For release 10:00 a.m. (EST), Tuesday, March 7, 2017 USDL-17-0297
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Third Quarter 2016
From September 2015 to September 2016, employment increased in 307 of the 344 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. York, S.C., had the largest percentage
increase with a gain of 6.0 percent over the year, above the national job growth rate of 1.7 percent.
Within York, the largest employment increase occurred in professional and business services, which
gained 1,408 jobs over the year (15.0 percent). Midland, Texas, had the largest over-the-year percentage
decrease in employment among the largest counties in the U.S., with a loss of 5.8 percent. Within
Midland, trade, transportation, and utilities had the largest decrease in employment, with a loss of 1,504
jobs (-8.2 percent). County employment and wage data are from the Quarterly Census of Employment
and Wages (QCEW) program, which provides the only detailed quarterly and annual universe count of
establishments, employment, and wages at the county, metropolitan statistical area, state, and national
levels by detailed industry. These data are published within 6 months following the end of each quarter.
The U.S. average weekly wage increased 5.4 percent over the year, growing to $1,027 in the third
quarter of 2016. Clark, Nev., had the largest over-the-year percentage increase in average weekly wages
with a gain of 12.2 percent. Within Clark, an average weekly wage gain of $151 (24.0 percent) in leisure
and hospitality made the largest contribution to the county’s increase in average weekly wages.
Rockland, N.Y., experienced the largest percentage decrease in average weekly wages with a loss of
14.9 percent over the year. Within Rockland, manufacturing had the largest impact on the county’s
average weekly wage decline with a decrease of $2,912 (-63.6 percent) over the year.
Large County Employment
In September 2016, national employment was 142.9 million (as measured by the QCEW program). Over
the year, employment increased 1.7 percent, or 2.4 million. In September 2016, the 344 U.S. counties
with 75,000 or more jobs accounted for 72.5 percent of total U.S. employment and 77.7 percent of total
wages. These 344 counties had a net job growth of 2.0 million over the year, accounting for 80.5 percent
of the overall U.S. employment increase. The 5 counties with the largest increases in employment levels
had a combined over-the-year employment gain of 261,700 jobs, which was 10.7 percent of the overall
job increase for the U.S. (See table A.)
Employment declined in 33 of the largest counties from September 2015 to September 2016. Midland,
Texas, had the largest over-the-year percentage decrease in employment (-5.8 percent), followed by
Lafayette, La.; Gregg, Texas; Anchorage, Alaska; and Washington, Pa. (See table 1.)
Table A. Large counties ranked by September 2016 employment, September 2015-16 employment increase, and
September 2015-16 percent increase in employment
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Employment in large counties
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September 2016 employment | Increase in employment, | Percent increase in employment,
(thousands) | September 2015-16 | September 2015-16
| (thousands) |
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| |
United States 142,940.5| United States 2,444.7| United States 1.7
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| |
Los Angeles, Calif. 4,357.4| Los Angeles, Calif. 70.7| York, S.C. 6.0
Cook, Ill. 2,577.2| Maricopa, Ariz. 62.0| Williamson, Tenn. 5.8
New York, N.Y. 2,411.9| Dallas, Texas 49.6| Utah, Utah 5.3
Harris, Texas 2,262.3| King, Wash. 42.3| Collier, Fla. 5.1
Maricopa, Ariz. 1,885.6| New York, N.Y. 37.1| Washoe, Nev. 5.0
Dallas, Texas 1,662.8| Clark, Nev. 34.1| Placer, Calif. 4.9
Orange, Calif. 1,563.4| Orange, Calif. 32.3| Seminole, Fla. 4.8
San Diego, Calif. 1,415.6| Fulton, Ga. 31.7| Brevard, Fla. 4.7
King, Wash. 1,331.3| San Diego, Calif. 30.4| Volusia, Fla. 4.7
Miami-Dade, Fla. 1,107.4| Cook, Ill. 29.6| Thurston, Wash. 4.7
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $1,027, a 5.4 percent increase, during the year ending
in the third quarter of 2016. Among the 344 largest counties, 339 had over-the-year increases in average
weekly wages. Clark, Nev., had the largest percentage wage increase among the largest U.S. counties
(12.2 percent). (See table B.)
Of the 344 largest counties, 5 experienced over-the-year decreases in average weekly wages. Rockland,
N.Y., had the largest percentage decrease in average weekly wages (-14.9 percent), followed by
Lafayette, La.; Benton, Ark.; Lake, Ill.; and Midland, Texas. (See table 1.)
Table B. Large counties ranked by third quarter 2016 average weekly wages, third quarter 2015-16
increase in average weekly wages, and third quarter 2015-16 percent increase in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Increase in average weekly | Percent increase in average
third quarter 2016 | wage, third quarter 2015-16 | weekly wage, third
| | quarter 2015-16
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| |
United States $1,027| United States $53| United States 5.4
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| |
Santa Clara, Calif. $2,260| Santa Clara, Calif. $186| Clark, Nev. 12.2
San Mateo, Calif. 2,098| San Mateo, Calif. 172| Manatee, Fla. 10.7
San Francisco, Calif. 1,892| San Francisco, Calif. 150| Hillsborough, N.H. 10.4
New York, N.Y. 1,879| Middlesex, Mass. 139| Elkhart, Ind. 10.3
Washington, D.C. 1,728| King, Wash. 119| Boone, Ky. 10.3
Suffolk, Mass. 1,660| Alameda, Calif. 112| McLean, Ill. 10.2
Arlington, Va. 1,648| Hillsborough, N.H. 107| Dane, Wis. 10.1
King, Wash. 1,582| Clark, Nev. 103| Middlesex, Mass. 9.8
Middlesex, Mass. 1,555| Suffolk, Mass. 96| Washington, Ark. 9.5
Fairfax, Va. 1,546| Ramsey, Minn. 95| Alachua, Fla. 9.5
| Dane, Wis. 95|
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Ten Largest U.S. Counties
Among the 10 largest counties, 9 had over-the-year percentage increases in employment in September
2016. Maricopa, Ariz., had the largest gain (3.4 percent). Within Maricopa, professional and business
services had the largest over-the-year employment level increase, with a gain of 12,662 jobs, or 4.0
percent. Harris, Texas, had the only percentage decrease in employment among the 10 largest counties
(-0.9 percent). (See table 2.)
Average weekly wages increased over the year in all of the 10 largest U.S. counties. King, Wash.,
experienced the largest percentage gain in average weekly wages (8.1 percent). Within King, trade,
transportation, and utilities had the largest impact on the county’s average weekly wage growth. Within
trade, transportation, and utilities, average weekly wages increased by $210, or 17.8 percent, over the
year. Harris, Texas, had the smallest percentage gain in average weekly wages among the 10 largest
counties (2.1 percent).
