For release 10:00 a.m. (EST), Thursday, March 8, 2018 USDL-18-0334
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 2017
From September 2016 to September 2017, employment increased in 283 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 10.4 percent over the year, above the national job growth rate of 1.0 percent.
Within Midland, the largest employment increase occurred in natural resources and mining, which
gained 4,526 jobs over the year (24.4 percent). Collier, Fla., had the largest over-the-year percentage
decrease in employment among the largest counties in the U.S., with a loss of 5.2 percent. Within
Collier, construction had the largest decrease in employment, with a loss of 1,879 jobs (-12.8 percent).
The U.S. average weekly wage decreased 0.6 percent over the year, declining to $1,021 in the third
quarter of 2017. This is the third decline since first quarter 2016, and one of only nine declines in the
history of the series, which dates back to 1978. Mercer, N.J., had the largest over-the-year percentage
decrease in average weekly wages with a loss of 8.8 percent. Within Mercer, an average weekly wage
loss of $260 (-13.1 percent) in professional and business services made the largest contribution to the
county’s decrease in average weekly wages. Midland, Texas, had the largest over-the-year percentage
increase in average weekly wages with a gain of 8.4 percent. Within Midland, natural resources and
mining had the largest impact on the county’s average weekly wage change with an increase of $180
(9.5 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 September 2017, national employment was 144.5 million (as measured by the QCEW program). Over
the year, employment increased 1.0 percent, or 1.5 million. In September 2017, the 346 U.S. counties
with 75,000 or more jobs accounted for 72.7 percent of total U.S. employment and 77.8 percent of total
wages. These 346 counties had a net job growth of 1.1 million over the year, accounting for 77.3 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 201,100 jobs, which was 13.8 percent of the overall
job increase for the U.S. (See table A.)
Employment declined in 60 of the largest counties from September 2016 to September 2017. Collier,
Fla., had the largest over-the-year percentage decrease in employment (-5.2 percent), followed by Lee,
Fla.; Jefferson, Texas; Sangamon, Ill.; and Brazoria, Texas. (See table 1.)
Table A. Large counties ranked by September 2017 employment, September 2016-17 employment increase, and
September 2016-17 percent increase in employment
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Employment in large counties
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September 2017 employment | Increase in employment, | Percent increase in employment,
(thousands) | September 2016-17 | September 2016-17
| (thousands) |
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| |
United States 144,464.4| United States 1,459.4| United States 1.0
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| |
Los Angeles, Calif. 4,408.1| Los Angeles, Calif. 58.1| Midland, Texas 10.4
Cook, Ill. 2,578.3| Maricopa, Ariz. 48.2| Elkhart, Ind. 5.2
New York, N.Y. 2,451.9| King, Wash. 36.7| Weld, Colo. 5.0
Harris, Texas 2,261.3| Dallas, Texas 31.1| Clark, Wash. 4.6
Maricopa, Ariz. 1,938.0| New York, N.Y. 27.0| Calcasieu, La. 4.5
Dallas, Texas 1,691.1| Kings, N.Y. 25.4| Rutherford, Tenn. 4.3
Orange, Calif. 1,598.6| Santa Clara, Calif. 23.2| Utah, Utah 4.2
San Diego, Calif. 1,439.5| Clark, Nev. 22.8| Montgomery, Texas 4.0
King, Wash. 1,367.1| San Bernardino, Calif. 22.6| Benton, Wash. 3.8
Miami-Dade, Fla. 1,092.6| Orange, Calif. 21.7| Kings, N.Y. 3.7
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Large County Average Weekly Wages
Average weekly wages for the nation decreased to $1,021, a 0.6 percent decrease, during the year ending
in the third quarter of 2017. Among the 346 largest counties, 265 had over-the-year decreases in average
weekly wages. Mercer, N.J., had the largest percentage wage decrease among the largest U.S. counties (-
8.8 percent). (See table B.)
Of the 346 largest counties, 71 experienced an over-the-year increase in average weekly wages.
Midland, Texas, had the largest percentage increase in average weekly wages (8.4 percent), followed by
Union, N.J.; Elkhart, Ind.; Forsyth, N.C.; and Maui + Kalawao, Hawaii. (See table 1.)
Table B. Large counties ranked by third quarter 2017 average weekly wages, third quarter 2016-17
decrease in average weekly wages, and third quarter 2016-17 percent decrease in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Decrease in average weekly | Percent decrease in average
third quarter 2017 | wage, third quarter 2016-17 | weekly wage, third
| | quarter 2016-17
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| |
United States $1,021| United States -$6| United States -0.6
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| |
Santa Clara, Calif. $2,320| Mercer, N.J. -$118| Mercer, N.J. -8.8
San Mateo, Calif. 2,123| Somerset, N.J. -74| Wyandotte, Kan. -6.0
San Francisco, Calif. 1,954| Wyandotte, Kan. -61| Clark, Nev. -5.3
New York, N.Y. 1,889| Fairfield, Conn. -58| Somerset, N.J. -5.0
Washington, D.C. 1,759| Middlesex, Mass. -57| Clay, Mo. -4.8
Suffolk, Mass. 1,691| Clark, Nev. -50| Washington, Ark. -4.7
Arlington, Va. 1,642| Clay, Mo. -43| Okaloosa, Fla. -4.3
King, Wash. 1,626| Jefferson, Ky. -42| McLean, Ill. -4.2
Fairfax, Va. 1,540| Dauphin, Pa. -42| Jefferson, Ky. -4.2
Middlesex, Mass. 1,498| Anchorage, Alaska -41| Montgomery, Ala. -4.1
| Washington, Ark. -41| Sedgwick, Kan. -4.1
| McLean, Ill. -41|
| Mecklenburg, N.C. -41|
| Norfolk City, Va. -41|
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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
2017. King, Wash., had the largest gain (2.8 percent). Within King, trade, transportation, and utilities
had the largest over-the-year employment level increase, with a gain of 16,733 jobs, or 6.6 percent.
Miami-Dade, Fla., had the only percentage decrease in employment among the 10 largest counties (-1.7
percent). Within Miami-Dade, leisure and hospitality had the largest over-the-year employment level
decrease, with a loss of 6,855 jobs, or -4.9 percent. (See table 2.)
Average weekly wages decreased over the year in 7 of the 10 largest U.S. counties. Dallas, Texas,
experienced the largest percentage loss in average weekly wages (-1.9 percent). Within Dallas, trade,
transportation, and utilities had the largest impact on the county’s average weekly wage loss. Within
trade, transportation, and utilities, average weekly wages decreased by $61, or -5.5 percent, over the
year. King, Wash., had the largest percentage gain in average weekly wages among the 10 largest
counties (2.7 percent). Within King, information had the largest impact on the county’s average weekly
wage growth with an increase of $169 (3.4 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. September 2017 employment and 2017 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 9.9 million employer reports cover 144.5 million full- and part-time workers. Data for the
third 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 fourth quarter 2017 is scheduled to be released
on Wednesday, May 23, 2018.
