For release 10:00 a.m. (EST), Thursday, December 18, 2014 USDL-14-2250
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 2014
From June 2013 to June 2014, employment increased in 305 of the 339 largest U.S. counties, the U.S.
Bureau of Labor Statistics reported today. Weld, Colo., had the largest increase, with a gain of 8.9
percent over the year, compared with national job growth of 2.0 percent. Within Weld, the largest
employment increase occurred in natural resources and mining, which gained 2,636 jobs over the year
(27.3 percent). Atlantic, N.J., had the largest over-the-year decrease in employment among the largest
counties in the U.S. with a loss of 1.6 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 6 months after the end of each quarter.
The U.S. average weekly wage increased 2.1 percent over the year, growing to $940 in the second
quarter of 2014. Midland, Texas, had the largest over-the-year increase in average weekly wages with a
gain of 9.0 percent. Within Midland, an average weekly wage gain of $142, or 7.5 percent, in natural
resources and mining made the largest contribution to the county’s increase in average weekly wages.
Williamson, Texas, experienced the largest decrease in average weekly wages with a loss of 2.7 percent
over the year.
Table A. Large counties ranked by June 2014 employment, June 2013-14 employment
increase, and June 2013-14 percent increase in employment
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Employment in large counties
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June 2014 employment | Increase in employment, | Percent increase in employment,
(thousands) | June 2013-14 | June 2013-14
| (thousands) |
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| |
United States 137,776.4| United States 2,674.6| United States 2.0
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| |
Los Angeles, Calif. 4,155.9| Los Angeles, Calif. 71.7| Weld, Colo. 8.9
Cook, Ill. 2,499.5| Harris, Texas 71.3| Benton, Ark. 6.8
New York, N.Y. 2,492.5| New York, N.Y. 65.8| Lee, Fla. 6.3
Harris, Texas 2,258.0| Dallas, Texas 52.0| Sarasota, Fla. 5.8
Maricopa, Ariz. 1,717.1| Cook, Ill. 43.9| Midland, Texas 5.5
Dallas, Texas 1,544.6| King, Wash. 42.7| Clark, Wash. 5.3
Orange, Calif. 1,477.2| Santa Clara, Calif. 37.8| Charleston, S.C. 5.2
San Diego, Calif. 1,338.5| Maricopa, Ariz. 36.9| Montgomery, Texas 5.1
King, Wash. 1,248.1| Clark, Nev. 33.1| Mecklenburg, N.C. 4.9
Miami-Dade, Fla. 1,026.2| Mecklenburg, N.C. 28.3| Lexington, S.C. 4.9
| |
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Large County Employment
In June 2014, national employment was 137.8 million (as measured by the QCEW program). Over the
year, employment increased 2.0 percent, or 2.7 million. The 339 U.S. counties with 75,000 or more jobs
accounted for 71.8 percent of total U.S. employment and 76.9 percent of total wages. These 339
counties had a net job growth of 2.0 million over the year, accounting for 73.6 percent of the overall
U.S. employment increase.
Weld, Colo., had the largest percentage increase in employment (8.9 percent) among the largest U.S.
counties. The five counties with the largest increases in employment level were Los Angeles, Calif.;
Harris, Texas; New York, N.Y.; Dallas, Texas; and Cook, Ill. These counties had a combined over-the-
year employment gain of 304,700 jobs, which was 11.4 percent of the overall job increase for the U.S.
(See table A.)
Employment declined in 29 of the largest counties from June 2013 to June 2014. Atlantic, N.J., had the
largest over-the-year percentage decrease in employment (-1.6 percent). Within Atlantic, leisure and
hospitality had the largest decrease in employment, with a loss of 2,817 jobs (-5.7 percent). Passaic,
N.J., had the second largest percentage decrease in employment, followed by McLean, Ill.; Arlington,
Va.; and Burlington, N.J. (See table 1.)
Table B. Large counties ranked by second quarter 2014 average weekly wages, second quarter 2013-14
increase in average weekly wages, and second quarter 2013-14 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 2014 | wage, second quarter 2013-14 | weekly wage, second
| | quarter 2013-14
--------------------------------------------------------------------------------------------------------
| |
United States $940| United States $19| United States 2.1
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| |
Santa Clara, Calif. $1,886| San Mateo, Calif. $107| Midland, Texas 9.0
San Mateo, Calif. 1,740| Midland, Texas 105| Douglas, Colo. 8.8
New York, N.Y. 1,732| Douglas, Colo. 89| Hillsborough, N.H. 7.4
San Francisco, Calif. 1,593| San Francisco, Calif. 76| Collier, Fla. 6.8
Washington, D.C. 1,569| Santa Clara, Calif. 76| San Mateo, Calif. 6.6
Arlington, Va. 1,516| Hillsborough, N.H. 73| Calcasieu, La. 6.4
Suffolk, Mass. 1,463| Washington, Ore. 61| Newport News City, Va. 6.2
Fairfax, Va. 1,457| Collier, Fla. 54| Weld, Colo. 5.8
Fairfield, Conn. 1,455| Newport News City, Va. 54| Washington, Ore. 5.5
Middlesex, Mass. 1,386| Suffolk, Mass. 52| Ingham, Mich. 5.4
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $940, a 2.1 percent increase, during the year ending in
the second quarter of 2014. Among the 339 largest counties, 312 had over-the-year increases in average
weekly wages. Midland, Texas, had the largest wage increase among the largest U.S. counties (9.0
percent).
Of the 339 largest counties, 22 experienced over-the-year decreases in average weekly wages.
Williamson, Texas, had the largest percentage decrease in average weekly wages, with a loss of 2.7
percent. Within Williamson, manufacturing had the largest impact on the county’s average weekly wage
decrease. Within this industry, average weekly wages declined by $168 (-9.5 percent) over the year.
Westchester, N.Y., had the second largest percentage decrease in average weekly wages, followed by
Lake, Ind.; Bibb, Ga.; Washington, D.C.; and Chittenden, Vt. (See table 1.) The decline in average
weekly wages in Washington, D.C., was largely due to a pay period effect in federal government wages.
For more information see the concepts and methodology section of the Technical Note.
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in June 2014.
Dallas, Texas, and King, Wash., had the largest gain (3.5 percent). Within Dallas, professional and
business services had the largest over-the-year employment level increase among all private industry
groups with a gain of 15,108 jobs, or 5.1 percent. Trade, transportation, and utilities had the largest
employment level increase among all private industry groups within King, with a gain of 11,204 jobs, or
5.1 percent. Cook, Ill., Orange, Calif., and Los Angeles, Calif., tied for the smallest percentage increase
in employment (1.8 percent) among the 10 largest counties. (See table 2.)
