For release 10:00 a.m. (EST), Tuesday, December 5, 2017 USDL-17-1613
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Second Quarter 2017
From June 2016 to June 2017, employment increased in 318 of the 346 largest U.S. counties, the U.S.
Bureau of Labor Statistics reported today. Midland, Texas, had the largest percentage increase with a
gain of 7.3 percent over the year, above the national job growth rate of 1.7 percent. Within Midland, the
largest employment increase occurred in natural resources and mining, which gained 3,497 jobs over the
year (19.6 percent). Lucas, Ohio, had the largest over-the-year percentage decrease in employment
among the largest counties in the U.S., with a loss of 1.9 percent. Within Lucas, construction had the
largest decrease in employment, with a loss of 1,534 jobs (-14.2 percent).
The U.S. average weekly wage increased 3.2 percent over the year, growing to $1,020 in the second
quarter of 2017. New Hanover, N.C., had the largest over-the-year percentage increase in average
weekly wages with a gain of 11.9 percent. Within New Hanover, an average weekly wage gain of $589
(62.7 percent) in professional and business services made the largest contribution to the county’s
increase in average weekly wages. McLean, Ill., had the largest over-the-year percentage decrease in
average weekly wages with a loss of 20.4 percent. Within McLean, financial activities had the largest
impact on the county’s average weekly wage change with a decrease of $953 (-38.9 percent) over the
year.
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.
Large County Employment
In June 2017, national employment was 145.2 million (as measured by the QCEW program). Over the
year, employment increased 1.7 percent, or 2.4 million. In June 2017, the 346 U.S. counties with 75,000
or more jobs accounted for 72.7 percent of total U.S. employment and 77.7 percent of total wages. These
346 counties had a net job growth of 1.8 million over the year, accounting for 76.8 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 258,900 jobs, which was 10.8 percent of the overall job
increase for the U.S. (See table A.)
Employment declined in 23 of the largest counties from June 2016 to June 2017. Lucas, Ohio, had the
largest over-the-year percentage decrease in employment (-1.9 percent), followed by Caddo, La.;
Kanawha, W.Va.; Shawnee, Kan.; and Anchorage, Alaska. (See table 1.)
Table A. Large counties ranked by June 2017 employment, June 2016-17 employment increase, and
June 2016-17 percent increase in employment
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Employment in large counties
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June 2017 employment | Increase in employment, | Percent increase in employment,
(thousands) | June 2016-17 | June 2016-17
| (thousands) |
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| |
United States 145,186.4| United States 2,407.0| United States 1.7
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| |
Los Angeles, Calif. 4,373.6| Los Angeles, Calif. 71.9| Midland, Texas 7.3
Cook, Ill. 2,598.4| Maricopa, Ariz. 61.2| Weld, Colo. 5.3
New York, N.Y. 2,469.1| King, Wash. 44.2| Utah, Utah 5.2
Harris, Texas 2,284.5| New York, N.Y. 41.1| York, S.C. 4.8
Maricopa, Ariz. 1,891.7| Dallas, Texas 40.5| Elkhart, Ind. 4.7
Dallas, Texas 1,686.9| Orange, Calif. 33.2| Davis, Utah 4.5
Orange, Calif. 1,598.1| San Diego, Calif. 28.9| Clark, Wash. 4.4
San Diego, Calif. 1,440.9| Fulton, Ga. 27.9| Deschutes, Ore. 4.3
King, Wash. 1,369.7| Clark, Nev. 26.9| Boone, Ky. 4.2
Miami-Dade, Fla. 1,111.0| Orange, Fla. 26.5| Williamson, Tenn. 4.1
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $1,020, a 3.2 percent increase, during the year ending
in the second quarter of 2017. Among the 346 largest counties, 325 had over-the-year increases in
average weekly wages. New Hanover, N.C., had the largest percentage wage increase among the largest
U.S. counties (11.9 percent). (See table B.)
Of the 346 largest counties, 19 experienced an over-the-year decrease in average weekly wages.
McLean, Ill., had the largest percentage decrease in average weekly wages (-20.4 percent), followed by
Union, N.J.; Warren, Ohio; Somerset, N.J.; Fairfield, Conn.; and Washington, Ore. (See table 1.)
Table B. Large counties ranked by second quarter 2017 average weekly wages, second quarter 2016-17
increase in average weekly wages, and second quarter 2016-17 percent increase in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Increase in average weekly | Percent increase in average
second quarter 2017 | wage, second quarter 2016-17 | weekly wage, second
| | quarter 2016-17
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| |
United States $1,020| United States $32| United States 3.2
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| |
Santa Clara, Calif. $2,392| San Mateo, Calif. $214| New Hanover, N.C. 11.9
San Mateo, Calif. 2,093| Santa Clara, Calif. 141| San Mateo, Calif. 11.4
San Francisco, Calif. 1,941| Midland, Texas 135| Midland, Texas 11.4
New York, N.Y. 1,907| San Francisco, Calif. 132| Kitsap, Wash. 11.0
Washington, D.C. 1,675| Morris, N.J. 102| Clackamas, Ore. 10.0
Suffolk, Mass. 1,651| Kitsap, Wash. 97| Bell, Texas 9.6
Arlington, Va. 1,609| New Hanover, N.C. 94| St. Louis, Minn. 9.5
Fairfax, Va. 1,542| Clackamas, Ore. 93| Newport News City, Va. 7.4
Morris, N.J. 1,525| King, Wash. 83| San Francisco, Calif. 7.3
Middlesex, Mass. 1,522| Bell, Texas 77| Washington, Ark. 7.2
| | Morris, N.J. 7.2
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Ten Largest U.S. Counties
All of the largest counties had over-the-year percentage increases in employment in June 2017. King,
Wash., and Maricopa, Ariz., had the largest gain (3.3 percent). Within King, trade, transportation, and
utilities had the largest over-the-year employment level increase, with a gain of 16,004 jobs, or 6.4
percent. Within Maricopa, education and health services had the largest over-the-year employment level
increase, with a gain of 11,768 jobs, or 4.2 percent. Cook, Ill., had the lowest percentage increase in
employment among the 10 largest counties (0.3 percent). Within Cook, leisure and hospitality had the
largest over-the-year employment level increase, with a gain of 7,020 jobs, or 2.4 percent. (See table 2.)
Average weekly wages increased over the year in 9 of the 10 largest U.S. counties. King, Wash.,
experienced the largest percentage gain in average weekly wages (6.0 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 $183, or 12.8 percent, over the
year. Harris, Texas, had the only percent loss in average weekly wages among the 10 largest counties
(-0.4 percent). Within Harris, natural resources and mining had the largest impact on the county’s average
weekly wage growth with a decrease of $290 (-9.0 percent) over the year.