For More Information
The tables included in this release contain data for the nation and for the 344 U.S. counties with annual
average employment levels of 75,000 or more in 2015. September 2016 employment and 2016 third
quarter average weekly wages for all states are provided in table 3 of this release.
The data are derived from reports submitted by employers who are subject to unemployment insurance
(UI) laws. The 9.8 million employer reports cover 142.9 million full- and part-time workers. Data for the
third quarter of 2016 will be available later at www.bls.gov/cew. Additional information about the
quarterly employment and wages data is available in the Technical Note. More information about
QCEW data may be obtained by calling (202) 691-6567.
The most current news release on quarterly measures of gross job flows is available from QCEW
Business Employment Dynamics at www.bls.gov/news.release/pdf/cewbd.pdf.
Several BLS regional offices issue QCEW news releases targeted to local data users. Links to these
releases are available at www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for fourth quarter 2016 is scheduled to be released
on Wednesday, June 7, 2017.
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 (NAICS). Data for 2016 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 345 counties
presented in this release were derived using 2015 preliminary annual averages of employment. For
2016 data, four counties have been added to the publication tables: Merced, Calif.; Napa, Calif.;
Bay, Fla.; and Merrimack, N.H. These counties will be included in all 2016 quarterly releases. Two
counties, Black Hawk, Iowa, and Ector, Texas, which were published in the 2015 releases, will be
excluded from this and future 2016 releases because their 2015 annual average employment levels
were less than 75,000. The counties in table 2 are selected and sorted each year based on the annual
average employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released by the
individual states. These potential differences result from the states' 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- | 634,000 establish-
| submitted by 9.7 | ministrative records| ments
| million establish- | submitted by 7.7 |
| ments in first | million private-sec-|
| quarter of 2016 | 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 | -Within 6 months | -7 months after the | -Usually first Friday
| after the end of | end of each quarter| of following month
| each quarter | |
-----------|---------------------|----------------------|------------------------
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, met-| and contractions at | industry
| ropolitan statisti-| the national level |
| cal area (MSA), | by NAICS supersec- |
| state, and national| tors and by size of |
| levels by detailed | firm, and at the |
| industry | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew |--www.bls.gov/bdm |--www.bls.gov/ces
Web sites | | |
---------------------------------------------------------------------------------
Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly
contribution reports submitted to the SWAs by employers. For federal 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 9.5
million employer reports of employment and wages submitted by states to the BLS in 2015. 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 2015, UI and UCFE programs
covered workers in 139.5 million jobs. The estimated 134.4 million workers in these jobs (after
adjustment for multiple jobholders) represented 96.5 percent of civilian wage and salary
employment. Covered workers received $7.385 trillion in pay, representing 94.0 percent of the
wage and salary component of personal income and 40.9 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 2015 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. Adjusted data account for improvements in
reporting employment and wages for individual and multi-unit establishments. To accomplish this,
adjustments were implemented to account for: administrative changes caused by multi-unit
employers who start reporting for each individual establishment rather than as a single entity (first
quarter of 2008); selected large administrative changes in employment and wages (second quarter
of 2011); and state verified improvements in reporting of employment and wages (third quarter of
2014). These 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 2015 edition
of this publication, which was published in September 2016, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2016 version of this news release. Tables and additional content from the 2015 edition
of Employment and Wages Annual Averages Online are now available at
www.bls.gov/cew/cewbultn15.htm. The 2016 edition of Employment and Wages Annual Averages
Online will be available in September 2017.
News releases on quarterly measures of gross job flows also are available from BED at
www.bls.gov/bdm, (202) 691-6467, or 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: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 345 largest counties,
third quarter 2016
Employment Average weekly wage(2)
Establishments,
County(1) third quarter Percent Ranking Percent Ranking
2016 September change, by Third change, by
(thousands) 2016 September percent quarter third percent
(thousands) 2015-16(3) change 2016 quarter change
2015-16(3)
United States(4)......... 9,800.8 142,940.5 1.7 - $1,027 5.4 -