Average weekly wages increased over the year in all of the 10 largest U.S. counties. Harris, Texas,
experienced the largest percentage gain in average weekly wages (3.4 percent). Within Harris, natural
resources and mining had the largest impact on the county’s average weekly wage growth. Within this
industry, average weekly wages increased by $154, or 5.0 percent, over the year. San Diego, Calif., and
Maricopa, Ariz., tied for the smallest increase in average weekly wages (1.2 percent) among the 10
largest counties.
For More Information
The tables included in this release contain data for the nation and for the 339 U.S. counties with annual
average employment levels of 75,000 or more in 2013. June 2014 employment and 2014 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 9.4 million employer reports cover 137.8 million full- and part-
time workers. The QCEW program provides a quarterly and annual universe count of establishments,
employment, and wages at the county, MSA, state, and national levels by detailed industry. Data for the
second quarter of 2014 will be available electronically later at www.bls.gov/cew/. For additional
information about the quarterly employment and wages data, please read the Technical Note. 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 www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for third quarter 2014 is scheduled to be released on
Thursday, March 19, 2015.
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 2014 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or greater. In
addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in
the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average
of employment for the previous year. The 340 counties presented in this release were derived using 2013
preliminary annual averages of employment. For 2014 data, five counties have been added to the publication
tables: Shelby, Ala.; Osceola, Fla.; Black Hawk, Iowa; Washington, Minn.; and Cleveland, Okla. These counties
will be included in all 2014 quarterly releases. The counties in table 2 are selected and sorted each year based on
the annual average employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released by the individual states. These
potential differences result from the states' continuing 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 differences and the intended uses of the program products. (See
table.) Additional information 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 9.4 | ministrative records| ments
| million establish- | submitted by 7.5 |
| ments in first | million private-sec-|
| quarter of 2014 | 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 | -6 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 counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports
submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation
for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports
submitted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of
a few agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports,
employers who operate 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 9.2 million
employer reports of employment and wages submitted by states to the BLS in 2013. These reports are based on
place of employment 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 effective, expanding coverage to include most State
and local government employees. In 2013, UI and UCFE programs covered workers in 134.0 million jobs. The
estimated 128.7 million workers in these jobs (after adjustment for multiple jobholders) represented 95.8 percent
of civilian wage and salary employment. Covered workers received $6.673 trillion in pay, representing 93.7
percent of the wage and salary component of personal income and 39.8 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. Coverage changes may affect the over-the-year
comparisons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received pay for the pay period
including the 12th of the month. With few exceptions, all 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 averages 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
compensation 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 workforce could increase significantly when there is a large decline in
the number of employees that had been receiving below-average wages. Wages may include payments to workers
not present in the employment counts because they did not work during the pay period including the 12th of the
month. When comparing average weekly wage levels between industries, states, or quarters, these factors should
be taken into consideration.
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 employers 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 individual 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 underlying 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 2013 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 unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from
the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year
changes presented in this news release.
The adjusted data used to calculate the over-the-year change measures presented in this release account for most
of the administrative changes--those occurring when employers update the industry, location, and ownership
information of their establishments. The most common adjustments for administrative change are the result of
updated information about the county location of individual establishments. Included in these adjustments are
administrative changes involving the classification of 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. Beginning with the second quarter of 2011, adjusted data account for