For More Information
The tables included in this release contain data for the nation and for the 346 U.S. counties with annual
average employment levels of 75,000 or more in 2016. June 2017 employment and 2017 second 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 9.9 million employer reports cover 145.2 million full- and part-time workers. Data for the
second quarter of 2017 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 third quarter 2017 is scheduled to be released on
Thursday, March 8, 2018.
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 2017 North American Industry
Classification System (NAICS). Data for 2017 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 347 counties
presented in this release were derived using 2016 preliminary annual averages of employment. For
2017 data, three counties have been added to the publication tables: Sussex, Del.; Maui + Kalawao,
Hawaii; and Deschutes, Ore. These counties will be included in all 2017 quarterly releases. One
county, Gregg, Texas, which was published in the 2016 releases, will be excluded from this and
future 2017 releases because its 2016 annual average employment level was 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 9.9 | ministrative records| ments
| million establish- | submitted by 7.9 |
| ments in first | million private-sec-|
| quarter of 2017 | 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 the 3rd Friday
| after the end of | end of each quarter| after the end of the
| each quarter | | week including
| | | the 12th of the month
-----------|---------------------|----------------------|------------------------
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 federal
| 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/sae/
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 9.7
million employer reports of employment and wages submitted by states to the BLS in 2016. 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 2016, UI and UCFE programs
covered workers in 141.9 million jobs. The estimated 136.6 million workers in these jobs (after
adjustment for multiple jobholders) represented 96.4 percent of civilian wage and salary
employment. Covered workers received $7.607 trillion in pay, representing 94.1 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 2016 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 2016 edition
of this publication, which was published in September 2017, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2017 version of this news release. Tables and additional content from the 2016 edition
of Employment and Wages Annual Averages Online are now available at
www.bls.gov/cew/cewbultn16.htm. The 2017 edition of Employment and Wages Annual Averages
Online will be available in September 2018.
News releases on quarterly measures of gross job flows also are available from BED at
www.bls.gov/bdm, (202) 691-6467, or data.bls.gov/cgi-bin/forms/bdm.
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 347 largest counties,
second quarter 2017
Employment Average weekly wage(2)
Establishments,
County(1) second quarter Percent Ranking Percent Ranking
2017 June change, by Second change, by
(thousands) 2017 June percent quarter second percent
(thousands) 2016-17(3) change 2017 quarter change
2016-17(3)
United States(4)......... 9,922.4 145,186.4 1.7 - $1,020 3.2 -
Jefferson, AL............ 18.5 345.1 1.2 212 1,008 4.3 71
Madison, AL.............. 9.6 197.0 2.7 70 1,072 2.3 220
Mobile, AL............... 10.1 170.3 -0.1 324 857 1.4 273
Montgomery, AL........... 6.4 133.2 1.1 222 840 0.2 322
Shelby, AL............... 5.8 85.2 0.6 274 948 2.6 196
Tuscaloosa, AL........... 4.6 91.7 0.7 264 850 4.8 48
Anchorage, AK............ 8.3 151.4 -1.1 342 1,064 1.0 300
Maricopa, AZ............. 96.5 1,891.7 3.3 29 986 1.6 261
Pima, AZ................. 18.8 359.5 1.8 146 861 4.2 75
Benton, AR............... 6.4 118.0 2.1 117 1,022 2.6 196
Pulaski, AR.............. 14.4 250.1 0.7 264 909 1.5 266
Washington, AR........... 6.0 106.3 1.9 134 867 7.2 10
Alameda, CA.............. 62.9 778.9 2.9 55 1,376 5.8 23
Butte, CA................ 8.5 82.8 2.2 109 771 3.4 134
Contra Costa, CA......... 32.0 370.7 1.5 183 1,240 3.9 92
Fresno, CA............... 35.1 392.9 2.2 109 805 3.9 92
Kern, CA................. 18.8 325.6 3.1 37 840 2.2 226
Los Angeles, CA.......... 483.9 4,373.6 1.7 157 1,130 3.8 102
Marin, CA................ 12.5 118.0 2.2 109 1,278 1.4 273
Merced, CA............... 6.6 80.0 1.9 134 790 3.8 102
Monterey, CA............. 13.7 205.9 0.3 304 878 4.6 57
Napa, CA................. 5.9 79.3 2.4 89 1,014 4.8 48
Orange, CA............... 119.3 1,598.1 2.1 117 1,130 2.5 207
Placer, CA............... 12.9 162.4 3.0 47 1,015 1.9 242
Riverside, CA............ 63.0 713.6 3.2 33 826 2.0 236
Sacramento, CA........... 57.4 652.4 2.6 80 1,107 3.9 92
San Bernardino, CA....... 58.1 727.0 3.3 29 863 2.6 196
San Diego, CA............ 109.8 1,440.9 2.0 125 1,101 2.8 183
San Francisco, CA........ 60.4 717.4 3.1 37 1,941 7.3 9
San Joaquin, CA.......... 17.6 248.9 3.4 24 863 4.5 60
San Luis Obispo, CA...... 10.4 120.6 3.5 21 870 3.7 114
San Mateo, CA............ 28.1 402.5 2.8 62 2,093 11.4 2