Jefferson, AL............ 18.3 340.8 0.6 273 1,010 5.1 211
Madison, AL.............. 9.4 192.8 3.0 69 1,122 6.4 100
Mobile, AL............... 10.0 169.2 1.0 238 887 6.4 100
Montgomery, AL........... 6.4 131.6 2.0 142 858 4.9 226
Shelby, AL............... 5.7 84.3 0.7 265 970 6.7 79
Tuscaloosa, AL........... 4.5 92.9 0.3 297 824 1.7 334
Anchorage Borough, AK.... 8.3 153.3 -2.2 340 1,102 1.8 332
Maricopa, AZ............. 95.0 1,885.6 3.4 46 996 7.1 56
Pima, AZ................. 18.7 360.4 1.4 203 866 6.4 100
Benton, AR............... 6.1 116.5 3.3 51 934 -2.0 342
Pulaski, AR.............. 14.4 248.8 1.2 226 922 6.0 130
Washington, AR........... 5.9 104.4 2.9 74 862 9.5 9
Alameda, CA.............. 61.6 757.1 2.3 123 1,394 8.7 18
Butte, CA................ 8.3 82.7 2.9 74 780 7.3 47
Contra Costa, CA......... 31.7 362.2 3.0 69 1,245 6.3 108
Fresno, CA............... 33.9 385.0 1.9 153 807 4.5 254
Kern, CA................. 18.2 324.8 -0.3 316 861 4.6 247
Los Angeles, CA.......... 472.3 4,357.4 1.6 183 1,132 5.8 152
Marin, CA................ 12.5 114.6 2.0 142 1,243 6.0 130
Merced, CA............... 6.4 82.1 2.4 112 802 7.2 52
Monterey, CA............. 13.5 204.0 0.7 265 891 7.7 33
Napa, CA................. 5.8 77.7 -0.8 328 1,016 6.2 117
Orange, CA............... 116.1 1,563.4 2.1 131 1,153 6.8 68
Placer, CA............... 12.5 158.4 4.9 6 1,040 5.4 187
Riverside, CA............ 60.4 688.2 3.5 44 844 7.9 27
Sacramento, CA........... 55.9 640.4 2.7 87 1,117 5.0 215
San Bernardino, CA....... 56.4 706.2 2.6 96 881 7.8 30
San Diego, CA............ 107.6 1,415.6 2.2 125 1,130 4.9 226
San Francisco, CA........ 60.1 709.5 3.1 58 1,892 8.6 20
San Joaquin, CA.......... 17.4 244.1 1.8 164 875 5.0 215
San Luis Obispo, CA...... 10.3 115.2 2.1 131 861 4.7 242
San Mateo, CA............ 27.8 395.3 2.7 87 2,098 8.9 16
Santa Barbara, CA........ 15.3 199.1 1.0 238 999 6.8 68
Santa Clara, CA.......... 70.9 1,052.5 2.7 87 2,260 9.0 15
Santa Cruz, CA........... 9.5 107.0 2.9 74 936 5.3 198
Solano, CA............... 11.1 136.9 2.0 142 1,054 7.2 52
Sonoma, CA............... 19.7 206.0 2.1 131 993 6.3 108
Stanislaus, CA........... 15.0 189.4 3.3 51 887 5.8 152
Tulare, CA............... 10.1 163.0 1.6 183 744 8.1 24
Ventura, CA.............. 26.3 316.7 0.6 273 1,019 6.5 90
Yolo, CA................. 6.6 103.2 1.4 203 1,114 7.7 33
Adams, CO................ 10.5 201.2 3.4 46 1,016 6.8 68
Arapahoe, CO............. 21.5 322.6 1.8 164 1,200 7.1 56
Boulder, CO.............. 14.8 178.2 2.7 87 1,216 4.6 247
Denver, CO............... 31.0 500.9 3.1 58 1,248 4.5 254
Douglas, CO.............. 11.6 117.1 2.5 105 1,118 7.0 59
El Paso, CO.............. 18.7 267.9 3.2 54 934 6.5 90
Jefferson, CO............ 20.6 234.4 1.8 164 1,046 5.4 187
Larimer, CO.............. 11.6 156.4 4.3 14 938 5.4 187
Weld, CO................. 7.0 100.8 -0.3 316 912 5.6 168
Fairfield, CT............ 35.2 424.3 0.2 300 1,479 5.0 215
Hartford, CT............. 27.6 507.2 0.2 300 1,196 4.9 226
New Haven, CT............ 23.8 363.4 0.6 273 1,063 4.0 286
New London, CT........... 7.4 123.2 0.5 283 1,016 7.6 39
New Castle, DE........... 19.7 286.0 0.5 283 1,131 5.3 198
Washington, DC........... 39.2 759.2 1.7 177 1,728 3.8 292
Alachua, FL.............. 7.0 128.7 3.1 58 880 9.5 9
Bay, FL.................. 5.5 77.4 0.9 250 754 5.0 215
Brevard, FL.............. 15.3 203.2 4.7 8 932 7.0 59
Broward, FL.............. 68.0 781.2 2.5 105 951 5.8 152
Collier, FL.............. 13.5 135.8 5.1 4 869 6.8 68
Duval, FL................ 28.6 490.3 3.4 46 967 6.4 100
Escambia, FL............. 8.1 131.4 3.8 29 809 6.3 108
Hillsborough, FL......... 40.9 666.3 3.7 34 993 8.4 21
Lake, FL................. 7.9 93.9 4.2 16 715 5.9 139
Lee, FL.................. 21.3 247.6 4.5 12 806 5.4 187
Leon, FL................. 8.6 147.9 3.1 58 841 5.9 139
Manatee, FL.............. 10.4 116.1 2.7 87 816 10.7 2
Marion, FL............... 8.1 100.0 3.8 29 719 9.3 12
Miami-Dade, FL........... 96.5 1,107.4 2.6 96 983 6.0 130
Okaloosa, FL............. 6.3 82.2 2.9 74 855 4.8 233
Orange, FL............... 40.6 797.1 3.2 54 904 6.0 130
Osceola, FL.............. 6.6 89.1 3.8 29 707 5.5 177
Palm Beach, FL........... 54.8 579.8 3.6 40 973 5.0 215
Pasco, FL................ 10.6 114.2 4.1 18 717 6.2 117
Pinellas, FL............. 32.3 418.6 2.6 96 900 6.3 108
Polk, FL................. 12.9 210.0 3.2 54 783 5.7 160
Sarasota, FL............. 15.5 162.6 2.9 74 838 7.9 27
Seminole, FL............. 14.6 184.7 4.8 7 852 6.0 130
Volusia, FL.............. 13.9 169.2 4.7 8 727 4.3 269
Bibb, GA................. 4.5 81.7 1.8 164 790 4.1 279
Chatham, GA.............. 8.7 148.5 1.0 238 873 6.5 90
Clayton, GA.............. 4.5 120.9 3.9 26 981 7.6 39
Cobb, GA................. 23.9 345.6 2.4 112 1,095 9.4 11
DeKalb, GA............... 19.8 295.7 2.0 142 1,045 7.2 52
Fulton, GA............... 47.2 830.7 4.0 24 1,339 5.4 187
Gwinnett, GA............. 27.1 345.0 3.0 69 980 3.5 304
Hall, GA................. 4.7 83.1 2.6 96 861 4.9 226
Muscogee, GA............. 5.0 92.7 0.1 305 814 6.5 90
Richmond, GA............. 4.8 104.4 1.2 226 887 7.5 41
Honolulu, HI............. 25.6 472.9 2.1 131 997 6.9 63
Ada, ID.................. 14.9 229.3 4.2 16 905 7.2 52
Champaign, IL............ 4.3 90.4 -0.9 329 894 2.8 320
Cook, IL................. 152.8 2,577.2 1.2 226 1,159 4.5 254
DuPage, IL............... 37.9 610.8 0.4 292 1,156 3.7 296
Kane, IL................. 13.7 209.9 0.2 300 930 7.0 59
Lake, IL................. 22.2 334.9 -0.5 321 1,279 -0.9 341