selected large administrative changes in employment and wages. These new adjustments allow QCEW to include
county employment and wage growth rates in this news release that would otherwise not meet publication
standards.
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 Standards 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 2013 edition of this publication, which
was published in September 2014, contains selected data produced by Business Employment Dynamics (BED) on
job gains and losses, as well as selected data from the first quarter 2014 version of this news release. Tables and
additional content from Employment and Wages Annual Averages 2013 are now available online at
http://www.bls.gov/cew/cewbultn13.htm. The 2014 edition of Employment and Wages Annual Averages Online
will be available in September 2015.
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 establishments, employment, and wages in the 340 largest counties,
second quarter 2014
Employment Average weekly wage(2)
Establishments,
County(1) second quarter Percent Ranking Percent Ranking
2014 June change, by Second change, by
(thousands) 2014 June percent quarter second percent
(thousands) 2013-14(3) change 2014 quarter change
2013-14(3)
United States(4)......... 9,360.5 137,776.4 2.0 - $940 2.1 -
Jefferson, AL............ 17.7 340.7 0.2 297 931 1.6 195
Madison, AL.............. 9.0 182.7 -0.3 321 1,047 1.7 177
Mobile, AL............... 9.6 166.8 1.0 244 809 0.7 276
Montgomery, AL........... 6.3 129.7 -0.2 317 798 1.8 167
Shelby, AL............... 5.1 78.9 1.8 152 878 2.0 143
Tuscaloosa, AL........... 4.3 88.0 2.8 78 800 0.4 295
Anchorage Borough, AK.... 8.3 155.1 -0.1 311 1,056 4.9 14
Maricopa, AZ............. 92.9 1,717.1 2.2 124 931 1.2 239
Pima, AZ................. 18.7 347.0 0.5 281 815 0.5 285
Benton, AR............... 5.7 105.8 6.8 2 894 -0.8 331
Pulaski, AR.............. 14.3 242.7 0.5 281 856 1.2 239
Washington, AR........... 5.7 97.0 2.3 115 758 0.8 269
Alameda, CA.............. 56.8 701.9 2.8 78 1,190 1.4 219
Contra Costa, CA......... 29.6 343.2 2.9 73 1,139 1.7 177
Fresno, CA............... 30.7 364.5 1.4 194 719 1.7 177
Kern, CA................. 17.1 311.8 0.5 281 820 1.9 156
Los Angeles, CA.......... 438.6 4,155.9 1.8 152 1,024 2.9 67
Marin, CA................ 12.0 112.0 2.3 115 1,171 3.3 41
Monterey, CA............. 12.8 197.0 1.7 167 790 1.7 177
Orange, CA............... 107.2 1,477.2 1.8 152 1,033 1.5 205
Placer, CA............... 11.3 143.2 3.3 57 918 2.2 123
Riverside, CA............ 52.7 626.9 4.0 34 763 0.3 306
Sacramento, CA........... 52.1 617.8 3.3 57 1,027 1.1 249
San Bernardino, CA....... 50.6 651.4 3.8 42 802 1.5 205
San Diego, CA............ 99.9 1,338.5 2.1 129 1,044 1.2 239
San Francisco, CA........ 57.0 638.5 4.3 25 1,593 5.0 13
San Joaquin, CA.......... 16.6 219.5 1.9 141 770 1.6 195
San Luis Obispo, CA...... 9.7 112.5 3.4 54 770 1.7 177
San Mateo, CA............ 25.8 372.6 4.7 13 1,740 6.6 5
Santa Barbara, CA........ 14.5 195.1 2.4 108 888 0.6 281
Santa Clara, CA.......... 65.5 978.4 4.0 34 1,886 4.2 20
Santa Cruz, CA........... 9.1 103.6 2.5 102 830 3.4 36
Solano, CA............... 10.2 128.3 1.8 152 966 3.3 41
Sonoma, CA............... 18.8 193.2 4.5 17 854 1.4 219
Stanislaus, CA........... 14.2 175.2 2.6 98 767 1.5 205
Tulare, CA............... 9.1 153.8 -0.2 317 652 2.5 100
Ventura, CA.............. 24.8 316.1 1.7 167 950 0.2 308
Yolo, CA................. 6.0 96.3 3.8 42 962 2.1 135
Adams, CO................ 9.2 184.9 4.8 11 916 3.4 36
Arapahoe, CO............. 19.3 307.7 2.7 87 1,074 1.1 249
Boulder, CO.............. 13.3 169.9 2.9 73 1,103 2.6 86
Denver, CO............... 27.2 459.8 4.2 30 1,126 2.9 67
Douglas, CO.............. 10.1 109.3 3.7 45 1,100 8.8 2
El Paso, CO.............. 16.9 251.4 2.3 115 849 1.9 156
Jefferson, CO............ 17.9 224.7 2.5 102 957 2.4 104
Larimer, CO.............. 10.4 144.5 3.3 57 828 5.2 11
Weld, CO................. 6.1 98.6 8.9 1 840 5.8 8
Fairfield, CT............ 33.8 425.6 1.1 235 1,455 1.5 205
Hartford, CT............. 26.3 506.6 0.6 273 1,159 3.6 32
New Haven, CT............ 23.0 362.6 0.3 295 986 1.8 167
New London, CT........... 7.0 123.8 -0.4 324 960 2.2 123
New Castle, DE........... 18.0 277.8 2.5 102 1,098 0.7 276
Washington, DC........... 35.9 732.6 1.8 152 1,569 -1.1 334
Alachua, FL.............. 6.7 118.6 1.7 167 816 2.1 135
Brevard, FL.............. 14.8 189.7 1.6 177 834 -0.4 327
Broward, FL.............. 65.7 733.1 3.1 64 873 1.2 239
Collier, FL.............. 12.6 120.4 4.3 25 853 6.8 4
Duval, FL................ 27.5 454.2 1.7 167 896 1.9 156
Escambia, FL............. 8.1 123.2 2.0 135 742 1.2 239
Hillsborough, FL......... 39.4 609.4 2.8 78 899 1.9 156
Lake, FL................. 7.6 81.8 2.7 87 645 2.1 135
Lee, FL.................. 19.8 217.4 6.3 3 750 1.4 219
Leon, FL................. 8.3 139.5 2.7 87 782 1.8 167
Manatee, FL.............. 9.9 105.6 2.2 124 736 2.4 104
Marion, FL............... 8.0 93.0 2.0 135 677 1.7 177
Miami-Dade, FL........... 93.4 1,026.2 2.5 102 913 3.3 41
Okaloosa, FL............. 6.2 78.3 0.2 297 776 1.3 226
Orange, FL............... 38.3 725.7 3.4 54 828 2.6 86
Osceola, FL.............. 6.0 78.8 4.3 25 665 2.9 67
Palm Beach, FL........... 52.0 537.2 3.6 50 910 1.9 156
Pasco, FL................ 10.2 98.3 3.7 45 701 2.6 86
Pinellas, FL............. 31.4 396.7 1.6 177 844 3.7 29
Polk, FL................. 12.6 191.5 2.3 115 723 1.5 205
Sarasota, FL............. 14.9 148.9 5.8 4 787 1.5 205
Seminole, FL............. 14.1 166.2 3.9 39 793 1.1 249
Volusia, FL.............. 13.6 151.5 1.9 141 694 3.0 58