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| |
| Effects of Hurricanes Irma and Maria on the Quarterly Census of Employment and Wages |
| |
| Hurricanes Irma and Maria made landfall in the United States on September 7 and September 20, |
| 2017, respectively, during the QCEW third quarter reference period. These events did not cause changes |
| to QCEW methodology. However, they did affect data collection in Puerto Rico and the U.S. Virgin |
| Islands. For more information, please visit this webpage: |
| www.bls.gov/bls/hurricanes-harvey-irma-maria.htm. |
| |
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| |
| QCEW Publication Acceleration and Conversion to Two Data Releases |
| |
| The QCEW publication process is accelerating for a more timely release. Beginning with the fourth |
| quarter 2017 release, QCEW data will be published in two parts. The current County Employment and |
| Wages news release and associated data will be accelerated and published first. The full QCEW data |
| release will occur two weeks later, accompanied by a data release notice. |
| |
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| |
| Alaska Area Name Changes Effective with QCEW Release for Third Quarter 2017 |
| These Alaska area names have been updated for the current and future QCEW releases |
| |
| ------------------------------------------------------------ |
| | Previous Name | Current Name | |
| |------------------------------------------------------------| |
| | Aleutian East Borough | Aleutians East Borough | |
| | Aleutian West Census Area | Aleutians West Census Area | |
| | Anchorage Borough | Anchorage Municipality | |
| | Juneau Borough | Juneau City and Borough | |
| | Petersburg Census Area | Petersburg Borough | |
| | Sitka Borough | Sitka City and Borough | |
| | Yakutat Borough | Yakutat City and Borough | |
| ------------------------------------------------------------ |
| |
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Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of
Employment and Wages (QCEW) program, also known as the ES-202 program. The data are
derived from summaries of employment and total pay of workers covered by state and federal
unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The
summaries are a result of the administration of state unemployment insurance programs that
require most employers to pay quarterly taxes based on the employment and wages of workers
covered by UI. QCEW data in this release are based on the 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: QCEW, Business Employment Dynamics (BED), and Current Employment
Statistics (CES). Each of these measures 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- | 651,000 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/ces/
Web sites | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly
contribution reports submitted to the SWAs by employers. For federal civilian workers covered by
the Unemployment Compensation for Federal Employees (UCFE) program, employment and
wage data are compiled from quarterly reports submitted by four major federal payroll processing
centers on behalf of all federal agencies, with the exception of a few agencies which still report
directly to the individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry of each of their
establishments. QCEW employment and wage data are derived from microdata summaries of 9.7
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
that reflect economic events or 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
eliminate the effect of 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,
third quarter 2017
Employment Average weekly wage(2)
Establishments,
County(1) third quarter Percent Ranking Percent Ranking
2017 September change, by Third change, by
(thousands) 2017 September percent quarter third percent
(thousands) 2016-17(3) change 2017 quarter change
2016-17(3)
United States(4)......... 9,916.5 144,464.4 1.0 - $1,021 -0.6 -
Jefferson, AL............ 18.7 344.2 1.1 145 990 -1.8 237
Madison, AL.............. 9.6 195.5 1.3 114 1,103 -1.6 217
Mobile, AL............... 10.2 170.5 0.6 209 874 -1.5 208
Montgomery, AL........... 6.4 132.7 1.0 157 825 -4.1 336
Shelby, AL............... 5.8 85.8 1.4 104 956 -1.4 195
Tuscaloosa, AL........... 4.6 94.0 1.0 157 831 1.0 35
Anchorage, AK............ 8.3 151.5 -1.2 332 1,063 -3.7 330
Maricopa, AZ............. 96.6 1,938.0 2.6 32 987 -1.1 168
Pima, AZ................. 18.7 365.6 1.1 145 869 0.6 49
Benton, AR............... 6.5 118.1 1.0 157 942 0.7 43
Pulaski, AR.............. 14.4 251.3 0.8 182 904 -2.2 266
Washington, AR........... 6.0 107.1 2.6 32 823 -4.7 341
Alameda, CA.............. 63.5 777.0 2.4 42 1,390 0.0 72
Butte, CA................ 8.5 84.2 1.2 132 789 1.3 27
Contra Costa, CA......... 32.4 368.0 0.6 209 1,240 0.0 72
Fresno, CA............... 35.5 393.4 1.6 80 804 -0.4 103
Kern, CA................. 19.0 330.3 1.7 79 844 -1.9 245
Los Angeles, CA.......... 488.1 4,408.1 1.3 114 1,147 1.1 32
Marin, CA................ 12.5 114.5 0.7 192 1,237 -0.1 82
Merced, CA............... 6.7 84.3 2.6 32 807 -0.5 111
Monterey, CA............. 13.7 204.8 0.3 244 885 -0.7 133
Napa, CA................. 5.9 79.2 1.9 67 1,020 0.7 43
Orange, CA............... 120.4 1,598.6 1.4 104 1,135 -1.1 168
