For release 10:00 a.m. (EDT), Wednesday, September 7, 2016 USDL-16-1806
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
First Quarter 2016
From March 2015 to March 2016, employment increased in 318 of the 344 largest U.S. counties, the
U.S. Bureau of Labor Statistics reported today. Williamson, Tenn., had the largest percentage increase
with a gain of 7.9 percent over the year, above the national job growth rate of 2.0 percent. Within
Williamson, the largest employment increase occurred in professional and business services, which
gained 3,598 jobs over the year (11.9 percent). Midland, Texas, had the largest over-the-year percentage
decrease in employment among the largest counties in the U.S., with a loss of 9.0 percent. Within
Midland, natural resources and mining had the largest decrease in employment, with a loss of 3,292 jobs
(-15.0 percent). County employment and wage data are from the Quarterly Census of Employment and
Wages (QCEW) program, which provides the only detailed quarterly and annual universe count of
establishments, employment, and wages at the county, MSA, state, and national levels by detailed
industry. These detailed data are published within 6 months following the end of each calendar quarter.
The U.S. average weekly wage decreased 0.5 percent over the year, declining to $1,043 in the first
quarter of 2016. This is one of only seven declines in the history of the series which dates back to 1978.
McLean, Ill., had the largest over-the-year percentage decrease in average weekly wages with a loss of
13.3 percent. Within McLean, an average weekly wage loss of $659 (-31.4 percent) in financial
activities made the largest contribution to the county’s decrease in average weekly wages. Clayton, Ga.,
experienced the largest percentage increase in average weekly wages with a gain of 15.5 percent over
the year. Within Clayton, trade, transportation, and utilities had the largest impact on the county’s
average weekly wage growth with an increase of $305 (23.7 percent) over the year.
Large County Employment
In March 2016, national employment was 140.1 million (as measured by the QCEW program). Over the
year, employment increased 2.0 percent, or 2.7 million. In March 2016, the 344 U.S. counties with
75,000 or more jobs accounted for 72.6 percent of total U.S. employment and 78.8 percent of total
wages. These 344 counties had a net job growth of 2.1 million over the year, accounting for 77.9 percent
of the overall U.S. employment increase. The five counties with the largest increases in employment
levels had a combined over-the-year employment gain of 277,300 jobs, which was 10.3 percent of the
overall job increase for the U.S. (See table A.)
Employment declined in 25 of the largest counties from March 2015 to March 2016. Midland, Texas,
had the largest over-the-year percentage decrease in employment (-9.0 percent), followed by Lafayette,
La.; Gregg, Texas; McLean, Ill.; and Weld, Colo. (See table 1.)
Table A. Large counties ranked by March 2016 employment, March 2015-16 employment increase, and
March 2015-16 percent increase in employment
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Employment in large counties
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March 2016 employment | Increase in employment, | Percent increase in employment,
(thousands) | March 2015-16 | March 2015-16
| (thousands) |
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| |
United States 140,070.8| United States 2,683.0| United States 2.0
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| |
Los Angeles, Calif. 4,309.9| Los Angeles, Calif. 79.7| Williamson, Tenn. 7.9
Cook, Ill. 2,515.9| Maricopa, Ariz. 58.9| Utah, Utah 6.7
New York, N.Y. 2,396.8| Dallas, Texas 49.4| Loudoun, Va. 6.2
Harris, Texas 2,256.9| New York, N.Y. 44.8| Rutherford, Tenn. 5.5
Maricopa, Ariz. 1,864.4| King, Wash. 44.5| Lee, Fla. 5.1
Dallas, Texas 1,614.7| Orange, Calif. 35.8| Benton, Ark. 5.0
Orange, Calif. 1,545.7| San Francisco, Calif. 32.1| Osceola, Fla. 5.0
San Diego, Calif. 1,388.4| Fulton, Ga. 31.4| San Francisco, Calif. 4.8
King, Wash. 1,294.1| Riverside, Calif. 31.0| Riverside, Calif. 4.7
Miami-Dade, Fla. 1,107.3| San Diego, Calif. 30.9| Washoe, Nev. 4.7
| Cook, Ill. 30.9| Horry, S.C. 4.7
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Large County Average Weekly Wages
Average weekly wages for the nation decreased to $1,043, a 0.5 percent decrease, during the year ending
in the first quarter of 2016. Among the 344 largest counties, 167 had over-the-year decreases in average
weekly wages. McLean, Ill., had the largest percentage wage decrease among the largest U.S. counties
(-13.3 percent). (See table B.)
Of the 344 largest counties, 164 experienced over-the-year increases in average weekly wages. Clayton,
Ga., had the largest percentage increase in average weekly wages (15.5 percent), followed by King,
Wash.; San Mateo, Calif.; Ventura, Calif.; and Merrimack, N.H. (See table 1.)