McHenry, IL.............. 8.7 98.9 1.3 216 863 6.7 79
McLean, IL............... 3.8 83.5 -2.0 338 985 10.2 6
Madison, IL.............. 6.0 97.9 -1.4 335 807 2.0 329
Peoria, IL............... 4.6 100.8 -0.7 325 975 7.5 41
St. Clair, IL............ 5.5 93.2 -0.7 325 821 4.6 247
Sangamon, IL............. 5.2 129.9 -0.4 318 1,011 0.6 337
Will, IL................. 16.1 231.4 1.5 195 919 7.7 33
Winnebago, IL............ 6.6 127.3 -1.2 333 863 6.9 63
Allen, IN................ 8.8 185.3 1.0 238 834 4.8 233
Elkhart, IN.............. 4.7 128.5 3.1 58 869 10.3 4
Hamilton, IN............. 9.2 138.5 2.5 105 963 5.2 205
Lake, IN................. 10.3 190.0 1.0 238 877 4.2 275
Marion, IN............... 23.9 598.0 1.9 153 1,036 7.0 59
St. Joseph, IN........... 5.7 124.7 1.8 164 834 4.8 233
Tippecanoe, IN........... 3.4 83.8 1.4 203 870 4.8 233
Vanderburgh, IN.......... 4.8 108.3 1.6 183 822 5.0 215
Johnson, IA.............. 4.1 83.9 2.1 131 970 5.8 152
Linn, IA................. 6.7 130.5 0.6 273 1,000 7.8 30
Polk, IA................. 17.1 296.9 2.9 74 1,040 5.9 139
Scott, IA................ 5.6 91.0 -0.4 318 843 5.5 177
Johnson, KS.............. 23.5 338.6 1.3 216 1,029 6.3 108
Sedgwick, KS............. 12.8 248.5 0.6 273 884 6.5 90
Shawnee, KS.............. 5.2 98.5 1.7 177 839 7.3 47
Wyandotte, KS............ 3.6 92.3 2.5 105 1,006 6.7 79
Boone, KY................ 4.4 84.4 2.4 112 909 10.3 4
Fayette, KY.............. 10.9 194.3 2.2 125 917 4.7 242
Jefferson, KY............ 25.4 465.3 3.0 69 1,007 7.8 30
Caddo, LA................ 7.2 113.5 -1.1 332 813 2.0 329
Calcasieu, LA............ 5.2 94.2 1.2 226 920 4.7 242
East Baton Rouge, LA..... 15.3 273.5 1.9 153 955 4.5 254
Jefferson, LA............ 13.6 193.1 0.4 292 922 5.5 177
Lafayette, LA............ 9.4 129.2 -5.6 343 892 -3.4 343
Orleans, LA.............. 12.3 192.7 1.8 164 964 4.8 233
St. Tammany, LA.......... 8.0 88.4 1.1 235 852 1.1 336
Cumberland, ME........... 13.7 180.4 1.4 203 937 9.3 12
Anne Arundel, MD......... 15.0 269.1 1.9 153 1,083 3.8 292
Baltimore, MD............ 21.2 374.6 0.7 265 1,032 5.8 152
Frederick, MD............ 6.4 100.5 0.9 250 968 6.3 108
Harford, MD.............. 5.8 92.4 1.6 183 1,008 8.4 21
Howard, MD............... 10.0 169.1 1.6 183 1,259 6.6 87
Montgomery, MD........... 32.6 466.2 0.8 257 1,353 5.9 139
Prince George's, MD...... 15.9 317.9 2.1 131 1,112 5.0 215
Baltimore City, MD....... 13.6 338.7 0.9 250 1,209 3.9 289
Barnstable, MA........... 9.4 101.8 0.5 283 856 5.7 160
Bristol, MA.............. 17.3 224.7 1.2 226 916 3.9 289
Essex, MA................ 24.4 325.5 1.3 216 1,069 5.8 152
Hampden, MA.............. 17.8 209.6 1.7 177 932 5.7 160
Middlesex, MA............ 53.9 889.4 1.6 183 1,555 9.8 8
Norfolk, MA.............. 24.9 349.8 1.8 164 1,144 4.1 279
Plymouth, MA............. 15.5 191.6 1.7 177 944 3.7 296
Suffolk, MA.............. 28.3 665.9 3.6 40 1,660 6.1 125
Worcester, MA............ 24.4 342.6 2.0 142 1,018 5.2 205
Genesee, MI.............. 6.9 134.3 0.9 250 854 4.4 262
Ingham, MI............... 6.0 151.3 2.7 87 955 4.9 226
Kalamazoo, MI............ 5.0 117.8 2.4 112 943 5.2 205
Kent, MI................. 14.3 390.8 3.0 69 908 4.2 275
Macomb, MI............... 17.7 322.7 1.4 203 1,022 7.7 33
Oakland, MI.............. 39.3 727.5 2.4 112 1,124 5.9 139
Ottawa, MI............... 5.6 124.5 1.3 216 865 6.3 108
Saginaw, MI.............. 4.0 85.3 0.2 300 832 7.5 41
Washtenaw, MI............ 8.1 209.4 3.6 40 1,109 5.5 177
Wayne, MI................ 30.6 715.1 1.8 164 1,120 5.6 168
Anoka, MN................ 6.7 121.0 0.7 265 1,027 6.2 117
Dakota, MN............... 9.4 188.0 1.8 164 991 5.2 205
Hennepin, MN............. 39.9 912.2 2.4 112 1,277 6.2 117
Olmsted, MN.............. 3.3 96.2 1.2 226 1,151 3.7 296
Ramsey, MN............... 12.7 331.1 0.8 257 1,162 8.9 16
St. Louis, MN............ 5.1 98.2 0.1 305 874 4.9 226
Stearns, MN.............. 4.2 85.7 0.6 273 884 7.3 47
Washington, MN........... 5.2 82.3 1.5 195 870 6.5 90
Harrison, MS............. 4.5 85.5 1.9 153 713 2.1 327
Hinds, MS................ 5.9 120.7 0.3 297 873 5.6 168
Boone, MO................ 4.8 93.5 1.5 195 833 4.8 233
Clay, MO................. 5.5 104.8 4.3 14 899 5.3 198
Greene, MO............... 8.5 164.7 1.8 164 802 5.9 139
Jackson, MO.............. 20.9 365.9 2.8 83 1,024 3.2 312
St. Charles, MO.......... 9.0 145.7 2.7 87 822 6.1 125
St. Louis, MO............ 36.3 599.8 1.0 238 1,057 5.3 198
St. Louis City, MO....... 13.2 228.7 1.0 238 1,104 5.7 160
Yellowstone, MT.......... 6.4 82.2 0.8 257 879 3.9 289
Douglas, NE.............. 19.3 338.7 1.6 183 983 5.4 187
Lancaster, NE............ 10.3 170.0 1.4 203 845 6.0 130
Clark, NV................ 56.4 947.0 3.7 34 947 12.2 1
Washoe, NV............... 15.0 214.8 5.0 5 932 6.2 117
Hillsborough, NH......... 12.3 200.4 1.4 203 1,137 10.4 3
Merrimack, NH............ 5.1 77.2 1.9 153 954 7.3 47
Rockingham, NH........... 11.0 149.5 2.1 131 989 5.5 177
Atlantic, NJ............. 6.5 128.6 1.0 238 843 2.9 318
Bergen, NJ............... 32.8 446.6 0.5 283 1,180 3.8 292
Burlington, NJ........... 10.9 203.3 2.6 96 1,044 5.0 215
Camden, NJ............... 11.9 203.0 2.2 125 988 5.0 215
Essex, NJ................ 20.4 338.0 1.8 164 1,251 6.1 125
Gloucester, NJ........... 6.3 106.8 3.5 44 875 4.5 254
Hudson, NJ............... 14.7 255.6 3.7 34 1,355 5.9 139
Mercer, NJ............... 11.1 245.8 1.8 164 1,335 7.1 56
Middlesex, NJ............ 22.0 418.1 2.8 83 1,191 4.1 279
Monmouth, NJ............. 20.1 259.4 1.5 195 975 4.5 254