Placer, CA............... 13.0 161.9 1.9 67 1,033 -1.0 156
Riverside, CA............ 63.9 711.3 2.8 28 831 -1.3 186
Sacramento, CA........... 58.1 649.7 2.1 54 1,110 -0.4 103
San Bernardino, CA....... 59.0 731.5 3.2 20 864 -1.3 186
San Diego, CA............ 110.9 1,439.5 1.2 132 1,112 -1.6 217
San Francisco, CA........ 60.7 722.3 2.4 42 1,954 3.2 8
San Joaquin, CA.......... 17.7 253.2 2.5 40 868 -0.7 133
San Luis Obispo, CA...... 10.4 118.8 3.1 22 860 -0.8 142
San Mateo, CA............ 28.3 400.2 1.3 114 2,123 1.1 32
Santa Barbara, CA........ 15.6 202.5 1.6 80 979 -2.1 263
Santa Clara, CA.......... 72.8 1,077.2 2.2 52 2,320 2.6 13
Santa Cruz, CA........... 9.6 107.6 0.6 209 924 -1.2 175
Solano, CA............... 11.5 139.7 1.8 75 1,058 -0.1 82
Sonoma, CA............... 20.1 209.0 1.5 92 993 -0.5 111
Stanislaus, CA........... 15.6 190.1 0.7 192 880 -0.8 142
Tulare, CA............... 10.4 162.9 0.5 223 737 -0.8 142
Ventura, CA.............. 27.1 321.2 1.2 132 988 -3.1 314
Yolo, CA................. 6.7 102.0 -0.5 306 1,094 -2.2 266
Adams, CO................ 11.0 207.1 3.1 22 1,015 0.0 72
Arapahoe, CO............. 22.1 329.5 1.9 67 1,187 -0.9 150
Boulder, CO.............. 15.4 181.0 2.1 54 1,237 1.5 26
Denver, CO............... 32.4 510.4 2.0 60 1,257 0.8 38
Douglas, CO.............. 12.1 121.3 2.5 40 1,114 -0.6 121
El Paso, CO.............. 19.8 272.5 2.0 60 948 1.7 17
Jefferson, CO............ 20.3 234.3 0.6 209 1,057 0.6 49
Larimer, CO.............. 12.2 160.2 2.1 54 967 3.0 10
Weld, CO................. 7.4 106.7 5.0 3 927 1.6 21
Fairfield, CT............ 35.5 420.9 -0.8 321 1,422 -3.9 333
Hartford, CT............. 28.0 509.9 0.6 209 1,185 -1.3 186
New Haven, CT............ 24.2 364.3 0.5 223 1,051 -1.2 175
New London, CT........... 7.5 124.5 0.8 182 993 -2.6 296
New Castle, DE........... 20.0 286.4 0.1 270 1,146 1.2 30
Sussex, DE............... 6.8 81.9 3.0 24 737 -1.9 245
Washington, DC........... 40.4 764.7 0.7 192 1,759 1.3 27
Alachua, FL.............. 7.1 129.1 0.6 209 881 0.0 72
Bay, FL.................. 5.6 77.5 0.3 244 729 -3.6 326
Brevard, FL.............. 15.6 201.6 -1.1 329 902 -3.0 311
Broward, FL.............. 68.8 778.9 -0.7 314 941 -1.1 168
Collier, FL.............. 13.8 128.3 -5.2 346 857 -1.6 217
Duval, FL................ 29.2 498.6 1.3 114 951 -1.7 231
Escambia, FL............. 8.0 133.7 1.2 132 802 -0.6 121
Hillsborough, FL......... 41.8 662.5 -0.5 306 976 -1.8 237
Lake, FL................. 8.1 93.8 -0.5 306 692 -3.5 323
Lee, FL.................. 21.7 239.7 -2.8 345 810 0.1 64
Leon, FL................. 8.7 146.8 -1.5 334 852 0.8 38
Manatee, FL.............. 10.7 116.1 -0.1 287 793 -3.2 316
Marion, FL............... 8.2 99.0 -0.1 287 695 -3.2 316
Miami-Dade, FL........... 97.5 1,092.6 -1.7 337 984 -0.1 82
Okaloosa, FL............. 6.3 82.9 0.7 192 819 -4.3 340
Orange, FL............... 41.9 811.7 1.3 114 895 -1.4 195
Osceola, FL.............. 6.9 90.6 2.0 60 689 -2.5 285
Palm Beach, FL........... 55.9 576.0 -1.0 328 951 -2.4 281
Pasco, FL................ 10.8 115.0 0.9 167 717 -0.1 82
Pinellas, FL............. 32.7 418.2 0.2 259 881 -1.9 245
Polk, FL................. 13.1 211.4 1.2 132 777 -0.8 142
Sarasota, FL............. 15.8 161.5 -0.8 321 841 0.5 52
Seminole, FL............. 14.9 185.3 0.4 233 866 0.1 64
Volusia, FL.............. 14.2 166.8 -0.7 314 720 -1.0 156
Bibb, GA................. 4.2 81.8 -1.5 334 799 0.8 38
Chatham, GA.............. 8.0 148.9 -0.1 287 852 -2.3 274
Clayton, GA.............. 4.0 122.9 0.9 167 1,027 4.3 6
Cobb, GA................. 21.8 356.1 1.6 80 1,066 -2.2 266
DeKalb, GA............... 17.9 298.0 1.4 104 1,031 -0.5 111
Fulton, GA............... 43.0 853.5 2.0 60 1,324 -1.5 208
Gwinnett, GA............. 24.8 351.3 1.4 104 968 -1.6 217
Hall, GA................. 4.4 85.7 2.7 31 853 -0.8 142
Muscogee, GA............. 4.5 93.2 0.7 192 842 2.8 11
Richmond, GA............. 4.4 104.5 0.6 209 866 -1.9 245
Honolulu, HI............. 26.1 472.5 0.2 259 989 -0.9 150
Maui + Kalawao, HI....... 6.2 76.4 0.2 259 891 4.6 5
Ada, ID.................. 15.9 236.5 3.2 20 902 -0.2 90
Champaign, IL............ 4.0 91.3 0.9 167 885 -1.4 195
Cook, IL................. 137.9 2,578.3 0.1 270 1,157 -0.3 98
DuPage, IL............... 34.5 621.8 0.8 182 1,161 0.5 52
Kane, IL................. 12.5 211.0 -0.4 300 921 -0.9 150
Lake, IL................. 20.1 339.7 1.6 80 1,263 -2.0 256
McHenry, IL.............. 7.8 99.1 0.2 259 833 -2.9 307
McLean, IL............... 3.4 84.0 -0.4 300 939 -4.2 338
Madison, IL.............. 5.4 99.4 1.3 114 782 -2.6 296
Peoria, IL............... 4.2 103.6 -0.4 300 1,077 1.6 21
St. Clair, IL............ 5.0 94.0 0.3 244 808 -1.9 245
Sangamon, IL............. 4.7 127.1 -2.1 343 1,012 -0.4 103
Will, IL................. 14.6 244.2 2.6 32 879 -3.5 323
Winnebago, IL............ 6.0 126.2 -0.7 314 888 1.8 16
Allen, IN................ 8.8 186.3 0.4 233 821 -1.6 217
Elkhart, IN.............. 4.7 135.7 5.2 2 924 6.5 3
Hamilton, IN............. 9.4 140.4 1.1 145 976 0.7 43
Lake, IN................. 10.4 189.3 -0.4 300 877 0.0 72
Marion, IN............... 24.0 598.2 0.1 270 1,020 -1.6 217
St. Joseph, IN........... 5.8 123.7 -1.1 329 827 -1.2 175
Tippecanoe, IN........... 3.4 83.9 -0.1 287 886 1.6 21
Vanderburgh, IN.......... 4.8 110.2 2.0 60 824 0.2 63
Johnson, IA.............. 4.2 84.5 0.7 192 965 -0.6 121