Santa Barbara, CA........ 15.5 201.7 1.8 146 988 4.4 66
Santa Clara, CA.......... 72.2 1,077.3 2.5 85 2,392 6.3 14
Santa Cruz, CA........... 9.6 110.3 1.8 146 950 5.7 25
Solano, CA............... 11.4 139.9 2.0 125 1,056 5.0 41
Sonoma, CA............... 20.0 208.4 2.5 85 973 4.6 57
Stanislaus, CA........... 15.4 189.3 2.5 85 860 5.0 41
Tulare, CA............... 10.3 169.4 3.1 37 711 0.7 309
Ventura, CA.............. 26.9 326.4 1.3 200 1,015 2.9 175
Yolo, CA................. 6.7 103.5 2.2 109 1,098 3.7 114
Adams, CO................ 10.9 206.7 3.0 47 975 2.1 231
Arapahoe, CO............. 22.0 331.5 2.3 99 1,166 4.4 66
Boulder, CO.............. 15.2 181.2 2.3 99 1,192 4.4 66
Denver, CO............... 32.1 510.0 3.1 37 1,214 3.4 134
Douglas, CO.............. 12.0 123.6 2.8 62 1,135 4.1 82
El Paso, CO.............. 19.6 274.0 3.0 47 899 2.6 196
Jefferson, CO............ 20.2 235.7 0.4 294 1,047 4.1 82
Larimer, CO.............. 12.0 160.5 2.8 62 899 3.2 148
Weld, CO................. 7.3 106.1 5.3 2 894 5.1 38
Fairfield, CT............ 35.3 429.3 -0.5 334 1,503 -1.9 341
Hartford, CT............. 27.9 514.9 0.8 252 1,214 1.6 261
New Haven, CT............ 24.0 369.2 1.2 212 1,067 2.3 220
New London, CT........... 7.5 126.7 1.9 134 1,003 -0.2 331
New Castle, DE........... 19.6 286.6 -0.3 327 1,135 3.3 140
Sussex, DE............... 6.7 84.6 3.4 24 732 2.2 226
Washington, DC........... 39.0 766.5 1.0 235 1,675 3.3 140
Alachua, FL.............. 7.2 127.3 2.8 62 845 -1.4 338
Bay, FL.................. 5.7 78.9 1.0 235 760 3.8 102
Brevard, FL.............. 15.8 206.5 2.9 55 932 6.5 13
Broward, FL.............. 69.7 793.0 2.4 89 958 3.1 156
Collier, FL.............. 13.9 135.4 2.3 99 874 0.9 303
Duval, FL................ 29.9 500.3 3.2 33 959 3.2 148
Escambia, FL............. 8.3 133.7 3.8 13 783 0.1 324
Hillsborough, FL......... 42.4 663.6 2.0 125 964 1.2 291
Lake, FL................. 8.2 92.2 3.3 29 702 3.2 148
Lee, FL.................. 22.1 248.2 3.1 37 830 2.9 175
Leon, FL................. 8.8 146.2 0.7 264 819 0.4 315
Manatee, FL.............. 10.9 116.7 3.0 47 792 1.7 253
Marion, FL............... 8.3 100.6 2.7 70 716 0.0 326
Miami-Dade, FL........... 98.8 1,111.0 1.8 146 971 1.8 247
Okaloosa, FL............. 6.4 82.7 0.9 247 868 5.7 25
Orange, FL............... 42.4 811.8 3.4 24 900 3.9 92
Osceola, FL.............. 7.0 89.1 3.0 47 716 3.6 124
Palm Beach, FL........... 56.4 593.4 2.4 89 1,002 3.7 114
Pasco, FL................ 11.0 109.5 3.2 33 749 2.3 220
Pinellas, FL............. 33.1 425.8 2.6 80 888 1.4 273
Polk, FL................. 13.3 209.9 2.3 99 773 0.8 307
Sarasota, FL............. 15.9 163.3 2.4 89 839 2.9 175
Seminole, FL............. 15.1 187.0 2.7 70 891 4.2 75
Volusia, FL.............. 14.4 167.2 2.6 80 751 2.7 189
Bibb, GA................. 4.2 83.0 0.2 311 775 -0.1 328
Chatham, GA.............. 8.2 152.7 1.5 183 857 3.5 125
Clayton, GA.............. 4.0 122.4 0.5 287 966 3.4 134
Cobb, GA................. 22.1 358.4 2.7 70 1,070 3.5 125
DeKalb, GA............... 18.0 298.6 1.0 235 1,030 1.4 273
Fulton, GA............... 43.5 854.1 3.4 24 1,329 3.4 134
Gwinnett, GA............. 25.0 352.5 1.7 157 969 0.4 315
Hall, GA................. 4.4 85.7 4.0 11 866 6.1 15
Muscogee, GA............. 4.6 93.1 0.8 252 782 1.4 273
Richmond, GA............. 4.4 104.2 0.6 274 843 2.6 196
Honolulu, HI............. 26.3 472.5 0.6 274 976 3.8 102
Maui + Kalawao, HI....... 6.2 77.3 1.6 170 840 2.8 183
Ada, ID.................. 15.4 235.8 4.0 11 884 3.3 140
Champaign, IL............ 4.4 89.6 -0.3 327 886 2.5 207
Cook, IL................. 155.4 2,598.4 0.3 304 1,179 3.1 156
DuPage, IL............... 38.6 631.9 1.1 222 1,149 3.0 168
Kane, IL................. 13.9 216.3 2.1 117 898 2.3 220
Lake, IL................. 22.6 349.0 2.7 70 1,300 2.1 231
McHenry, IL.............. 8.8 100.4 0.7 264 827 1.2 291
McLean, IL............... 3.7 83.5 -0.2 325 920 -20.4 346
Madison, IL.............. 6.0 98.1 1.3 200 791 2.2 226
Peoria, IL............... 4.6 100.3 0.3 304 907 0.8 307
St. Clair, IL............ 5.5 93.3 0.5 287 812 5.2 37
Sangamon, IL............. 5.2 129.5 0.4 294 988 0.5 312
Will, IL................. 16.4 242.3 2.3 99 886 1.3 282
Winnebago, IL............ 6.6 127.8 0.0 319 844 1.2 291
Allen, IN................ 8.8 186.6 1.5 183 829 3.2 148
Elkhart, IN.............. 4.7 135.0 4.7 5 915 3.5 125
Hamilton, IN............. 9.4 142.0 2.4 89 955 2.8 183
Lake, IN................. 10.4 188.5 0.2 311 854 1.8 247
Marion, IN............... 24.1 597.4 1.2 212 1,027 4.7 52
St. Joseph, IN........... 5.8 123.7 0.0 319 828 3.0 168
Tippecanoe, IN........... 3.4 82.4 -0.5 334 878 5.0 41
Vanderburgh, IN.......... 4.8 107.9 0.6 274 827 4.9 45
Johnson, IA.............. 4.2 84.1 1.7 157 944 3.1 156
Linn, IA................. 6.8 132.3 0.5 287 971 2.6 196
Polk, IA................. 17.3 303.5 1.9 134 1,018 4.3 71
Scott, IA................ 5.6 93.0 1.6 170 812 2.4 215
Johnson, KS.............. 23.7 342.0 1.0 235 1,031 1.2 291
Sedgwick, KS............. 12.7 247.2 -0.5 334 860 0.4 315
Shawnee, KS.............. 5.2 96.5 -1.2 343 842 4.9 45
Wyandotte, KS............ 3.5 91.0 0.7 264 987 5.9 18
Boone, KY................ 4.3 87.7 4.2 9 888 -1.8 340