Bibb, GA................. 4.6 81.8 2.3 115 733 -1.3 336
Chatham, GA.............. 8.2 141.2 3.2 62 798 3.9 24
Clayton, GA.............. 4.3 114.0 3.8 42 895 2.6 86
Cobb, GA................. 22.5 325.8 4.3 25 994 1.0 257
De Kalb, GA.............. 18.7 282.4 2.7 87 968 0.9 264
Fulton, GA............... 44.1 763.2 3.0 70 1,222 1.4 219
Gwinnett, GA............. 25.1 326.7 4.4 22 912 1.0 257
Muscogee, GA............. 4.8 94.9 0.5 281 742 1.6 195
Richmond, GA............. 4.7 101.5 1.3 214 799 2.8 79
Honolulu, HI............. 24.9 456.6 0.9 250 877 2.6 86
Ada, ID.................. 13.9 211.8 2.4 108 818 3.2 49
Champaign, IL............ 4.5 89.3 1.4 194 817 2.9 67
Cook, IL................. 156.6 2,499.5 1.8 152 1,085 1.7 177
Du Page, IL.............. 38.8 608.8 1.0 244 1,074 1.5 205
Kane, IL................. 14.0 207.4 1.4 194 809 1.4 219
Lake, IL................. 23.1 340.2 0.6 273 1,226 1.7 177
McHenry, IL.............. 9.0 97.3 1.6 177 769 0.5 285
McLean, IL............... 3.9 84.3 -1.4 336 947 -1.0 333
Madison, IL.............. 6.2 97.2 1.8 152 760 1.5 205
Peoria, IL............... 4.8 101.6 -0.6 328 890 1.9 156
St. Clair, IL............ 5.7 91.6 -0.8 331 745 0.8 269
Sangamon, IL............. 5.4 130.2 2.2 124 959 1.7 177
Will, IL................. 16.2 217.9 1.2 222 835 2.3 115
Winnebago, IL............ 6.9 128.2 1.5 185 797 0.9 264
Allen, IN................ 8.8 179.5 2.2 124 748 0.4 295
Elkhart, IN.............. 4.7 122.9 4.2 30 797 4.0 22
Hamilton, IN............. 8.8 127.9 4.5 17 875 1.5 205
Lake, IN................. 10.2 188.5 -0.8 331 834 -1.4 337
Marion, IN............... 23.6 578.3 1.1 235 927 0.5 285
St. Joseph, IN........... 5.8 118.4 3.3 57 759 0.9 264
Tippecanoe, IN........... 3.3 80.0 1.4 194 798 1.7 177
Vanderburgh, IN.......... 4.8 105.8 1.4 194 757 0.4 295
Black Hawk, IA........... 3.8 76.1 0.3 295 780 2.8 79
Johnson, IA.............. 4.0 80.9 1.1 235 874 3.3 41
Linn, IA................. 6.5 130.4 1.2 222 894 1.6 195
Polk, IA................. 16.3 290.3 3.0 70 920 2.4 104
Scott, IA................ 5.5 91.5 1.2 222 766 2.0 143
Johnson, KS.............. 21.4 331.4 2.8 78 976 3.0 58
Sedgwick, KS............. 12.3 245.5 1.2 222 836 -0.8 331
Shawnee, KS.............. 4.8 97.3 2.5 102 791 0.6 281
Wyandotte, KS............ 3.3 88.0 4.4 22 872 4.3 19
Boone, KY................ 4.1 79.3 2.4 108 846 1.8 167
Fayette, KY.............. 10.3 184.7 2.4 108 835 1.8 167
Jefferson, KY............ 24.3 442.5 2.4 108 926 2.7 84
Caddo, LA................ 7.4 114.9 -0.2 317 774 3.2 49
Calcasieu, LA............ 5.0 87.7 1.7 167 827 6.4 6
East Baton Rouge, LA..... 14.9 264.1 1.4 194 895 1.2 239
Jefferson, LA............ 13.7 194.8 1.4 194 837 1.7 177
Lafayette, LA............ 9.3 140.5 -0.1 311 930 2.9 67
Orleans, LA.............. 11.7 186.7 4.6 16 912 0.3 306
St. Tammany, LA.......... 7.7 82.8 2.7 87 789 2.3 115
Cumberland, ME........... 12.6 177.8 1.4 194 843 2.1 135
Anne Arundel, MD......... 14.6 257.0 0.9 250 996 0.5 285
Baltimore, MD............ 21.2 368.3 0.5 281 941 2.1 135
Frederick, MD............ 6.3 96.4 -0.4 324 899 1.5 205
Harford, MD.............. 5.6 89.1 -0.7 330 939 0.5 285
Howard, MD............... 9.5 164.0 0.1 302 1,118 0.4 295
Montgomery, MD........... 32.9 462.7 1.2 222 1,244 0.0 313
Prince Georges, MD....... 15.6 308.0 1.2 222 998 1.5 205
Baltimore City, MD....... 13.8 333.5 0.8 261 1,068 1.6 195
Barnstable, MA........... 9.0 103.5 0.9 250 789 2.9 67
Bristol, MA.............. 16.3 222.3 1.1 235 856 2.0 143
Essex, MA................ 22.5 320.0 1.8 152 1,007 2.9 67
Hampden, MA.............. 16.3 202.5 0.6 273 856 2.8 79
Middlesex, MA............ 50.7 861.8 1.5 185 1,386 1.1 249
Norfolk, MA.............. 23.7 343.0 1.7 167 1,077 0.7 276
Plymouth, MA............. 14.2 185.9 0.6 273 911 2.6 86
Suffolk, MA.............. 25.0 619.5 1.5 185 1,463 3.7 29
Worcester, MA............ 22.2 332.1 1.0 244 946 2.4 104
Genesee, MI.............. 7.1 134.7 1.3 214 768 2.3 115
Ingham, MI............... 6.1 150.4 0.2 297 901 5.4 10
Kalamazoo, MI............ 5.1 114.2 1.5 185 849 1.8 167
Kent, MI................. 13.9 362.4 3.7 45 823 2.0 143
Macomb, MI............... 17.2 311.3 1.3 214 947 1.7 177
Oakland, MI.............. 38.1 701.9 1.4 194 1,048 2.8 79
Ottawa, MI............... 5.5 117.1 4.0 34 787 2.6 86
Saginaw, MI.............. 4.1 84.0 0.1 302 748 2.2 123
Washtenaw, MI............ 8.1 195.8 1.2 222 997 2.4 104
Wayne, MI................ 30.5 699.2 0.9 250 1,031 3.3 41
Anoka, MN................ 6.8 118.0 0.9 250 905 3.0 58
Dakota, MN............... 9.5 181.9 1.3 214 924 2.9 67
Hennepin, MN............. 40.2 879.2 1.7 167 1,151 1.0 257
Olmsted, MN.............. 3.3 93.4 -0.6 328 1,065 1.2 239
Ramsey, MN............... 13.1 325.6 0.9 250 1,067 3.8 27
St. Louis, MN............ 5.2 98.3 0.9 250 757 0.9 264
Stearns, MN.............. 4.2 84.3 2.1 129 775 3.5 35
Washington, MN........... 5.2 78.4 0.7 267 784 2.0 143
Harrison, MS............. 4.4 84.0 0.8 261 685 1.2 239
Hinds, MS................ 5.9 118.9 -0.9 333 826 1.7 177
Boone, MO................ 4.7 90.0 1.2 222 733 1.8 167
Clay, MO................. 5.2 94.4 3.0 70 837 0.6 281
Greene, MO............... 8.2 158.9 2.3 115 715 0.8 269
Jackson, MO.............. 19.7 354.3 0.7 267 925 0.5 285
St. Charles, MO.......... 8.6 135.5 2.1 129 781 3.3 41
St. Louis, MO............ 33.7 587.4 1.4 194 992 2.2 123
St. Louis City, MO....... 10.8 222.7 1.0 244 992 2.1 135
Yellowstone, MT.......... 6.3 79.8 1.6 177 802 -0.2 323
Douglas, NE.............. 18.6 327.9 1.8 152 853 2.9 67
Lancaster, NE............ 10.1 163.4 1.8 152 758 2.0 143
Clark, NV................ 51.4 877.4 3.9 39 825 0.5 285
Washoe, NV............... 13.9 195.5 2.9 73 827 1.3 226