Linn, IA................. 6.9 130.6 -0.2 296 966 -3.4 321
Polk, IA................. 17.4 298.5 0.3 244 1,012 -2.7 300
Scott, IA................ 5.6 91.4 0.1 270 825 -2.3 274
Johnson, KS.............. 23.9 343.6 1.6 80 1,008 -1.9 245
Sedgwick, KS............. 12.7 247.0 -0.5 306 849 -4.1 336
Shawnee, KS.............. 5.2 96.5 -1.8 340 820 -2.3 274
Wyandotte, KS............ 3.5 92.4 1.5 92 953 -6.0 345
Boone, KY................ 4.4 87.7 3.3 16 877 -3.8 332
Fayette, KY.............. 10.9 195.8 0.5 223 893 -2.7 300
Jefferson, KY............ 25.0 468.0 0.7 192 959 -4.2 338
Caddo, LA................ 7.3 111.5 -1.7 337 812 0.0 72
Calcasieu, LA............ 5.3 99.2 4.5 5 909 -0.8 142
East Baton Rouge, LA..... 15.8 264.9 -0.1 287 940 -3.0 311
Jefferson, LA............ 14.0 189.4 -1.8 340 899 -2.5 285
Lafayette, LA............ 9.6 129.6 0.2 259 858 -3.6 326
Orleans, LA.............. 12.7 192.9 0.2 259 937 -2.5 285
St. Tammany, LA.......... 8.4 88.0 -0.7 314 844 -1.2 175
Cumberland, ME........... 14.0 184.2 1.8 75 932 -0.4 103
Anne Arundel, MD......... 15.2 271.4 0.5 223 1,071 -1.4 195
Baltimore, MD............ 21.2 373.7 -0.9 325 1,012 -1.8 237
Frederick, MD............ 6.4 101.4 1.0 157 939 -2.6 296
Harford, MD.............. 5.8 94.1 1.1 145 984 -2.5 285
Howard, MD............... 10.0 171.3 0.6 209 1,230 -2.5 285
Montgomery, MD........... 32.8 469.9 0.2 259 1,336 -1.3 186
Prince George's, MD...... 15.9 316.4 -0.3 299 1,080 -2.7 300
Baltimore City, MD....... 13.6 346.0 2.1 54 1,199 -1.2 175
Barnstable, MA........... 9.6 102.1 0.1 270 849 -0.7 133
Bristol, MA.............. 17.7 227.6 1.1 145 901 -1.3 186
Essex, MA................ 25.9 327.3 0.0 284 1,072 -0.2 90
Hampden, MA.............. 18.4 210.3 0.8 182 915 -1.8 237
Middlesex, MA............ 55.4 904.1 1.6 80 1,498 -3.7 330
Norfolk, MA.............. 25.5 352.5 0.4 233 1,142 -0.2 90
Plymouth, MA............. 16.0 194.8 1.3 114 937 -0.7 133
Suffolk, MA.............. 29.9 675.0 0.9 167 1,691 1.7 17
Worcester, MA............ 25.5 349.3 0.7 192 1,011 -0.3 98
Genesee, MI.............. 6.8 134.8 0.2 259 845 -1.2 175
Ingham, MI............... 6.0 153.2 1.1 145 935 -2.2 266
Kalamazoo, MI............ 5.0 117.7 -0.1 287 944 0.3 59
Kent, MI................. 14.5 396.4 1.5 92 891 -2.0 256
Macomb, MI............... 17.6 327.7 0.1 270 1,016 -0.2 90
Oakland, MI.............. 39.4 731.3 1.0 157 1,116 -1.3 186
Ottawa, MI............... 5.7 126.9 1.0 157 864 -0.2 90
Saginaw, MI.............. 3.9 84.3 -0.7 314 812 -2.2 266
Washtenaw, MI............ 8.2 213.1 1.5 92 1,101 0.5 52
Wayne, MI................ 30.9 722.3 0.7 192 1,092 -1.8 237
Anoka, MN................ 7.2 123.0 2.0 60 1,008 -1.9 245
Dakota, MN............... 9.9 188.7 0.7 192 959 -3.6 326
Hennepin, MN............. 41.0 927.2 1.8 75 1,236 -2.9 307
Olmsted, MN.............. 3.4 97.7 1.6 80 1,180 2.5 14
Ramsey, MN............... 13.4 334.9 0.4 233 1,124 -2.5 285
St. Louis, MN............ 5.3 99.1 0.6 209 844 -3.1 314
Stearns, MN.............. 4.3 87.2 0.9 167 877 -0.8 142
Washington, MN........... 5.5 85.4 3.3 16 851 -2.0 256
Harrison, MS............. 4.6 85.0 -1.1 329 697 -2.4 281
Hinds, MS................ 5.8 120.4 -0.7 314 855 -1.8 237
Boone, MO................ 5.1 94.6 1.2 132 819 -1.9 245
Clay, MO................. 5.7 107.4 2.8 28 856 -4.8 342
Greene, MO............... 9.1 166.2 1.3 114 781 -2.6 296
Jackson, MO.............. 22.4 369.2 1.0 157 1,019 -0.6 121
St. Charles, MO.......... 9.6 147.9 0.9 167 807 -1.7 231
St. Louis, MO............ 39.7 607.8 0.8 182 1,048 -0.7 133
St. Louis City, MO....... 14.8 228.0 0.2 259 1,066 -3.6 326
Yellowstone, MT.......... 6.8 82.0 -0.6 311 865 -1.7 231
Douglas, NE.............. 19.3 338.7 0.1 270 957 -2.5 285
Lancaster, NE............ 10.4 168.9 -0.4 300 842 -0.4 103
Clark, NV................ 55.3 970.2 2.4 42 898 -5.3 344
Washoe, NV............... 14.8 218.8 2.1 54 933 0.1 64
Hillsborough, NH......... 12.2 201.9 0.6 209 1,126 -0.8 142
Merrimack, NH............ 5.2 77.2 0.1 270 962 0.8 38
Rockingham, NH........... 11.0 151.0 0.9 167 993 0.3 59
Atlantic, NJ............. 6.5 126.2 -1.7 337 841 -0.7 133
Bergen, NJ............... 33.0 445.4 0.6 209 1,166 -0.5 111
Burlington, NJ........... 11.0 206.0 1.4 104 1,019 -3.3 319
Camden, NJ............... 12.1 206.9 1.5 92 966 -1.5 208
Essex, NJ................ 20.4 342.5 1.6 80 1,228 -2.1 263
Gloucester, NJ........... 6.3 108.3 1.1 145 848 -2.8 306
Hudson, NJ............... 15.1 262.2 1.9 67 1,363 0.1 64
Mercer, NJ............... 11.2 250.0 0.4 233 1,219 -8.8 346
Middlesex, NJ............ 22.3 425.0 1.3 114 1,152 -2.9 307
Monmouth, NJ............. 20.1 261.9 1.0 157 972 -0.5 111
Morris, NJ............... 17.1 290.8 1.5 92 1,466 -0.7 133
Ocean, NJ................ 13.2 169.6 2.3 48 797 -2.3 274
Passaic, NJ.............. 12.7 167.1 0.3 244 976 -2.3 274
Somerset, NJ............. 10.2 187.1 0.2 259 1,415 -5.0 343
Union, NJ................ 14.3 220.4 0.4 233 1,332 8.2 2
Bernalillo, NM........... 18.3 327.4 -0.2 296 876 -1.6 217
Albany, NY............... 10.4 234.4 -0.4 300 1,049 -1.0 156
Bronx, NY................ 18.9 300.9 0.1 270 1,005 1.6 21
Broome, NY............... 4.5 86.6 -0.9 325 818 1.2 30
Dutchess, NY............. 8.5 113.2 0.7 192 974 -0.6 121
Erie, NY................. 24.9 473.3 0.3 244 893 -1.7 231
Kings, NY................ 63.2 714.5 3.7 10 856 -1.2 175
Monroe, NY............... 18.9 386.8 0.7 192 947 -2.7 300