Fayette, KY.............. 10.8 193.7 0.8 252 918 4.2 75
Jefferson, KY............ 24.9 468.0 1.2 212 1,014 4.3 71
Caddo, LA................ 7.3 112.5 -1.7 345 804 1.5 266
Calcasieu, LA............ 5.3 98.3 2.6 80 871 2.8 183
East Baton Rouge, LA..... 15.7 261.4 0.5 287 959 1.9 242
Jefferson, LA............ 14.0 192.7 -0.8 339 905 4.5 60
Lafayette, LA............ 9.6 128.7 -0.4 331 860 0.1 324
Orleans, LA.............. 12.6 191.9 0.4 294 928 0.5 312
St. Tammany, LA.......... 8.3 88.4 -0.3 327 850 2.9 175
Cumberland, ME........... 14.0 186.7 1.9 134 909 0.9 303
Anne Arundel, MD......... 15.2 274.4 1.6 170 1,089 4.1 82
Baltimore, MD............ 21.4 379.4 0.0 319 1,005 3.1 156
Frederick, MD............ 6.4 102.0 1.5 183 931 1.5 266
Harford, MD.............. 5.8 94.6 1.9 134 952 1.1 298
Howard, MD............... 10.1 172.4 0.2 311 1,220 2.1 231
Montgomery, MD........... 33.0 477.9 1.3 200 1,333 1.4 273
Prince George's, MD...... 16.0 322.4 3.5 21 1,064 3.7 114
Baltimore City, MD....... 13.7 341.5 1.5 183 1,183 4.1 82
Barnstable, MA........... 9.5 108.6 2.0 125 869 4.2 75
Bristol, MA.............. 17.5 230.5 1.3 200 950 1.3 282
Essex, MA................ 25.4 330.4 0.0 319 1,093 3.7 114
Hampden, MA.............. 18.1 209.8 0.6 274 900 1.7 253
Middlesex, MA............ 55.0 912.0 2.1 117 1,522 3.3 140
Norfolk, MA.............. 25.4 357.7 0.8 252 1,182 3.7 114
Plymouth, MA............. 15.9 198.2 1.9 134 1,000 4.8 48
Suffolk, MA.............. 29.5 677.3 2.3 99 1,651 4.4 66
Worcester, MA............ 25.2 350.0 1.1 222 1,012 2.0 236
Genesee, MI.............. 6.8 136.3 0.7 264 832 0.6 310
Ingham, MI............... 6.0 151.7 1.6 170 969 2.1 231
Kalamazoo, MI............ 5.0 119.1 1.3 200 938 2.7 189
Kent, MI................. 14.4 398.2 2.0 125 884 3.9 92
Macomb, MI............... 17.6 334.7 1.2 212 1,007 3.2 148
Oakland, MI.............. 39.2 741.5 2.1 117 1,131 3.1 156
Ottawa, MI............... 5.6 126.5 0.9 247 858 2.3 220
Saginaw, MI.............. 3.9 84.5 -0.3 327 818 3.7 114
Washtenaw, MI............ 8.2 207.1 2.0 125 1,094 2.0 236
Wayne, MI................ 30.8 725.2 1.2 212 1,111 2.5 207
Anoka, MN................ 7.1 124.4 2.4 89 980 1.9 242
Dakota, MN............... 9.9 190.3 2.1 117 998 3.1 156
Hennepin, MN............. 39.0 919.1 1.8 146 1,273 4.8 48
Olmsted, MN.............. 3.4 99.1 2.1 117 1,073 3.8 102
Ramsey, MN............... 13.3 334.8 2.2 109 1,131 1.6 261
St. Louis, MN............ 5.3 99.8 1.1 222 855 9.5 7
Stearns, MN.............. 4.4 88.1 1.9 134 831 -0.1 328
Washington, MN........... 5.5 86.5 3.0 47 882 5.9 18
Harrison, MS............. 4.6 86.9 1.8 146 718 3.0 168
Hinds, MS................ 5.8 120.7 -0.8 339 849 1.0 300
Boone, MO................ 5.1 93.5 1.2 212 822 3.8 102
Clay, MO................. 5.7 106.9 2.8 62 904 2.8 183
Greene, MO............... 9.0 166.2 1.9 134 789 2.7 189
Jackson, MO.............. 22.1 371.6 1.8 146 1,021 3.5 125
St. Charles, MO.......... 9.5 149.4 1.6 170 823 -0.4 333
St. Louis, MO............ 39.0 610.4 0.8 252 1,059 1.7 253
St. Louis City, MO....... 14.6 227.7 0.9 247 1,077 4.7 52
Yellowstone, MT.......... 6.7 82.7 0.3 304 875 2.9 175
Douglas, NE.............. 19.1 341.3 1.1 222 938 2.9 175
Lancaster, NE............ 10.3 168.8 0.3 304 820 3.8 102
Clark, NV................ 55.0 967.0 2.9 55 886 2.2 226
Washoe, NV............... 14.6 217.7 3.6 16 906 3.5 125
Hillsborough, NH......... 12.2 204.1 1.5 183 1,080 3.1 156
Merrimack, NH............ 5.2 77.8 1.1 222 944 4.1 82
Rockingham, NH........... 10.9 153.0 2.3 99 1,009 1.0 300
Atlantic, NJ............. 6.6 132.1 0.3 304 855 2.0 236
Bergen, NJ............... 33.2 453.0 0.8 252 1,179 1.3 282
Burlington, NJ........... 11.0 210.1 2.7 70 1,036 1.2 291
Camden, NJ............... 12.1 207.8 1.5 183 987 4.0 89
Essex, NJ................ 20.6 346.7 1.7 157 1,231 4.5 60
Gloucester, NJ........... 6.4 109.1 1.6 170 872 0.9 303
Hudson, NJ............... 15.2 263.9 3.8 13 1,350 3.7 114
Mercer, NJ............... 11.2 251.9 1.4 194 1,279 3.5 125
Middlesex, NJ............ 22.4 426.1 2.2 109 1,181 1.8 247
Monmouth, NJ............. 20.2 271.7 1.6 170 988 0.5 312
Morris, NJ............... 17.2 295.0 1.1 222 1,525 7.2 10
Ocean, NJ................ 13.3 176.8 2.4 89 806 1.4 273
Passaic, NJ.............. 12.7 169.9 0.4 294 992 3.1 156
Somerset, NJ............. 10.3 192.2 1.1 222 1,464 -3.4 343
Union, NJ................ 14.4 223.5 1.6 170 1,237 -3.7 345
Bernalillo, NM........... 18.4 327.1 1.1 222 865 1.3 282
Albany, NY............... 10.4 235.0 0.1 316 1,084 0.6 310
Bronx, NY................ 18.8 303.2 0.9 247 978 3.7 114
Broome, NY............... 4.5 87.6 0.4 294 817 2.1 231
Dutchess, NY............. 8.5 113.5 0.4 294 1,023 3.0 168
Erie, NY................. 24.9 474.9 0.6 274 904 2.7 189
Kings, NY................ 62.8 714.0 3.7 15 850 3.2 148
Monroe, NY............... 19.0 390.9 0.6 274 968 3.9 92
Nassau, NY............... 54.4 643.6 1.7 157 1,150 -1.5 339
New York, NY............. 129.2 2,469.1 1.7 157 1,907 2.4 215
Oneida, NY............... 5.4 106.9 0.8 252 810 3.1 156
Onondaga, NY............. 13.0 247.7 0.4 294 936 1.8 247
Orange, NY............... 10.5 145.5 1.4 194 905 2.7 189
Queens, NY............... 53.2 666.3 2.9 55 965 2.4 215
