For release 10:00 a.m. (EDT), Thursday, September 18, 2014 USDL-14-1713
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
First Quarter 2014
From March 2013 to March 2014, employment increased in 281 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 7.5
percent over the year, compared with national job growth of 1.7 percent. Within Weld, the largest
employment increase occurred in natural resources and mining, which gained 2,145 jobs over the year
(24.1 percent). Peoria, Ill., had the largest over-the-year decrease in employment among the largest
counties in the U.S. with a loss of 2.6 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 3.8 percent over the year, growing to $1,027 in the first
quarter of 2014. Chester, Pa., had the largest over-the-year increase in average weekly wages with a gain
of 13.9 percent. Within Chester, an average weekly wage gain of $520, or 49.1 percent, in trade,
transportation, and utilities made the largest contribution to the county’s increase in average weekly
wages. Benton, Ark., experienced the largest decrease in average weekly wages with a loss of 3.2
percent over the year.
Table A. Large counties ranked by March 2014 employment, March 2013-14 employment
increase, and March 2013-14 percent increase in employment
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Employment in large counties
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March 2014 employment | Increase in employment, | Percent increase in employment,
(thousands) | March 2013-14 | March 2013-14
| (thousands) |
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| |
United States 134,555.0| United States 2,254.3| United States 1.7
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| |
Los Angeles, Calif. 4,125.8| Los Angeles, Calif. 79.4| Weld, Colo. 7.5
New York, N.Y. 2,453.1| Harris, Texas 64.0| York, S.C. 6.4
Cook, Ill. 2,413.6| New York, N.Y. 58.7| Lee, Fla. 6.3
Harris, Texas 2,226.8| Dallas, Texas 45.4| Sarasota, Fla. 5.8
Maricopa, Ariz. 1,749.9| King, Wash. 39.1| Wyandotte, Kan. 5.5
Dallas, Texas 1,515.6| Maricopa, Ariz. 39.0| Midland, Texas 5.4
Orange, Calif. 1,459.9| Santa Clara, Calif. 36.9| Montgomery, Texas 5.2
San Diego, Calif. 1,321.0| Orange, Calif. 35.0| Collier, Fla. 4.9
King, Wash. 1,214.7| Clark, Nev. 32.3| Sonoma, Calif. 4.8
Miami-Dade, Fla. 1,043.4| San Diego, Calif. 26.7| Fort Bend, Texas 4.8
| |
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Large County Employment
In March 2014, national employment was 134.6 million (as measured by the QCEW program). Over the
year, employment increased 1.7 percent, or 2.3 million. The 339 U.S. counties with 75,000 or more jobs
accounted for 72.0 percent of total U.S. employment and 78.3 percent of total wages. These 339
counties had a net job growth of 1.7 million over the year, accounting for 74.4 percent of the overall
U.S. employment increase.
Weld, Colo., had the largest percentage increase in employment (7.5 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 King, Wash. These counties had a combined over-
the-year employment gain of 286,600 jobs, which was 12.7 percent of the overall job increase for the
U.S. (See table A.)
Employment declined in 50 of the largest counties from March 2013 to March 2014. Peoria, Ill., had the
largest over-the-year percentage decrease in employment (-2.6 percent). Within Peoria, professional and
business services had the largest decrease in employment, with a loss of 1,240 jobs (-7.4 percent). St.
Clair, Ill. had the second largest percentage decrease in employment, followed by Atlantic, N.J.; Lake,
Ind.; and Arlington, Va. (See table 1.)
Table B. Large counties ranked by first quarter 2014 average weekly wages, first quarter 2013-14
increase in average weekly wages, and first 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
first quarter 2014 | wage, first quarter 2013-14 | weekly wage, first
| | quarter 2013-14
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| |
United States $1,027| United States $38| United States 3.8
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| |
New York, N.Y. $2,749| New York, N.Y. $294| Chester, Pa. 13.9
Santa Clara, Calif. 2,074| San Mateo, Calif. 181| New York, N.Y. 12.0
San Mateo, Calif. 2,058| Chester, Pa. 173| San Mateo, Calif. 9.6
Somerset, N.J. 2,048| San Francisco, Calif. 166| Forsyth, N.C. 9.6
San Francisco, Calif. 1,944| Suffolk, Mass. 150| San Francisco, Calif. 9.3
Fairfield, Conn. 1,922| Santa Clara, Calif. 137| Suffolk, Mass. 8.8
Suffolk, Mass. 1,852| Midland, Texas 104| Midland, Texas 8.5
Washington, D.C. 1,701| Middlesex, Mass. 90| Palm Beach, Fla. 7.8
Arlington, Va. 1,669| Forsyth, N.C. 90| Washington, Pa. 7.3
Morris, N.J. 1,646| Lake, Ill. 86| Elkhart, Ind. 7.2
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $1,027, a 3.8 percent increase, during the year ending
in the first quarter of 2014. Among the 339 largest counties, 323 had over-the-year increases in average
weekly wages. Chester, Pa., had the largest wage increase among the largest U.S. counties (13.9
percent).
Of the 339 largest counties, 15 experienced over-the-year decreases in average weekly wages. Benton,
Ark., had the largest percentage decrease in average weekly wages, with a loss of 3.2 percent. Within
Benton, professional and business services had the largest impact on the county’s average weekly wage
decrease. Within this industry, average weekly wages declined by $253 (-8.9 percent) over the year.
Cumberland, N.C., had the second largest percentage decrease in average weekly wages, followed by
Dutchess, N.Y.; Ocean, N.J.; and McLean, Ill. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in March 2014.
King, Wash., had the largest gain (3.3 percent). Within King, trade, transportation, and utilities had the
largest over-the-year employment level increase among all private industry groups with a gain of 10,023
jobs, or 4.7 percent. Cook, Ill., had the smallest percentage increase in employment (1.0 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. New York, N.Y.,
experienced the largest percentage gain in average weekly wages (12.0 percent). Within New York,
financial services had the largest impact on the county’s average weekly wage growth. Within this
industry, average weekly wages increased by $1,607, or 21.0 percent, over the year. Orange, Calif., had
the smallest increase in average weekly wages (2.7 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. March 2014 employment and 2014 first 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 134.6 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
first quarter of 2014 will be available 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 second quarter 2014 is scheduled to be released
on Thursday, December 18, 2014.
