For release 10:00 a.m. (EDT), Thursday, June 27, 2013 USDL-13-1244
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
County Employment and Wages
Fourth Quarter 2012
From December 2011 to December 2012, employment increased in 287 of
the 328 largest U.S. counties, the U.S. Bureau of Labor Statistics
reported today. Elkhart, Ind., posted the largest increase, with a
gain of 7.4 percent over the year, compared with national job growth
of 1.9 percent. Within Elkhart, the largest employment increase
occurred in manufacturing, which gained 5,479 jobs over the year
(11.6 percent). Sangamon, Ill., had the largest over-the-year
decrease in employment among the largest counties in the U.S. with a
loss of 2.5 percent. County employment and wage data are compiled
under the Quarterly Census of Employment and Wages (QCEW) program,
which produces detailed information on county employment and wages
within 7 months after the end of each quarter.
The U.S. average weekly wage increased over the year by 4.7 percent
to $1,000 in the fourth quarter of 2012. San Mateo, Calif., had the
largest over-the-year increase in average weekly wages with a gain of
107.3 percent. Within San Mateo, a total wage gain of $6.9 billion
(379.6 percent) in professional and business services had the largest
contribution to the increase in average weekly wages. Lake, Ohio,
experienced the largest decrease in average weekly wages with a loss
of 3.2 percent over the year.
Table A. Large counties ranked by December 2012 employment, December 2011-12 employment
increase, and December 2011-12 percent increase in employment
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Employment in large counties
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December 2012 employment | Increase in employment, | Percent increase in employment,
(thousands) | December 2011-12 | December 2011-12
| (thousands) |
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| |
United States 133,726.8| United States 2,440.6| United States 1.9
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| |
Los Angeles, Calif. 4,082.2| Harris, Texas 82.2| Elkhart, Ind. 7.4
Cook, Ill. 2,441.2| Los Angeles, Calif. 74.2| Lexington, S.C. 6.9
New York, N.Y. 2,437.9| New York, N.Y. 50.2| Rutherford, Tenn. 6.4
Harris, Texas 2,160.8| Dallas, Texas 49.6| Utah, Utah 6.0
Maricopa, Ariz. 1,721.1| Maricopa, Ariz. 46.0| Montgomery, Texas 5.7
Dallas, Texas 1,499.2| Orange, Calif. 37.9| Fort Bend, Texas 5.3
Orange, Calif. 1,436.6| King, Wash. 34.5| Douglas, Colo. 5.1
San Diego, Calif. 1,302.0| Santa Clara, Calif. 33.0| Collin, Texas 4.8
King, Wash. 1,185.3| San Diego, Calif. 29.2| Brazos, Texas 4.4
Miami-Dade, Fla. 1,020.6| Cook, Ill. 28.9| Travis, Texas 4.3
| | Salt Lake, Utah 4.3
| |
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Large County Employment
In December 2012, national employment, as measured by the QCEW
program, was 133.7 million, up by 1.9 percent or 2.4 million from
December 2011. The 328 U.S. counties with 75,000 or more jobs
accounted for 71.3 percent of total U.S. employment and 77.0 percent
of total wages. These 328 counties had a net job growth of 1.8
million over the year, accounting for 73.3 percent of the overall
U.S. employment increase.
Elkhart, Ind., had the largest percentage increase in employment (7.4
percent) among the largest U.S. counties. The five counties with the
largest increases in employment level were Harris, Texas; Los
Angeles, Calif.; New York, N.Y.; Dallas, Texas; and Maricopa, Ariz.
These counties had a combined over-the-year employment gain of
302,200, which was 12.4 percent of the overall job increase for the
U.S. (See table A.)
Employment declined in 38 of the large counties from December 2011 to
December 2012. Sangamon, Ill., had the largest over-the-year
percentage decrease in employment (-2.5 percent). Within Sangamon,
public administration within state government had the largest
decrease in employment with a loss of 1,067 jobs (-2.9 percent).
Caddo, La., had the second largest percentage decrease in employment,
followed by Jefferson, Texas. Two counties, Vanderburgh, Ind., and
Benton, Wash., tied for the fourth largest percentage decrease. (See
table 1.)
Table B. Large counties ranked by fourth quarter 2012 average weekly wages, fourth quarter 2011-12
increase in average weekly wages, and fourth quarter 2011-12 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
fourth quarter 2012 | wage, fourth quarter 2011-12 | weekly wage, fourth
| | quarter 2011-12
--------------------------------------------------------------------------------------------------------
| |
United States $1,000| United States $45| United States 4.7
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| |
San Mateo, Calif. $3,240| San Mateo, Calif. $1,677| San Mateo, Calif. 107.3
New York, N.Y. 2,107| Douglas, Colo. 516| Douglas, Colo. 48.0
Santa Clara, Calif. 1,906| New York, N.Y. 217| Virginia Beach City, Va. 13.3
Suffolk, Mass. 1,724| Suffolk, Mass. 127| Rockingham, N.H. 12.0
Fairfield, Conn. 1,704| San Francisco, Calif. 119| New York, N.Y. 11.5
Washington, D.C. 1,703| Rockingham, N.H. 111| Washington, Pa. 11.5
San Francisco, Calif. 1,694| Fairfield, Conn. 109| McHenry, Ill. 11.2
Arlington, Va. 1,625| Washington, Pa. 105| Utah, Utah 9.4
Douglas, Colo. 1,591| Virginia Beach City, Va. 101| Elkhart, Ind. 8.9
Fairfax, Va. 1,588| Santa Clara, Calif. 91| Yolo, Calif. 8.6
| McHenry, Ill. 91|
| Harris, Texas 91|
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased by 4.7 percent during
the year ending in the fourth quarter of 2012. Among the 328 largest
counties, 316 had over-the-year increases in average weekly wages.
San Mateo, Calif., had the largest wage increase among the largest
U.S. counties (107.3 percent).
Of the 328 largest counties, 10 experienced over-the-year decreases
in average weekly wages. Lake, Ohio, had the largest average weekly
wage decrease with a loss of 3.2 percent. Within Lake, total wages in
manufacturing declined by $45.3 million (-12.3 percent) over the
year. Passaic, N.J., had the second largest decrease in average
weekly wages, followed by Genesee, Mich.; Atlantic, N.J.; and Benton,
Wash. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases
in employment in December 2012. Harris, Texas, had the largest gain
(4.0 percent). Within Harris, professional and business services had
the largest over-the-year employment level increase among all private
industry groups with a gain of 20,112 jobs (5.9 percent). Cook, Ill.,
had the smallest percentage increase in employment (1.2 percent)
among the 10 largest counties. (See table 2.)