Table B. Large counties ranked by first quarter 2016 average weekly wages, first quarter 2015-16
decrease in average weekly wages, and first quarter 2015-16 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
first quarter 2016 | wage, first quarter 2015-16 | weekly wage, first
| | quarter 2015-16
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| |
United States $1,043| United States -$5| United States -0.5
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| |
New York, N.Y. $2,783| Washington, Pa. -$146| McLean, Ill. -13.3
Santa Clara, Calif. 2,210| McLean, Ill. -137| Washington, Pa. -12.0
San Mateo, Calif. 2,195| Mercer, N.J. -129| Lafayette, La. -10.3
San Francisco, Calif. 2,054| Lafayette, La. -98| Mercer, N.J. -8.5
Somerset, N.J. 2,022| Somerset, N.J. -93| Williamson, Texas -7.8
Fairfield, Conn. 1,899| Williamson, Texas -85| Orange, Calif. -6.4
Suffolk, Mass. 1,890| Orange, Calif. -78| Allegheny, Pa. -6.2
Washington, D.C. 1,766| Midland, Texas -76| Tulsa, Okla. -5.9
Arlington, Va. 1,734| Allegheny, Pa. -75| Gregg, Texas -5.9
Morris, N.J. 1,696| Morris, N.J. -74| St. Louis, Minn. -5.8
| Harris, Texas -74|
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Ten Largest U.S. Counties
Among the 10 largest counties, 9 had over-the-year percentage increases in employment in March 2016.
King, Wash., had the largest gain (3.6 percent). Within King, professional and business services had the
largest over-the-year employment level increase, with a gain of 9,047 jobs, or 4.4 percent. Harris, Texas,
had the only percentage decrease in employment among the 10 largest counties (-1.2 percent). (See table
2.)
Average weekly wages decreased over the year in 8 of the 10 largest U.S. counties. Orange, Calif.,
experienced the largest percentage loss in average weekly wages (-6.4 percent). Within Orange,
professional and business services had the largest impact on the county’s average weekly wage decline.
Within professional and business services, average weekly wages decreased by $388, or -22.4 percent,
over the year. King, Wash., had the largest percentage gain in average weekly wages among the 10
largest counties (5.1 percent).
For More Information
The tables included in this release contain data for the nation and for the 344 U.S. counties with annual
average employment levels of 75,000 or more in 2015. March 2016 employment and 2016 first quarter
average weekly wages for all states are provided in table 3 of this release.
The data are derived from reports submitted by every employer subject to unemployment insurance (UI)
laws. The 9.7 million employer reports cover 140.1 million full- and part-time workers. Data for the first
quarter of 2016 will be available electronically later at www.bls.gov/cew/. For additional information
about the quarterly employment and wages data, please read the Technical Note. Additional information
about the QCEW data may be obtained by calling (202) 691-6567.
Several BLS regional offices issue QCEW news releases targeted to local data users. For links to these
releases, see www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for second quarter 2016 is scheduled to be released
on Wednesday, December 7, 2016.
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| |
| County Changes for the 2016 County Employment and Wages News Releases |
| |
| Counties with annual average employment of 75,000 or more in 2015 are included in this release and |
| will be included in future 2016 releases. Four counties have been added to the publication tables: |
| Merced, Calif.; Napa, Calif.; Bay, Fla.; and Merrimack, N.H. Two counties, Black Hawk, Iowa, and |
| Ector, Texas, which were published in the 2015 releases, will be excluded from this and future 2016 |
| releases because their 2015 annual average employment levels were less than 75,000. |
| |
| |
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| |
| Change in Oregon Public University Classification |
| |
| Prior to this release, public universities in the state of Oregon were classified in QCEW under state |
| government ownership. Beginning with data in this release for first quarter 2016, QCEW classifies |
| these establishments in local government ownership. The industry classification for these institutions |
| has not changed. |
| |
| This change in ownership resulted from the passage in 2011 and 2013 of state legislation which |
| created a new legal entity called "universities with governing boards." Public universities in Oregon |
| were reorganized in 2014 and 2015 under this new legal entity. They are now independent public |
| bodies that can establish their budgets without state approval. This new political subdivision will be |
| classified under local government ownership. |
| |
| For more information, contact the Oregon Labor Market Information group at sf202_or@bls.gov. |
| |
| |
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Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of
Employment and Wages (QCEW) program, also known as the ES-202 program. The data are
derived from summaries of employment and total pay of workers covered by state and federal
unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The
summaries are a result of the administration of state unemployment insurance programs that
require most employers to pay quarterly taxes based on the employment and wages of workers
covered by UI. QCEW data in this release are based on the 2012 North American Industry
Classification System. Data for 2016 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or
greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S.
averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the
basis of the preliminary annual average of employment for the previous year. The 345 counties
presented in this release were derived using 2015 preliminary annual averages of employment. For
2016 data, four counties have been added to the publication tables: Merced, Calif.; Napa, Calif.;
Bay, Fla.; and Merrimack, N.H. These counties will be included in all 2016 quarterly releases. Two
counties, Black Hawk, Iowa, and Ector, Texas, which were published in the 2015 releases, will be
excluded from this and future 2016 releases because their 2015 annual average employment levels
were less than 75,000. The counties in table 2 are selected and sorted each year based on the annual
average employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released by the
individual states. These potential differences result from the states' continuing receipt of UI data
over time and ongoing review and editing. The individual states determine their data release
timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for any given
quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current
Employment Statistics (CES)--makes use of the quarterly UI employment reports in producing
data; however, each measure has a somewhat different universe coverage, estimation procedure,
and publication product.
Differences in coverage and estimation methods can result in somewhat different measures of
employment change over time. It is important to understand program differences and the intended
uses of the program products. (See table.) Additional information on each program can be obtained
from the program Web sites shown in the table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
----------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 623,000 establish-
| submitted by 9.7 | ministrative records| ments
| million establish- | submitted by 7.6 |
| ments in first | million private-sec-|
| quarter of 2016 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -Within 6 months | -7 months after the | -Usually first Friday
| after the end of | end of each quarter| of following month
| each quarter | |
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
---------------------------------------------------------------------------------
Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly
contribution reports submitted to the SWAs by employers. For federal civilian workers covered by
the Unemployment Compensation for Federal Employees (UCFE) program, employment and
wage data are compiled from quarterly reports submitted by four major federal payroll processing
centers on behalf of all federal agencies, with the exception of a few agencies which still report
directly to the individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry of each of their
establishments. QCEW employment and wage data are derived from microdata summaries of 9.5
million employer reports of employment and wages submitted by states to the BLS in 2015. These
reports are based on place of employment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state since 1978,
when the 1976 amendments to the Federal Unemployment Tax Act became effective, expanding
coverage to include most state and local government employees. In 2015, UI and UCFE programs
covered workers in 139.5 million jobs. The estimated 134.4 million workers in these jobs (after
adjustment for multiple jobholders) represented 96.5 percent of civilian wage and salary
employment. Covered workers received $7.385 trillion in pay, representing 94.0 percent of the
wage and salary component of personal income and 40.9 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural workers on
small farms, all members of the Armed Forces, elected officials in most states, most employees of
railroads, some domestic workers, most student workers at schools, and employees of certain small
nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on the
employment and wages reported by employers covered under the UI program. Coverage changes
may affect the over-the-year comparisons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received pay for
the pay period including the 12th of the month. With few exceptions, all employees of covered
firms are reported, including production and sales workers, corporation officials, executives,
supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also
are included.
Average weekly wage values are calculated by dividing quarterly total wages by the average of the
three monthly employment levels (all employees, as described above) and dividing the result by
13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and
wage values. The average wage values that can be calculated using rounded data from the BLS
database may differ from the averages reported. Included in the quarterly wage data are non-wage
cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other
gratuities, and, in some states, employer contributions to certain deferred compensation plans such
as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may
reflect fluctuations in average monthly employment and/or total quarterly wages between the
current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as well as the
number of individuals in high-paying and low-paying occupations and the incidence of pay periods
within a quarter. For instance, the average weekly wage of the workforce could increase
significantly when there is a large decline in the number of employees that had been receiving
below-average wages. Wages may include payments to workers not present in the employment
counts because they did not work during the pay period including the 12th of the month. When
comparing average weekly wage levels between industries, states, or quarters, these factors should
be taken into consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This
variability may be due to calendar effects resulting from some quarters having more pay dates than
others. The effect is most visible in counties with a dominant employer. In particular, this effect
has been observed in counties where government employers represent a large fraction of overall
employment. Similar calendar effects can result from private sector pay practices. However, these
effects are typically less pronounced for two reasons: employment is less concentrated in a single
private employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal
government due to the uniform nature of federal payroll processing. Most federal employees are
paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates,
while in other quarters there are seven pay dates. Over-the-year comparisons of average weekly
wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in
part, to a comparison of quarterly wages for the current year, which include seven pay dates, with
year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the
current quarter reflecting six pay dates are compared with year-ago wages for a quarter including
seven pay dates.
In order to ensure the highest possible quality of data, states verify with employers and update, if
necessary, the industry, location, and ownership classification of all establishments on a 3 year
cycle. Changes in establishment classification codes resulting from this process are introduced with
the data reported for the first quarter of the year. Changes resulting from improved employer
reporting also are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of individual
establishment records and reflect the number of establishments that exist in a county or industry at
a point in time. Establishments can move in or out of a county or industry for a number of reasons-
-some reflecting economic events, others reflecting administrative changes. For example,
economic change would come from a firm relocating into the county; administrative change would
come from a company correcting its county designation.