Nassau, NY............... 54.5 631.2 0.9 167 1,108 1.7 17
New York, NY............. 129.2 2,451.9 1.1 145 1,889 0.5 52
Oneida, NY............... 5.4 104.6 -0.5 306 789 -0.6 121
Onondaga, NY............. 12.9 245.6 0.0 284 924 -1.4 195
Orange, NY............... 10.5 143.8 1.4 104 850 -1.2 175
Queens, NY............... 53.4 665.8 2.3 48 970 -0.5 111
Richmond, NY............. 9.9 116.0 1.3 114 928 0.5 52
Rockland, NY............. 10.9 125.0 1.6 80 953 -3.4 321
Saratoga, NY............. 6.0 87.0 2.4 42 917 -1.1 168
Suffolk, NY.............. 53.4 665.9 0.5 223 1,098 -2.7 300
Westchester, NY.......... 36.6 428.4 1.0 157 1,235 0.1 64
Buncombe, NC............. 9.2 130.3 1.5 92 789 0.4 58
Catawba, NC.............. 4.4 87.0 1.5 92 774 -1.4 195
Cumberland, NC........... 6.2 118.3 -0.1 287 802 -1.5 208
Durham, NC............... 8.3 197.9 0.7 192 1,255 -0.3 98
Forsyth, NC.............. 9.2 184.8 0.4 233 952 5.3 4
Guilford, NC............. 14.3 279.5 0.1 270 886 0.3 59
Mecklenburg, NC.......... 37.6 685.8 2.4 42 1,132 -3.5 323
New Hanover, NC.......... 8.1 111.8 1.5 92 820 -0.1 82
Wake, NC................. 34.3 544.1 2.6 32 1,039 -1.0 156
Cass, ND................. 7.2 118.4 -0.1 287 934 -1.6 217
Butler, OH............... 7.9 155.2 2.0 60 901 -1.4 195
Cuyahoga, OH............. 36.0 721.1 0.3 244 1,028 0.1 64
Delaware, OH............. 5.3 88.1 0.5 223 974 -0.5 111
Franklin, OH............. 32.3 753.6 1.6 80 1,032 -1.1 168
Hamilton, OH............. 24.0 516.8 0.7 192 1,094 -1.9 245
Lake, OH................. 6.3 95.4 0.7 192 820 -1.4 195
Lorain, OH............... 6.2 98.3 0.5 223 787 -2.2 266
Lucas, OH................ 10.1 208.0 -0.6 311 878 -1.5 208
Mahoning, OH............. 5.9 97.8 0.3 244 730 -1.5 208
Montgomery, OH........... 11.9 255.9 1.1 145 866 -1.8 237
Stark, OH................ 8.6 159.3 0.3 244 769 0.1 64
Summit, OH............... 14.4 267.9 0.0 284 886 -2.1 263
Warren, OH............... 4.9 92.4 1.3 114 977 -1.0 156
Cleveland, OK............ 5.8 81.1 0.5 223 748 -1.7 231
Oklahoma, OK............. 28.2 451.9 0.9 167 949 -2.2 266
Tulsa, OK................ 22.6 353.3 0.8 182 908 -2.5 285
Clackamas, OR............ 14.9 163.3 2.1 54 963 -0.4 103
Deschutes, OR............ 8.5 81.4 3.5 13 858 4.1 7
Jackson, OR.............. 7.4 89.5 2.6 32 788 -1.0 156
Lane, OR................. 12.1 155.2 1.2 132 804 -0.9 150
Marion, OR............... 10.8 155.8 1.3 114 845 1.0 35
Multnomah, OR............ 35.0 504.4 1.6 80 1,070 -0.4 103
Washington, OR........... 19.3 290.9 2.4 42 1,318 -0.6 121
Allegheny, PA............ 35.6 699.0 1.1 145 1,076 -1.6 217
Berks, PA................ 9.0 172.3 0.6 209 923 -2.5 285
Bucks, PA................ 20.0 264.7 1.2 132 934 -2.4 281
Butler, PA............... 5.1 85.7 0.1 270 943 -0.6 121
Chester, PA.............. 15.5 250.8 1.2 132 1,207 -1.5 208
Cumberland, PA........... 6.5 133.6 0.6 209 917 -2.3 274
Dauphin, PA.............. 7.6 182.5 0.9 167 996 -4.0 334
Delaware, PA............. 14.1 223.3 0.9 167 1,058 -1.0 156
Erie, PA................. 7.0 123.2 -0.2 296 787 -0.6 121
Lackawanna, PA........... 5.7 98.7 0.4 233 773 -2.3 274
Lancaster, PA............ 13.5 238.4 1.1 145 855 -1.0 156
Lehigh, PA............... 8.8 191.0 0.9 167 992 -1.1 168
Luzerne, PA.............. 7.5 146.5 1.0 157 800 -3.3 319
Montgomery, PA........... 27.7 493.6 1.2 132 1,212 -1.8 237
Northampton, PA.......... 6.8 115.3 1.3 114 871 -1.5 208
Philadelphia, PA......... 35.1 676.8 1.2 132 1,212 -1.2 175
Washington, PA........... 5.5 87.8 1.5 92 985 0.0 72
Westmoreland, PA......... 9.3 134.7 0.5 223 839 1.1 32
York, PA................. 9.2 179.3 0.3 244 898 -0.1 82
Providence, RI........... 18.3 288.1 0.5 223 1,026 -2.0 256
Charleston, SC........... 15.1 244.7 0.4 233 902 -1.4 195
Greenville, SC........... 13.9 266.1 1.4 104 877 -1.6 217
Horry, SC................ 8.7 127.8 1.3 114 633 0.0 72
Lexington, SC............ 6.6 118.5 2.2 52 778 -1.6 217
Richland, SC............. 10.1 218.1 -0.6 311 893 0.8 38
Spartanburg, SC.......... 6.2 138.4 3.5 13 856 -1.0 156
York, SC................. 5.6 93.7 3.6 11 825 -0.5 111
Minnehaha, SD............ 7.3 125.8 0.8 182 902 -0.6 121
Davidson, TN............. 22.5 488.8 2.3 48 1,062 0.0 72
Hamilton, TN............. 9.7 202.0 1.5 92 903 0.7 43
Knox, TN................. 12.3 238.6 0.6 209 874 -1.6 217
Rutherford, TN........... 5.6 126.3 4.3 6 901 -1.0 156
Shelby, TN............... 20.5 493.5 0.3 244 1,028 -1.6 217
Williamson, TN........... 8.7 129.9 3.4 15 1,133 -3.2 316
Bell, TX................. 5.4 117.5 0.3 244 863 -0.3 98
Bexar, TX................ 41.0 857.8 1.3 114 905 -0.7 133
Brazoria, TX............. 5.8 107.2 -1.9 342 1,074 -0.9 150
Brazos, TX............... 4.6 102.9 1.4 104 775 1.3 27
Cameron, TX.............. 6.5 138.2 0.4 233 612 -3.0 311
Collin, TX............... 24.8 398.0 3.3 16 1,190 -0.7 133
Dallas, TX............... 76.7 1,691.1 1.9 67 1,213 -1.9 245
Denton, TX............... 14.9 239.6 3.0 24 929 -2.5 285
El Paso, TX.............. 15.1 300.9 0.8 182 717 -1.5 208
Fort Bend, TX............ 13.2 177.3 0.9 167 942 -2.0 256
Galveston, TX............ 6.2 108.5 -0.1 287 886 -1.3 186
Harris, TX............... 114.7 2,261.3 0.1 270 1,247 -1.7 231
Hidalgo, TX.............. 12.3 252.7 1.6 80 649 -0.6 121
Jefferson, TX............ 5.9 119.7 -2.3 344 1,052 -1.4 195
Lubbock, TX.............. 7.5 139.1 1.3 114 790 -2.7 300