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| |
| County Changes for the 2014 County Employment and Wages News Releases |
| |
| Counties with annual average employment of 75,000 or more in 2013 are included in this release and |
| will be included in future 2014 releases. Five counties have been added to the publication tables: |
| Shelby, Ala.; Osceola, Fla.; Black Hawk, Iowa; Washington, Minn.; and Cleveland, Okla. |
| |
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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 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 time-tables.
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 7.3 |
| 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 2012 edition of
this publication, which was published in September 2013, contains selected data produced by Business
Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2013
version of this news release. Tables and additional content from Employment and Wages Annual
Averages 2012 are now available online at http://www.bls.gov/cew/cewbultn12.htm. The 2013 edition
of Employment and Wages Annual Averages Online will be available in September 2014.
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,
first quarter 2014
Employment Average weekly wage(2)
Establishments,
County(1) first quarter Percent Ranking Percent Ranking
2014 March change, by First change, by
(thousands) 2014 March percent quarter first percent
(thousands) 2013-14(3) change 2014 quarter change
2013-14(3)
United States(4)......... 9,358.3 134,555.0 1.7 - $1,027 3.8 -
Jefferson, AL............ 17.7 336.3 0.1 280 997 1.3 268
Madison, AL.............. 9.1 180.3 -0.1 290 1,049 1.7 241
Mobile, AL............... 9.6 164.8 0.8 218 819 0.9 292
Montgomery, AL........... 6.4 128.0 -0.6 312 785 0.6 303
Shelby, AL............... 5.0 77.5 1.9 128 963 4.9 41
Tuscaloosa, AL........... 4.3 87.2 1.9 128 800 0.3 314
Anchorage Borough, AK.... 8.3 150.6 0.3 265 1,070 3.0 136
Maricopa, AZ............. 92.9 1,749.9 2.3 102 977 3.3 117
Pima, AZ................. 18.6 353.0 0.0 282 821 1.5 252
Benton, AR............... 5.7 103.1 4.6 13 1,298 -3.2 339
Pulaski, AR.............. 14.4 242.1 0.0 282 881 3.2 124
Washington, AR........... 5.7 95.0 1.8 137 782 3.0 136
Alameda, CA.............. 57.0 690.3 2.5 88 1,298 4.3 59
Contra Costa, CA......... 29.7 335.9 2.7 80 1,268 1.5 252
Fresno, CA............... 31.0 345.0 2.9 72 755 2.6 167
Kern, CA................. 17.3 292.2 2.4 97 856 1.2 275
Los Angeles, CA.......... 441.9 4,125.8 2.0 118 1,096 3.8 84
Marin, CA................ 11.9 109.0 2.4 97 1,195 4.8 44
Monterey, CA............. 12.9 162.5 3.2 55 837 0.6 303
Orange, CA............... 106.5 1,459.9 2.5 88 1,121 2.7 159
Placer, CA............... 11.2 140.0 2.9 72 952 2.1 207
Riverside, CA............ 52.9 619.5 4.0 27 785 2.1 207
Sacramento, CA........... 52.3 607.1 1.6 148 1,083 3.0 136
San Bernardino, CA....... 51.3 646.0 3.7 39 798 1.1 279
San Diego, CA............ 99.6 1,321.0 2.1 114 1,131 6.8 13
San Francisco, CA........ 57.2 629.7 3.9 31 1,944 9.3 5
San Joaquin, CA.......... 16.8 211.7 2.0 118 803 2.6 167
San Luis Obispo, CA...... 9.7 109.7 2.9 72 788 0.3 314
San Mateo, CA............ 25.5 365.7 4.6 13 2,058 9.6 3
Santa Barbara, CA........ 14.6 189.0 3.3 50 915 2.1 207
Santa Clara, CA.......... 65.3 960.4 4.0 27 2,074 7.1 11
Santa Cruz, CA........... 9.1 93.5 3.2 55 871 0.5 310
Solano, CA............... 10.2 125.2 1.5 153 1,038 2.1 207
Sonoma, CA............... 19.0 188.8 4.8 9 871 0.9 292
Stanislaus, CA........... 14.4 168.5 2.1 114 802 1.4 261
Tulare, CA............... 9.2 144.0 2.0 118 687 6.3 17
Ventura, CA.............. 24.7 317.3 1.7 140 1,072 4.4 56
Yolo, CA................. 6.0 90.6 1.4 162 1,014 4.0 73
Adams, CO................ 9.1 176.9 4.7 11 915 2.6 167
Arapahoe, CO............. 19.3 298.9 2.6 83 1,250 4.8 44
Boulder, CO.............. 13.3 165.9 2.6 83 1,161 3.8 84
Denver, CO............... 27.0 449.9 3.9 31 1,329 4.8 44
Douglas, CO.............. 10.0 103.9 4.2 22 1,143 3.6 105
El Paso, CO.............. 16.8 242.9 1.4 162 876 2.3 187
Jefferson, CO............ 17.8 216.4 2.0 118 993 4.9 41
Larimer, CO.............. 10.3 137.0 2.7 80 860 4.2 69
Weld, CO................. 6.0 94.7 7.5 1 868 5.6 23
Fairfield, CT............ 33.6 410.5 0.6 238 1,922 2.3 187
Hartford, CT............. 26.2 493.6 0.7 224 1,383 5.2 35
New Haven, CT............ 22.9 354.9 0.6 238 1,026 1.3 268
New London, CT........... 7.0 119.6 -1.2 324 1,022 4.7 47
New Castle, DE........... 17.7 272.7 1.9 128 1,285 4.3 59
Washington, DC........... 35.6 727.3 1.2 177 1,701 5.3 31
Alachua, FL.............. 6.7 119.2 1.0 195 785 1.9 224
Brevard, FL.............. 14.7 189.2 0.7 224 856 0.9 292
Broward, FL.............. 65.6 736.8 2.8 78 911 3.4 111
Collier, FL.............. 12.5 133.5 4.9 8 828 0.2 319
Duval, FL................ 27.7 453.7 1.6 148 977 1.3 268