All of the 10 largest U.S. counties had over-the-year increases in
average weekly wages. New York, N.Y., experienced the largest gain in
average weekly wages (11.5 percent). Within New York, financial
activities had the largest impact on the county’s average weekly wage
growth. Within this industry, employment declined by 2,288 (-0.6
percent) while total wages increased by $4.8 billion (25.6 percent).
Maricopa, Ariz., had the smallest average weekly wage increase (3.4
percent) among the 10 largest counties.
For More Information
The tables included in this release contain data for the nation and
for the 328 U.S. counties with annual average employment levels of
75,000 or more in 2011. December 2012 employment and 2012 fourth
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 9.2 million employer reports cover 133.7 million full-
and part-time workers. For additional information about the quarterly
employment and wages data, please read the Technical Note. Data for
the fourth quarter of 2012 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 first quarter 2013 is
scheduled to be released on Thursday, September 26, 2013.
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| |
| Hurricane Sandy |
| |
| Hurricane Sandy made landfall in the United States on October 29, |
| 2012, during the QCEW fourth quarter reference period. This event |
| did not warrant changes to QCEW methodology. |
| |
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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 2012 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 329 counties presented
in this release were derived using 2011 preliminary annual averages of employment.
For 2012 data, seven counties have been added to the publication tables: Okaloosa,
Fla.; Tippecanoe, Ind.; Johnson, Iowa; St. Tammany, La.; Saratoga, N.Y.; Delaware,
Ohio; and Gregg, Texas. These counties will be included in all 2012 quarterly re-
leases. One county, Jackson, Ore., which was published in the 2011 releases, will
be excluded from this and future 2012 releases because its 2011 annual average
employment levels were less than 75,000. The counties in table 2 are selected and
sorted each year based on the annual average employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released
by the individual states. These potential differences result from the states' con-
tinuing receipt of UI data over time and ongoing review and editing. The individual
states determine their data release timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for
any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED),
and Current Employment Statistics (CES)--makes use of the quarterly UI employment
reports in producing data; however, each measure has a somewhat different universe
coverage, estimation procedure, and publication product.
Differences in coverage and estimation methods can result in somewhat different
measures of employment change over time. It is important to understand program dif-
ferences and the intended uses of the program products. (See table.) Additional in-
formation on each program can be obtained from the program Web sites shown in the
table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
---------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 557,000 establish-
| submitted by 9.2 | ministrative records| ments
| million establish- | submitted by 6.8 |
| ments in first | million private-sec-|
| quarter of 2012 | 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 to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation estimates to
| | losses | population counts (ben-
| | | chmarking)
-----------|---------------------|----------------------|------------------------
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 9.1 million employer reports of employment and wages
submitted by states to the BLS in 2011. 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 2011, UI and UCFE programs covered workers in 129.4 million jobs. The estimated
124.8 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.7 percent of civilian wage and salary employment. Covered workers
received $6.217 trillion in pay, representing 93.3 percent of the wage and salary
component of personal income and 41.2 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.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations.
This variability may be due to calendar effects resulting from some quarters having
more pay dates than others. The effect is most visible in counties with a dominant
employer. In particular, this effect has been observed in counties where government
employers represent a large fraction of overall employment. Similar calendar effects
can result from private sector pay practices. However, these effects are typically
less pronounced for two reasons: employment is less concentrated in a single private
employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal
government due to the uniform nature of federal payroll processing. Most federal
employees are paid on a biweekly pay schedule. As a result, in some quarters federal
wages include six pay dates, while in other quarters there are seven pay dates. Over-
the-year comparisons of average weekly wages may also reflect this calendar effect.
Growth in average weekly wages may be attributed, in part, to a comparison of
quarterly wages for the current year, which include seven pay dates, with year-ago
wages that reflect only six pay dates. An opposite effect will occur when wages in
the current quarter reflecting six pay dates are compared with year-ago wages for a
