.1 1,228 4.2
Information.............................. 4.3 137.9 2.3 1,999 2.4
Financial activities..................... 19.0 358.3 3.1 3,199 -11.3
Professional and business services....... 25.4 471.5 3.0 2,000 4.5
Education and health services............ 9.3 296.3 0.8 1,140 3.7
Leisure and hospitality.................. 12.6 238.3 5.8 762 1.2
Other services........................... 18.9 89.5 2.6 993 2.8
Government................................. 0.3 435.8 -3.3 1,115 2.3
Harris, TX................................... 101.2 2,043.2 2.4 1,120 5.0
Private industry........................... 100.7 1,784.0 3.2 1,141 5.1
Natural resources and mining............. 1.6 80.7 8.1 3,052 11.7
Construction............................. 6.5 132.4 -1.0 1,092 2.4
Manufacturing............................ 4.5 176.5 5.5 1,380 4.7
Trade, transportation, and utilities..... 22.7 424.7 2.5 1,012 5.9
Information.............................. 1.3 28.5 -1.4 1,276 4.8
Financial activities..................... 10.5 111.7 -0.2 1,384 6.1
Professional and business services....... 20.0 336.2 5.3 1,341 2.9
Education and health services............ 11.3 242.5 2.4 896 1.5
Leisure and hospitality.................. 8.2 187.6 4.0 392 0.5
Other services........................... 13.5 62.2 2.8 643 5.4
Government................................. 0.6 259.2 -2.8 974 3.0
Maricopa, AZ................................. 94.9 1,593.3 1.8 878 2.2
Private industry........................... 94.2 1,414.0 2.0 865 2.9
Natural resources and mining............. 0.5 8.4 16.1 750 0.3
Construction............................. 8.5 82.1 0.8 893 1.6
Manufacturing............................ 3.2 109.0 0.6 1,341 6.3
Trade, transportation, and utilities..... 22.0 333.9 2.2 818 3.2
Information.............................. 1.5 27.3 0.1 1,101 4.1
Financial activities..................... 11.1 134.7 3.1 1,062 2.1
Professional and business services....... 22.4 263.5 0.9 915 3.4
Education and health services............ 10.5 235.4 2.7 912 2.6
Leisure and hospitality.................. 7.0 171.4 2.6 401 -1.2
Other services........................... 6.7 48.0 2.4 591 3.3
Government................................. 0.7 179.3 0.0 968 -1.4
Dallas, TX................................... 68.0 1,438.3 1.9 1,055 2.0
Private industry........................... 67.5 1,272.4 2.6 1,061 2.0
Natural resources and mining............. 0.6 9.0 8.5 3,318 0.3
Construction............................. 3.9 68.3 0.4 958 3.7
Manufacturing............................ 2.8 115.0 0.3 1,244 1.8
Trade, transportation, and utilities..... 14.8 284.8 2.1 975 3.0
Information.............................. 1.6 45.5 -0.9 1,592 6.1
Financial activities..................... 8.5 138.9 1.8 1,389 3.0
Professional and business services....... 14.9 269.8 4.8 1,175 0.6
Education and health services............ 7.2 167.9 3.1 982 0.4
Leisure and hospitality.................. 5.6 132.4 3.1 444 0.0
Other services........................... 7.1 40.1 3.5 667 4.2
Government................................. 0.5 165.9 -3.5 1,015 2.2
Orange, CA................................... 103.5 1,379.2 0.8 999 3.2
Private industry........................... 102.1 1,231.0 1.2 982 3.3
Natural resources and mining............. 0.2 4.0 -9.0 658 9.7
Construction............................. 6.2 69.8 0.7 1,081 2.9
Manufacturing............................ 4.9 153.3 1.0 1,209 3.1
Trade, transportation, and utilities..... 15.8 243.9 0.2 953 4.2
Information.............................. 1.2 23.6 -5.3 1,399 5.2
Financial activities..................... 9.5 103.6 0.5 1,454 5.2
Professional and business services....... 18.2 244.1 0.4 1,137 2.8
Education and health services............ 10.3 156.5 2.2 914 3.7
Leisure and hospitality.................. 7.1 175.8 2.2 425 2.2
Other services........................... 21.5 48.6 -0.3 535 1.9
Government................................. 1.4 148.2 -2.3 1,138 2.3
San Diego, CA................................ 99.6 1,249.3 0.4 982 4.7
Private industry........................... 98.2 1,026.7 0.9 949 5.2
Natural resources and mining............. 0.7 11.6 0.6 538 -0.7
Construction............................. 6.0 55.3 -1.1 1,027 3.2
Manufacturing............................ 2.9 93.5 0.5 1,335 2.7
Trade, transportation, and utilities..... 13.4 197.4 0.5 771 2.8
Information.............................. 1.2 23.9 -4.5 1,488 8.3
Financial activities..................... 8.4 66.9 0.6 1,151 4.3
Professional and business services....... 15.8 210.5 0.4 1,372 10.1
Education and health services............ 8.4 146.0 2.1 905 3.2
Leisure and hospitality.................. 7.0 158.3 0.5 408 2.8
Other services........................... 28.1 56.5 -1.0 516 4.0
Government................................. 1.4 222.6 -1.9 1,138 3.5
King, WA..................................... 81.2 1,145.6 2.0 1,134 3.0
Private industry........................... 80.7 986.4 2.7 1,135 3.3
Natural resources and mining............. 0.3 2.8 6.8 1,494 20.3
Construction............................. 5.6 46.6 -2.0 1,128 2.5
Manufacturing............................ 2.3 99.2 2.1 1,414 -0.3
Trade, transportation, and utilities..... 14.6 208.7 3.1 997 5.8
Information.............................. 1.7 80.3 1.9 2,048 2.7
Financial activities..................... 6.3 64.6 -1.3 1,361 5.1
Professional and business services....... 13.9 181.7 4.7 1,400 5.2
Education and health services............ 7.1 135.3 3.3 930 2.0
Leisure and hospitality.................. 6.4 113.8 3.3 433 0.2
Other services........................... 22.3 53.5 3.1 580 -3.7
Government................................. 0.6 159.2 -1.9 1,129 2.0
Miami-Dade, FL............................... 86.3 953.4 2.2 876 3.2
Private industry........................... 85.9 826.4 3.3 829 2.0
Natural resources and mining............. 0.5 7.2 1.8 521 2.4
Construction............................. 5.0 30.0 -3.5 858 -0.9
Manufacturing............................ 2.6 36.0 -0.1 819 6.4
Trade, transportation, and utilities..... 24.6 245.2 3.4 778 2.1
Information.............................. 1.4 17.2 -0.6 1,332 2.4
Financial activities..................... 9.0 61.7 2.2 1,275 3.9
Professional and business services....... 17.8 125.4 4.3 1,032 3.4
Education and health services............ 9.7 154.4 2.9 847 1.7
Leisure and hospitality.................. 6.5 111.6 5.3 481 -1.2
Other services........................... 7.7 36.6 4.1 545 0.7
Government................................. 0.4 127.0 -4.0 1,159 10.3
(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.