Morris, NJ............... 16.9 285.8 0.0 308 1,478 6.8 68
Ocean, NJ................ 13.0 165.9 1.6 183 814 6.3 108
Passaic, NJ.............. 12.3 166.4 1.3 216 996 5.8 152
Somerset, NJ............. 10.0 184.5 1.9 153 1,482 2.4 325
Union, NJ................ 14.2 220.0 1.3 216 1,234 4.1 279
Bernalillo, NM........... 18.3 327.2 2.0 142 890 5.6 168
Albany, NY............... 10.4 233.5 1.6 183 1,065 2.7 322
Bronx, NY................ 18.8 299.8 0.5 283 992 5.6 168
Broome, NY............... 4.6 87.4 0.9 250 808 7.4 44
Dutchess, NY............. 8.5 111.5 0.7 265 979 5.4 187
Erie, NY................. 24.9 471.3 0.9 250 907 6.0 130
Kings, NY................ 62.0 688.1 4.1 18 866 4.1 279
Monroe, NY............... 19.1 384.9 0.6 273 974 4.6 247
Nassau, NY............... 54.4 627.2 2.1 131 1,092 2.8 320
New York, NY............. 130.1 2,411.9 1.6 183 1,879 2.6 323
Oneida, NY............... 5.4 105.0 0.8 257 794 6.9 63
Onondaga, NY............. 13.1 246.1 0.7 265 938 2.5 324
Orange, NY............... 10.5 141.6 2.2 125 860 6.2 117
Queens, NY............... 52.8 651.9 1.9 153 974 4.4 262
Richmond, NY............. 9.9 115.3 2.6 96 918 4.6 247
Rockland, NY............. 10.8 122.9 2.1 131 987 -14.9 344
Saratoga, NY............. 6.0 84.8 0.5 283 925 7.4 44
Suffolk, NY.............. 53.2 659.4 0.8 257 1,126 6.7 79
Westchester, NY.......... 36.8 424.3 1.0 238 1,232 0.5 338
Buncombe, NC............. 9.0 128.5 3.7 34 788 3.7 296
Catawba, NC.............. 4.4 86.1 4.1 18 783 5.5 177
Cumberland, NC........... 6.2 118.8 0.7 265 813 6.7 79
Durham, NC............... 8.1 195.3 1.6 183 1,265 3.3 310
Forsyth, NC.............. 9.2 182.6 0.6 273 912 3.4 307
Guilford, NC............. 14.2 279.0 0.6 273 883 3.0 316
Mecklenburg, NC.......... 37.1 669.0 4.4 13 1,175 5.4 187
New Hanover, NC.......... 7.9 110.8 2.7 87 820 6.2 117
Wake, NC................. 33.5 532.8 3.9 26 1,045 6.0 130
Cass, ND................. 7.1 118.6 1.4 203 950 4.3 269
Butler, OH............... 7.6 151.6 2.5 105 905 6.5 90
Cuyahoga, OH............. 35.6 720.4 0.8 257 1,025 4.2 275
Delaware, OH............. 5.1 86.0 2.9 74 979 6.3 108
Franklin, OH............. 31.5 741.4 2.8 83 1,040 6.4 100
Hamilton, OH............. 23.7 513.2 1.5 195 1,095 4.3 269
Lake, OH................. 6.3 94.4 -0.5 321 834 4.8 233
Lorain, OH............... 6.2 97.2 0.9 250 811 4.5 254
Lucas, OH................ 10.1 210.1 1.0 238 901 6.9 63
Mahoning, OH............. 5.9 98.7 0.0 308 734 4.3 269
Montgomery, OH........... 11.9 253.6 1.3 216 883 5.6 168
Stark, OH................ 8.6 158.2 -0.1 312 770 4.2 275
Summit, OH............... 14.2 267.8 1.2 226 909 4.0 286
Warren, OH............... 4.8 91.2 1.5 195 948 6.9 63
Cleveland, OK............ 5.6 80.7 0.1 305 763 6.7 79
Oklahoma, OK............. 27.7 446.8 -1.4 335 967 3.4 307
Tulsa, OK................ 22.0 348.8 -0.6 324 934 3.5 304
Clackamas, OR............ 14.5 159.1 2.4 112 971 5.1 211
Jackson, OR.............. 7.2 87.3 2.6 96 798 4.7 242
Lane, OR................. 11.9 153.0 3.1 58 813 5.3 198
Marion, OR............... 10.4 153.8 2.3 123 836 6.1 125
Multnomah, OR............ 33.9 493.4 2.5 105 1,073 6.6 87
Washington, OR........... 18.9 283.8 3.1 58 1,327 3.2 312
Allegheny, PA............ 35.8 690.8 0.5 283 1,098 4.6 247
Berks, PA................ 9.0 171.8 0.4 292 947 9.2 14
Bucks, PA................ 20.0 261.4 2.0 142 960 5.5 177
Butler, PA............... 5.0 85.2 -0.1 312 947 3.0 316
Chester, PA.............. 15.5 250.2 1.7 177 1,228 1.8 332
Cumberland, PA........... 6.4 133.0 1.1 235 929 5.6 168
Dauphin, PA.............. 7.6 180.2 0.6 273 1,036 7.7 33
Delaware, PA............. 14.1 220.4 1.4 203 1,065 5.2 205
Erie, PA................. 7.0 124.0 -1.6 337 791 1.9 331
Lackawanna, PA........... 5.8 97.9 0.8 257 793 5.9 139
Lancaster, PA............ 13.4 236.4 2.4 112 862 5.6 168
Lehigh, PA............... 8.8 187.9 1.3 216 1,004 6.8 68
Luzerne, PA.............. 7.5 144.5 1.2 226 825 5.9 139
Montgomery, PA........... 27.6 485.3 1.4 203 1,234 6.4 100
Northampton, PA.......... 6.8 113.1 3.6 40 887 4.7 242
Philadelphia, PA......... 35.0 671.5 3.2 54 1,226 5.5 177
Washington, PA........... 5.5 85.8 -2.2 340 973 3.3 310
Westmoreland, PA......... 9.3 133.4 -1.0 331 827 4.9 226
York, PA................. 9.1 177.6 0.7 265 900 5.9 139
Providence, RI........... 17.6 285.6 0.5 283 1,046 8.7 18
Charleston, SC........... 14.6 243.7 3.7 34 916 4.4 262
Greenville, SC........... 13.6 262.2 1.9 153 898 4.3 269
Horry, SC................ 8.5 124.7 3.1 58 632 5.5 177
Lexington, SC............ 6.4 115.7 2.0 142 791 7.3 47
Richland, SC............. 9.9 219.0 2.0 142 885 6.0 130
Spartanburg, SC.......... 6.1 133.0 3.7 34 861 5.9 139
York, SC................. 5.3 89.8 6.0 1 830 8.2 23
Minnehaha, SD............ 7.1 125.0 1.5 195 907 6.8 68
Davidson, TN............. 21.5 479.1 4.6 11 1,058 2.3 326
Hamilton, TN............. 9.3 198.4 1.9 153 897 3.6 302
Knox, TN................. 11.9 237.4 2.1 131 887 6.2 117
Rutherford, TN........... 5.3 119.3 3.8 29 917 7.9 27
Shelby, TN............... 20.1 491.9 1.2 226 1,045 6.7 79
Williamson, TN........... 8.3 124.7 5.8 2 1,154 5.7 160
Bell, TX................. 5.3 116.3 0.0 308 868 5.7 160
Bexar, TX................ 39.9 846.6 2.4 112 914 4.6 247
Brazoria, TX............. 5.6 106.1 1.9 153 1,045 5.3 198
Brazos, TX............... 4.4 101.3 0.8 257 772 5.8 152
Cameron, TX.............. 6.5 138.4 2.2 125 636 4.3 269
Collin, TX............... 23.5 381.5 3.8 29 1,191 5.9 139
Dallas, TX............... 74.9 1,662.8 3.1 58 1,239 6.8 68