Hillsborough, NH......... 12.1 193.9 0.8 261 1,059 7.4 3
Rockingham, NH........... 10.6 144.3 1.9 141 944 4.0 22
Atlantic, NJ............. 6.6 137.0 -1.6 339 794 1.0 257
Bergen, NJ............... 32.8 445.7 1.4 194 1,147 2.0 143
Burlington, NJ........... 11.0 201.2 -1.1 335 988 0.5 285
Camden, NJ............... 11.9 200.7 1.6 177 909 0.4 295
Essex, NJ................ 20.4 334.7 -0.3 321 1,124 -0.2 323
Gloucester, NJ........... 6.1 100.8 1.1 235 828 2.3 115
Hudson, NJ............... 14.2 237.9 0.4 289 1,276 2.1 135
Mercer, NJ............... 11.0 236.0 0.2 297 1,186 0.4 295
Middlesex, NJ............ 21.9 395.8 1.1 235 1,115 1.5 205
Monmouth, NJ............. 20.0 257.4 1.4 194 943 1.3 226
Morris, NJ............... 17.0 285.2 0.6 273 1,343 1.2 239
Ocean, NJ................ 12.6 166.9 3.2 62 759 -0.1 318
Passaic, NJ.............. 12.2 167.5 -1.5 338 943 1.8 167
Somerset, NJ............. 10.0 183.5 1.4 194 1,379 0.4 295
Union, NJ................ 14.3 223.0 -0.4 324 1,209 -0.4 327
Bernalillo, NM........... 18.0 313.7 0.7 267 816 2.0 143
Albany, NY............... 10.3 227.3 0.4 289 985 3.1 54
Bronx, NY................ 17.6 251.6 1.4 194 889 0.2 308
Broome, NY............... 4.6 89.2 0.1 302 756 1.1 249
Dutchess, NY............. 8.4 110.6 0.0 306 969 0.7 276
Erie, NY................. 24.5 462.7 0.7 267 826 2.2 123
Kings, NY................ 56.9 566.7 4.7 13 759 1.5 205
Monroe, NY............... 18.5 381.2 0.4 289 889 2.2 123
Nassau, NY............... 53.3 618.4 1.5 185 1,061 1.6 195
New York, NY............. 126.6 2,492.5 2.7 87 1,732 3.0 58
Oneida, NY............... 5.3 104.0 -0.1 311 760 2.2 123
Onondaga, NY............. 13.0 243.7 -0.1 311 866 1.3 226
Orange, NY............... 10.1 138.9 1.2 222 825 -0.2 323
Queens, NY............... 49.6 553.6 3.1 64 884 3.2 49
Richmond, NY............. 9.4 99.1 2.9 73 797 0.9 264
Rockland, NY............. 10.2 118.3 2.8 78 1,003 1.7 177
Saratoga, NY............. 5.8 83.2 -0.2 317 868 1.3 226
Suffolk, NY.............. 51.9 656.2 0.4 289 1,014 1.7 177
Westchester, NY.......... 36.3 421.1 1.2 222 1,215 -1.6 338
Buncombe, NC............. 8.2 119.0 2.2 124 702 1.7 177
Catawba, NC.............. 4.2 81.7 1.1 235 713 2.9 67
Cumberland, NC........... 6.2 118.2 -0.9 333 746 -0.4 327
Durham, NC............... 7.6 189.4 1.9 141 1,208 0.5 285
Forsyth, NC.............. 9.0 178.3 2.1 129 837 0.0 313
Guilford, NC............. 14.1 267.3 0.9 250 809 -0.2 323
Mecklenburg, NC.......... 33.6 606.6 4.9 9 1,040 1.3 226
New Hanover, NC.......... 7.4 102.9 3.4 54 752 1.8 167
Wake, NC................. 30.4 494.8 4.2 30 933 0.4 295
Cass, ND................. 6.6 115.2 4.5 17 832 2.8 79
Butler, OH............... 7.5 143.0 2.3 115 819 1.9 156
Cuyahoga, OH............. 35.4 715.5 0.0 306 954 2.4 104
Delaware, OH............. 4.6 83.7 0.0 306 915 0.8 269
Franklin, OH............. 30.0 705.5 2.7 87 945 1.3 226
Hamilton, OH............. 23.1 504.5 1.5 185 1,011 1.3 226
Lake, OH................. 6.3 96.4 0.6 273 781 3.9 24
Lorain, OH............... 6.0 98.0 0.6 273 775 1.7 177
Lucas, OH................ 10.0 206.1 1.3 214 819 2.6 86
Mahoning, OH............. 5.9 98.5 1.0 244 664 2.2 123
Montgomery, OH........... 11.9 247.5 1.4 194 816 1.9 156
Stark, OH................ 8.7 160.2 1.6 177 713 1.1 249
Summit, OH............... 14.0 262.0 1.4 194 828 1.3 226
Warren, OH............... 4.4 86.4 1.3 214 816 1.7 177
Cleveland, OK............ 5.2 78.4 2.1 129 716 1.8 167
Oklahoma, OK............. 26.2 442.4 1.0 244 891 1.9 156
Tulsa, OK................ 21.3 342.9 1.6 177 894 3.6 32
Clackamas, OR............ 13.2 148.0 1.3 214 884 2.6 86
Jackson, OR.............. 6.7 80.4 2.0 135 707 -0.1 318
Lane, OR................. 11.1 143.1 1.5 185 742 1.0 257
Marion, OR............... 9.6 143.0 3.1 64 764 2.3 115
Multnomah, OR............ 30.9 467.3 3.1 64 965 2.4 104
Washington, OR........... 17.1 265.3 2.3 115 1,165 5.5 9
Allegheny, PA............ 35.3 695.1 0.0 306 1,002 0.1 311
Berks, PA................ 8.9 167.8 2.0 135 872 3.0 58
Bucks, PA................ 19.7 257.1 1.2 222 903 1.2 239
Butler, PA............... 5.0 85.8 0.0 306 866 -0.1 318
Chester, PA.............. 15.2 244.4 1.8 152 1,231 1.6 195
Cumberland, PA........... 6.2 128.3 1.2 222 910 3.8 27
Dauphin, PA.............. 7.3 178.8 0.5 281 920 1.3 226
Delaware, PA............. 13.9 219.0 1.7 167 989 1.6 195
Erie, PA................. 7.2 126.4 0.7 267 731 -0.1 318
Lackawanna, PA........... 5.9 97.9 0.7 267 715 2.6 86
Lancaster, PA............ 12.9 229.1 1.9 141 777 2.6 86
Lehigh, PA............... 8.6 183.9 1.7 167 943 3.1 54
Luzerne, PA.............. 7.6 141.7 0.6 273 744 2.9 67
Montgomery, PA........... 27.4 478.9 0.8 261 1,163 1.4 219
Northampton, PA.......... 6.6 107.1 1.9 141 818 2.0 143
Philadelphia, PA......... 35.1 638.2 0.8 261 1,105 0.4 295
Washington, PA........... 5.3 88.7 1.3 214 934 4.5 17
Westmoreland, PA......... 9.3 134.8 0.2 297 763 3.2 49
York, PA................. 9.0 174.6 1.1 235 811 1.1 249
Providence, RI........... 17.4 279.2 1.7 167 928 2.2 123
Charleston, SC........... 12.4 232.3 5.2 7 822 3.4 36
Greenville, SC........... 12.6 250.5 4.5 17 820 2.2 123
Horry, SC................ 7.9 124.9 3.1 64 548 2.0 143
Lexington, SC............ 5.8 107.7 4.9 9 720 0.8 269
Richland, SC............. 9.1 208.8 2.8 78 823 2.6 86
Spartanburg, SC.......... 5.8 123.4 2.6 98 835 3.0 58
York, SC................. 4.9 82.8 4.5 17 769 4.5 17
Minnehaha, SD............ 6.8 122.9 2.4 108 796 3.2 49
Davidson, TN............. 19.5 452.7 2.3 115 951 2.6 86
Hamilton, TN............. 8.8 187.1 0.9 250 844 2.4 104
Knox, TN................. 11.2 222.6 1.9 141 823 3.3 41
Rutherford, TN........... 4.7 111.5 4.2 30 840 3.6 32