Table 3. Covered(1) establishments, employment, and wages by state,
second quarter 2011(2)
Employment Average weekly
wage(3)
Establishments,
second quarter
State 2011 Percent Percent
(thousands) June change, Second change,
2011 June quarter second
(thousands) 2010-11 2011 quarter
2010-11
United States(4)......... 9,084.2 130,469.9 0.9 $891 3.0
Alabama.................. 116.3 1,824.8 -0.4 767 2.3
Alaska................... 21.7 335.9 1.6 941 2.6
Arizona.................. 145.6 2,336.3 1.1 842 2.7
Arkansas................. 85.8 1,140.4 -1.3 703 2.6
California............... 1,379.3 14,664.6 0.3 1,019 4.0
Colorado................. 169.7 2,234.7 1.4 900 3.4
Connecticut.............. 110.7 1,630.2 0.8 1,116 3.8
Delaware................. 28.2 408.4 0.5 926 5.9
District of Columbia..... 35.5 711.3 1.4 1,541 2.4
Florida.................. 594.3 7,092.3 0.8 802 2.6
Georgia.................. 266.5 3,803.1 1.0 832 2.5
Hawaii................... 38.3 590.5 0.7 799 2.4
Idaho.................... 54.1 616.6 0.0 667 2.3
Illinois................. 384.2 5,633.0 1.0 939 3.2
Indiana.................. 159.7 2,769.2 1.3 749 2.2
Iowa..................... 93.5 1,476.9 0.7 726 2.5
Kansas................... 88.2 1,313.2 -0.1 754 2.9
Kentucky................. 109.7 1,751.8 0.9 760 2.3
Louisiana................ 126.8 1,844.3 -0.1 794 3.1
Maine.................... 49.0 593.8 0.3 712 1.9
Maryland................. 165.4 2,513.5 0.5 987 3.1
Massachusetts............ 227.8 3,230.4 0.9 1,120 5.6
Michigan................. 242.1 3,896.9 1.8 845 2.4
Minnesota................ 164.6 2,645.4 1.4 898 3.5
Mississippi.............. 68.8 1,079.4 -0.6 664 1.8
Missouri................. 174.3 2,617.7 0.3 774 1.6
Montana.................. 42.1 434.1 0.5 681 3.5
Nebraska................. 60.4 911.6 0.1 714 2.4
Nevada................... 71.4 1,123.0 0.5 816 2.5
New Hampshire............ 47.9 615.2 0.4 888 2.4
New Jersey............... 264.6 3,836.2 -0.3 1,056 2.6
New Mexico............... 54.8 788.7 -0.5 763 2.8
New York................. 597.1 8,575.3 1.0 1,092 1.0
North Carolina........... 253.1 3,865.9 1.5 783 2.5
North Dakota............. 27.1 382.4 5.1 769 8.2
Ohio..................... 287.5 5,009.1 0.9 795 2.6
Oklahoma................. 102.6 1,510.3 0.7 749 4.5
Oregon................... 131.1 1,637.5 0.7 819 4.2
Pennsylvania............. 347.1 5,606.5 1.0 875 3.1
Rhode Island............. 34.9 458.1 0.3 862 3.5
South Carolina........... 111.1 1,801.6 1.1 726 2.3
South Dakota............. 31.1 404.8 0.8 656 3.8
Tennessee................ 139.1 2,616.9 1.3 794 2.3
Texas.................... 578.9 10,462.4 2.1 900 4.0
Utah..................... 83.4 1,183.9 2.0 756 3.1
Vermont.................. 24.3 297.0 1.0 773 2.8
Virginia................. 233.2 3,619.7 0.9 949 2.2
Washington............... 229.5 2,875.8 0.6 928 3.5
West Virginia............ 48.7 702.9 0.3 765 5.4
Wisconsin................ 158.0 2,712.0 0.9 767 3.0
Wyoming.................. 25.1 284.7 1.2 819 3.7
Puerto Rico.............. 50.7 915.1 -1.4 496 0.6
Virgin Islands........... 3.5 44.1 0.6 747 5.5
(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