quarter including seven pay dates.
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 3-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 2011 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 2011 edition of this publication, which was published in October 2012,
contains selected data produced by Business Employment Dynamics (BED) on job gains
and losses, as well as selected data from the first quarter 2012 version of this
news release. Tables and additional content from Employment and Wages Annual Aver-
ages 2011 are now available online at http://www.bls.gov/cew/cewbultn11.htm. The
2012 edition of Employment and Wages Annual Averages Online will be available later
in 2013.
News releases on quarterly measures of gross job flows also are available upon re-
quest from the Division of Administrative Statistics and Labor Turnover (Business
Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail:
BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals
upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-
877-8339.
Table 1. Covered(1) establishments, employment, and wages in the 329 largest counties,
fourth quarter 2012(2)
Employment Average weekly wage(4)
Establishments,
County(3) fourth quarter Percent Ranking Percent Ranking
2012 December change, by Fourth change, by
(thousands) 2012 December percent quarter fourth percent
(thousands) 2011-12(5) change 2012 quarter change
2011-12(5)
United States(6)......... 9,205.6 133,726.8 1.9 - $1,000 4.7 -
Jefferson, AL............ 17.8 339.3 0.9 223 1,011 5.0 71
Madison, AL.............. 9.0 181.4 1.4 181 1,077 1.5 265
Mobile, AL............... 9.7 165.3 0.1 280 881 0.6 301
Montgomery, AL........... 6.4 128.4 1.0 213 883 0.3 306
Tuscaloosa, AL........... 4.3 86.4 2.3 94 848 2.3 219
Anchorage Borough, AK.... 8.3 153.1 0.6 244 1,046 3.9 119
Maricopa, AZ............. 95.2 1,721.1 2.7 69 964 3.4 150
Pima, AZ................. 19.0 353.5 1.5 172 839 2.1 234
Benton, AR............... 5.6 99.2 1.9 134 900 3.9 119
Pulaski, AR.............. 14.5 246.3 1.0 213 927 6.9 24
Washington, AR........... 5.6 94.6 3.7 23 837 0.8 294
Alameda, CA.............. 54.7 670.7 4.0 17 1,265 3.9 119
Contra Costa, CA......... 29.0 331.8 2.9 59 1,168 2.9 183
Fresno, CA............... 29.2 335.2 1.8 143 777 2.9 183
Kern, CA................. 17.0 295.3 3.0 52 842 2.1 234
Los Angeles, CA.......... 421.5 4,082.2 1.9 134 1,185 6.6 29
Marin, CA................ 11.7 109.0 3.8 20 1,225 3.4 150
Monterey, CA............. 12.4 152.4 3.2 41 809 1.4 271
Orange, CA............... 104.2 1,436.6 2.7 69 1,131 4.4 91
Placer, CA............... 10.9 132.5 2.8 65 979 4.5 85
Riverside, CA............ 49.2 585.6 3.4 33 765 1.5 265
Sacramento, CA........... 50.1 595.1 2.7 69 1,056 1.3 276
San Bernardino, CA....... 48.6 629.4 2.2 106 830 2.6 202
San Diego, CA............ 100.5 1,302.0 2.3 94 1,099 5.5 45
San Francisco, CA........ 54.7 603.3 4.2 12 1,694 7.6 15
San Joaquin, CA.......... 16.3 205.2 1.5 172 810 1.6 261
San Luis Obispo, CA...... 9.5 103.9 4.1 15 809 1.3 276
San Mateo, CA............ 24.8 349.2 3.6 25 3,240 107.3 1
Santa Barbara, CA........ 14.3 180.5 3.6 25 961 7.4 17
Santa Clara, CA.......... 63.0 928.0 3.7 23 1,906 5.0 71
Santa Cruz, CA........... 8.9 90.4 3.5 29 849 0.1 313
Solano, CA............... 9.7 123.9 3.3 39 998 7.4 17
Sonoma, CA............... 18.3 179.8 3.2 41 918 2.6 202
Stanislaus, CA........... 13.8 162.3 2.5 80 793 2.3 219
Tulare, CA............... 8.9 139.8 0.4 265 697 3.6 139
Ventura, CA.............. 23.9 311.0 3.1 48 984 3.3 157
Yolo, CA................. 6.0 89.4 1.2 194 997 8.6 10
Adams, CO................ 9.0 162.3 3.3 39 886 3.1 166
Arapahoe, CO............. 19.2 292.3 3.5 29 1,159 4.5 85
Boulder, CO.............. 13.3 163.5 2.5 80 1,134 2.0 246
Denver, CO............... 26.5 443.1 4.2 12 1,222 4.6 81
Douglas, CO.............. 9.9 98.5 5.1 7 1,591 48.0 2
El Paso, CO.............. 17.0 241.2 1.9 134 884 0.8 294
Jefferson, CO............ 18.0 215.8 2.6 76 1,010 5.1 64
Larimer, CO.............. 10.3 134.0 2.7 69 887 4.1 104
Weld, CO................. 5.9 86.9 4.2 12 831 2.8 189
Fairfield, CT............ 33.1 416.4 1.0 213 1,704 6.8 26
Hartford, CT............. 25.8 499.9 1.2 194 1,210 5.1 64
New Haven, CT............ 22.6 361.7 1.2 194 1,034 2.9 183
New London, CT........... 7.0 123.3 -0.6 308 971 1.5 265
New Castle, DE........... 17.1 272.7 1.0 213 1,178 7.0 21
Washington, DC........... 36.8 721.5 1.7 154 1,703 2.2 227
Alachua, FL.............. 6.6 117.9 1.0 213 843 2.1 234
Brevard, FL.............. 14.5 188.7 -1.1 316 874 1.0 290
Broward, FL.............. 64.5 720.5 2.3 94 920 3.4 150
Collier, FL.............. 12.1 125.6 2.0 123 839 4.1 104
Duval, FL................ 27.5 449.6 2.0 123 953 5.0 71
Escambia, FL............. 8.0 120.6 0.6 244 787 2.7 193
Hillsborough, FL......... 38.7 604.4 2.5 80 953 3.5 146
Lake, FL................. 7.4 83.9 3.5 29 653 1.1 287
Lee, FL.................. 19.1 210.6 2.7 69 774 2.2 227
Leon, FL................. 8.3 140.2 0.8 236 810 0.2 308
Manatee, FL.............. 9.5 110.2 2.8 65 733 -0.1 319
Marion, FL............... 8.0 92.5 2.6 76 688 2.1 234
Miami-Dade, FL........... 91.3 1,020.6 2.3 94 976 4.1 104
Okaloosa, FL............. 6.1 75.4 -0.1 291 779 2.6 202
Orange, FL............... 36.9 698.7 3.2 41 860 3.9 119
Palm Beach, FL........... 50.6 522.9 2.1 114 1,003 7.6 15
Pasco, FL................ 10.1 102.2 2.1 114 681 2.6 202
Pinellas, FL............. 31.1 389.9 1.6 162 901 1.7 256
Polk, FL................. 12.4 194.6 1.4 181 740 3.1 166
Sarasota, FL............. 14.7 142.5 3.2 41 824 3.1 166
Seminole, FL............. 14.0 162.0 1.8 143 818 5.1 64