The over-the-year changes of employment and wages presented in this release have been adjusted
to account for most of the administrative corrections made to the underlying establishment reports.
This is done by modifying the prior-year levels used to calculate the over-the-year changes.
Percent changes are calculated using an adjusted version of the final 2015 quarterly data as the
base data. The adjusted prior-year levels used to calculate the over-the-year percent change in
employment and wages are not published. These adjusted prior-year levels do not match the
unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data
from the Web site, or from data published in prior BLS news releases, may differ substantially
from the over-the-year changes presented in this news release.
The adjusted data used to calculate the over-the-year change measures presented in this release
account for most of the administrative changes--those occurring when employers update the
industry, location, and ownership information of their establishments. The most common
adjustments for administrative change are the result of updated information about the county
location of individual establishments. Included in these adjustments are administrative changes
involving the classification of establishments that were previously reported in the unknown or
statewide county or unknown industry categories. Adjusted data account for improvements in
reporting employment and wages for individual and multi-unit establishments. To accomplish this,
adjustments were implemented to account for: administrative changes caused by multi-unit
employers who start reporting for each individual establishment rather than as a single entity (first
quarter of 2008); selected large administrative changes in employment and wages (second quarter
of 2011); and state verified improvements in reporting of employment and wages (third quarter of
2014). These adjustments allow QCEW to include county employment and wage growth rates in
this news release that would otherwise not meet publication standards.
The adjusted data used to calculate the over-the-year change measures presented in any County
Employment and Wages news release are valid for comparisons between the starting and ending
points (a 12-month period) used in that particular release. Comparisons may not be valid for any
time period other than the one featured in a release even if the changes were calculated using
adjusted data.
County definitions are assigned according to Federal Information Processing Standards
Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after
approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology
Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104-106.
Areas shown as counties include those designated as independent cities in some jurisdictions and,
in Alaska, those designated as census areas where counties have not been created. County data also
are presented for the New England states for comparative purposes even though townships are the
more common designation used in New England (and New Jersey). The regions referred to in this
release are defined as census regions.
Additional statistics and other information
Employment and Wages Annual Averages Online features comprehensive information by detailed
industry on establishments, employment, and wages for the nation and all states. The 2014 edition
of this publication, which was published in September 2015, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2015 version of this news release. Tables and additional content from the 2014 edition
of Employment and Wages Annual Averages Online are now available at