Richmond, NY............. 9.8 116.7 1.7 157 911 2.4 215
Rockland, NY............. 10.9 126.5 2.4 89 989 -0.7 336
Saratoga, NY............. 6.0 89.2 2.4 89 949 1.3 282
Suffolk, NY.............. 53.3 682.8 1.0 235 1,086 0.4 315
Westchester, NY.......... 36.6 437.6 1.3 200 1,327 2.6 196
Buncombe, NC............. 9.2 129.3 1.7 157 783 3.2 148
Catawba, NC.............. 4.4 87.9 1.8 146 793 4.1 82
Cumberland, NC........... 6.2 119.2 -0.6 337 795 5.7 25
Durham, NC............... 8.3 199.1 0.6 274 1,231 3.0 168
Forsyth, NC.............. 9.2 183.1 0.0 319 906 4.5 60
Guilford, NC............. 14.2 279.1 1.4 194 890 3.9 92
Mecklenburg, NC.......... 37.3 683.2 3.1 37 1,152 4.0 89
New Hanover, NC.......... 8.0 112.4 2.8 62 884 11.9 1
Wake, NC................. 34.0 549.7 3.1 37 1,040 4.7 52
Cass, ND................. 7.2 119.1 1.0 235 917 3.9 92
Butler, OH............... 7.8 153.8 2.9 55 901 3.0 168
Cuyahoga, OH............. 35.8 728.8 0.5 287 1,029 3.5 125
Delaware, OH............. 5.3 90.4 2.6 80 971 1.3 282
Franklin, OH............. 31.9 753.3 2.7 70 1,007 1.9 242
Hamilton, OH............. 23.8 520.9 1.2 212 1,072 2.7 189
Lake, OH................. 6.3 96.9 0.3 304 838 5.4 33
Lorain, OH............... 6.2 100.0 1.3 200 792 2.9 175
Lucas, OH................ 10.2 209.5 -1.9 346 856 -0.6 335
Mahoning, OH............. 5.9 97.2 1.1 222 720 4.3 71
Montgomery, OH........... 11.8 254.9 1.6 170 871 2.6 196
Stark, OH................ 8.5 160.7 0.7 264 762 4.7 52
Summit, OH............... 14.3 269.1 0.6 274 886 1.8 247
Warren, OH............... 4.9 94.2 1.1 222 898 -3.6 344
Cleveland, OK............ 5.8 79.7 0.2 311 749 0.9 303
Oklahoma, OK............. 28.0 450.0 0.4 294 943 2.5 207
Tulsa, OK................ 22.4 353.0 1.1 222 914 2.5 207
Clackamas, OR............ 14.8 165.0 2.7 70 1,027 10.0 5
Deschutes, OR............ 8.3 81.9 4.3 8 844 5.9 18
Jackson, OR.............. 7.3 88.1 3.0 47 791 5.6 29
Lane, OR................. 12.0 156.3 2.4 89 816 4.1 82
Marion, OR............... 10.6 155.6 1.5 183 854 4.0 89
Multnomah, OR............ 34.5 505.1 2.0 125 1,071 5.7 25
Washington, OR........... 19.1 293.7 2.8 62 1,264 -1.9 341
Allegheny, PA............ 35.8 703.6 0.6 274 1,082 3.8 102
Berks, PA................ 9.0 172.2 0.8 252 929 3.3 140
Bucks, PA................ 20.0 269.2 1.8 146 949 1.5 266
Butler, PA............... 5.1 86.2 0.2 311 949 4.5 60
Chester, PA.............. 15.5 252.6 1.5 183 1,322 5.1 38
Cumberland, PA........... 6.5 133.6 0.7 264 931 3.9 92
Dauphin, PA.............. 7.6 185.4 0.6 274 997 4.9 45
Delaware, PA............. 14.3 223.9 1.0 235 1,063 -0.7 336
Erie, PA................. 7.1 123.4 -0.4 331 771 0.0 326
Lackawanna, PA........... 5.7 97.7 0.6 274 777 2.5 207
Lancaster, PA............ 13.5 239.7 1.2 212 840 2.2 226
Lehigh, PA............... 8.9 191.5 1.4 194 977 -0.3 332
Luzerne, PA.............. 7.5 146.3 0.7 264 797 3.5 125
Montgomery, PA........... 27.8 498.8 1.7 157 1,205 0.2 322
Northampton, PA.......... 6.8 114.7 1.7 157 878 3.8 102
Philadelphia, PA......... 35.5 671.5 1.9 134 1,170 1.7 253
Washington, PA........... 5.5 89.3 3.1 37 990 4.2 75
Westmoreland, PA......... 9.3 136.2 0.8 252 829 5.9 18
York, PA................. 9.2 177.8 0.1 316 896 5.5 32
Providence, RI........... 18.2 287.2 0.5 287 1,016 1.5 266
Charleston, SC........... 14.9 249.2 1.7 157 915 4.2 75
Greenville, SC........... 13.7 267.8 1.9 134 903 5.4 33
Horry, SC................ 8.6 136.2 3.4 24 622 3.8 102
Lexington, SC............ 6.5 117.5 1.4 194 775 2.9 175
Richland, SC............. 10.1 220.0 1.3 200 854 0.4 315
Spartanburg, SC.......... 6.2 137.3 2.9 55 888 1.3 282
York, SC................. 5.6 94.3 4.8 4 824 4.7 52
Minnehaha, SD............ 7.2 127.5 1.3 200 876 3.4 134
Davidson, TN............. 22.3 485.4 3.6 16 1,053 3.7 114
Hamilton, TN............. 9.6 201.9 1.6 170 893 1.8 247
Knox, TN................. 12.2 236.6 0.8 252 877 3.1 156
Rutherford, TN........... 5.5 124.6 3.6 16 927 2.0 236
Shelby, TN............... 20.4 495.2 1.0 235 1,008 3.8 102
Williamson, TN........... 8.6 130.1 4.1 10 1,124 2.8 183
Bell, TX................. 5.4 119.1 1.5 183 883 9.6 6
Bexar, TX................ 40.7 853.6 1.8 146 914 4.6 57
Brazoria, TX............. 5.7 106.0 -0.2 325 1,030 1.7 253
Brazos, TX............... 4.5 97.7 1.0 235 763 5.0 41
Cameron, TX.............. 6.5 140.2 1.3 200 615 2.3 220
Collin, TX............... 24.5 398.6 3.6 16 1,169 1.7 253
Dallas, TX............... 76.1 1,686.9 2.5 85 1,213 2.6 196
Denton, TX............... 14.7 240.2 3.6 16 933 4.4 66
El Paso, TX.............. 15.0 299.6 1.7 157 717 3.3 140
Fort Bend, TX............ 13.1 179.7 1.6 170 936 0.4 315
Galveston, TX............ 6.2 111.3 1.7 157 904 3.1 156
Harris, TX............... 114.2 2,284.5 0.7 264 1,231 -0.4 333
Hidalgo, TX.............. 12.3 254.8 2.7 70 632 1.1 298
Jefferson, TX............ 5.9 123.4 0.4 294 1,026 1.2 291
Lubbock, TX.............. 7.5 138.7 1.3 200 801 5.1 38
McLennan, TX............. 5.2 113.4 2.0 125 830 1.6 261
Midland, TX.............. 5.4 89.3 7.3 1 1,321 11.4 2
Montgomery, TX........... 11.2 175.2 3.2 33 1,008 2.0 236
Nueces, TX............... 8.3 164.5 1.3 200 861 1.4 273
Potter, TX............... 4.0 78.6 -0.4 331 832 6.1 15