Volusia, FL.............. 13.4 150.8 0.8 236 709 4.9 76
Bibb, GA................. 4.6 81.4 1.2 194 760 2.3 219
Chatham, GA.............. 7.8 134.2 2.0 123 828 2.3 219
Clayton, GA.............. 4.3 112.0 -0.1 291 914 1.4 271
Cobb, GA................. 21.8 306.0 1.1 207 1,033 4.3 94
De Kalb, GA.............. 18.2 278.8 -0.1 291 1,026 4.4 91
Fulton, GA............... 42.4 738.0 3.1 48 1,317 7.2 20
Gwinnett, GA............. 24.5 312.0 1.6 162 968 4.8 78
Muscogee, GA............. 4.7 94.6 0.1 280 783 3.2 161
Richmond, GA............. 4.7 99.3 0.2 274 826 3.0 173
Honolulu, HI............. 24.8 455.0 1.8 143 908 3.1 166
Ada, ID.................. 13.6 202.4 2.7 69 843 1.0 290
Champaign, IL............ 4.3 88.1 0.6 244 806 2.5 209
Cook, IL................. 150.3 2,441.2 1.2 194 1,184 5.3 60
Du Page, IL.............. 37.4 578.3 2.1 114 1,168 4.5 85
Kane, IL................. 13.4 195.7 1.2 194 874 2.0 246
Lake, IL................. 22.3 326.3 2.1 114 1,272 6.7 28
McHenry, IL.............. 8.7 93.6 0.8 236 907 11.2 7
McLean, IL............... 3.8 87.3 1.5 172 948 1.2 281
Madison, IL.............. 6.0 94.7 -0.4 305 804 1.5 265
Peoria, IL............... 4.7 103.7 0.6 244 936 1.1 287
St. Clair, IL............ 5.6 94.0 -1.2 318 781 0.4 303
Sangamon, IL............. 5.3 126.8 -2.5 328 986 3.0 173
Will, IL................. 15.4 204.8 0.7 242 847 2.8 189
Winnebago, IL............ 6.8 124.6 -0.9 315 824 1.2 281
Allen, IN................ 9.0 177.8 1.8 143 774 -0.3 322
Elkhart, IN.............. 4.8 112.6 7.4 1 782 8.9 9
Hamilton, IN............. 8.6 115.3 1.7 154 921 5.3 60
Lake, IN................. 10.4 191.3 1.3 186 902 3.9 119
Marion, IN............... 24.1 570.6 2.3 94 992 4.2 100
St. Joseph, IN........... 6.0 117.2 -0.6 308 786 4.4 91
Tippecanoe, IN........... 3.3 79.7 1.9 134 809 0.1 313
Vanderburgh, IN.......... 4.8 105.3 -1.5 324 792 0.9 293
Johnson, IA.............. 3.7 79.1 2.3 94 854 3.5 146
Linn, IA................. 6.3 127.3 0.1 280 948 0.7 298
Polk, IA................. 15.3 274.1 1.6 162 981 4.1 104
Scott, IA................ 5.3 89.0 1.2 194 845 5.5 45
Johnson, KS.............. 21.3 316.2 2.7 69 1,046 6.1 38
Sedgwick, KS............. 12.4 243.5 1.4 181 915 4.1 104
Shawnee, KS.............. 4.8 95.4 0.4 265 856 8.5 11
Wyandotte, KS............ 3.2 84.2 1.6 162 874 0.3 306
Fayette, KY.............. 9.8 183.7 2.2 106 852 2.0 246
Jefferson, KY............ 23.2 436.8 3.8 20 936 2.3 219
Caddo, LA................ 7.5 119.2 -2.4 327 818 0.2 308
Calcasieu, LA............ 4.9 84.6 2.9 59 846 3.4 150
East Baton Rouge, LA..... 14.7 262.3 2.1 114 948 6.9 24
Jefferson, LA............ 13.7 195.5 -0.3 301 916 2.6 202
Lafayette, LA............ 9.1 139.1 1.9 134 989 4.0 115
Orleans, LA.............. 11.2 180.7 2.2 106 992 1.2 281
St. Tammany, LA.......... 7.5 80.9 2.0 123 843 4.5 85
Cumberland, ME........... 12.7 172.4 0.9 223 890 2.9 183
Anne Arundel, MD......... 14.8 244.7 2.6 76 1,050 1.9 251
Baltimore, MD............ 21.4 371.0 1.2 194 1,014 2.7 193
Frederick, MD............ 6.3 94.5 1.9 134 963 2.3 219
Harford, MD.............. 5.7 89.1 2.2 106 974 4.8 78
Howard, MD............... 9.4 161.8 2.3 94 1,212 3.5 146
Montgomery, MD........... 33.8 456.9 0.6 244 1,345 1.9 251
Prince Georges, MD....... 15.9 304.4 -0.2 295 1,019 0.2 308
Baltimore City, MD....... 14.2 333.0 0.2 274 1,180 6.1 38
Barnstable, MA........... 8.9 85.3 2.5 80 842 2.1 234
Bristol, MA.............. 16.0 213.4 0.4 265 901 5.5 45
Essex, MA................ 21.7 309.3 1.5 172 1,056 2.2 227
Hampden, MA.............. 15.5 197.4 0.2 274 898 3.9 119
Middlesex, MA............ 49.4 841.1 1.9 134 1,434 4.5 85
Norfolk, MA.............. 23.4 328.3 1.5 172 1,212 4.6 81
Plymouth, MA............. 14.0 179.0 2.5 80 924 2.3 219
Suffolk, MA.............. 23.6 602.9 1.6 162 1,724 8.0 12
Worcester, MA............ 21.4 319.2 -0.3 301 964 -0.1 319
Genesee, MI.............. 7.3 131.9 0.9 223 816 -1.7 326
Ingham, MI............... 6.4 155.8 0.1 280 924 1.7 256
Kalamazoo, MI............ 5.4 110.9 0.9 223 892 4.0 115
Kent, MI................. 14.2 341.8 3.0 52 879 2.9 183
Macomb, MI............... 17.4 294.7 1.7 154 1,012 1.2 281
Oakland, MI.............. 38.8 675.6 3.4 33 1,141 3.4 150
Ottawa, MI............... 5.6 110.1 3.8 20 831 0.1 313
Saginaw, MI.............. 4.2 84.6 0.5 257 789 0.8 294
Washtenaw, MI............ 8.2 198.6 2.3 94 1,030 3.6 139
Wayne, MI................ 31.9 696.3 1.1 207 1,083 0.6 301
Anoka, MN................ 7.2 112.7 2.1 114 900 3.0 173
Dakota, MN............... 10.0 174.8 1.8 143 943 4.9 76
Hennepin, MN............. 42.4 857.2 1.9 134 1,236 6.5 31
Olmsted, MN.............. 3.4 92.4 3.2 41 1,047 1.7 256
Ramsey, MN............... 14.0 321.0 0.6 244 1,071 4.1 104
St. Louis, MN............ 5.6 94.9 1.7 154 776 0.1 313
Stearns, MN.............. 4.4 81.7 1.2 194 799 5.5 45
Harrison, MS............. 4.4 82.5 -0.2 295 693 1.0 290
Hinds, MS................ 6.0 120.9 -1.3 319 856 3.8 128
Boone, MO................ 4.6 87.7 2.4 91 762 4.0 115
Clay, MO................. 5.2 86.3 -1.4 320 879 1.9 251
Greene, MO............... 8.1 156.0 2.9 59 737 3.8 128
Jackson, MO.............. 19.0 351.3 1.2 194 1,026 6.2 36
St. Charles, MO.......... 8.4 129.8 3.4 33 763 2.6 202
St. Louis, MO............ 32.6 574.9 0.6 244 1,088 7.0 21
St. Louis City, MO....... 9.5 218.1 -0.5 307 1,059 2.7 193
Yellowstone, MT.......... 6.1 78.7 1.7 154 846 5.4 55
Douglas, NE.............. 17.8 321.6 2.0 123 905 5.5 45
Lancaster, NE............ 9.5 160.1 2.5 80 792 3.8 128
Clark, NV................ 49.3 827.6 2.3 94 867 3.1 166
Washoe, NV............... 13.7 186.7 0.9 223 886 3.0 173