http://www.bls.gov/cew/cewbultn14.htm. The 2015 edition of Employment and Wages Annual
Averages Online will be available in September 2016.
News releases on quarterly measures of gross job flows also are available upon request from the
Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics),
telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals upon request.
Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-877-8339.
Table 1. Covered establishments, employment, and wages in the 345 largest counties,
first quarter 2016
Employment Average weekly wage(2)
Establishments,
County(1) first quarter Percent Ranking Percent Ranking
2016 March change, by First change, by
(thousands) 2016 March percent quarter first percent
(thousands) 2015-16(3) change 2016 quarter change
2015-16(3)
United States(4)......... 9,693.5 140,070.8 2.0 - $1,043 -0.5 -
Jefferson, AL............ 18.1 337.5 0.9 273 1,030 -3.5 311
Madison, AL.............. 9.3 189.9 3.4 55 1,066 1.1 88
Mobile, AL............... 9.9 168.4 0.8 283 819 -1.7 254
Montgomery, AL........... 6.4 130.0 1.5 224 810 0.7 114
Shelby, AL............... 5.6 83.5 1.6 211 991 0.3 144
Tuscaloosa, AL........... 4.4 90.9 0.1 316 800 0.5 129
Anchorage Borough, AK.... 8.4 149.3 -1.1 331 1,065 -2.9 300
Maricopa, AZ............. 94.8 1,864.4 3.3 63 972 -1.5 247
Pima, AZ................. 18.7 359.2 1.2 251 829 1.3 72
Benton, AR............... 6.0 113.4 5.0 6 1,266 -2.8 298
Pulaski, AR.............. 14.4 246.4 1.9 176 896 1.0 98
Washington, AR........... 5.9 102.4 3.6 44 798 3.2 14
Alameda, CA.............. 60.6 739.0 2.0 168 1,353 1.3 72
Butte, CA................ 8.1 79.1 2.4 130 723 0.1 155
Contra Costa, CA......... 31.2 354.0 3.4 55 1,285 -1.2 233
Fresno, CA............... 33.1 365.1 2.4 130 774 0.8 108
Kern, CA................. 17.9 294.7 -0.8 326 847 -2.4 287
Los Angeles, CA.......... 464.3 4,309.9 1.9 176 1,138 2.1 34
Marin, CA................ 12.3 112.5 1.7 200 1,282 3.8 8
Merced, CA............... 6.3 72.8 0.9 273 742 1.2 79
Monterey, CA............. 13.4 169.4 0.3 310 852 0.5 129
Napa, CA................. 5.7 73.6 0.7 289 957 1.8 47
Orange, CA............... 113.9 1,545.7 2.4 130 1,143 -6.4 338
Placer, CA............... 12.3 153.6 4.6 12 995 1.0 98
Riverside, CA............ 58.7 686.0 4.7 9 823 -4.5 325
Sacramento, CA........... 54.7 630.6 2.7 109 1,102 -0.3 191
San Bernardino, CA....... 54.9 694.1 2.4 130 822 1.2 79
San Diego, CA............ 105.9 1,388.4 2.3 142 1,108 -2.0 270
San Francisco, CA........ 59.4 696.4 4.8 8 2,054 -2.1 277
San Joaquin, CA.......... 17.4 234.2 3.6 44 821 0.7 114
San Luis Obispo, CA...... 10.2 115.7 1.4 235 821 2.0 38
San Mateo, CA............ 27.4 383.9 2.6 116 2,195 4.8 3
Santa Barbara, CA........ 15.1 192.4 0.3 310 933 0.1 155
Santa Clara, CA.......... 69.7 1,025.7 3.1 78 2,210 1.9 42
Santa Cruz, CA........... 9.5 98.3 2.0 168 881 3.2 14
Solano, CA............... 10.8 134.2 3.5 50 1,070 1.9 42
Sonoma, CA............... 19.5 198.9 2.4 130 923 0.0 165
Stanislaus, CA........... 14.9 179.3 2.7 109 840 1.7 59
Tulare, CA............... 9.8 152.7 1.9 176 708 2.8 18
Ventura, CA.............. 25.8 319.6 0.2 314 1,083 4.4 4
Yolo, CA................. 6.5 97.2 1.0 263 1,028 0.7 114
Adams, CO................ 10.3 193.6 2.7 109 941 1.1 88
Arapahoe, CO............. 21.3 317.2 2.6 116 1,248 -0.2 187
Boulder, CO.............. 14.6 174.0 2.3 142 1,176 -1.6 250
Denver, CO............... 30.4 485.3 2.8 99 1,312 -3.0 301
Douglas, CO.............. 11.4 113.9 3.0 86 1,195 -2.1 277
El Paso, CO.............. 18.5 259.3 3.6 44 877 -0.8 219
Jefferson, CO............ 19.4 229.6 2.4 130 1,024 0.5 129
Larimer, CO.............. 11.5 148.3 3.8 33 897 -1.0 222
Weld, CO................. 6.9 99.2 -2.6 339 895 -3.8 316