Escambia, FL............. 8.1 123.1 0.8 218 739 2.5 174
Hillsborough, FL......... 39.3 620.7 2.9 72 950 2.8 149
Lake, FL................. 7.6 85.8 3.0 64 639 1.9 224
Lee, FL.................. 19.8 228.1 6.3 3 749 1.2 275
Leon, FL................. 8.3 140.8 2.5 88 763 2.0 215
Manatee, FL.............. 9.9 111.5 3.4 47 706 0.4 311
Marion, FL............... 8.0 93.6 1.6 148 657 1.1 279
Miami-Dade, FL........... 93.4 1,043.4 2.6 83 948 4.4 56
Okaloosa, FL............. 6.2 78.4 0.9 207 785 0.6 303
Orange, FL............... 38.1 728.9 3.3 50 873 2.8 149
Osceola, FL.............. 5.9 80.2 4.1 25 683 1.3 268
Palm Beach, FL........... 51.7 545.2 3.5 44 1,010 7.8 8
Pasco, FL................ 10.2 103.9 2.6 83 657 3.0 136
Pinellas, FL............. 31.4 396.6 1.3 170 843 1.7 241
Polk, FL................. 12.6 200.4 2.0 118 727 3.0 136
Sarasota, FL............. 14.9 153.5 5.8 4 790 3.7 98
Seminole, FL............. 14.1 165.1 3.1 58 811 2.5 174
Volusia, FL.............. 13.5 157.6 2.5 88 685 3.8 84
Bibb, GA................. 4.6 81.3 2.9 72 772 3.5 108
Chatham, GA.............. 8.2 137.1 1.2 177 833 2.8 149
Clayton, GA.............. 4.3 111.1 1.7 140 962 4.3 59
Cobb, GA................. 22.4 322.8 4.6 13 1,101 1.1 279
De Kalb, GA.............. 18.6 282.3 3.8 37 1,056 4.0 73
Fulton, GA............... 43.7 749.8 2.3 102 1,500 5.4 27
Gwinnett, GA............. 24.9 319.4 3.5 44 988 3.2 124
Muscogee, GA............. 4.7 94.6 1.0 195 800 1.9 224
Richmond, GA............. 4.7 102.0 1.4 162 801 1.4 261
Honolulu, HI............. 24.9 457.2 1.1 184 893 1.8 230
Ada, ID.................. 13.8 208.8 3.9 31 857 5.9 21
Champaign, IL............ 4.5 87.6 -0.3 297 837 1.6 248
Cook, IL................. 156.3 2,413.6 1.0 195 1,248 5.0 39
Du Page, IL.............. 38.6 588.6 0.9 207 1,183 2.7 159
Kane, IL................. 13.9 198.6 1.7 140 841 2.6 167
Lake, IL................. 23.0 317.8 -0.4 303 1,484 6.2 18
McHenry, IL.............. 8.9 93.0 2.6 83 807 3.2 124
McLean, IL............... 3.9 83.3 -1.7 334 1,041 -1.0 335
Madison, IL.............. 6.1 93.9 -0.3 297 801 2.3 187
Peoria, IL............... 4.8 98.6 -2.6 339 963 -0.9 333
St. Clair, IL............ 5.7 90.1 -2.3 338 762 1.5 252
Sangamon, IL............. 5.4 125.5 0.6 238 993 3.2 124
Will, IL................. 16.1 209.7 2.3 102 867 4.2 69
Winnebago, IL............ 6.9 122.7 -0.9 317 834 3.1 132
Allen, IN................ 8.8 174.3 1.1 184 825 2.0 215
Elkhart, IN.............. 4.7 118.1 4.0 27 809 7.2 10
Hamilton, IN............. 8.7 121.9 4.2 22 1,022 3.7 98
Lake, IN................. 10.3 183.1 -1.9 336 863 -0.7 331
Marion, IN............... 23.8 568.0 1.0 195 1,052 0.0 324
St. Joseph, IN........... 5.9 115.8 1.0 195 777 1.0 288
Tippecanoe, IN........... 3.3 79.3 0.7 224 828 1.5 252
Vanderburgh, IN.......... 4.8 104.0 -0.4 303 804 3.3 117
Black Hawk, IA........... 3.7 74.7 0.4 260 826 1.1 279
Johnson, IA.............. 3.9 79.8 1.5 153 876 3.7 98
Linn, IA................. 6.5 126.0 0.6 238 958 3.8 84
Polk, IA................. 16.1 279.8 3.0 64 1,044 2.7 159
Scott, IA................ 5.5 87.6 0.5 253 780 1.6 248
Johnson, KS.............. 21.1 319.8 2.4 97 1,072 5.3 31
Sedgwick, KS............. 12.3 242.7 0.9 207 909 5.1 38
Shawnee, KS.............. 4.7 96.0 2.3 102 818 1.1 279
Wyandotte, KS............ 3.2 85.1 5.5 5 938 5.2 35
Boone, KY................ 4.1 77.1 1.5 153 822 1.2 275
Fayette, KY.............. 10.3 180.0 1.3 170 869 2.8 149
Jefferson, KY............ 24.3 431.6 1.0 195 994 3.8 84
Caddo, LA................ 7.4 114.3 -1.6 331 779 2.4 182
Calcasieu, LA............ 5.0 87.2 0.3 265 856 2.0 215
East Baton Rouge, LA..... 14.9 265.7 1.2 177 915 1.1 279
Jefferson, LA............ 13.8 191.2 0.3 265 875 2.1 207
Lafayette, LA............ 9.3 140.0 0.9 207 954 4.3 59
Orleans, LA.............. 11.5 187.2 3.6 42 980 1.8 230
St. Tammany, LA.......... 7.7 82.0 3.0 64 841 1.0 288
Cumberland, ME........... 12.6 167.6 0.9 207 912 1.7 241
Anne Arundel, MD......... 14.6 250.4 0.7 224 1,061 -0.4 329
Baltimore, MD............ 21.2 361.0 -0.1 290 985 0.6 303
Frederick, MD............ 6.3 94.0 -0.6 312 964 1.9 224
Harford, MD.............. 5.6 86.4 -0.9 317 910 -0.3 327
Howard, MD............... 9.5 156.3 -0.2 293 1,220 2.3 187
Montgomery, MD........... 33.0 450.7 0.6 238 1,364 3.6 105
Prince Georges, MD....... 15.6 298.9 -0.4 303 1,007 2.1 207
Baltimore City, MD....... 13.8 327.0 -0.5 307 1,192 1.7 241
Barnstable, MA........... 9.0 82.6 0.8 218 830 1.6 248
Bristol, MA.............. 16.3 214.4 1.6 148 874 2.6 167
Essex, MA................ 22.2 305.7 1.7 140 1,044 1.9 224
Hampden, MA.............. 16.2 196.7 0.6 238 923 2.8 149
Middlesex, MA............ 50.1 835.2 1.0 195 1,553 6.2 18
Norfolk, MA.............. 23.6 328.2 1.3 170 1,159 1.8 230
Plymouth, MA............. 14.2 176.7 1.1 184 894 2.2 199
Suffolk, MA.............. 24.7 611.6 2.2 110 1,852 8.8 6