Shelby, TN............... 19.5 477.2 0.4 289 949 0.4 295
Williamson, TN........... 7.0 107.9 4.0 34 1,057 0.0 313
Bell, TX................. 4.9 111.9 1.4 194 774 2.5 100
Bexar, TX................ 36.7 794.7 2.6 98 834 2.7 84
Brazoria, TX............. 5.2 98.7 2.7 87 959 4.7 15
Brazos, TX............... 4.2 92.3 3.7 45 722 3.1 54
Cameron, TX.............. 6.3 134.9 1.9 141 585 2.1 135
Collin, TX............... 21.0 347.4 4.4 22 1,101 2.0 143
Dallas, TX............... 70.8 1,544.6 3.5 52 1,122 1.5 205
Denton, TX............... 12.4 206.5 4.7 13 846 2.3 115
El Paso, TX.............. 14.3 285.1 1.2 222 673 2.3 115
Fort Bend, TX............ 11.0 163.6 4.8 11 945 1.0 257
Galveston, TX............ 5.7 103.3 2.6 98 832 2.6 86
Gregg, TX................ 4.2 78.5 1.5 185 863 2.9 67
Harris, TX............... 107.8 2,258.0 3.3 57 1,231 3.4 36
Hidalgo, TX.............. 11.8 240.2 2.5 102 608 3.1 54
Jefferson, TX............ 5.8 124.0 2.4 108 965 3.7 29
Lubbock, TX.............. 7.2 130.8 1.8 152 726 3.3 41
McLennan, TX............. 5.0 103.6 0.4 289 769 2.4 104
Midland, TX.............. 5.3 91.1 5.5 5 1,269 9.0 1
Montgomery, TX........... 9.9 157.4 5.1 8 958 4.6 16
Nueces, TX............... 8.1 163.5 2.8 78 843 3.9 24
Potter, TX............... 4.0 78.2 1.1 235 741 0.8 269
Smith, TX................ 5.9 97.6 1.5 185 791 2.5 100
Tarrant, TX.............. 39.7 827.5 2.0 135 952 5.1 12
Travis, TX............... 34.9 657.8 3.9 39 1,051 3.4 36
Webb, TX................. 5.0 94.4 2.7 87 647 0.0 313
Williamson, TX........... 8.8 146.5 3.6 50 876 -2.7 339
Davis, UT................ 7.7 114.9 2.8 78 742 0.5 285
Salt Lake, UT............ 40.2 625.2 2.7 87 887 1.4 219
Utah, UT................. 13.7 195.1 4.0 34 755 3.0 58
Weber, UT................ 5.6 95.7 2.0 135 720 3.0 58
Chittenden, VT........... 6.3 100.7 1.4 194 933 -1.1 334
Arlington, VA............ 8.8 165.4 -1.4 336 1,516 -0.6 330
Chesterfield, VA......... 8.1 125.7 1.9 141 823 0.2 308
Fairfax, VA.............. 35.1 588.4 -0.3 321 1,457 0.7 276
Henrico, VA.............. 10.4 181.0 1.8 152 904 0.0 313
Loudoun, VA.............. 10.6 151.9 1.9 141 1,090 0.4 295
Prince William, VA....... 8.3 122.4 1.8 152 822 0.1 311
Alexandria City, VA...... 6.3 95.8 0.1 302 1,321 -0.1 318
Chesapeake City, VA...... 5.7 96.4 -0.1 311 751 1.3 226
Newport News City, VA.... 3.7 98.9 0.9 250 928 6.2 7
Norfolk City, VA......... 5.6 139.8 3.1 64 904 2.3 115
Richmond City, VA........ 7.1 148.6 0.8 261 1,010 2.5 100
Virginia Beach City, VA.. 11.3 177.0 1.4 194 732 0.8 269
Benton, WA............... 5.7 86.8 4.3 25 939 0.6 281
Clark, WA................ 13.9 141.9 5.3 6 858 2.0 143
King, WA................. 84.1 1,248.1 3.5 52 1,235 2.4 104
Kitsap, WA............... 6.7 83.4 2.8 78 843 1.9 156
Pierce, WA............... 21.7 279.7 2.9 73 858 1.1 249
Snohomish, WA............ 20.1 270.5 2.1 129 1,012 1.9 156
Spokane, WA.............. 15.6 207.9 1.8 152 796 2.2 123
Thurston, WA............. 7.8 104.0 3.7 45 846 1.3 226
Whatcom, WA.............. 7.1 85.6 2.7 87 768 1.6 195
Yakima, WA............... 8.1 116.3 1.9 141 638 1.6 195
Kanawha, WV.............. 6.0 104.9 -0.1 311 830 1.3 226
Brown, WI................ 6.4 152.1 0.9 250 813 1.0 257
Dane, WI................. 14.1 317.4 1.4 194 952 3.0 58
Milwaukee, WI............ 24.7 479.2 0.5 281 909 2.2 123
Outagamie, WI............ 5.0 105.9 1.6 177 779 2.4 104
Waukesha, WI............. 12.3 235.4 0.5 281 925 2.0 143
Winnebago, WI............ 3.6 90.5 -0.5 327 875 4.2 20
San Juan, PR............. 11.3 252.6 -1.3 (5) 599 0.2 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(5) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 339 U.S. counties comprise 71.8 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
second quarter 2014
Employment Average weekly
wage(1)
Establishments,
second quarter
County by NAICS supersector 2014 Percent Percent
(thousands) June change, Second change,
2014 June quarter second
(thousands) 2013-14(2) 2014 quarter
2013-14(2)
United States(3) ............................ 9,360.5 137,776.4 2.0 $940 2.1
Private industry........................... 9,065.9 116,600.4 2.3 930 2.3
Natural resources and mining............. 135.8 2,180.7 1.5 1,072 4.8
Construction............................. 753.6 6,268.9 4.7 1,012 2.7
Manufacturing............................ 338.3 12,225.7 1.2 1,158 2.8
Trade, transportation, and utilities..... 1,912.4 26,104.1 2.1 799 2.4
Information.............................. 150.3 2,740.5 0.4 1,603 5.0
Financial activities..................... 832.4 7,713.2 0.8 1,394 2.5
Professional and business services....... 1,672.4 19,151.8 3.0 1,203 1.5
Education and health services............ 1,472.7 20,457.9 1.6 859 1.9
Leisure and hospitality.................. 794.5 15,222.3 2.7 389 2.6
Other services........................... 811.6 4,309.6 2.1 637 2.4
Government................................. 294.6 21,175.9 0.4 995 1.5
Los Angeles, CA.............................. 438.6 4,155.9 1.8 1,024 2.9
Private industry........................... 432.8 3,614.8 1.9 992 3.0
Natural resources and mining............. 0.5 10.0 -2.0 1,404 -3.2
Construction............................. 13.4 119.5 1.9 1,060 0.2
Manufacturing............................ 12.6 364.0 -1.0 1,110 2.4
Trade, transportation, and utilities..... 53.6 781.7 2.2 858 2.6
Information.............................. 9.6 188.0 -3.5 1,871 10.7
Financial activities..................... 24.3 207.9 -0.7 1,595 7.3
Professional and business services....... 47.5 601.6 2.0 1,244 2.7
Education and health services............ 201.4 711.5 1.6 798 2.3
Leisure and hospitality.................. 30.6 467.4 4.3 556 1.6
Other services........................... 27.8 147.3 3.4 645 1.7