Denton, TX............... 14.1 228.8 3.4 46 954 6.8 68
El Paso, TX.............. 14.7 299.3 2.4 112 728 4.4 262
Fort Bend, TX............ 12.5 174.2 2.1 131 951 0.3 339
Galveston, TX............ 6.1 108.0 4.1 18 896 5.4 187
Gregg, TX................ 4.2 74.0 -3.4 342 858 1.2 335
Harris, TX............... 112.9 2,262.3 -0.9 329 1,267 2.1 327
Hidalgo, TX.............. 12.2 248.5 1.8 164 654 4.8 233
Jefferson, TX............ 5.8 122.3 -0.2 315 1,061 5.7 160
Lubbock, TX.............. 7.5 137.0 1.4 203 811 4.0 286
McLennan, TX............. 5.2 111.4 2.6 96 850 7.7 33
Midland, TX.............. 5.4 83.0 -5.8 344 1,176 -0.3 340
Montgomery, TX........... 10.8 168.4 1.0 238 1,007 4.1 279
Nueces, TX............... 8.3 161.6 -0.5 321 893 4.1 279
Potter, TX............... 4.0 78.9 0.0 308 831 3.1 315
Smith, TX................ 6.1 102.6 1.3 216 849 5.3 198
Tarrant, TX.............. 41.9 860.4 2.4 112 1,029 6.6 87
Travis, TX............... 39.0 710.0 2.9 74 1,174 5.1 211
Webb, TX................. 5.3 99.1 2.2 125 680 2.9 318
Williamson, TX........... 10.1 158.7 4.1 18 1,009 6.8 68
Davis, UT................ 8.2 123.2 4.1 18 831 5.7 160
Salt Lake, UT............ 43.6 676.2 3.9 26 993 6.5 90
Utah, UT................. 15.2 223.1 5.3 3 825 7.4 44
Weber, UT................ 5.9 103.0 2.9 74 784 5.9 139
Chittenden, VT........... 6.7 102.1 0.2 300 996 6.8 68
Arlington, VA............ 9.5 173.0 1.3 216 1,648 3.8 292
Chesterfield, VA......... 8.9 132.4 -0.1 312 877 5.4 187
Fairfax, VA.............. 37.6 598.1 1.7 177 1,546 5.6 168
Henrico, VA.............. 11.6 190.0 1.0 238 992 5.0 215
Loudoun, VA.............. 12.1 160.3 3.1 58 1,155 3.5 304
Prince William, VA....... 9.3 125.5 1.6 183 913 6.5 90
Alexandria City, VA...... 6.6 94.3 -0.7 325 1,447 5.0 215
Chesapeake City, VA...... 6.0 97.2 -0.4 318 812 6.4 100
Newport News City, VA.... 3.9 96.0 -2.1 339 995 3.6 302
Norfolk City, VA......... 5.9 140.9 0.3 297 1,030 3.4 307
Richmond City, VA........ 7.8 153.7 1.4 203 1,124 3.2 312
Virginia Beach City, VA.. 12.2 176.8 1.1 235 792 3.7 296
Benton, WA............... 5.7 86.9 2.5 105 1,042 8.1 24
Clark, WA................ 14.3 150.6 2.8 83 971 6.1 125
King, WA................. 85.7 1,331.3 3.3 51 1,582 8.1 24
Kitsap, WA............... 6.6 85.9 0.4 292 981 6.4 100
Pierce, WA............... 21.7 299.9 4.0 24 951 5.5 177
Snohomish, WA............ 20.6 284.9 2.0 142 1,108 5.4 187
Spokane, WA.............. 15.6 217.6 3.4 46 883 4.4 262
Thurston, WA............. 8.1 112.0 4.7 8 949 3.7 296
Whatcom, WA.............. 7.2 88.3 3.1 58 844 5.1 211
Yakima, WA............... 7.7 124.0 2.7 87 712 4.4 262
Kanawha, WV.............. 5.9 101.5 -1.3 334 890 6.5 90
Brown, WI................ 6.7 154.6 1.5 195 904 6.7 79
Dane, WI................. 15.0 330.7 2.6 96 1,032 10.1 7
Milwaukee, WI............ 25.6 487.0 0.5 283 970 4.5 254
Outagamie, WI............ 5.2 107.0 1.4 203 875 4.8 233
Waukesha, WI............. 12.8 239.0 0.4 292 1,006 5.2 205
Winnebago, WI............ 3.7 93.1 2.0 142 924 4.4 262
San Juan, PR............. 10.8 245.0 -1.4 (5) 634 2.8 (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 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 344 U.S. counties comprise 72.5 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
third quarter 2016
Employment Average weekly
wage(1)
Establishments,
third quarter
County by NAICS supersector 2016 Percent Percent
(thousands) September change, Third change,
2016 September quarter third
(thousands) 2015-16(2) 2016 quarter
2015-16(2)
United States(3) ............................ 9,800.8 142,940.5 1.7 $1,027 5.4
Private industry........................... 9,501.7 121,392.9 1.8 1,019 5.7
Natural resources and mining............. 137.6 1,963.4 -5.7 1,020 -0.8
Construction............................. 780.3 6,898.0 3.4 1,143 5.5
Manufacturing............................ 345.2 12,317.5 -0.6 1,242 6.2
Trade, transportation, and utilities..... 1,922.9 26,939.5 1.1 867 5.6
Information.............................. 157.8 2,792.9 1.0 1,927 8.4
Financial activities..................... 859.7 7,973.7 1.7 1,530 6.0
Professional and business services....... 1,766.7 20,200.9 2.2 1,308 5.3
Education and health services............ 1,590.0 21,741.6 2.8 958 6.0
Leisure and hospitality.................. 822.8 15,811.2 2.4 441 6.8
Other services........................... 835.9 4,390.8 1.7 705 6.0
Government................................. 299.1 21,547.5 1.2 1,077 4.6
Los Angeles, CA.............................. 472.3 4,357.4 1.6 1,132 5.8
Private industry........................... 466.1 3,793.2 1.7 1,096 6.4
Natural resources and mining............. 0.5 8.7 0.0 1,406 4.8
Construction............................. 13.9 134.1 3.9 1,192 5.5
Manufacturing............................ 12.4 353.8 -3.2 1,287 6.7
Trade, transportation, and utilities..... 53.7 811.3 0.5 936 7.6
Information.............................. 9.6 224.5 0.3 1,981 9.1
Financial activities..................... 25.3 217.1 0.4 1,789 4.5
Professional and business services....... 47.6 603.8 2.0 1,338 4.4
Education and health services............ 216.2 749.5 2.1 898 9.5
Leisure and hospitality.................. 32.1 508.7 2.8 633 6.7
Other services........................... 27.0 147.2 0.3 743 8.6
Government................................. 6.2 564.2 1.3 1,383 2.4
Cook, IL..................................... 152.8 2,577.2 1.2 1,159 4.5
Private industry........................... 151.5 2,279.3 1.2 1,166 4.9