Worcester, MA............ 22.1 321.6 1.0 195 976 2.7 159
Genesee, MI.............. 7.1 131.2 -0.3 297 804 4.3 59
Ingham, MI............... 6.2 148.4 -0.3 297 961 1.4 261
Kalamazoo, MI............ 5.2 111.0 0.6 238 917 2.0 215
Kent, MI................. 13.9 353.9 3.7 39 862 3.1 132
Macomb, MI............... 17.2 301.7 1.1 184 995 2.3 187
Oakland, MI.............. 38.1 677.8 1.1 184 1,107 2.9 143
Ottawa, MI............... 5.5 111.6 3.1 58 782 2.8 149
Saginaw, MI.............. 4.1 81.6 -0.7 315 814 4.5 53
Washtenaw, MI............ 8.2 196.8 0.5 253 996 1.3 268
Wayne, MI................ 30.7 684.1 0.4 260 1,121 6.4 16
Anoka, MN................ 6.8 113.9 2.0 118 887 2.1 207
Dakota, MN............... 9.4 174.4 1.2 177 997 4.3 59
Hennepin, MN............. 41.7 849.5 1.0 195 1,325 3.8 84
Olmsted, MN.............. 3.3 90.3 -1.4 328 1,031 2.7 159
Ramsey, MN............... 13.1 317.3 0.5 253 1,192 1.8 230
St. Louis, MN............ 5.3 93.9 0.0 282 813 3.3 117
Stearns, MN.............. 4.2 80.3 0.2 275 761 1.7 241
Washington, MN........... 5.2 73.7 0.9 207 841 3.8 84
Harrison, MS............. 4.5 81.9 -0.2 293 708 0.7 301
Hinds, MS................ 6.0 119.6 -0.2 293 840 2.9 143
Boone, MO................ 4.6 88.8 1.8 137 745 0.8 296
Clay, MO................. 5.2 91.0 3.6 42 880 3.4 111
Greene, MO............... 8.1 156.1 1.9 128 738 3.7 98
Jackson, MO.............. 19.4 345.6 0.3 265 992 0.8 296
St. Charles, MO.......... 8.5 130.5 2.1 114 828 4.3 59
St. Louis, MO............ 33.4 571.5 1.2 177 1,066 3.3 117
St. Louis City, MO....... 10.5 217.4 -1.3 325 1,170 4.3 59
Yellowstone, MT.......... 6.3 76.9 0.5 253 813 3.7 98
Douglas, NE.............. 18.3 323.1 2.3 102 933 2.2 199
Lancaster, NE............ 9.9 160.6 1.9 128 779 2.5 174
Clark, NV................ 51.2 861.4 3.9 31 856 3.0 136
Washoe, NV............... 13.9 190.1 3.3 50 856 2.8 149
Hillsborough, NH......... 12.0 190.2 1.0 195 1,086 4.4 56
Rockingham, NH........... 10.5 134.9 1.3 170 944 2.8 149
Atlantic, NJ............. 6.6 125.8 -2.1 337 808 1.5 252
Bergen, NJ............... 32.9 431.3 1.5 153 1,222 2.4 182
Burlington, NJ........... 11.0 193.8 -1.4 328 1,017 0.4 311
Camden, NJ............... 11.9 191.8 0.2 275 937 0.6 303
Essex, NJ................ 20.4 330.0 -1.3 325 1,343 1.1 279
Gloucester, NJ........... 6.1 97.5 1.6 148 839 1.8 230
Hudson, NJ............... 14.2 234.6 0.5 253 1,569 2.5 174
Mercer, NJ............... 11.0 233.5 1.4 162 1,490 0.7 301
Middlesex, NJ............ 21.9 388.4 0.6 238 1,307 3.7 98
Monmouth, NJ............. 20.1 240.8 1.1 184 1,003 1.5 252
Morris, NJ............... 17.1 275.6 0.7 224 1,646 4.3 59
Ocean, NJ................ 12.6 151.1 3.1 58 779 -1.3 336
Passaic, NJ.............. 12.3 165.0 -1.1 322 968 0.3 314
Somerset, NJ............. 10.1 175.3 0.8 218 2,048 -0.3 327
Union, NJ................ 14.3 218.5 -0.9 317 1,263 1.5 252
Bernalillo, NM........... 17.9 310.0 0.8 218 836 1.1 279
Albany, NY............... 10.2 222.2 0.1 280 1,008 2.9 143
Bronx, NY................ 17.5 249.2 1.9 128 881 2.2 199
Broome, NY............... 4.6 86.4 -1.1 322 750 2.3 187
Dutchess, NY............. 8.4 107.5 0.0 282 946 -1.6 337
Erie, NY................. 24.4 449.9 0.2 275 875 2.3 187
Kings, NY................ 56.4 556.1 4.6 13 760 0.8 296
Monroe, NY............... 18.5 372.8 0.7 224 919 1.5 252
Nassau, NY............... 53.2 595.9 1.7 140 1,091 1.8 230
New York, NY............. 125.9 2,453.1 2.5 88 2,749 12.0 2
Oneida, NY............... 5.3 101.0 -0.3 297 751 0.3 314
Onondaga, NY............. 13.0 238.4 -0.2 293 911 3.5 108
Orange, NY............... 10.1 133.8 0.3 265 798 0.4 311
Queens, NY............... 49.1 539.3 2.5 88 911 1.3 268
Richmond, NY............. 9.4 97.4 3.1 58 802 1.8 230
Rockland, NY............. 10.1 113.6 2.9 72 1,054 0.2 319
Saratoga, NY............. 5.8 78.5 1.1 184 865 0.6 303
Suffolk, NY.............. 51.7 618.4 0.3 265 1,029 -0.4 329
Westchester, NY.......... 36.2 402.6 0.3 265 1,430 5.4 27
Buncombe, NC............. 8.2 117.3 1.9 128 727 1.4 261
Catawba, NC.............. 4.2 81.2 1.7 140 720 1.7 241
Cumberland, NC........... 6.2 117.5 -1.0 320 732 -2.0 338
Durham, NC............... 7.5 185.7 1.3 170 1,373 3.9 79
Forsyth, NC.............. 9.0 175.8 1.0 195 1,029 9.6 3
Guilford, NC............. 14.1 267.9 0.9 207 883 1.7 241
Mecklenburg, NC.......... 33.4 600.1 3.1 58 1,382 5.2 35
New Hanover, NC.......... 7.4 100.0 2.2 110 775 1.6 248
Wake, NC................. 30.2 479.6 3.5 44 1,013 2.3 187
Cass, ND................. 6.5 110.6 3.0 64 869 3.8 84
Butler, OH............... 7.5 140.1 2.4 97 872 2.7 159
Cuyahoga, OH............. 35.4 696.5 0.0 282 1,054 4.0 73
Delaware, OH............. 4.6 79.6 0.4 260 1,123 4.0 73
Franklin, OH............. 29.8 686.6 1.9 128 1,024 4.1 72
Hamilton, OH............. 23.1 489.7 1.2 177 1,116 0.8 296
Lake, OH................. 6.3 92.2 -0.3 297 824 0.2 319