Hillsborough, NH......... 12.1 192.2 0.5 257 1,137 3.7 136
Rockingham, NH........... 10.6 137.1 1.0 213 1,034 12.0 4
Atlantic, NJ............. 6.7 131.7 -0.2 295 816 -1.4 325
Bergen, NJ............... 33.1 435.0 0.3 272 1,272 6.2 36
Burlington, NJ........... 11.0 198.1 2.8 65 1,035 1.8 255
Camden, NJ............... 12.1 195.2 -0.2 295 1,002 1.4 271
Essex, NJ................ 20.6 343.5 -0.3 301 1,221 3.6 139
Gloucester, NJ........... 6.1 98.6 0.5 257 873 2.3 219
Hudson, NJ............... 14.1 238.6 2.0 123 1,285 1.3 276
Mercer, NJ............... 11.0 233.0 1.5 172 1,312 3.6 139
Middlesex, NJ............ 21.8 393.4 1.9 134 1,162 1.6 261
Monmouth, NJ............. 20.0 243.6 0.2 274 1,031 2.6 202
Morris, NJ............... 17.3 276.1 0.9 223 1,476 5.4 55
Ocean, NJ................ 12.3 147.3 1.2 194 835 4.6 81
Passaic, NJ.............. 12.2 175.1 0.3 272 998 -2.1 327
Somerset, NJ............. 10.1 174.1 0.9 223 1,429 2.2 227
Union, NJ................ 14.4 222.2 0.5 257 1,228 0.2 308
Bernalillo, NM........... 17.9 313.9 1.3 186 836 0.7 298
Albany, NY............... 10.0 222.6 0.9 223 976 2.0 246
Bronx, NY................ 17.2 239.7 1.8 143 932 2.5 209
Broome, NY............... 4.5 90.2 -0.8 313 764 2.1 234
Dutchess, NY............. 8.2 112.3 -0.8 313 975 2.2 227
Erie, NY................. 24.0 461.8 0.5 257 853 3.0 173
Kings, NY................ 54.0 529.5 2.0 123 821 2.8 189
Monroe, NY............... 18.3 380.0 0.1 280 890 0.2 308
Nassau, NY............... 53.0 609.0 1.8 143 1,134 2.0 246
New York, NY............. 123.7 2,437.9 2.1 114 2,107 11.5 5
Oneida, NY............... 5.3 105.4 -1.4 320 777 3.7 136
Onondaga, NY............. 13.0 244.7 0.5 257 929 5.9 41
Orange, NY............... 9.9 134.2 0.4 265 820 1.9 251
Queens, NY............... 47.9 533.4 2.9 59 938 2.2 227
Richmond, NY............. 9.1 95.3 1.6 162 843 3.8 128
Rockland, NY............. 9.9 116.6 -0.2 295 1,054 6.3 35
Saratoga, NY............. 5.6 78.8 2.0 123 876 3.8 128
Suffolk, NY.............. 50.9 630.9 1.1 207 1,056 0.0 317
Westchester, NY.......... 36.1 413.1 0.6 244 1,346 5.4 55
Buncombe, NC............. 8.1 116.2 2.5 80 752 2.7 193
Catawba, NC.............. 4.4 79.3 0.1 280 733 0.4 303
Cumberland, NC........... 6.3 118.5 -1.4 320 770 -0.1 319
Durham, NC............... 7.5 188.2 2.6 76 1,225 1.6 261
Forsyth, NC.............. 9.0 177.3 1.7 154 883 3.3 157
Guilford, NC............. 14.2 266.9 1.1 207 863 5.5 45
Mecklenburg, NC.......... 33.5 584.2 3.0 52 1,103 5.1 64
New Hanover, NC.......... 7.5 97.1 1.6 162 797 2.4 214
Wake, NC................. 30.1 464.2 3.4 33 967 2.4 214
Cass, ND................. 6.3 108.7 3.6 25 883 6.6 29
Butler, OH............... 7.4 140.6 -0.2 295 844 2.8 189
Cuyahoga, OH............. 35.7 711.1 2.0 123 1,020 5.4 55
Delaware, OH............. 4.4 81.4 4.1 15 950 3.9 119
Franklin, OH............. 29.8 687.2 2.5 80 970 1.6 261
Hamilton, OH............. 23.2 491.7 0.4 265 1,092 6.0 40
Lake, OH................. 6.4 94.7 0.0 288 813 -3.2 328
Lorain, OH............... 6.0 94.5 -0.7 310 816 2.5 209
Lucas, OH................ 10.1 203.3 0.8 236 866 2.1 234
Mahoning, OH............. 5.9 98.8 1.3 186 718 3.8 128
Montgomery, OH........... 12.1 245.6 0.6 244 864 2.7 193
Stark, OH................ 8.8 155.6 0.8 236 752 3.0 173
Summit, OH............... 14.2 258.8 1.1 207 893 4.3 94
Oklahoma, OK............. 25.2 434.9 1.6 162 947 5.3 60
Tulsa, OK................ 20.7 341.5 1.8 143 962 0.0 317
Clackamas, OR............ 12.9 142.2 2.2 106 893 4.2 100
Lane, OR................. 10.9 138.1 1.0 213 758 2.7 193
Marion, OR............... 9.5 129.9 0.5 257 760 3.4 150
Multnomah, OR............ 30.4 447.5 2.0 123 988 2.1 234
Washington, OR........... 16.8 252.7 1.2 194 1,101 1.4 271
Allegheny, PA............ 35.8 689.7 0.7 242 1,058 5.0 71
Berks, PA................ 9.0 166.3 1.3 186 869 2.1 234
Bucks, PA................ 19.8 250.5 0.4 265 957 2.9 183
Butler, PA............... 4.9 82.9 -0.3 301 895 3.6 139
Chester, PA.............. 15.1 238.7 0.1 280 1,283 0.7 298
Cumberland, PA........... 6.2 125.7 0.6 244 866 3.0 173
Dauphin, PA.............. 7.5 174.5 0.5 257 955 4.3 94
Delaware, PA............. 14.0 215.7 1.3 186 1,076 6.4 33
Erie, PA................. 7.6 124.6 -0.7 310 775 1.7 256
Lackawanna, PA........... 5.9 98.1 -0.4 305 726 1.4 271
Lancaster, PA............ 12.8 221.7 0.9 223 816 3.7 136
Lehigh, PA............... 8.7 177.8 -0.1 291 964 3.0 173
Luzerne, PA.............. 7.7 139.5 -1.4 320 746 3.2 161
Montgomery, PA........... 27.5 473.9 1.0 213 1,250 5.9 41
Northampton, PA.......... 6.6 104.9 2.0 123 842 1.3 276
Philadelphia, PA......... 36.5 638.0 1.1 207 1,180 4.1 104
Washington, PA........... 5.6 86.3 0.8 236 1,016 11.5 5
Westmoreland, PA......... 9.5 133.8 0.9 223 795 -0.7 323
York, PA................. 9.1 172.6 0.4 265 837 3.6 139
Providence, RI........... 17.5 272.7 1.4 181 992 3.0 173
Charleston, SC........... 12.1 219.0 2.5 80 837 1.5 265
Greenville, SC........... 12.3 238.5 2.3 94 838 2.7 193
Horry, SC................ 7.7 104.7 2.5 80 576 1.2 281
Lexington, SC............ 5.7 105.1 6.9 2 732 2.4 214
Richland, SC............. 9.0 206.5 1.4 181 843 2.4 214
Spartanburg, SC.......... 5.8 117.4 2.4 91 832 2.1 234
Minnehaha, SD............ 6.7 118.1 2.5 80 850 4.3 94
Davidson, TN............. 18.5 441.6 3.0 52 1,090 6.5 31
Hamilton, TN............. 8.5 188.0 2.2 106 897 3.8 128
Knox, TN................. 10.9 222.5 0.6 244 875 3.9 119
Rutherford, TN........... 4.4 107.4 6.4 3 877 4.2 100
Shelby, TN............... 19.1 481.9 1.7 154 1,023 5.5 45
Williamson, TN........... 6.4 100.6 4.0 17 1,121 6.4 33