Natural resources and mining............. 0.1 1.2 5.7 1,194 -1.6
Construction............................. 12.2 75.7 2.5 1,451 1.8
Manufacturing............................ 6.3 184.8 -1.3 1,250 7.3
Trade, transportation, and utilities..... 29.8 472.5 0.5 946 4.8
Information.............................. 2.7 52.6 -0.9 1,752 7.4
Financial activities..................... 15.1 193.0 0.9 2,013 4.9
Professional and business services....... 32.3 476.5 0.6 1,462 3.0
Education and health services............ 16.3 439.3 2.2 1,016 8.1
Leisure and hospitality.................. 14.1 282.9 3.6 547 7.5
Other services........................... 17.3 95.9 0.3 919 4.9
Government................................. 1.3 297.9 0.9 1,107 2.2
New York, NY................................. 130.1 2,411.9 1.6 1,879 2.6
Private industry........................... 129.2 2,148.9 1.7 1,946 2.7
Natural resources and mining............. 0.0 0.2 1.2 1,896 1.3
Construction............................. 2.2 41.2 3.6 1,876 4.7
Manufacturing............................ 2.1 26.5 -3.1 1,343 -0.3
Trade, transportation, and utilities..... 19.5 253.0 -2.3 1,352 5.9
Information.............................. 4.9 155.6 1.8 2,613 0.5
Financial activities..................... 19.2 368.6 0.2 3,373 2.6
Professional and business services....... 27.5 556.9 2.6 2,178 2.6
Education and health services............ 9.8 337.0 2.8 1,341 2.4
Leisure and hospitality.................. 13.7 293.6 1.8 896 5.5
Other services........................... 20.3 101.6 0.9 1,167 6.1
Government................................. 0.8 263.0 0.0 1,319 0.1
Harris, TX................................... 112.9 2,262.3 -0.9 1,267 2.1
Private industry........................... 112.3 1,990.6 -1.4 1,281 2.2
Natural resources and mining............. 1.8 73.7 -15.6 3,173 5.2
Construction............................. 7.2 160.8 -1.8 1,354 4.2
Manufacturing............................ 4.8 167.9 -9.5 1,576 6.5
Trade, transportation, and utilities..... 25.0 462.7 -1.2 1,137 5.1
Information.............................. 1.2 27.1 0.7 1,437 2.3
Financial activities..................... 11.7 123.8 1.9 1,591 2.3
Professional and business services....... 23.0 386.5 -2.3 1,569 1.9
Education and health services............ 15.6 291.7 3.7 1,041 3.1
Leisure and hospitality.................. 9.7 229.7 3.2 464 5.7
Other services........................... 11.7 65.5 0.1 797 0.6
Government................................. 0.6 271.7 2.6 1,166 1.7
Maricopa, AZ................................. 95.0 1,885.6 3.4 996 7.1
Private industry........................... 94.2 1,673.1 3.7 985 6.7
Natural resources and mining............. 0.4 7.5 0.9 936 1.4
Construction............................. 6.8 103.6 6.0 1,048 8.4
Manufacturing............................ 3.1 115.3 -0.8 1,405 7.6
Trade, transportation, and utilities..... 18.5 366.1 1.8 900 5.4
Information.............................. 1.5 34.0 0.5 1,498 21.2
Financial activities..................... 10.7 168.9 5.9 1,281 7.5
Professional and business services....... 20.6 325.4 4.0 1,055 5.3
Education and health services............ 10.6 285.1 3.6 1,011 6.9
Leisure and hospitality.................. 7.4 205.2 3.5 473 8.7
Other services........................... 6.0 49.8 1.1 715 7.2
Government................................. 0.7 212.6 1.5 1,086 9.6
Dallas, TX................................... 74.9 1,662.8 3.1 1,239 6.8
Private industry........................... 74.4 1,489.3 3.3 1,245 7.0
Natural resources and mining............. 0.6 8.6 -8.3 3,515 -0.2
Construction............................. 4.4 86.0 5.2 1,236 8.1
Manufacturing............................ 2.7 108.9 0.1 1,472 14.1
Trade, transportation, and utilities..... 16.0 338.5 3.2 1,149 9.0
Information.............................. 1.3 49.4 2.8 1,813 3.8
Financial activities..................... 9.2 159.3 3.7 1,659 5.9
Professional and business services....... 16.8 339.2 3.4 1,426 6.7
Education and health services............ 9.3 196.0 4.2 1,094 3.8
Leisure and hospitality.................. 6.6 160.4 3.9 509 7.4
Other services........................... 7.0 42.2 1.2 821 8.2
Government................................. 0.6 173.5 1.4 1,182 5.0
Orange, CA................................... 116.1 1,563.4 2.1 1,153 6.8
Private industry........................... 114.6 1,418.5 2.0 1,139 6.9
Natural resources and mining............. 0.2 2.9 -2.6 918 10.6
Construction............................. 6.7 97.2 3.5 1,317 7.5
Manufacturing............................ 4.9 155.9 -0.5 1,398 6.2
Trade, transportation, and utilities..... 16.9 255.8 -0.4 1,021 8.0
Information.............................. 1.3 25.6 2.5 1,922 12.7
Financial activities..................... 11.0 116.3 0.5 1,778 6.1
Professional and business services....... 20.5 295.3 2.3 1,364 5.7
Education and health services............ 30.1 200.2 2.8 977 8.8
Leisure and hospitality.................. 8.4 212.7 2.7 515 10.0
Other services........................... 6.9 45.9 2.5 723 8.4
Government................................. 1.5 144.9 3.1 1,301 4.8
San Diego, CA................................ 107.6 1,415.6 2.2 1,130 4.9
Private industry........................... 105.7 1,185.5 2.0 1,084 4.8
Natural resources and mining............. 0.7 9.5 -2.4 723 14.0
Construction............................. 6.7 76.6 5.4 1,213 8.4
Manufacturing............................ 3.2 107.0 -0.6 1,578 9.8
Trade, transportation, and utilities..... 14.2 217.4 -0.5 862 4.9