McLennan, TX............. 5.3 112.5 0.4 233 841 0.5 52
Midland, TX.............. 5.5 91.4 10.4 1 1,283 8.4 1
Montgomery, TX........... 11.3 176.4 4.0 8 1,003 -0.5 111
Nueces, TX............... 8.3 160.5 -0.7 314 883 -0.2 90
Potter, TX............... 4.0 78.0 -0.8 321 821 -1.0 156
Smith, TX................ 6.2 102.4 0.9 167 843 0.6 49
Tarrant, TX.............. 43.2 877.8 2.3 48 1,000 -2.9 307
Travis, TX............... 40.6 728.0 2.6 32 1,188 0.9 37
Webb, TX................. 5.4 100.1 1.2 132 672 -1.0 156
Williamson, TX........... 10.7 164.6 2.9 27 1,010 -1.3 186
Davis, UT................ 8.4 128.1 3.6 11 816 -1.4 195
Salt Lake, UT............ 44.7 688.0 1.8 75 993 -0.1 82
Utah, UT................. 16.1 232.7 4.2 7 822 0.0 72
Weber, UT................ 6.0 104.4 1.9 67 781 0.1 64
Chittenden, VT........... 6.9 102.6 0.3 244 983 -1.2 175
Arlington, VA............ 9.3 176.0 0.9 167 1,642 -0.4 103
Chesterfield, VA......... 9.1 136.1 3.0 24 865 -2.0 256
Fairfax, VA.............. 37.4 603.0 0.7 192 1,540 -0.6 121
Henrico, VA.............. 11.6 194.0 1.5 92 960 -2.5 285
Loudoun, VA.............. 12.3 163.9 2.6 32 1,179 1.6 21
Prince William, VA....... 9.4 127.4 1.9 67 894 -2.2 266
Alexandria City, VA...... 6.4 92.7 -1.6 336 1,438 -0.2 90
Chesapeake City, VA...... 6.1 97.6 -0.9 325 807 -0.1 82
Newport News City, VA.... 3.9 98.0 1.3 114 993 -0.3 98
Norfolk City, VA......... 5.9 142.1 0.8 182 990 -4.0 334
Richmond City, VA........ 7.7 153.9 0.3 244 1,113 -1.2 175
Virginia Beach City, VA.. 12.2 178.7 0.3 244 775 -1.4 195
Benton, WA............... 5.7 89.6 3.8 9 1,030 -1.6 217
Clark, WA................ 14.5 158.0 4.6 4 975 0.7 43
King, WA................. 86.2 1,367.1 2.8 28 1,626 2.7 12
Kitsap, WA............... 6.7 87.5 1.4 104 947 -2.4 281
Pierce, WA............... 21.7 305.1 1.1 145 953 0.3 59
Snohomish, WA............ 20.7 283.4 -0.8 321 1,102 -0.5 111
Spokane, WA.............. 15.6 220.8 1.4 104 889 0.7 43
Thurston, WA............. 8.3 114.8 3.3 16 946 1.9 15
Whatcom, WA.............. 7.3 89.8 1.9 67 858 1.7 17
Yakima, WA............... 7.7 125.0 1.3 114 735 3.2 8
Kanawha, WV.............. 5.7 100.0 -1.4 333 880 -1.1 168
Brown, WI................ 6.9 157.1 1.2 132 884 -2.0 256
Dane, WI................. 15.7 333.1 0.7 192 1,017 -1.4 195
Milwaukee, WI............ 26.6 487.0 0.1 270 955 -1.3 186
Outagamie, WI............ 5.3 108.1 0.8 182 871 -0.2 90
Waukesha, WI............. 13.2 242.7 0.2 259 986 -1.9 245
Winnebago, WI............ 3.8 93.5 0.1 270 921 -0.9 150
San Juan, PR............. 10.8 240.6 -2.4 (5) 617 -2.2 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from 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,
third quarter 2017
Employment Average weekly
wage(1)
Establishments,
third quarter
County by NAICS supersector 2017 Percent Percent
(thousands) September change, Third change,
2017 September quarter third
(thousands) 2016-17(2) 2017 quarter
2016-17(2)
United States(3) ............................ 9,916.5 144,464.4 1.0 $1,021 -0.6
Private industry........................... 9,617.8 122,881.9 1.2 1,013 -0.6
Natural resources and mining............. 137.0 1,997.3 2.5 1,016 1.2
Construction............................. 788.8 7,093.0 2.3 1,140 -0.3
Manufacturing............................ 347.3 12,443.1 0.6 1,221 -1.8
Trade, transportation, and utilities..... 1,919.2 27,119.8 0.6 861 -0.5
Information.............................. 163.5 2,788.4 -0.4 1,977 2.2
Financial activities..................... 872.5 8,101.5 1.3 1,517 -0.8
Professional and business services....... 1,789.9 20,414.8 0.9 1,310 -0.2
Education and health services............ 1,656.2 22,170.0 1.7 941 -1.6
Leisure and hospitality.................. 841.9 16,027.9 0.9 440 -0.2
Other services........................... 843.4 4,410.1 0.1 714 1.1
Government................................. 298.7 21,582.5 0.2 1,070 -0.7
Los Angeles, CA.............................. 488.1 4,408.1 1.3 1,147 1.1
Private industry........................... 481.8 3,839.8 1.4 1,113 1.4
Natural resources and mining............. 0.5 7.6 -0.3 1,094 -9.6
Construction............................. 14.4 139.7 3.3 1,202 1.6
Manufacturing............................ 12.2 344.6 -3.2 1,281 -1.0
Trade, transportation, and utilities..... 54.6 819.7 0.3 937 0.8
Information.............................. 10.3 214.9 0.2 2,194 3.6
Financial activities..................... 26.3 218.7 0.3 1,766 0.3
Professional and business services....... 48.9 615.4 2.2 1,369 1.3
Education and health services............ 230.9 780.0 2.6 874 -1.4
Leisure and hospitality.................. 33.7 521.6 1.6 626 -0.3
Other services........................... 26.6 148.3 -0.3 1,061 37.6
Government................................. 6.3 568.3 0.6 1,385 0.1
Cook, IL..................................... 137.9 2,578.3 0.1 1,157 -0.3
Private industry........................... 136.6 2,282.2 0.2 1,160 -0.5
Natural resources and mining............. 0.1 1.3 12.1 1,131 -5.0
Construction............................. 10.6 76.9 1.3 1,451 0.1
Manufacturing............................ 5.8 183.9 0.3 1,205 -3.2
Trade, transportation, and utilities..... 27.5 467.5 -0.5 955 1.0
Information.............................. 2.3 50.4 -1.6 1,805 0.2
Financial activities..................... 13.7 194.8 0.6 2,006 -0.4
Professional and business services....... 28.6 474.3 -0.5 1,480 -0.1