Smith, TX................ 6.2 103.4 0.8 252 823 1.5 266
Tarrant, TX.............. 42.8 877.0 2.7 70 1,011 3.9 92
Travis, TX............... 40.2 728.7 3.1 37 1,186 5.6 29
Webb, TX................. 5.4 99.9 2.2 109 667 1.4 273
Williamson, TX........... 10.6 166.8 3.1 37 992 5.6 29
Davis, UT................ 8.3 128.1 4.5 6 837 5.4 33
Salt Lake, UT............ 44.0 687.6 2.8 62 967 2.7 189
Utah, UT................. 15.8 232.4 5.2 3 814 1.6 261
Weber, UT................ 6.0 103.9 2.3 99 762 1.7 253
Chittenden, VT........... 6.9 103.5 0.8 252 978 0.3 321
Arlington, VA............ 9.2 178.7 2.3 99 1,609 3.3 140
Chesterfield, VA......... 9.0 137.1 1.4 194 862 2.5 207
Fairfax, VA.............. 37.3 610.3 1.2 212 1,542 3.5 125
Henrico, VA.............. 11.6 195.4 1.6 170 972 1.7 253
Loudoun, VA.............. 12.2 168.2 2.9 55 1,165 2.6 196
Prince William, VA....... 9.3 130.7 1.5 183 880 2.4 215
Alexandria City, VA...... 6.5 94.8 -0.9 341 1,389 3.1 156
Chesapeake City, VA...... 6.1 100.0 1.0 235 806 1.9 242
Newport News City, VA.... 3.9 97.8 0.9 247 975 7.4 8
Norfolk City, VA......... 5.9 142.4 2.1 117 1,029 5.8 23
Richmond City, VA........ 7.7 154.1 1.6 170 1,087 3.3 140
Virginia Beach City, VA.. 12.2 183.8 0.6 274 786 3.8 102
Benton, WA............... 5.7 93.1 3.0 47 1,010 1.3 282
Clark, WA................ 14.4 157.8 4.4 7 954 5.9 18
King, WA................. 86.1 1,369.7 3.3 29 1,472 6.0 17
Kitsap, WA............... 6.7 88.6 2.0 125 978 11.0 4
Pierce, WA............... 21.7 303.9 1.8 146 934 3.4 134
Snohomish, WA............ 20.7 286.2 0.1 316 1,106 3.0 168
Spokane, WA.............. 15.6 222.0 2.3 99 868 4.5 60
Thurston, WA............. 8.2 113.4 3.5 21 934 5.3 36
Whatcom, WA.............. 7.3 90.4 1.9 134 860 6.8 12
Yakima, WA............... 7.7 121.6 -0.6 337 716 4.2 75
Kanawha, WV.............. 5.7 100.7 -1.5 344 876 1.5 266
Brown, WI................ 6.8 159.3 2.2 109 868 1.2 291
Dane, WI................. 15.1 334.2 1.0 235 1,004 -0.1 328
Milwaukee, WI............ 25.7 488.8 0.4 294 970 2.6 196
Outagamie, WI............ 5.2 110.2 1.0 235 860 3.2 148
Waukesha, WI............. 12.8 246.6 0.5 287 996 1.3 282
Winnebago, WI............ 3.7 94.8 1.1 222 928 2.5 207
San Juan, PR............. 11.0 242.0 -0.9 (5) 622 2.6 (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 346 U.S. counties comprise 72.7 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
second quarter 2017
Employment Average weekly
wage(1)
Establishments,
second quarter
County by NAICS supersector 2017 Percent Percent
(thousands) June change, Second change,
2017 June quarter second
(thousands) 2016-17(2) 2017 quarter
2016-17(2)
United States(3) ............................ 9,922.4 145,186.4 1.7 $1,020 3.2
Private industry........................... 9,624.1 123,579.7 1.9 1,010 3.2
Natural resources and mining............. 136.9 2,001.8 1.9 1,017 2.2
Construction............................. 791.8 7,102.1 3.6 1,119 4.0
Manufacturing............................ 348.4 12,484.2 0.7 1,239 3.0
Trade, transportation, and utilities..... 1,925.9 27,199.7 1.0 863 3.1
Information.............................. 162.6 2,794.8 0.5 1,880 5.6
Financial activities..................... 871.5 8,131.0 1.7 1,537 3.0
Professional and business services....... 1,789.3 20,439.6 1.9 1,318 2.7
Education and health services............ 1,640.5 22,056.4 2.5 929 3.0
Leisure and hospitality.................. 840.1 16,514.3 2.1 431 3.9
Other services........................... 847.2 4,514.9 1.4 701 3.9
Government................................. 298.3 21,606.6 0.7 1,075 3.4
Los Angeles, CA.............................. 483.9 4,373.6 1.7 1,130 3.8
Private industry........................... 477.5 3,791.8 1.7 1,099 4.0
Natural resources and mining............. 0.5 8.6 7.0 1,082 2.5
Construction............................. 14.1 138.4 4.6 1,187 5.0
Manufacturing............................ 12.2 347.5 -3.3 1,307 6.4
Trade, transportation, and utilities..... 54.0 818.4 0.8 924 3.8
Information.............................. 10.1 185.8 -0.2 2,192 4.5
Financial activities..................... 25.7 219.2 0.9 1,782 3.2
Professional and business services....... 48.0 598.6 1.0 1,406 5.3
Education and health services............ 228.1 773.2 3.2 862 1.8
Leisure and hospitality.................. 33.4 525.9 3.1 625 3.6
Other services........................... 26.6 148.8 0.9 753 10.1
Government................................. 6.3 581.8 1.2 1,334 3.0
Cook, IL..................................... 155.4 2,598.4 0.3 1,179 3.1
Private industry........................... 154.1 2,299.4 0.4 1,166 3.1
Natural resources and mining............. 0.1 1.3 5.2 1,182 4.9
Construction............................. 12.5 76.3 1.2 1,436 2.9
Manufacturing............................ 6.3 186.1 0.0 1,232 3.4
Trade, transportation, and utilities..... 30.1 469.1 -0.3 973 5.2
Information.............................. 2.7 51.4 0.5 1,778 -0.4
Financial activities..................... 15.3 194.7 0.3 2,051 2.4
Professional and business services....... 32.8 475.4 -0.6 1,495 4.2
Education and health services............ 16.5 443.0 0.5 962 1.7
Leisure and hospitality.................. 14.6 297.5 2.4 539 3.5