Bell, TX................. 4.9 109.4 1.7 154 783 1.7 256
Bexar, TX................ 35.7 765.3 2.9 59 877 1.5 265
Brazoria, TX............. 5.1 94.2 3.6 25 934 4.1 104
Brazos, TX............... 4.0 90.3 4.4 9 735 3.5 146
Cameron, TX.............. 6.4 131.7 2.1 114 609 2.7 193
Collin, TX............... 19.7 318.7 4.8 8 1,158 5.5 45
Dallas, TX............... 70.1 1,499.2 3.4 33 1,209 5.5 45
Denton, TX............... 11.7 189.8 3.1 48 877 5.0 71
El Paso, TX.............. 14.2 282.0 2.2 106 697 3.4 150
Fort Bend, TX............ 10.0 149.5 5.3 6 1,007 5.1 64
Galveston, TX............ 5.5 97.3 1.3 186 903 4.0 115
Gregg, TX................ 4.2 78.1 0.6 244 913 3.8 128
Harris, TX............... 104.3 2,160.8 4.0 17 1,331 7.3 19
Hidalgo, TX.............. 11.5 235.2 2.3 94 612 2.2 227
Jefferson, TX............ 5.8 121.3 -2.3 326 1,006 4.1 104
Lubbock, TX.............. 7.1 128.2 1.8 143 772 8.0 12
McLennan, TX............. 4.9 103.2 2.1 114 813 5.4 55
Montgomery, TX........... 9.3 146.4 5.7 5 985 7.7 14
Nueces, TX............... 7.9 157.6 3.2 41 885 5.1 64
Smith, TX................ 5.7 94.6 0.1 280 867 6.8 26
Tarrant, TX.............. 39.0 800.8 3.0 52 974 4.5 85
Travis, TX............... 32.7 619.4 4.3 10 1,114 3.1 166
Webb, TX................. 4.9 92.6 1.3 186 683 5.1 64
Williamson, TX........... 8.1 136.2 3.5 29 934 2.5 209
Davis, UT................ 7.5 108.7 2.4 91 778 0.8 294
Salt Lake, UT............ 38.8 606.5 4.3 10 947 5.5 45
Utah, UT................. 13.3 183.9 6.0 4 834 9.4 8
Weber, UT................ 5.5 91.8 2.2 106 721 2.4 214
Chittenden, VT........... 6.1 99.0 0.2 274 981 4.1 104
Arlington, VA............ 8.7 165.9 -1.1 316 1,625 2.1 234
Chesterfield, VA......... 8.0 121.2 3.1 48 881 3.2 161
Fairfax, VA.............. 35.3 597.8 0.9 223 1,588 4.3 94
Henrico, VA.............. 10.3 181.9 2.9 59 949 1.3 276
Loudoun, VA.............. 10.2 144.2 3.2 41 1,171 2.7 193
Prince William, VA....... 8.1 115.9 3.4 33 863 2.1 234
Alexandria City, VA...... 6.3 97.2 1.6 162 1,460 2.5 209
Chesapeake City, VA...... 5.8 96.8 0.0 288 775 3.3 157
Newport News City, VA.... 3.7 98.3 1.8 143 912 3.1 166
Norfolk City, VA......... 5.7 138.7 0.0 288 972 4.2 100
Richmond City, VA........ 7.2 149.1 0.6 244 1,066 4.1 104
Virginia Beach City, VA.. 11.5 165.4 1.8 143 862 13.3 3
Benton, WA............... 5.9 76.4 -1.5 324 969 -1.0 324
Clark, WA................ 14.0 132.3 3.0 52 894 5.8 44
King, WA................. 84.1 1,185.3 3.0 52 1,276 4.7 80
Kitsap, WA............... 6.8 80.7 0.2 274 860 3.2 161
Pierce, WA............... 22.1 266.8 1.2 194 869 3.2 161
Snohomish, WA............ 19.6 261.7 2.8 65 1,005 0.4 303
Spokane, WA.............. 16.2 200.1 1.0 213 809 3.3 157
Thurston, WA............. 7.7 97.8 1.5 172 839 1.1 287
Whatcom, WA.............. 7.0 81.0 2.3 94 801 3.6 139
Yakima, WA............... 9.0 95.1 1.5 172 679 4.6 81
Kanawha, WV.............. 6.0 105.5 -0.7 310 843 1.2 281
Brown, WI................ 6.6 148.3 1.0 213 892 5.2 63
Dane, WI................. 14.3 310.5 1.5 172 957 5.9 41
Milwaukee, WI............ 23.8 476.8 0.9 223 969 3.0 173
Outagamie, WI............ 5.1 104.1 1.6 162 830 4.3 94
Waukesha, WI............. 12.7 229.5 0.9 223 1,004 7.0 21
Winnebago, WI............ 3.6 90.2 1.3 186 924 3.9 119
San Juan, PR............. 11.0 275.6 1.5 (7) 661 0.8 (7)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 328 U.S. counties comprise 71.3 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) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties,
fourth quarter 2012(2)
Employment Average weekly
wage(3)
Establishments,
fourth quarter
County by NAICS supersector 2012 Percent Percent
(thousands) December change, Fourth change,
2012 December quarter fourth
(thousands) 2011-12(4) 2012 quarter
2011-12(4)
United States(5)............................. 9,205.6 133,726.8 1.9 $1,000 4.7
Private industry........................... 8,911.3 112,271.7 2.3 1,008 5.3
Natural resources and mining............. 131.9 1,888.3 2.0 1,148 6.1
Construction............................. 750.2 5,627.0 2.8 1,102 5.0
Manufacturing............................ 335.7 11,950.0 1.4 1,207 3.2
Trade, transportation, and utilities..... 1,894.7 26,179.3 1.5 827 3.9
Information.............................. 143.9 2,696.7 0.4 1,620 8.0
Financial activities..................... 815.1 7,595.9 1.7 1,629 11.4
Professional and business services....... 1,617.5 18,205.1 3.3 1,370 8.2
Education and health services............ 942.0 19,708.0 2.0 928 2.7
Leisure and hospitality.................. 776.6 13,631.9 3.7 416 3.0
Other services........................... 1,280.2 4,575.7 3.4 604 1.0
Government................................. 294.2 21,455.1 -0.2 960 1.6
Los Angeles, CA.............................. 421.5 4,082.2 1.9 1,185 6.6
Private industry........................... 415.8 3,546.4 2.5 1,179 6.9
Natural resources and mining............. 0.5 10.0 1.4 1,731 19.5
Construction............................. 12.2 110.9 3.6 1,160 4.2
Manufacturing............................ 12.5 364.8 0.2 1,182 3.6
Trade, transportation, and utilities..... 51.3 792.1 2.0 907 4.3
Information.............................. 8.5 203.9 5.3 2,224 6.3
Financial activities..................... 22.2 213.7 1.4 1,841 19.4
Professional and business services....... 43.1 584.3 3.7 1,483 5.1
Education and health services............ 30.0 541.1 2.4 1,096 4.3
Leisure and hospitality.................. 27.9 421.3 4.7 985 6.5
Other services........................... 182.1 284.5 -2.4 418 9.4
Government................................. 5.7 535.8 -2.5 1,221 4.3