Information.............................. 1.2 23.3 -0.8 1,930 8.7
Financial activities..................... 9.7 72.1 2.2 1,438 7.1
Professional and business services....... 18.1 232.1 1.0 1,526 -0.3
Education and health services............ 29.7 192.9 2.5 978 8.7
Leisure and hospitality.................. 8.0 194.4 3.7 506 4.8
Other services........................... 7.4 51.3 0.7 632 8.8
Government................................. 1.9 230.1 3.1 1,378 5.1
King, WA..................................... 85.7 1,331.3 3.3 1,582 8.1
Private industry........................... 85.2 1,165.9 3.4 1,615 8.6
Natural resources and mining............. 0.4 3.2 4.0 1,208 3.0
Construction............................. 6.4 69.2 7.1 1,365 8.5
Manufacturing............................ 2.5 104.1 -3.7 1,615 3.1
Trade, transportation, and utilities..... 14.5 253.9 4.7 1,392 17.8
Information.............................. 2.2 98.1 8.1 4,960 3.1
Financial activities..................... 6.5 68.1 3.1 1,655 6.2
Professional and business services....... 17.2 220.9 2.1 1,668 8.3
Education and health services............ 19.4 166.9 4.6 1,051 8.5
Leisure and hospitality.................. 7.1 137.5 3.3 588 8.1
Other services........................... 9.0 43.9 2.7 907 11.4
Government................................. 0.5 165.4 2.1 1,348 3.9
Miami-Dade, FL............................... 96.5 1,107.4 2.6 983 6.0
Private industry........................... 96.2 969.7 2.7 957 5.3
Natural resources and mining............. 0.5 7.6 9.4 643 13.0
Construction............................. 6.2 44.3 9.6 969 4.1
Manufacturing............................ 2.9 40.4 2.0 977 14.1
Trade, transportation, and utilities..... 26.3 276.9 0.4 896 5.9
Information.............................. 1.5 17.8 0.6 1,781 22.1
Financial activities..................... 10.4 74.0 0.7 1,484 4.4
Professional and business services....... 21.2 154.8 3.7 1,114 3.2
Education and health services............ 10.3 174.3 3.2 979 2.1
Leisure and hospitality.................. 7.2 138.4 4.1 592 7.2
Other services........................... 8.2 39.8 2.7 628 6.1
Government................................. 0.3 137.8 1.9 1,172 10.9
(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 2015 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,
third quarter 2016
Employment Average weekly
wage(1)
Establishments,
third quarter
State 2016 Percent Percent
(thousands) September change, Third change,
2016 September quarter third
(thousands) 2015-16 2016 quarter
2015-16
United States(2)........... 9,800.8 142,940.5 1.7 $1,027 5.4
Alabama.................... 122.7 1,923.8 1.5 870 4.9
Alaska..................... 22.3 337.4 -2.6 1,055 1.2
Arizona.................... 154.6 2,695.5 3.1 950 6.9
Arkansas................... 89.1 1,205.4 1.0 794 5.2
California................. 1,493.8 16,871.1 2.4 1,210 6.7
Colorado................... 193.5 2,576.5 2.6 1,062 5.6
Connecticut................ 117.5 1,674.2 0.3 1,204 5.0
Delaware................... 31.7 440.7 0.8 1,022 5.6
District of Columbia....... 39.2 759.2 1.7 1,728 3.8
Florida.................... 664.8 8,320.2 3.7 905 6.2
Georgia.................... 302.2 4,290.4 2.9 969 5.9
Hawaii..................... 40.5 648.4 1.8 956 6.7
Idaho...................... 58.9 703.7 3.5 782 6.3
Illinois................... 405.1 5,933.6 0.6 1,062 4.4
Indiana.................... 162.2 3,025.9 1.8 866 5.9
Iowa....................... 101.6 1,548.6 0.8 873 6.2
Kansas..................... 90.6 1,377.2 0.5 857 5.9
Kentucky................... 123.6 1,880.2 1.5 857 6.5
Louisiana.................. 129.0 1,908.8 -0.9 883 2.9
Maine...................... 53.3 616.2 0.9 825 5.9
Maryland................... 168.7 2,648.1 1.4 1,124 5.3
Massachusetts.............. 246.1 3,522.9 2.0 1,277 6.8
Michigan................... 242.3 4,292.2 2.1 976 5.9
Minnesota.................. 162.7 2,849.5 1.6 1,053 6.4
Mississippi................ 73.7 1,126.9 0.7 739 4.7
Missouri................... 193.7 2,782.1 1.6 888 5.0
Montana.................... 46.2 464.5 1.5 792 4.3
Nebraska................... 73.2 973.9 0.9 857 5.5
Nevada..................... 82.6 1,300.7 3.8 949 10.1
New Hampshire.............. 52.0 655.0 1.8 1,027 7.9
New Jersey................. 267.7 4,000.0 1.8 1,173 5.0
New Mexico................. 58.4 811.5 0.2 830 4.0
New York................... 646.8 9,216.6 1.6 1,222 3.5
North Carolina............. 268.4 4,290.3 2.3 909 5.3
North Dakota............... 32.1 423.2 -3.4 964 0.7
Ohio....................... 293.9 5,347.3 1.1 924 5.4
Oklahoma................... 109.4 1,578.7 -1.3 854 3.5
Oregon..................... 148.4 1,866.5 2.6 970 5.2
Pennsylvania............... 356.6 5,776.7 1.0 1,013 5.4
Rhode Island............... 37.0 481.1 0.8 990 7.6
South Carolina............. 125.0 2,008.6 2.5 832 5.6
South Dakota............... 33.0 424.2 1.1 809 7.0
Tennessee.................. 154.0 2,918.8 2.5 912 5.4
Texas...................... 657.1 11,830.7 1.3 1,042 4.3
Utah....................... 96.8 1,407.4 3.8 881 6.3
Vermont.................... 25.1 309.9 0.5 880 6.2
Virginia................... 267.0 3,801.0 1.0 1,063 5.0
Washington................. 238.5 3,278.9 3.0 1,188 6.9
West Virginia.............. 50.6 691.5 -1.6 816 3.9
Wisconsin.................. 171.2 2,850.1 1.0 885 6.2
Wyoming.................... 26.3 274.8 -4.7 865 0.0
Puerto Rico................ 46.0 888.2 -0.4 524 2.3
Virgin Islands............. 3.4 37.4 1.4 778 5.9
(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