Other services........................... 17.7 98.4 0.5 947 6.2
Government................................. 1.3 299.0 -0.7 1,276 2.8
New York, NY................................. 129.2 2,469.1 1.7 1,907 2.4
Private industry........................... 128.4 2,206.4 1.8 1,976 2.2
Natural resources and mining............. 0.0 0.2 7.0 2,059 4.4
Construction............................. 2.3 40.4 -2.4 1,865 3.8
Manufacturing............................ 2.1 25.5 -3.8 1,418 2.5
Trade, transportation, and utilities..... 19.5 253.3 -0.5 1,367 0.2
Information.............................. 5.0 164.2 5.4 2,509 0.4
Financial activities..................... 19.5 377.1 0.0 3,591 2.0
Professional and business services....... 27.0 578.4 2.1 2,201 1.5
Education and health services............ 10.0 342.7 1.0 1,334 7.8
Leisure and hospitality.................. 14.1 303.0 2.3 875 3.3
Other services........................... 20.5 104.4 1.1 1,253 7.4
Government................................. 0.8 262.7 0.7 1,339 5.1
Harris, TX................................... 114.2 2,284.5 0.7 1,231 -0.4
Private industry........................... 113.7 2,007.4 0.6 1,247 -0.6
Natural resources and mining............. 1.6 66.7 -2.3 2,940 -9.0
Construction............................. 7.4 158.9 -1.4 1,328 2.5
Manufacturing............................ 4.8 169.5 -1.1 1,558 1.3
Trade, transportation, and utilities..... 25.0 468.0 0.1 1,118 1.4
Information.............................. 1.2 26.9 -4.2 1,400 -2.1
Financial activities..................... 12.1 126.2 2.1 1,633 2.6
Professional and business services....... 23.1 394.6 0.6 1,529 -3.0
Education and health services............ 15.9 291.2 2.8 1,034 3.1
Leisure and hospitality.................. 10.0 236.5 1.7 451 4.6
Other services........................... 11.6 66.4 1.5 800 3.0
Government................................. 0.6 277.1 1.5 1,113 1.1
Maricopa, AZ................................. 96.5 1,891.7 3.3 986 1.6
Private industry........................... 95.8 1,705.9 3.5 975 1.8
Natural resources and mining............. 0.4 8.6 2.3 908 5.5
Construction............................. 6.9 110.6 7.1 1,038 4.2
Manufacturing............................ 3.1 117.9 1.0 1,432 -1.1
Trade, transportation, and utilities..... 18.2 370.5 2.1 899 2.5
Information.............................. 1.5 34.7 -0.5 1,376 -0.7
Financial activities..................... 10.7 174.5 5.2 1,258 0.2
Professional and business services....... 20.6 323.8 2.0 1,065 2.1
Education and health services............ 10.6 289.1 4.2 978 2.5
Leisure and hospitality.................. 7.6 211.6 3.7 477 5.8
Other services........................... 5.9 50.6 -2.3 718 4.8
Government................................. 0.7 185.7 1.9 1,075 0.4
Dallas, TX................................... 76.1 1,686.9 2.5 1,213 2.6
Private industry........................... 75.5 1,514.5 2.7 1,218 2.4
Natural resources and mining............. 0.5 8.7 5.1 3,279 -4.9
Construction............................. 4.5 89.0 4.1 1,222 7.6
Manufacturing............................ 2.8 112.7 0.9 1,440 -1.2
Trade, transportation, and utilities..... 15.9 342.8 3.2 1,046 0.6
Information.............................. 1.4 48.6 -1.3 1,818 -1.7
Financial activities..................... 9.4 165.4 4.2 1,704 3.1
Professional and business services....... 17.1 340.5 2.5 1,417 3.5
Education and health services............ 9.5 197.2 2.9 1,105 5.6
Leisure and hospitality.................. 6.8 164.1 2.4 489 2.5
Other services........................... 7.0 43.9 -0.6 803 6.2
Government................................. 0.6 172.4 0.4 1,170 5.0
Orange, CA................................... 119.3 1,598.1 2.1 1,130 2.5
Private industry........................... 117.8 1,443.1 2.3 1,115 2.6
Natural resources and mining............. 0.2 2.9 -1.0 899 6.8
Construction............................. 6.7 101.0 3.4 1,320 6.2
Manufacturing............................ 4.9 158.0 -0.4 1,397 2.0
Trade, transportation, and utilities..... 16.9 258.5 1.3 1,003 1.4
Information.............................. 1.3 27.0 1.2 1,930 9.4
Financial activities..................... 11.1 117.9 1.3 1,724 1.6
Professional and business services....... 20.4 294.6 1.2 1,344 3.9
Education and health services............ 32.9 208.2 4.0 911 -0.4
Leisure and hospitality.................. 8.7 219.9 3.2 502 6.1
Other services........................... 6.8 45.9 1.1 725 5.7
Government................................. 1.5 155.0 0.6 1,267 2.2
San Diego, CA................................ 109.8 1,440.9 2.0 1,101 2.8
Private industry........................... 107.9 1,205.0 2.0 1,058 1.5
Natural resources and mining............. 0.6 9.0 -8.3 720 2.3
Construction............................. 6.8 79.2 4.2 1,181 3.3
Manufacturing............................ 3.2 107.9 0.7 1,504 3.0
Trade, transportation, and utilities..... 14.2 224.1 0.9 873 0.3
Information.............................. 1.2 24.2 0.3 1,890 5.8
Financial activities..................... 9.9 73.3 0.9 1,446 4.1
Professional and business services....... 18.0 229.2 0.5 1,481 0.1
Education and health services............ 31.3 198.4 3.0 933 0.5