Cook, IL..................................... 150.3 2,441.2 1.2 1,184 5.3
Private industry........................... 148.9 2,144.5 1.4 1,189 5.6
Natural resources and mining............. 0.1 0.7 -6.1 1,088 -1.0
Construction............................. 12.4 61.7 0.1 1,482 5.8
Manufacturing............................ 6.6 194.1 0.4 1,255 4.6
Trade, transportation, and utilities..... 29.2 462.5 1.2 899 4.2
Information.............................. 2.7 54.1 0.1 1,627 3.4
Financial activities..................... 15.6 184.1 -0.2 2,350 16.6
Professional and business services....... 31.8 432.2 2.4 1,565 5.0
Education and health services............ 15.8 414.1 1.1 968 1.0
Leisure and hospitality.................. 13.3 241.5 3.6 473 2.8
Other services........................... 16.7 95.8 -0.3 843 4.1
Government................................. 1.4 296.7 -0.4 1,149 3.6
New York, NY................................. 123.7 2,437.9 2.1 2,107 11.5
Private industry........................... 123.5 1,999.2 2.5 2,331 12.5
Natural resources and mining............. 0.0 0.1 5.0 1,862 18.1
Construction............................. 2.1 32.5 8.5 2,003 2.1
Manufacturing............................ 2.4 26.6 1.0 1,552 -7.1
Trade, transportation, and utilities..... 20.9 267.0 2.6 1,529 13.0
Information.............................. 4.4 142.9 1.7 2,447 5.5
Financial activities..................... 18.8 353.5 -0.5 5,186 26.4
Professional and business services....... 25.7 500.5 3.8 2,430 6.3
Education and health services............ 9.4 314.8 1.5 1,226 3.2
Leisure and hospitality.................. 13.1 259.9 3.2 907 2.4
Other services........................... 19.2 94.5 3.7 1,094 2.1
Government................................. 0.3 438.7 0.4 1,100 1.3
Harris, TX................................... 104.3 2,160.8 4.0 1,331 7.3
Private industry........................... 103.8 1,904.6 4.4 1,372 7.6
Natural resources and mining............. 1.7 91.3 6.9 3,544 9.5
Construction............................. 6.5 142.3 5.3 1,335 7.9
Manufacturing............................ 4.6 192.7 5.0 1,704 9.8
Trade, transportation, and utilities..... 23.4 459.4 3.0 1,196 8.4
Information.............................. 1.2 27.2 -2.9 1,463 5.3
Financial activities..................... 10.7 116.0 2.9 1,708 10.7
Professional and business services....... 20.8 362.7 5.9 1,639 4.5
Education and health services............ 11.9 256.5 3.4 1,021 6.4
Leisure and hospitality.................. 8.6 192.7 5.9 430 3.6
Other services........................... 13.7 62.6 3.1 718 5.4
Government................................. 0.5 256.2 0.8 1,026 3.0
Maricopa, AZ................................. 95.2 1,721.1 2.7 964 3.4
Private industry........................... 94.5 1,512.0 3.0 967 3.8
Natural resources and mining............. 0.5 8.5 3.7 918 0.7
Construction............................. 7.6 88.3 7.2 1,049 7.7
Manufacturing............................ 3.2 113.7 1.8 1,290 0.8
Trade, transportation, and utilities..... 21.2 357.3 1.7 917 2.5
Information.............................. 1.6 28.5 2.3 1,253 5.4
Financial activities..................... 10.9 146.2 3.0 1,194 5.7
Professional and business services....... 22.0 285.1 3.5 1,086 5.6
Education and health services............ 10.6 252.8 2.4 1,001 1.5
Leisure and hospitality.................. 7.2 180.6 2.7 444 3.3
Other services........................... 6.5 46.9 1.1 634 4.1
Government................................. 0.7 209.1 1.1 946 1.4
Dallas, TX................................... 70.1 1,499.2 3.4 1,209 5.5
Private industry........................... 69.6 1,335.4 3.8 1,228 5.8
Natural resources and mining............. 0.6 10.1 8.5 3,980 -9.0
Construction............................. 4.0 71.0 6.4 1,171 6.1
Manufacturing............................ 2.8 111.8 -0.1 1,398 7.0
Trade, transportation, and utilities..... 15.2 305.9 3.3 1,065 5.7
Information.............................. 1.5 47.6 3.6 1,640 1.9
Financial activities..................... 8.6 145.1 2.6 1,663 12.1
Professional and business services....... 15.5 292.6 5.6 1,451 4.8
Education and health services............ 7.9 176.8 3.9 1,068 3.1
Leisure and hospitality.................. 5.9 133.2 4.6 528 6.5
Other services........................... 7.3 40.4 1.4 756 8.2
Government................................. 0.5 163.8 0.2 1,059 3.6
Orange, CA................................... 104.2 1,436.6 2.7 1,131 4.4
Private industry........................... 102.8 1,300.9 3.1 1,138 4.5
Natural resources and mining............. 0.2 3.0 -5.3 746 7.8
Construction............................. 6.0 73.2 5.3 1,269 7.2
Manufacturing............................ 4.8 158.6 0.5 1,352 3.8
Trade, transportation, and utilities..... 16.2 258.9 1.8 1,002 2.3
Information.............................. 1.2 24.2 -0.4 1,692 11.8
Financial activities..................... 9.6 111.6 4.3 2,030 6.8
Professional and business services....... 19.1 264.5 4.3 1,329 5.1
Education and health services............ 10.7 165.4 2.1 1,064 2.7
Leisure and hospitality.................. 7.4 182.5 4.2 431 4.6
Other services........................... 19.6 52.9 4.2 534 -1.1
Government................................. 1.4 135.7 -1.3 1,064 3.5
San Diego, CA................................ 100.5 1,302.0 2.3 1,099 5.5
Private industry........................... 99.1 1,084.0 2.6 1,090 5.7
Natural resources and mining............. 0.7 9.0 1.1 665 4.2
Construction............................. 5.8 57.5 3.6 1,133 0.7
Manufacturing............................ 2.9 93.7 -0.6 1,534 5.6
Trade, transportation, and utilities..... 13.6 219.0 2.1 843 6.7