Lorain, OH............... 6.0 93.4 0.6 238 807 1.9 224
Lucas, OH................ 10.0 201.2 1.4 162 867 2.0 215
Mahoning, OH............. 5.9 95.9 0.4 260 686 2.5 174
Montgomery, OH........... 11.9 241.8 0.9 207 854 2.2 199
Stark, OH................ 8.7 155.1 0.9 207 751 2.2 199
Summit, OH............... 14.0 255.4 1.5 153 926 3.8 84
Warren, OH............... 4.4 80.1 4.1 25 862 2.7 159
Cleveland, OK............ 5.2 78.7 2.3 102 693 1.8 230
Oklahoma, OK............. 26.0 436.4 0.7 224 971 3.9 79
Tulsa, OK................ 21.3 337.1 0.7 224 976 4.7 47
Clackamas, OR............ 13.1 143.1 1.4 162 875 3.1 132
Jackson, OR.............. 6.7 77.7 2.0 118 733 5.0 39
Lane, OR................. 11.0 140.5 2.5 88 740 3.2 124
Marion, OR............... 9.6 135.1 3.4 47 757 2.3 187
Multnomah, OR............ 30.8 458.5 3.3 50 1,009 2.2 199
Washington, OR........... 17.1 260.6 3.7 39 1,213 4.6 52
Allegheny, PA............ 35.0 674.5 -0.6 312 1,130 4.5 53
Berks, PA................ 8.9 164.7 0.6 238 867 4.0 73
Bucks, PA................ 19.6 246.1 0.6 238 921 1.8 230
Butler, PA............... 5.0 83.4 0.6 238 905 1.0 288
Chester, PA.............. 15.1 238.3 0.8 218 1,415 13.9 1
Cumberland, PA........... 6.1 124.5 0.5 253 921 3.3 117
Dauphin, PA.............. 7.3 173.2 -0.1 290 1,038 4.5 53
Delaware, PA............. 13.7 214.1 1.2 177 1,121 5.5 25
Erie, PA................. 7.1 121.4 -0.5 307 759 0.1 323
Lackawanna, PA........... 5.9 96.3 -0.5 307 744 3.5 108
Lancaster, PA............ 12.8 221.6 1.8 137 803 2.0 215
Lehigh, PA............... 8.6 176.2 0.7 224 979 3.4 111
Luzerne, PA.............. 7.5 138.6 0.0 282 773 3.8 84
Montgomery, PA........... 27.1 465.9 0.3 265 1,346 4.2 69
Northampton, PA.......... 6.6 104.4 1.1 184 874 3.8 84
Philadelphia, PA......... 34.6 634.3 0.3 265 1,187 2.9 143
Washington, PA........... 5.3 84.9 0.7 224 1,067 7.3 9
Westmoreland, PA......... 9.3 129.6 -0.7 315 772 1.8 230
York, PA................. 8.9 170.4 0.3 265 845 1.1 279
Providence, RI........... 17.4 272.0 1.5 153 1,057 5.6 23
Charleston, SC........... 12.4 222.1 2.8 78 863 2.9 143
Greenville, SC........... 12.8 244.0 4.4 17 855 2.6 167
Horry, SC................ 7.9 110.1 2.5 88 571 1.4 261
Lexington, SC............ 5.9 104.4 4.4 17 717 -0.1 325
Richland, SC............. 9.1 206.6 2.1 114 845 2.2 199
Spartanburg, SC.......... 5.9 122.2 2.5 88 818 3.3 117
York, SC................. 4.8 81.0 6.4 2 785 2.5 174
Minnehaha, SD............ 6.7 118.7 2.3 102 852 5.4 27
Davidson, TN............. 19.4 448.5 3.0 64 1,041 3.3 117
Hamilton, TN............. 8.7 184.1 0.2 275 863 2.3 187
Knox, TN................. 11.2 220.6 1.0 195 837 0.8 296
Rutherford, TN........... 4.7 110.6 3.9 31 837 2.3 187
Shelby, TN............... 19.4 470.1 -0.4 303 1,017 3.9 79
Williamson, TN........... 7.0 105.1 4.0 27 1,189 -0.9 333
Bell, TX................. 5.0 110.6 0.9 207 821 4.3 59
Bexar, TX................ 36.7 784.5 2.3 102 917 3.0 136
Brazoria, TX............. 5.2 97.4 1.7 140 1,032 6.6 15
Brazos, TX............... 4.2 95.1 3.9 31 711 2.3 187
Cameron, TX.............. 6.4 133.9 1.9 128 581 1.4 261
Collin, TX............... 20.9 337.0 4.3 19 1,213 2.5 174
Dallas, TX............... 70.8 1,515.6 3.1 58 1,281 5.4 27
Denton, TX............... 12.3 200.2 4.2 22 895 2.4 182
El Paso, TX.............. 14.3 283.7 1.1 184 690 3.9 79
Fort Bend, TX............ 10.9 159.5 4.8 9 1,034 4.0 73
Galveston, TX............ 5.7 101.2 3.0 64 905 2.1 207
Gregg, TX................ 4.2 76.9 -1.6 331 879 3.8 84
Harris, TX............... 107.5 2,226.8 3.0 64 1,399 4.7 47
Hidalgo, TX.............. 11.8 238.5 1.4 162 597 2.9 143
Jefferson, TX............ 5.8 120.7 -0.5 307 1,016 3.6 105
Lubbock, TX.............. 7.2 130.0 2.0 118 750 4.7 47
McLennan, TX............. 4.9 101.9 0.0 282 781 2.2 199
Midland, TX.............. 5.2 88.0 5.4 6 1,322 8.5 7
Montgomery, TX........... 9.8 154.8 5.2 7 1,022 2.5 174
Nueces, TX............... 8.1 161.8 1.5 153 867 3.8 84
Potter, TX............... 3.9 77.4 1.3 170 775 2.8 149
Smith, TX................ 5.9 95.3 0.7 224 799 3.8 84
Tarrant, TX.............. 39.6 814.0 2.0 118 1,010 5.5 25
Travis, TX............... 34.6 646.6 4.3 19 1,100 3.4 111
Webb, TX................. 5.0 93.5 2.2 110 650 3.2 124
Williamson, TX........... 8.7 143.5 4.3 19 1,127 6.7 14
Davis, UT................ 7.6 110.7 3.4 47 778 1.0 288
Salt Lake, UT............ 39.6 614.6 2.7 80 947 3.4 111
Utah, UT................. 13.6 189.6 4.7 11 771 5.9 21
Weber, UT................ 5.6 94.3 1.4 162 721 4.9 41
Chittenden, VT........... 6.3 97.0 0.6 238 937 0.2 319
Arlington, VA............ 8.8 163.1 -1.8 335 1,669 3.2 124
Chesterfield, VA......... 8.1 121.8 2.0 118 866 1.3 268
Fairfax, VA.............. 35.3 576.4 -1.5 330 1,580 1.2 275
Henrico, VA.............. 10.4 178.5 0.9 207 1,110 6.2 18
Loudoun, VA.............. 10.5 145.9 1.5 153 1,244 3.9 79