Government................................. 5.8 541.1 0.9 1,240 3.1
New York, NY................................. 126.6 2,492.5 2.7 1,732 3.0
Private industry........................... 126.2 2,059.6 3.1 1,859 2.9
Natural resources and mining............. 0.0 0.2 -1.9 2,118 -13.7
Construction............................. 2.2 34.5 2.6 1,691 2.2
Manufacturing............................ 2.2 25.4 -1.2 1,236 3.1
Trade, transportation, and utilities..... 20.7 260.0 1.0 1,311 2.5
Information.............................. 4.7 148.3 0.5 2,434 9.1
Financial activities..................... 19.2 363.3 3.0 3,418 2.8
Professional and business services....... 26.5 523.8 3.3 2,082 2.4
Education and health services............ 9.6 318.2 3.2 1,185 1.8
Leisure and hospitality.................. 13.5 280.6 5.1 793 4.2
Other services........................... 19.7 98.4 2.7 1,074 1.4
Government................................. 0.4 432.9 1.0 1,136 3.2
Cook, IL..................................... 156.6 2,499.5 1.8 1,085 1.7
Private industry........................... 155.2 2,200.1 2.1 1,068 2.0
Natural resources and mining............. 0.1 0.9 7.3 1,087 7.2
Construction............................. 12.9 70.1 5.8 1,312 1.6
Manufacturing............................ 6.7 187.0 0.0 1,121 4.1
Trade, transportation, and utilities..... 31.0 455.3 2.1 875 2.7
Information.............................. 2.8 55.0 0.3 1,649 3.5
Financial activities..................... 16.0 186.3 0.3 1,876 2.9
Professional and business services....... 33.4 455.4 3.1 1,368 2.2
Education and health services............ 16.4 422.0 1.7 893 0.4
Leisure and hospitality.................. 14.1 264.6 2.5 475 -0.6
Other services........................... 17.6 99.4 3.7 808 1.3
Government................................. 1.3 299.4 -0.7 1,207 -0.3
Harris, TX................................... 107.8 2,258.0 3.3 1,231 3.4
Private industry........................... 107.3 1,997.0 3.3 1,258 3.5
Natural resources and mining............. 1.8 93.3 5.4 3,224 5.0
Construction............................. 6.8 154.5 6.0 1,272 5.6
Manufacturing............................ 4.6 196.2 2.9 1,508 5.9
Trade, transportation, and utilities..... 24.3 465.2 3.2 1,101 4.2
Information.............................. 1.2 28.5 -1.4 1,396 2.3
Financial activities..................... 11.1 118.3 2.0 1,503 5.2
Professional and business services....... 21.7 394.4 2.3 1,528 0.7
Education and health services............ 14.8 266.9 2.4 928 1.1
Leisure and hospitality.................. 9.1 214.8 5.8 419 4.8
Other services........................... 11.6 64.2 2.9 746 5.5
Government................................. 0.6 261.1 2.6 1,025 1.8
Maricopa, AZ................................. 92.9 1,717.1 2.2 931 1.2
Private industry........................... 92.2 1,538.7 2.3 915 1.2
Natural resources and mining............. 0.5 8.4 3.3 816 -4.4
Construction............................. 7.3 93.5 0.4 944 0.0
Manufacturing............................ 3.2 114.4 0.6 1,364 2.9
Trade, transportation, and utilities..... 20.0 345.8 2.5 836 1.0
Information.............................. 1.5 33.8 6.6 1,232 6.8
Financial activities..................... 10.9 152.0 2.6 1,152 -1.1
Professional and business services....... 21.7 294.1 0.7 1,003 2.6
Education and health services............ 10.7 254.8 2.3 948 0.7
Leisure and hospitality.................. 7.3 190.6 3.6 437 3.6
Other services........................... 6.3 47.6 1.8 654 -0.6
Government................................. 0.7 178.5 1.4 1,053 1.4
Dallas, TX................................... 70.8 1,544.6 3.5 1,122 1.5
Private industry........................... 70.3 1,379.7 3.7 1,131 1.7
Natural resources and mining............. 0.6 9.9 2.8 3,831 -8.4
Construction............................. 4.0 77.2 8.2 1,070 4.5
Manufacturing............................ 2.7 107.5 -1.3 1,331 1.3
Trade, transportation, and utilities..... 15.4 309.7 4.6 1,008 0.8
Information.............................. 1.4 49.5 3.1 1,697 -4.1
Financial activities..................... 8.5 149.9 1.7 1,532 3.7
Professional and business services....... 15.9 308.7 5.1 1,293 4.3
Education and health services............ 8.7 178.6 2.5 979 1.2
Leisure and hospitality.................. 6.1 147.3 4.3 451 1.3
Other services........................... 6.8 41.0 2.6 723 1.3
Government................................. 0.5 164.9 1.8 1,048 0.1
Orange, CA................................... 107.2 1,477.2 1.8 1,033 1.5
Private industry........................... 105.9 1,330.8 1.8 1,021 1.6
Natural resources and mining............. 0.2 3.4 0.7 804 16.4
Construction............................. 6.5 80.9 3.3 1,144 2.2
Manufacturing............................ 4.9 157.6 -0.4 1,275 2.0
Trade, transportation, and utilities..... 16.7 253.4 1.8 949 2.0
Information.............................. 1.3 23.7 -4.3 1,574 8.6
Financial activities..................... 10.6 113.9 0.7 1,518 -2.3
Professional and business services....... 20.7 271.8 1.4 1,221 4.2
Education and health services............ 26.7 183.5 2.1 889 0.7
Leisure and hospitality.................. 7.9 194.8 3.0 438 0.7
Other services........................... 6.8 43.0 2.1 647 2.4
Government................................. 1.3 146.4 1.2 1,141 0.5
San Diego, CA................................ 99.9 1,338.5 2.1 1,044 1.2
Private industry........................... 98.5 1,117.3 2.4 1,025 1.0
Natural resources and mining............. 0.7 10.6 -3.9 692 5.3
Construction............................. 6.4 63.2 3.3 1,060 0.9
Manufacturing............................ 3.0 96.4 1.4 1,428 -1.9
Trade, transportation, and utilities..... 14.2 211.6 1.5 795 0.8