Fairfield, CT............ 35.0 419.6 0.9 273 1,899 -1.7 254
Hartford, CT............. 27.3 501.1 0.3 310 1,363 -3.1 305
New Haven, CT............ 23.6 359.4 1.0 263 1,042 0.7 114
New London, CT........... 7.3 120.6 1.4 235 1,033 -0.1 177
New Castle, DE........... 19.2 282.8 0.7 289 1,224 -3.7 314
Washington, DC........... 38.7 749.6 2.0 168 1,766 0.4 137
Alachua, FL.............. 7.0 126.5 3.3 63 807 0.6 123
Bay, FL.................. 5.5 78.2 1.6 211 711 0.1 155
Brevard, FL.............. 15.2 197.4 2.0 168 846 -1.6 250
Broward, FL.............. 67.8 780.4 2.8 99 926 0.2 147
Collier, FL.............. 13.3 143.8 2.4 130 844 2.2 32
Duval, FL................ 28.3 482.9 3.1 78 991 -0.2 187
Escambia, FL............. 8.1 129.2 3.1 78 783 2.1 34
Hillsborough, FL......... 40.4 667.4 3.8 33 977 0.4 137
Lake, FL................. 7.8 92.7 3.2 69 653 0.8 108
Lee, FL.................. 20.9 254.1 5.1 5 771 1.3 72
Leon, FL................. 8.5 145.3 1.3 242 780 0.6 123
Manatee, FL.............. 10.3 121.0 3.2 69 749 3.5 11
Marion, FL............... 8.1 98.9 2.2 150 671 1.4 70
Miami-Dade, FL........... 95.9 1,107.3 2.7 109 972 -0.3 191
Okaloosa, FL............. 6.2 81.4 3.0 86 795 -1.1 224
Orange, FL............... 40.0 789.2 3.7 41 895 0.6 123
Osceola, FL.............. 6.4 88.4 5.0 6 665 -0.7 216
Palm Beach, FL........... 54.2 591.1 4.3 18 995 -0.6 211
Pasco, FL................ 10.5 113.1 3.7 41 670 1.8 47
Pinellas, FL............. 32.1 416.8 2.5 124 865 0.0 165
Polk, FL................. 12.8 209.8 2.9 94 754 2.4 24
Sarasota, FL............. 15.4 164.1 2.8 99 800 1.1 88
Seminole, FL............. 14.5 180.0 4.5 14 833 0.2 147
Volusia, FL.............. 13.9 167.6 3.3 63 694 0.3 144
Bibb, GA................. 4.5 81.2 2.3 142 778 0.9 102
Chatham, GA.............. 8.6 147.0 2.7 109 833 -1.9 264
Clayton, GA.............. 4.5 120.4 4.0 28 1,146 15.5 1
Cobb, GA................. 23.7 342.8 3.4 55 1,128 0.6 123
DeKalb, GA............... 19.7 290.6 1.5 224 1,085 1.5 66
Fulton, GA............... 46.8 808.9 4.0 28 1,562 2.8 18
Gwinnett, GA............. 26.8 339.4 3.2 69 989 -0.9 220
Hall, GA................. 4.7 81.9 4.6 12 810 -1.6 250
Muscogee, GA............. 4.9 92.8 0.1 316 851 1.2 79
Richmond, GA............. 4.8 103.9 -0.3 320 825 -0.4 201
Honolulu, HI............. 25.5 470.1 1.3 242 935 1.9 42
Ada, ID.................. 14.4 222.3 4.2 21 839 -3.9 317
Champaign, IL............ 4.4 87.8 -0.8 326 859 0.9 102
Cook, IL................. 154.9 2,515.9 1.2 251 1,278 -0.2 187
DuPage, IL............... 38.4 605.2 1.2 251 1,204 0.4 137
Kane, IL................. 13.8 202.0 1.0 263 860 0.2 147
Lake, IL................. 22.4 325.4 1.0 263 1,532 -3.2 307
McHenry, IL.............. 8.8 93.8 1.2 251 805 -0.5 207
McLean, IL............... 3.8 82.6 -2.7 340 893 -13.3 343
Madison, IL.............. 6.0 96.0 0.3 310 782 -2.1 277
Peoria, IL............... 4.6 99.1 -0.9 328 1,035 -3.2 307
St. Clair, IL............ 5.5 92.5 0.5 303 761 0.9 102
Sangamon, IL............. 5.3 128.2 -0.1 319 988 -1.1 224
Will, IL................. 16.2 219.9 2.0 168 851 0.2 147
Winnebago, IL............ 6.7 126.1 0.9 273 832 -1.4 243
Allen, IN................ 8.8 180.4 1.9 176 835 -0.7 216
Elkhart, IN.............. 4.7 126.3 3.4 55 849 1.8 47
Hamilton, IN............. 9.1 134.0 4.4 16 1,027 -0.4 201
Lake, IN................. 10.4 183.3 -0.4 321 850 -4.2 319
Marion, IN............... 23.9 583.6 1.4 235 1,069 -0.4 201
St. Joseph, IN........... 5.8 121.3 3.0 86 781 -1.1 224
Tippecanoe, IN........... 3.4 81.8 0.8 283 871 0.2 147
Vanderburgh, IN.......... 4.8 105.6 0.8 283 799 -3.0 301
Johnson, IA.............. 4.1 82.0 1.1 260 906 1.1 88
Linn, IA................. 6.6 128.3 0.4 306 954 -4.6 328