Prince William, VA....... 8.2 116.3 0.6 238 832 -0.1 325
Alexandria City, VA...... 6.3 93.8 -1.6 331 1,368 5.3 31
Chesapeake City, VA...... 5.7 95.1 0.2 275 758 -0.7 331
Newport News City, VA.... 3.7 97.9 1.1 184 989 2.8 149
Norfolk City, VA......... 5.6 134.2 -0.5 307 969 3.7 98
Richmond City, VA........ 7.1 147.4 0.7 224 1,147 3.2 124
Virginia Beach City, VA.. 11.3 167.2 0.5 253 769 1.5 252
Benton, WA............... 6.1 77.0 0.7 224 959 0.6 303
Clark, WA................ 14.9 136.0 3.8 37 887 2.4 182
King, WA................. 88.8 1,214.7 3.3 50 1,353 4.7 47
Kitsap, WA............... 7.1 80.9 2.2 110 888 1.4 261
Pierce, WA............... 23.5 273.0 3.0 64 867 0.3 314
Snohomish, WA............ 21.1 264.2 1.7 140 1,161 6.9 12
Spokane, WA.............. 16.8 202.1 1.3 170 822 0.9 292
Thurston, WA............. 8.1 101.9 3.2 55 861 1.8 230
Whatcom, WA.............. 7.4 82.1 1.5 153 801 3.1 132
Yakima, WA............... 9.2 99.2 2.4 97 653 2.0 215
Kanawha, WV.............. 5.9 102.6 -1.3 325 845 2.7 159
Brown, WI................ 6.5 146.2 0.6 238 881 5.3 31
Dane, WI................. 14.1 308.1 1.1 184 970 3.4 111
Milwaukee, WI............ 24.5 471.3 0.0 282 992 2.0 215
Outagamie, WI............ 5.0 101.0 0.4 260 827 2.6 167
Waukesha, WI............. 12.3 226.5 0.7 224 992 2.0 215
Winnebago, WI............ 3.6 88.2 -1.0 320 928 2.4 182
San Juan, PR............. 11.2 256.0 -1.2 (5) 621 0.8 (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 72.0 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
first quarter 2014
Employment Average weekly
wage(1)
Establishments,
first quarter
County by NAICS supersector 2014 Percent Percent
(thousands) March change, First change,
2014 March quarter first
(thousands) 2013-14(2) 2014 quarter
2013-14(2)
United States(3) ............................ 9,358.3 134,555.0 1.7 $1,027 3.8
Private industry........................... 9,064.0 113,150.6 2.1 1,035 4.1
Natural resources and mining............. 135.2 1,920.1 2.5 1,249 6.4
Construction............................. 748.0 5,721.2 4.2 1,002 2.6
Manufacturing............................ 338.0 12,033.7 0.9 1,266 3.4
Trade, transportation, and utilities..... 1,910.1 25,564.3 2.1 842 3.1
Information.............................. 148.7 2,710.2 0.6 1,905 7.1
Financial activities..................... 829.1 7,588.3 0.7 2,115 10.0
Professional and business services....... 1,661.8 18,631.2 2.4 1,346 4.2
Education and health services............ 1,489.9 20,451.4 1.3 852 1.9
Leisure and hospitality.................. 789.7 14,134.7 3.0 388 1.8
Other services........................... 805.0 4,172.3 1.7 641 3.2
Government................................. 294.2 21,404.4 -0.2 983 2.5
Los Angeles, CA.............................. 441.9 4,125.8 2.0 1,096 3.8
Private industry........................... 436.2 3,587.4 2.2 1,073 4.0
Natural resources and mining............. 0.5 10.4 1.1 1,730 8.3
Construction............................. 12.8 117.3 2.5 1,065 1.3
Manufacturing............................ 12.5 363.2 -0.8 1,215 0.1
Trade, transportation, and utilities..... 52.9 775.4 2.0 880 3.0
Information.............................. 9.1 198.4 1.7 2,084 10.3
Financial activities..................... 23.6 206.9 -0.7 2,143 12.4
Professional and business services....... 46.0 597.8 2.6 1,350 3.0
Education and health services............ 204.7 708.1 1.1 795 3.2
Leisure and hospitality.................. 29.6 450.5 5.6 551 2.4
Other services........................... 26.5 144.2 3.1 646 2.4
Government................................. 5.7 538.3 0.2 1,253 3.0
New York, NY................................. 125.9 2,453.1 2.5 2,749 12.0
Private industry........................... 125.6 2,020.4 2.9 3,092 12.5
Natural resources and mining............. 0.0 0.2 9.5 3,901 61.5
Construction............................. 2.2 33.1 -0.4 1,702 2.0
Manufacturing............................ 2.3 25.1 -1.6 1,736 16.0
Trade, transportation, and utilities..... 20.7 255.9 1.3 1,339 3.6
Information.............................. 4.6 145.9 1.8 3,207 9.0
Financial activities..................... 19.1 354.2 1.7 9,261 21.0
Professional and business services....... 26.5 509.7 3.1 2,603 6.4
Education and health services............ 9.6 324.1 3.3 1,206 1.8
Leisure and hospitality.................. 13.5 269.8 5.1 809 2.5
Other services........................... 19.7 96.0 2.3 1,086 4.3
Government................................. 0.3 432.7 0.4 1,147 2.6
Cook, IL..................................... 156.3 2,413.6 1.0 1,248 5.0
Private industry........................... 155.0 2,120.7 1.4 1,258 5.4
Natural resources and mining............. 0.1 0.7 4.9 855 2.0
Construction............................. 12.8 58.8 3.9 1,323 2.3
Manufacturing............................ 6.7 185.7 -0.8 1,224 4.9
Trade, transportation, and utilities..... 30.8 442.4 1.3 937 4.1