Information.............................. 1.2 24.3 -1.6 1,620 6.8
Financial activities..................... 9.3 70.0 -2.2 1,308 0.2
Professional and business services....... 18.1 226.8 2.4 1,603 2.6
Education and health services............ 27.5 181.8 3.0 891 1.9
Leisure and hospitality.................. 7.7 179.7 3.8 432 2.4
Other services........................... 7.3 48.7 4.2 559 0.5
Government................................. 1.4 221.2 0.6 1,136 1.9
King, WA..................................... 84.1 1,248.1 3.5 1,235 2.4
Private industry........................... 83.6 1,086.5 3.9 1,239 2.4
Natural resources and mining............. 0.4 2.7 -8.2 1,233 -10.2
Construction............................. 6.0 57.5 9.6 1,171 1.4
Manufacturing............................ 2.3 105.8 0.1 1,508 1.5
Trade, transportation, and utilities..... 14.8 232.5 5.1 1,109 3.6
Information.............................. 2.0 85.9 4.1 2,435 4.5
Financial activities..................... 6.4 65.9 1.8 1,441 -0.4
Professional and business services....... 15.7 205.5 3.8 1,501 2.0
Education and health services............ 20.9 160.8 4.0 920 1.3
Leisure and hospitality.................. 6.8 128.0 3.7 496 9.0
Other services........................... 8.4 41.9 5.1 796 1.4
Government................................. 0.5 161.6 1.1 1,207 2.3
Miami-Dade, FL............................... 93.4 1,026.2 2.5 913 3.3
Private industry........................... 93.0 904.8 3.0 875 3.8
Natural resources and mining............. 0.5 7.4 2.6 556 2.8
Construction............................. 5.3 35.9 11.2 863 3.7
Manufacturing............................ 2.7 37.3 2.1 854 3.6
Trade, transportation, and utilities..... 27.2 267.5 2.4 831 4.5
Information.............................. 1.5 18.3 4.4 1,462 0.6
Financial activities..................... 9.8 71.0 4.4 1,380 5.7
Professional and business services....... 19.7 139.6 3.1 1,050 2.4
Education and health services............ 10.0 160.5 0.9 894 3.0
Leisure and hospitality.................. 7.1 128.3 2.8 512 4.7
Other services........................... 8.1 37.9 4.2 582 3.2
Government................................. 0.3 121.4 -0.7 1,169 1.2
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Counties selected are based on 2013 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
second quarter 2014
Employment Average weekly
wage(1)
Establishments,
second quarter
State 2014 Percent Percent
(thousands) June change, Second change,
2014 June quarter second
(thousands) 2013-14 2014 quarter
2013-14
United States(2)........... 9,360.5 137,776.4 2.0 $940 2.1
Alabama.................... 117.7 1,872.9 0.7 806 1.6
Alaska..................... 21.9 344.9 0.5 1,014 4.6
Arizona.................... 146.0 2,486.0 1.9 888 1.3
Arkansas................... 87.1 1,168.1 1.5 745 1.5
California................. 1,371.9 15,905.6 2.8 1,072 2.4
Colorado................... 178.8 2,439.3 3.4 960 2.9
Connecticut................ 113.9 1,676.6 0.6 1,155 2.5
Delaware................... 29.6 429.0 2.5 976 1.2
District of Columbia....... 35.9 732.6 1.0 1,569 -0.5
Florida.................... 636.0 7,628.6 3.1 839 2.1
Georgia.................... 281.5 4,036.3 3.1 882 1.7
Hawaii..................... 38.9 624.6 1.1 845 2.7
Idaho...................... 54.4 659.2 2.5 697 2.2
Illinois................... 413.4 5,836.9 1.5 988 1.9
Indiana.................... 159.0 2,916.9 1.8 784 1.2
Iowa....................... 99.5 1,547.8 1.6 780 3.0
Kansas..................... 85.5 1,372.8 1.7 797 2.3
Kentucky................... 120.6 1,820.8 1.7 798 2.0
Louisiana.................. 129.3 1,921.6 1.4 843 2.4
Maine...................... 49.1 610.4 0.8 746 2.1
Maryland................... 166.6 2,594.4 0.9 1,020 1.6
Massachusetts.............. 228.3 3,407.0 1.4 1,158 2.4
Michigan................... 236.2 4,164.7 2.3 897 2.3
Minnesota.................. 163.6 2,782.0 1.3 947 1.9
Mississippi................ 70.9 1,101.1 0.5 705 2.0
Missouri................... 183.5 2,703.2 1.3 818 1.9
Montana.................... 43.9 453.4 1.1 734 2.4
Nebraska................... 71.3 956.2 1.4 756 2.7
Nevada..................... 75.8 1,210.1 3.4 833 0.6
New Hampshire.............. 49.8 637.2 1.2 955 4.3
New Jersey................. 264.9 3,944.8 0.8 1,097 1.2
New Mexico................. 56.6 801.0 0.6 794 1.7
New York................... 624.8 8,965.2 1.8 1,146 2.4
North Carolina............. 259.6 4,080.7 2.4 818 1.2
North Dakota............... 31.5 453.0 4.4 936 5.5
Ohio....................... 288.3 5,233.8 1.4 846 2.1
Oklahoma................... 106.9 1,578.0 1.0 816 2.6
Oregon..................... 136.2 1,748.4 2.4 874 2.9
Pennsylvania............... 351.2 5,719.8 1.0 933 1.6
Rhode Island............... 35.8 472.9 1.6 898 2.0
South Carolina............. 116.1 1,916.4 2.7 765 2.5
South Dakota............... 31.9 422.9 1.4 712 3.3
Tennessee.................. 145.3 2,755.7 1.8 836 2.0
Texas...................... 618.3 11,402.8 3.0 973 3.1
Utah....................... 89.9 1,297.5 2.9 796 1.7
Vermont.................... 24.4 307.0 1.0 813 0.7
Virginia................... 242.9 3,710.8 0.7 976 0.8
Washington................. 236.4 3,109.6 3.2 990 2.1
West Virginia.............. 49.8 711.3 -0.3 792 1.4
Wisconsin.................. 164.4 2,809.1 1.3 816 2.0
Wyoming.................... 25.5 295.3 1.6 871 3.1
Puerto Rico................ 48.6 897.0 -2.0 504 0.6
Virgin Islands............. 3.4 37.8 -2.2 728 2.8
(1) Average weekly wages were calculated using unrounded data.
(2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and
Unemployment Compensation for Federal Employees (UCFE) programs.
United States Department of Labor