Polk, IA................. 16.9 288.5 2.5 124 1,058 -1.3 239
Scott, IA................ 5.5 89.1 0.9 273 793 0.0 165
Johnson, KS.............. 22.9 331.4 0.9 273 1,041 -4.3 322
Sedgwick, KS............. 12.7 248.3 0.9 273 871 -4.2 319
Shawnee, KS.............. 5.3 95.9 0.6 295 844 3.3 13
Wyandotte, KS............ 3.6 89.0 1.8 192 951 -1.9 264
Boone, KY................ 4.3 82.3 3.8 33 853 2.2 32
Fayette, KY.............. 10.7 187.6 1.7 200 861 -2.4 287
Jefferson, KY............ 25.1 454.0 2.8 99 1,013 -0.3 191
Caddo, LA................ 7.2 114.3 -1.0 330 776 -2.0 270
Calcasieu, LA............ 5.0 94.2 2.8 99 889 3.6 10
East Baton Rouge, LA..... 15.0 269.8 1.0 263 930 -1.5 247
Jefferson, LA............ 13.4 191.9 -1.2 332 875 -1.0 222
Lafayette, LA............ 9.3 132.1 -5.5 342 857 -10.3 341
Orleans, LA.............. 12.0 193.1 1.8 192 981 -2.0 270
St. Tammany, LA.......... 7.8 87.1 2.0 168 852 -3.0 301
Cumberland, ME........... 13.5 173.0 1.9 176 935 1.1 88
Anne Arundel, MD......... 15.0 260.9 2.1 158 1,068 -0.5 207
Baltimore, MD............ 21.2 372.6 1.7 200 993 0.0 165
Frederick, MD............ 6.4 98.5 1.8 192 940 -2.5 293
Harford, MD.............. 5.8 89.6 1.5 224 961 -2.1 277
Howard, MD............... 9.9 165.6 2.6 116 1,233 -0.4 201
Montgomery, MD........... 32.7 459.0 1.4 235 1,403 -0.6 211
Prince George's, MD...... 15.8 306.6 1.5 224 1,022 -1.9 264
Baltimore City, MD....... 13.6 333.3 1.2 251 1,210 -2.6 295
Barnstable, MA........... 9.3 85.7 3.0 86 846 0.7 114
Bristol, MA.............. 17.1 219.1 2.3 142 896 -4.3 322
Essex, MA................ 24.0 317.1 1.7 200 1,069 1.8 47
Hampden, MA.............. 17.5 204.2 1.5 224 921 0.7 114
Middlesex, MA............ 53.4 873.3 1.8 192 1,568 -3.5 311
Norfolk, MA.............. 24.7 343.1 2.4 130 1,191 0.4 137
Plymouth, MA............. 15.2 184.0 2.4 130 916 1.8 47
Suffolk, MA.............. 27.8 646.0 2.7 109 1,890 -1.2 233
Worcester, MA............ 24.0 334.6 1.7 200 996 1.8 47
Genesee, MI.............. 6.9 131.5 0.8 283 808 -1.8 260
Ingham, MI............... 6.0 147.7 2.9 94 951 0.0 165
Kalamazoo, MI............ 5.0 115.8 2.2 150 961 0.8 108
Kent, MI................. 14.2 388.1 3.0 86 870 1.6 63
Macomb, MI............... 17.6 314.2 2.3 142 1,028 2.7 20
Oakland, MI.............. 39.0 706.1 2.3 142 1,147 -0.1 177
Ottawa, MI............... 5.6 120.0 4.5 14 816 -2.4 287
Saginaw, MI.............. 4.0 83.7 1.9 176 801 1.9 42
Washtenaw, MI............ 8.1 205.6 2.1 158 1,047 1.2 79
Wayne, MI................ 30.6 699.6 1.0 263 1,156 1.1 88
Anoka, MN................ 6.7 118.0 1.4 235 901 -1.2 233
Dakota, MN............... 9.3 181.9 0.5 303 997 -1.7 254
Hennepin, MN............. 38.3 888.5 2.2 150 1,361 -1.9 264
Olmsted, MN.............. 3.2 95.1 4.1 25 1,162 1.1 88
Ramsey, MN............... 12.6 323.1 1.0 263 1,215 -3.1 305
St. Louis, MN............ 5.1 94.5 -0.9 328 786 -5.8 334
Stearns, MN.............. 4.1 83.4 0.4 306 822 3.5 11
Washington, MN........... 5.2 78.5 3.2 69 856 -1.7 254
Harrison, MS............. 4.5 83.7 1.6 211 702 -1.1 224
Hinds, MS................ 5.9 120.5 0.7 289 850 1.1 88
Boone, MO................ 4.8 92.2 2.6 116 770 -0.3 191
Clay, MO................. 5.5 99.8 3.8 33 896 1.2 79
Greene, MO............... 8.5 161.7 1.7 200 740 -1.9 264
Jackson, MO.............. 20.9 359.3 1.6 211 1,030 2.1 34
St. Charles, MO.......... 9.0 141.3 2.5 124 856 0.1 155
St. Louis, MO............ 36.0 592.2 1.6 211 1,074 -2.3 284
St. Louis City, MO....... 13.1 222.2 1.3 242 1,147 -2.4 287
Yellowstone, MT.......... 6.5 80.4 1.6 211 822 -1.4 243
Douglas, NE.............. 18.8 332.8 1.9 176 947 -1.5 247
Lancaster, NE............ 10.0 166.6 1.9 176 802 0.6 123
Clark, NV................ 55.6 923.8 2.8 99 866 1.5 66