Information.............................. 2.8 53.0 -1.0 2,027 2.9
Financial activities..................... 16.1 181.7 -0.2 3,270 16.9
Professional and business services....... 33.3 433.7 2.6 1,539 1.2
Education and health services............ 16.4 421.6 1.1 878 0.3
Leisure and hospitality.................. 14.0 242.6 2.0 453 1.6
Other services........................... 17.5 95.9 1.7 964 17.7
Government................................. 1.3 292.9 -1.7 1,176 1.4
Harris, TX................................... 107.5 2,226.8 3.0 1,399 4.7
Private industry........................... 107.0 1,962.6 3.0 1,447 4.9
Natural resources and mining............. 1.8 92.9 6.0 4,113 7.0
Construction............................. 6.7 150.4 4.1 1,314 4.5
Manufacturing............................ 4.7 193.8 2.3 1,648 3.1
Trade, transportation, and utilities..... 24.2 458.1 3.1 1,291 3.5
Information.............................. 1.2 28.4 0.8 1,485 2.3
Financial activities..................... 11.0 117.2 2.7 2,122 7.7
Professional and business services....... 21.6 385.2 1.5 1,729 6.4
Education and health services............ 14.8 265.0 2.2 955 2.5
Leisure and hospitality.................. 9.0 207.8 5.4 413 0.5
Other services........................... 11.6 62.8 2.9 769 6.7
Government................................. 0.5 264.2 2.6 1,042 2.6
Maricopa, AZ................................. 92.9 1,749.9 2.3 977 3.3
Private industry........................... 92.2 1,539.9 2.5 987 3.5
Natural resources and mining............. 0.5 8.3 -0.1 1,194 1.1
Construction............................. 7.3 91.8 3.4 980 4.6
Manufacturing............................ 3.2 114.3 0.7 1,502 2.4
Trade, transportation, and utilities..... 20.3 345.1 2.9 894 2.4
Information.............................. 1.5 32.7 3.2 1,450 12.7
Financial activities..................... 11.0 151.8 3.5 1,514 4.6
Professional and business services....... 22.0 291.7 1.5 1,058 5.4
Education and health services............ 10.8 256.3 1.3 903 1.2
Leisure and hospitality.................. 7.4 198.1 4.1 440 2.1
Other services........................... 6.4 47.7 2.2 670 5.8
Government................................. 0.7 210.0 0.4 900 1.2
Dallas, TX................................... 70.8 1,515.6 3.1 1,281 5.4
Private industry........................... 70.3 1,348.6 3.2 1,307 5.6
Natural resources and mining............. 0.6 9.7 5.2 4,429 12.7
Construction............................. 4.0 74.3 7.0 1,104 6.5
Manufacturing............................ 2.7 106.1 -3.3 1,606 3.4
Trade, transportation, and utilities..... 15.3 303.2 4.7 1,085 4.9
Information.............................. 1.4 48.2 2.1 2,369 1.2
Financial activities..................... 8.5 147.5 1.9 2,124 10.1
Professional and business services....... 15.8 300.9 3.8 1,402 6.1
Education and health services............ 8.7 178.3 2.5 1,063 5.7
Leisure and hospitality.................. 6.1 140.6 4.5 482 4.1
Other services........................... 6.8 39.3 2.1 731 2.2
Government................................. 0.5 167.0 2.4 1,068 2.8
Orange, CA................................... 106.5 1,459.9 2.5 1,121 2.7
Private industry........................... 105.2 1,315.1 2.6 1,100 2.9
Natural resources and mining............. 0.2 3.6 -2.5 670 5.3
Construction............................. 6.2 79.7 6.6 1,166 5.9
Manufacturing............................ 4.9 157.2 -0.6 1,426 4.3
Trade, transportation, and utilities..... 16.6 249.8 1.7 983 1.2
Information.............................. 1.2 24.0 -2.3 1,810 4.3
Financial activities..................... 10.3 111.7 0.0 1,839 4.7
Professional and business services....... 20.1 269.7 3.8 1,336 4.6
Education and health services............ 26.5 184.6 2.3 861 -0.1
Leisure and hospitality.................. 7.7 188.6 3.9 438 2.6
Other services........................... 6.4 41.9 3.0 636 2.3
Government................................. 1.3 144.8 1.6 1,317 2.3
San Diego, CA................................ 99.6 1,321.0 2.1 1,131 6.8
Private industry........................... 98.2 1,100.6 2.3 1,115 7.8
Natural resources and mining............. 0.7 10.0 -2.7 615 6.8
Construction............................. 6.1 61.6 4.7 1,065 3.5
Manufacturing............................ 3.0 96.0 0.6 1,786 16.4
Trade, transportation, and utilities..... 14.0 209.6 1.6 915 7.3
Information.............................. 1.2 24.4 -0.5 1,765 9.9
Financial activities..................... 9.0 69.6 -2.1 1,665 8.2
Professional and business services....... 17.6 226.0 2.0 1,642 11.9
Education and health services............ 27.5 181.7 1.8 870 1.6
Leisure and hospitality.................. 7.5 170.4 4.3 428 0.9
Other services........................... 6.9 47.5 5.9 557 0.7
Government................................. 1.4 220.3 0.9 1,213 2.8
King, WA..................................... 88.8 1,214.7 3.3 1,353 4.7