Information.............................. 1.1 24.9 1.7 1,580 -1.8
Financial activities..................... 8.5 71.5 3.4 1,381 12.3
Professional and business services....... 16.6 221.5 2.7 1,670 8.8
Education and health services............ 8.8 157.7 1.4 1,044 3.0
Leisure and hospitality.................. 7.2 160.2 3.3 439 0.5
Other services........................... 26.6 63.5 5.2 503 2.2
Government................................. 1.4 218.1 0.7 1,143 4.4
King, WA..................................... 84.1 1,185.3 3.0 1,276 4.7
Private industry........................... 83.5 1,028.2 3.4 1,291 5.1
Natural resources and mining............. 0.4 2.5 -8.0 2,021 35.5
Construction............................. 5.3 50.4 9.5 1,248 -1.3
Manufacturing............................ 2.2 103.6 3.5 1,482 -2.8
Trade, transportation, and utilities..... 14.4 223.0 3.6 1,086 6.2
Information.............................. 1.8 80.9 0.7 2,489 11.8
Financial activities..................... 6.2 64.2 2.5 1,587 8.3
Professional and business services....... 14.1 195.1 4.8 1,689 6.7
Education and health services............ 7.3 140.3 2.3 1,007 2.2
Leisure and hospitality.................. 6.5 115.6 4.0 485 1.7
Other services........................... 25.2 52.7 -0.7 627 6.8
Government................................. 0.5 157.1 0.5 1,177 1.2
Miami-Dade, FL............................... 91.3 1,020.6 2.3 976 4.1
Private industry........................... 90.9 882.1 2.8 957 5.4
Natural resources and mining............. 0.5 8.9 -2.7 609 2.5
Construction............................. 5.0 30.8 2.8 1,017 10.8
Manufacturing............................ 2.7 35.7 -1.2 930 3.8
Trade, transportation, and utilities..... 26.4 266.5 1.9 852 4.8
Information.............................. 1.5 17.7 1.3 1,489 10.9
Financial activities..................... 9.3 69.2 3.8 1,483 8.4
Professional and business services....... 19.1 134.1 4.2 1,342 8.8
Education and health services............ 10.1 159.0 1.0 929 0.7
Leisure and hospitality.................. 7.0 122.8 6.0 554 3.2
Other services........................... 8.0 35.7 2.8 586 3.9
Government................................. 0.4 138.5 -1.2 1,092 -2.3
(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 2011 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,
fourth quarter 2012(2)
Employment Average weekly
wage(3)
Establishments,
fourth quarter
State 2012 Percent Percent
(thousands) December change, Fourth change,
2012 December quarter fourth
(thousands) 2011-12 2012 quarter
2011-12
United States(4)......... 9,205.6 133,726.8 1.9 $1,000 4.7
Alabama.................. 117.0 1,847.3 1.1 854 2.6
Alaska................... 21.9 314.8 1.1 1,007 2.7
Arizona.................. 147.5 2,509.2 2.4 912 3.3
Arkansas................. 85.1 1,160.3 0.2 767 4.2
California............... 1,337.1 15,216.3 3.3 1,186 7.8
Colorado................. 173.6 2,311.4 2.7 1,032 5.8
Connecticut.............. 111.9 1,657.6 1.0 1,253 5.3
Delaware................. 27.8 411.0 1.2 1,044 6.1
District of Columbia..... 36.8 721.5 1.7 1,703 2.2
Florida.................. 618.3 7,535.5 2.3 880 3.9
Georgia.................. 273.7 3,889.9 1.7 927 4.7
Hawaii................... 38.6 620.7 2.1 868 2.7
Idaho.................... 53.4 618.4 2.0 732 2.1
Illinois................. 396.4 5,697.9 1.1 1,058 4.4
Indiana.................. 160.4 2,850.5 1.8 816 3.4
Iowa..................... 96.0 1,486.6 1.3 821 3.7
Kansas................... 84.9 1,339.2 1.5 835 4.4
Kentucky................. 113.2 1,796.0 1.4 801 1.8
Louisiana................ 127.1 1,891.9 1.0 884 4.1
Maine.................... 49.7 582.2 0.2 773 2.4
Maryland................. 169.1 2,544.1 1.2 1,086 2.5
Massachusetts............ 221.0 3,279.3 1.3 1,248 4.8
Michigan................. 238.9 3,988.9 1.9 954 2.3
Minnesota................ 170.1 2,677.2 1.6 985 5.1
Mississippi.............. 69.4 1,096.5 1.1 720 3.2
Missouri................. 179.3 2,641.9 0.9 863 4.6
Montana.................. 42.8 434.6 1.9 757 4.1
Nebraska................. 68.0 931.3 2.2 797 4.6
Nevada................... 73.5 1,145.8 1.9 877 2.9
New Hampshire............ 49.5 620.8 0.8 1,023 5.5
New Jersey............... 263.8 3,846.4 1.1 1,172 2.9
New Mexico............... 55.7 796.8 1.5 802 0.4
New York................. 608.4 8,741.9 1.4 1,280 6.9
North Carolina........... 259.9 3,963.9 1.9 854 3.6
North Dakota............. 30.1 421.0 6.1 944 8.4
Ohio..................... 287.1 5,098.0 1.3 887 3.6
Oklahoma................. 104.9 1,565.3 1.9 847 3.9
Oregon................... 134.8 1,654.1 1.4 871 2.5
Pennsylvania............. 354.4 5,629.8 0.5 972 3.8
Rhode Island............. 35.4 456.4 1.0 945 2.7
South Carolina........... 113.9 1,832.2 2.0 784 2.8
South Dakota............. 31.6 401.7 1.2 749 3.5
Tennessee................ 142.1 2,710.4 2.1 903 5.2
Texas.................... 599.6 10,956.4 3.2 1,027 5.5
Utah..................... 87.2 1,246.6 3.7 844 4.5
Vermont.................. 24.5 306.1 0.7 829 2.5
Virginia................. 242.5 3,663.7 1.1 1,042 3.7
Washington............... 239.6 2,902.0 2.1 1,017 4.0
West Virginia............ 49.6 714.3 0.0 788 1.5
Wisconsin................ 162.9 2,723.6 1.2 855 4.8
Wyoming.................. 25.6 277.6 0.2 908 3.7
Puerto Rico.............. 47.3 978.6 1.6 550 -0.4
Virgin Islands........... 3.4 39.8 -7.9 738 -3.9
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs.
(2) Data are preliminary.
(3) Average weekly wages were calculated using unrounded data.
(4) Totals for the United States do not include data for Puerto Rico or the
Virgin Islands.
United States Department of Labor