Leisure and hospitality.................. 8.3 200.0 2.8 504 5.7
Other services........................... 7.2 51.9 2.4 625 4.3
Government................................. 1.9 235.9 2.1 1,317 8.2
King, WA..................................... 86.1 1,369.7 3.3 1,472 6.0
Private industry........................... 85.6 1,198.8 3.6 1,495 6.6
Natural resources and mining............. 0.4 3.1 1.1 1,240 1.2
Construction............................. 6.8 71.1 5.9 1,334 3.3
Manufacturing............................ 2.5 102.6 -2.7 1,617 -1.8
Trade, transportation, and utilities..... 14.5 265.8 6.4 1,618 12.8
Information.............................. 2.2 103.4 5.9 2,991 8.6
Financial activities..................... 6.7 67.9 2.7 1,650 3.8
Professional and business services....... 17.9 224.5 2.6 1,668 5.0
Education and health services............ 18.0 170.8 2.5 1,040 4.6
Leisure and hospitality.................. 7.3 143.8 4.5 578 4.1
Other services........................... 9.3 45.7 1.6 882 5.6
Government................................. 0.5 171.0 1.7 1,318 1.7
Miami-Dade, FL............................... 98.8 1,111.0 1.8 971 1.8
Private industry........................... 98.5 985.9 1.8 949 3.3
Natural resources and mining............. 0.5 8.1 4.4 627 1.6
Construction............................. 6.7 46.0 5.6 926 1.6
Manufacturing............................ 2.9 41.4 2.7 879 1.9
Trade, transportation, and utilities..... 25.9 280.3 0.5 898 3.8
Information.............................. 1.6 17.9 -0.8 1,696 8.0
Financial activities..................... 10.6 75.5 1.4 1,498 3.7
Professional and business services....... 22.1 157.2 2.9 1,128 2.8
Education and health services............ 10.5 176.8 2.1 972 2.3
Leisure and hospitality.................. 7.3 141.5 1.8 581 3.8
Other services........................... 8.4 39.6 -0.4 621 2.5
Government................................. 0.3 125.2 1.3 1,125 -6.9
(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 2016 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
second quarter 2017
Employment Average weekly
wage(1)
Establishments,
second quarter
State 2017 Percent Percent
(thousands) June change, Second change,
2017 June quarter second
(thousands) 2016-17 2017 quarter
2016-17
United States(2)........... 9,922.4 145,186.4 1.7 $1,020 3.2
Alabama.................... 124.2 1,946.4 1.2 858 2.8
Alaska..................... 22.1 338.4 -0.7 1,005 -0.5
Arizona.................... 158.2 2,699.6 2.9 943 2.5
Arkansas................... 89.5 1,206.0 0.7 810 3.2
California................. 1,522.5 17,150.9 2.2 1,210 4.7
Colorado................... 198.4 2,638.8 2.5 1,042 4.2
Connecticut................ 118.6 1,701.2 0.6 1,216 0.4
Delaware................... 31.7 446.6 0.6 1,012 2.2
District of Columbia....... 39.0 766.5 1.0 1,675 3.3
Florida.................... 684.9 8,390.6 2.8 905 2.5
Georgia.................... 278.1 4,357.8 2.1 956 2.9
Hawaii..................... 41.7 653.0 1.0 935 3.5
Idaho...................... 61.0 723.5 3.4 765 3.4
Illinois................... 412.2 6,006.6 0.9 1,062 2.4
Indiana.................... 164.3 3,041.0 1.5 859 3.7
Iowa....................... 101.7 1,571.4 0.4 853 3.3
Kansas..................... 90.3 1,377.8 -0.1 849 2.4
Kentucky................... 120.8 1,889.4 0.8 862 2.9
Louisiana.................. 131.4 1,907.7 0.0 869 2.0
Maine...................... 54.4 629.1 0.9 814 2.5
Maryland................... 171.8 2,694.8 1.4 1,103 3.1
Massachusetts.............. 252.3 3,604.5 1.6 1,278 3.6
Michigan................... 242.9 4,365.3 1.6 969 2.9
Minnesota.................. 169.0 2,902.1 2.0 1,037 3.9
Mississippi................ 73.4 1,128.9 0.7 732 0.8
Missouri................... 206.6 2,818.7 1.2 889 3.0
Montana.................... 48.3 473.6 1.3 797 3.9
Nebraska................... 72.6 984.0 0.4 833 3.5
Nevada..................... 80.7 1,333.5 3.4 900 2.9
New Hampshire.............. 52.1 665.4 1.6 1,015 1.2
New Jersey................. 272.8 4,123.5 1.8 1,173 2.3
New Mexico................. 58.5 815.4 0.7 823 1.5
New York................... 648.6 9,417.4 1.6 1,237 2.2
North Carolina............. 272.0 4,361.4 1.8 902 4.3
North Dakota............... 31.9 422.7 -0.2 953 5.0
Ohio....................... 295.2 5,422.8 1.2 912 3.3
Oklahoma................... 110.4 1,583.8 0.8 845 2.5
Oregon..................... 150.2 1,912.6 2.2 967 3.8
Pennsylvania............... 360.1 5,859.4 1.3 1,000 3.0
Rhode Island............... 37.3 487.3 1.0 980 2.6
South Carolina............. 128.1 2,053.9 2.0 834 3.6
South Dakota............... 33.2 435.5 0.6 785 3.4
Tennessee.................. 157.2 2,948.1 1.8 906 3.5
Texas...................... 671.5 12,059.6 2.1 1,027 2.7
Utah....................... 98.5 1,440.3 3.4 862 2.6
Vermont.................... 25.6 314.2 1.0 870 2.1
Virginia................... 269.6 3,886.6 1.5 1,047 3.7
Washington................. 239.2 3,352.5 2.2 1,141 5.6
West Virginia.............. 50.1 690.9 -0.3 828 3.4
Wisconsin.................. 171.7 2,905.3 1.1 876 2.3
Wyoming.................... 26.1 280.2 -0.7 875 3.1
Puerto Rico................ 46.9 873.6 -1.0 515 1.2
Virgin Islands............. 3.3 38.6 0.4 762 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