Private industry........................... 88.2 1,054.3 3.6 1,373 4.8
Natural resources and mining............. 0.4 2.4 -1.6 1,515 -4.7
Construction............................. 5.9 53.4 7.7 1,166 0.1
Manufacturing............................ 2.3 104.9 0.8 1,921 10.5
Trade, transportation, and utilities..... 15.0 225.4 4.7 1,159 4.1
Information.............................. 1.9 83.7 3.7 2,764 9.5
Financial activities..................... 6.5 64.7 0.7 1,913 2.8
Professional and business services....... 15.7 200.1 3.4 1,651 4.2
Education and health services............ 25.2 160.1 3.5 894 0.7
Leisure and hospitality.................. 6.8 119.3 4.3 473 4.2
Other services........................... 8.6 40.2 3.7 803 1.9
Government................................. 0.5 160.4 1.8 1,227 4.2
Miami-Dade, FL............................... 93.4 1,043.4 2.6 948 4.4
Private industry........................... 93.0 906.3 3.1 933 4.7
Natural resources and mining............. 0.5 10.4 11.3 478 -5.2
Construction............................. 5.2 34.7 9.5 897 9.9
Manufacturing............................ 2.7 36.7 2.5 913 5.3
Trade, transportation, and utilities..... 27.4 266.3 2.9 865 3.8
Information.............................. 1.6 18.0 3.4 1,571 7.0
Financial activities..................... 9.8 70.3 4.3 1,787 9.4
Professional and business services....... 19.7 140.5 3.2 1,103 4.6
Education and health services............ 10.2 160.8 0.8 904 1.8
Leisure and hospitality.................. 7.1 130.4 3.0 525 2.5
Other services........................... 8.2 37.8 3.4 573 3.8
Government................................. 0.3 137.1 -0.8 1,047 3.5
(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,
first quarter 2014
Employment Average weekly
wage(1)
Establishments,
first quarter
State 2014 Percent Percent
(thousands) March change, First change,
2014 March quarter first
(thousands) 2013-14 2014 quarter
2013-14
United States(2)........... 9,358.3 134,555.0 1.7 $1,027 3.8
Alabama.................... 117.5 1,849.5 0.6 825 1.6
Alaska..................... 22.0 319.1 0.3 1,023 3.5
Arizona.................... 145.8 2,540.8 1.9 918 3.1
Arkansas................... 87.2 1,152.6 0.3 784 2.5
California................. 1,377.6 15,572.9 2.8 1,165 4.5
Colorado................... 177.4 2,370.1 3.1 1,046 4.2
Connecticut................ 113.5 1,627.2 0.5 1,362 3.3
Delaware................... 29.1 412.5 2.0 1,110 3.9
District of Columbia....... 35.6 727.3 1.2 1,701 5.3
Florida.................... 633.6 7,752.4 2.9 868 3.0
Georgia.................... 280.1 3,974.8 2.6 972 3.4
Hawaii..................... 39.0 624.9 1.2 857 1.9
Idaho...................... 54.0 631.5 3.3 722 3.9
Illinois................... 411.8 5,651.2 0.9 1,104 4.2
Indiana.................... 159.6 2,842.5 1.2 845 1.7
Iowa....................... 98.8 1,485.4 1.5 824 3.0
Kansas..................... 84.8 1,343.0 1.7 840 4.1
Kentucky................... 120.0 1,784.1 1.1 811 2.7
Louisiana.................. 129.5 1,909.8 1.2 868 2.6
Maine...................... 48.8 565.9 0.7 786 1.9
Maryland................... 166.3 2,512.8 0.1 1,086 1.8
Massachusetts.............. 226.0 3,272.2 1.3 1,300 5.3
Michigan................... 236.6 4,013.5 1.7 950 3.1
Minnesota.................. 164.6 2,652.3 0.8 1,036 3.4
Mississippi................ 71.3 1,096.8 0.6 707 1.7
Missouri................... 182.4 2,634.6 1.0 866 2.9
Montana.................... 43.7 429.9 0.7 730 3.3
Nebraska................... 70.2 930.7 1.7 797 2.6
Nevada..................... 75.6 1,183.5 3.4 867 2.7
New Hampshire.............. 49.6 614.2 1.3 970 3.4
New Jersey................. 265.3 3,794.3 0.6 1,263 2.2
New Mexico................. 56.2 787.0 0.2 793 1.9
New York................... 621.7 8,699.5 1.6 1,460 7.3
North Carolina............. 259.7 4,003.2 1.7 914 3.4
North Dakota............... 31.1 428.9 3.3 944 6.7
Ohio....................... 288.3 5,071.5 1.3 909 2.8
Oklahoma................... 106.8 1,565.2 0.7 854 3.9
Oregon..................... 135.9 1,688.5 2.8 893 3.4
Pennsylvania............... 348.2 5,560.9 0.3 1,007 4.1
Rhode Island............... 35.6 449.7 1.1 996 4.4
South Carolina............. 118.7 1,873.6 2.7 787 1.9
South Dakota............... 31.7 400.2 1.4 741 4.5
Tennessee.................. 145.0 2,718.2 1.7 874 2.2
Texas...................... 616.5 11,220.6 2.6 1,062 4.5
Utah....................... 88.7 1,270.8 3.1 831 3.4
Vermont.................... 24.4 301.1 0.5 807 1.9
Virginia................... 242.4 3,613.2 0.0 1,050 2.2
Washington................. 251.8 2,966.3 2.6 1,068 3.8
West Virginia.............. 49.6 694.6 -0.9 779 1.4
Wisconsin.................. 163.2 2,694.5 1.0 856 2.9
Wyoming.................... 25.5 275.4 1.0 877 2.1
Puerto Rico................ 48.3 914.9 -1.8 521 1.4
Virgin Islands............. 3.4 38.3 -3.6 744 2.6
(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