Education and health services............ 15.3 443.9 0.7 989 -0.4
Leisure and hospitality.................. 13.6 289.7 1.4 534 -2.6
Other services........................... 15.6 96.4 -0.4 915 -0.3
Government................................. 1.3 296.1 -0.6 1,134 2.4
New York, NY................................. 129.2 2,451.9 1.1 1,889 0.5
Private industry........................... 128.4 2,188.8 1.2 1,955 0.4
Natural resources and mining............. 0.0 0.2 12.8 1,853 -0.9
Construction............................. 2.3 41.3 -1.5 1,865 0.8
Manufacturing............................ 2.1 25.2 -5.0 1,543 12.3
Trade, transportation, and utilities..... 19.7 251.9 -0.8 1,380 2.9
Information.............................. 5.0 165.0 2.7 2,608 2.6
Financial activities..................... 19.6 373.5 0.8 3,366 -0.3
Professional and business services....... 27.3 575.1 1.6 2,185 0.2
Education and health services............ 10.1 341.1 0.7 1,336 -0.3
Leisure and hospitality.................. 14.7 300.1 1.5 903 0.6
Other services........................... 20.6 103.7 0.9 1,174 0.6
Government................................. 0.8 263.1 0.1 1,332 1.4
Harris, TX................................... 114.7 2,261.3 0.1 1,247 -1.7
Private industry........................... 114.1 1,990.5 0.1 1,257 -2.0
Natural resources and mining............. 1.6 66.4 0.3 2,994 -1.7
Construction............................. 7.4 155.9 -2.9 1,287 -4.5
Manufacturing............................ 4.8 170.0 1.4 1,598 1.8
Trade, transportation, and utilities..... 25.0 463.7 0.0 1,137 -0.6
Information.............................. 1.2 25.5 -6.4 1,530 6.4
Financial activities..................... 12.1 126.3 1.4 1,579 -0.8
Professional and business services....... 23.2 397.3 0.6 1,545 -4.7
Education and health services............ 16.1 290.1 0.4 1,024 -1.5
Leisure and hospitality.................. 10.1 227.9 -0.4 460 -0.6
Other services........................... 11.7 65.6 -0.8 781 -2.4
Government................................. 0.5 270.7 0.4 1,177 0.4
Maricopa, AZ................................. 96.6 1,938.0 2.6 987 -1.1
Private industry........................... 95.9 1,724.5 2.8 976 -1.2
Natural resources and mining............. 0.4 7.6 0.0 962 2.8
Construction............................. 6.8 112.4 7.5 1,056 0.8
Manufacturing............................ 3.1 119.7 3.2 1,347 -4.1
Trade, transportation, and utilities..... 18.1 372.4 1.8 892 -0.8
Information.............................. 1.5 34.0 0.4 1,392 -6.9
Financial activities..................... 10.7 176.3 4.0 1,253 -2.2
Professional and business services....... 20.5 327.9 0.5 1,057 0.0
Education and health services............ 10.8 297.0 2.7 1,000 -2.2
Leisure and hospitality.................. 7.8 213.0 2.9 490 4.0
Other services........................... 6.1 50.1 -2.5 730 2.1
Government................................. 0.7 213.5 0.4 1,089 0.3
Dallas, TX................................... 76.7 1,691.1 1.9 1,213 -1.9
Private industry........................... 76.1 1,518.6 2.2 1,218 -2.0
Natural resources and mining............. 0.5 8.8 7.9 3,601 0.3
Construction............................. 4.6 88.6 2.5 1,233 -1.0
Manufacturing............................ 2.8 112.7 1.3 1,438 -6.0
Trade, transportation, and utilities..... 16.0 346.1 2.9 1,052 -5.5
Information.............................. 1.4 48.2 -3.3 1,813 -0.2
Financial activities..................... 9.5 166.7 4.1 1,673 0.4
Professional and business services....... 17.2 343.4 1.5 1,408 -1.2
Education and health services............ 9.6 198.4 2.0 1,078 -1.1
Leisure and hospitality.................. 6.9 161.5 2.0 515 2.0
Other services........................... 6.9 42.8 0.5 812 -0.5
Government................................. 0.6 172.5 -0.5 1,171 -1.0
Orange, CA................................... 120.4 1,598.6 1.4 1,135 -1.1
Private industry........................... 119.0 1,454.8 1.6 1,122 -1.1
Natural resources and mining............. 0.2 2.8 -4.4 894 -4.9
Construction............................. 6.8 103.1 3.8 1,338 1.4
Manufacturing............................ 4.9 157.4 -1.0 1,385 -2.7
Trade, transportation, and utilities..... 17.1 259.1 0.8 1,029 0.5
Information.............................. 1.4 26.5 0.4 1,945 1.8
Financial activities..................... 11.3 117.9 0.3 1,813 1.2
Professional and business services....... 20.8 303.8 0.7 1,285 -2.1
Education and health services............ 33.6 211.1 3.7 953 -1.4
Leisure and hospitality.................. 8.7 218.0 1.6 506 -0.8
Other services........................... 6.8 45.3 -1.1 713 -0.6
Government................................. 1.4 143.9 -0.7 1,267 -2.6
San Diego, CA................................ 110.9 1,439.5 1.2 1,112 -1.6
Private industry........................... 108.9 1,208.2 1.4 1,073 -1.0
Natural resources and mining............. 0.6 9.4 -1.5 774 5.6
Construction............................. 7.0 81.0 4.6 1,203 0.1
Manufacturing............................ 3.2 108.4 0.3 1,533 -2.9
Trade, transportation, and utilities..... 14.3 225.3 0.9 891 -0.8
Information.............................. 1.2 24.1 -1.4 2,107 7.0
Financial activities..................... 10.1 73.5 1.0 1,419 -1.3
Professional and business services....... 18.3 232.1 0.6 1,471 -1.6
Education and health services............ 31.8 199.2 2.2 963 -0.7
Leisure and hospitality.................. 8.4 195.5 0.2 502 -0.6
Other services........................... 7.3 51.5 0.3 625 -0.8
Government................................. 1.9 231.3 0.2 1,329 -3.5
King, WA..................................... 86.2 1,367.1 2.8 1,626 2.7
Private industry........................... 85.6 1,202.3 3.2 1,659 2.5
Natural resources and mining............. 0.4 3.2 -0.9 1,355 11.9
Construction............................. 6.8 71.9 4.3 1,363 0.0
Manufacturing............................ 2.5 101.9 -2.5 1,601 -1.0
Trade, transportation, and utilities..... 14.4 269.1 6.6 1,513 8.1
Information.............................. 2.3 105.0 5.2 5,099 3.4
Financial activities..................... 6.7 68.2 1.8 1,631 -0.8
Professional and business services....... 18.0 225.9 2.2 1,669 0.4
Education and health services............ 18.0 171.1 2.7 1,033 -1.9
Leisure and hospitality.................. 7.3 141.2 2.8 596 2.2
Other services........................... 9.3 44.7 1.3 873 -4.2
Government................................. 0.5 164.8 -0.3 1,382 2.5
Miami-Dade, FL............................... 97.5 1,092.6 -1.7 984 -0.1
Private industry........................... 97.2 954.5 -1.9 951 -0.7
Natural resources and mining............. 0.5 7.1 -6.8 628 -1.9
Construction............................. 6.5 42.9 -3.8 955 -1.1
Manufacturing............................ 2.8 40.0 -1.4 881 -9.0
Trade, transportation, and utilities..... 25.3 274.4 -1.4 892 -0.7
Information.............................. 1.5 17.4 -1.6 1,602 -10.2
Financial activities..................... 10.6 74.7 0.4 1,476 -0.7
Professional and business services....... 21.9 151.7 -2.1 1,105 -1.1
Education and health services............ 10.5 175.4 -0.6 965 -1.1
Leisure and hospitality.................. 7.2 132.0 -4.9 639 8.1
Other services........................... 8.3 37.3 -5.1 625 -0.2
Government................................. 0.3 138.1 0.2 1,225 3.2
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Counties selected are based on 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,
third quarter 2017
Employment Average weekly
wage(1)
Establishments,
third quarter
State 2017 Percent Percent
(thousands) September change, Third change,
2017 September quarter third
(thousands) 2016-17 2017 quarter
2016-17
United States(2)........... 9,916.5 144,464.4 1.0 $1,021 -0.6
Alabama.................... 125.5 1,941.1 0.8 859 -1.3
Alaska..................... 22.2 335.4 -0.7 1,025 -2.8
Arizona.................... 158.6 2,760.1 2.4 948 -0.2
Arkansas................... 89.7 1,213.0 0.6 788 -0.6
California................. 1,534.7 17,153.4 1.7 1,215 0.5
Colorado................... 200.0 2,625.9 1.9 1,067 0.5
Connecticut................ 119.2 1,676.3 0.1 1,179 -2.2
Delaware................... 32.3 443.0 0.4 1,026 0.4
District of Columbia....... 40.4 764.7 0.7 1,759 1.3
Florida.................... 677.2 8,305.8 -0.2 896 -1.1
Georgia.................... 276.0 4,343.5 1.3 961 -0.9
Hawaii..................... 41.9 652.5 0.4 953 -0.3
Idaho...................... 61.7 722.3 2.7 778 -0.5
Illinois................... 367.3 5,969.6 0.5 1,057 -0.3
Indiana.................... 164.6 3,044.0 0.6 861 -0.6
Iowa....................... 102.2 1,546.1 -0.2 855 -2.2
Kansas..................... 90.4 1,376.4 -0.1 839 -2.1
Kentucky................... 121.9 1,890.4 0.5 837 -2.4
Louisiana.................. 131.9 1,904.3 -0.1 869 -1.7
Maine...................... 54.7 621.9 0.7 821 -0.5
Maryland................... 170.1 2,661.8 0.5 1,105 -1.7
Massachusetts.............. 255.0 3,568.0 0.9 1,265 -0.9
Michigan................... 245.2 4,334.3 0.9 964 -1.1
Minnesota.................. 171.2 2,883.0 1.1 1,030 -2.0
Mississippi................ 73.4 1,129.1 -0.1 729 -1.4
Missouri................... 209.3 2,805.8 0.9 878 -1.2
Montana.................... 49.1 468.6 0.9 793 0.1
Nebraska................... 73.5 973.3 -0.2 850 -0.8
Nevada..................... 81.3 1,337.7 2.9 914 -3.8
New Hampshire.............. 52.5 659.1 0.6 1,022 -0.4
New Jersey................. 270.6 4,043.6 1.1 1,156 -1.5
New Mexico................. 58.2 816.0 0.3 823 -0.8
New York................... 650.3 9,329.8 1.2 1,219 -0.2
North Carolina............. 274.0 4,348.0 1.3 904 -0.7
North Dakota............... 32.0 419.2 -1.0 953 -1.2
Ohio....................... 297.0 5,383.6 0.6 920 -0.8
Oklahoma................... 111.0 1,593.3 0.7 843 -1.2
Oregon..................... 152.1 1,905.3 1.8 969 -0.1
Pennsylvania............... 358.1 5,836.5 1.0 1,002 -1.1
Rhode Island............... 37.5 484.5 0.8 973 -1.8
South Carolina............. 129.5 2,027.2 0.8 828 -0.5
South Dakota............... 33.4 426.2 0.4 803 -0.7
Tennessee.................. 158.2 2,953.3 1.1 903 -1.2
Texas...................... 675.5 12,008.9 1.4 1,032 -1.0
Utah....................... 99.8 1,444.1 2.6 879 -0.2
Vermont.................... 25.6 310.3 0.1 869 -1.4
Virginia................... 272.2 3,843.6 1.0 1,053 -0.8
Washington................. 238.6 3,343.4 2.0 1,208 1.7
West Virginia.............. 50.6 694.0 0.2 826 1.1
Wisconsin.................. 173.4 2,866.9 0.5 876 -1.0
Wyoming.................... 26.3 276.2 0.3 868 0.3
Puerto Rico................ 46.3 862.8 -3.1 509 -2.7
Virgin Islands............. 3.4 36.9 -1.1 763 -1.9
(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