Washoe, NV............... 14.8 205.6 4.7 9 853 0.2 147
Hillsborough, NH......... 12.2 197.7 1.9 176 1,085 1.3 72
Merrimack, NH............ 5.1 75.6 1.2 251 907 4.3 5
Rockingham, NH........... 10.8 142.8 3.1 78 982 0.0 165
Atlantic, NJ............. 6.6 121.7 1.2 251 838 0.5 129
Bergen, NJ............... 33.1 440.3 1.3 242 1,227 -0.3 191
Burlington, NJ........... 11.1 198.0 2.3 142 1,035 -2.2 282
Camden, NJ............... 12.1 198.7 3.5 50 960 0.2 147
Essex, NJ................ 20.6 338.4 1.9 176 1,362 0.3 144
Gloucester, NJ........... 6.3 103.1 3.3 63 840 -1.2 233
Hudson, NJ............... 14.7 248.6 3.2 69 1,523 -1.4 243
Mercer, NJ............... 11.2 241.8 2.9 94 1,395 -8.5 340
Middlesex, NJ............ 22.1 409.0 2.2 150 1,299 -2.1 277
Monmouth, NJ............. 20.2 251.8 3.1 78 1,006 1.2 79
Morris, NJ............... 17.1 283.9 2.1 158 1,696 -4.2 319
Ocean, NJ................ 13.0 156.8 3.7 41 809 2.3 29
Passaic, NJ.............. 12.4 164.9 1.0 263 981 1.3 72
Somerset, NJ............. 10.1 181.4 2.9 94 2,022 -4.4 324
Union, NJ................ 14.3 216.4 (5) - 1,324 (5) -
Bernalillo, NM........... 18.3 319.4 1.3 242 841 -0.4 201
Albany, NY............... 10.4 230.0 0.7 289 1,023 2.0 38
Bronx, NY................ 18.7 300.2 1.2 251 927 2.5 23
Broome, NY............... 4.6 86.2 0.5 303 758 0.4 137
Dutchess, NY............. 8.5 109.5 0.6 295 954 -0.6 211
Erie, NY................. 24.8 459.9 1.1 260 893 0.9 102
Kings, NY................ 61.1 678.4 3.8 33 825 1.5 66
Monroe, NY............... 18.9 381.3 1.7 200 923 -1.1 224
Nassau, NY............... 54.1 614.0 2.2 150 1,128 2.4 24
New York, NY............. 130.3 2,396.8 1.9 176 2,783 -1.9 264
Oneida, NY............... 5.4 102.3 0.7 289 771 1.3 72
Onondaga, NY............. 13.1 241.0 0.9 273 916 1.9 42
Orange, NY............... 10.4 138.3 1.6 211 826 1.8 47
Queens, NY............... 52.2 639.1 3.0 86 963 2.6 21
Richmond, NY............. 9.8 113.5 2.6 116 865 4.2 6
Rockland, NY............. 10.6 118.1 1.8 192 1,007 -0.5 207
Saratoga, NY............. 5.9 82.4 2.1 158 881 0.0 165
Suffolk, NY.............. 52.7 635.9 1.5 224 1,060 1.2 79
Westchester, NY.......... 36.7 417.1 1.9 176 1,416 0.1 155
Buncombe, NC............. 8.9 125.6 4.3 18 738 1.7 59
Catawba, NC.............. 4.4 84.6 4.0 28 748 -1.2 233
Cumberland, NC........... 6.3 119.6 1.5 224 751 1.8 47
Durham, NC............... 8.1 193.1 1.8 192 1,315 -3.7 314
Forsyth, NC.............. 9.3 181.3 1.2 251 1,019 0.4 137
Guilford, NC............. 14.4 275.3 1.6 211 871 -3.4 310
Mecklenburg, NC.......... 36.8 652.1 4.1 25 1,365 -1.8 260
New Hanover, NC.......... 7.8 107.2 3.4 55 802 2.4 24
Wake, NC................. 32.9 517.6 4.2 21 1,053 1.2 79
Cass, ND................. 6.9 114.3 0.6 295 895 -2.2 282
Butler, OH............... 7.6 147.9 3.6 44 900 -0.1 177
Cuyahoga, OH............. 35.6 707.5 0.9 273 1,048 -2.0 270
Delaware, OH............. 5.0 82.7 3.3 63 1,096 0.0 165
Franklin, OH............. 31.1 724.2 3.1 78 1,041 0.1 155
Hamilton, OH............. 23.6 501.2 1.6 211 1,106 -1.1 224
Lake, OH................. 6.3 93.3 0.8 283 833 0.0 165
Lorain, OH............... 6.2 95.3 1.0 263 782 -2.7 297
Lucas, OH................ 10.1 207.5 2.4 130 886 0.5 129
Mahoning, OH............. 5.9 96.6 0.2 314 683 -2.6 295
Montgomery, OH........... 12.0 251.5 2.4 130 843 -1.3 239
Stark, OH................ 8.6 155.9 0.6 295 726 -4.5 325
Summit, OH............... 14.1 261.1 0.6 295 946 1.0 98
Warren, OH............... 4.7 88.8 3.9 31 912 0.2 147
Cleveland, OK............ 5.5 81.3 0.7 289 700 -0.3 191
Oklahoma, OK............. 27.4 444.8 -0.6 324 951 -5.2 332
Tulsa, OK................ 22.0 347.1 -0.5 322 921 -5.9 335
Clackamas, OR............ 14.5 154.7 3.2 69 916 0.5 129
Jackson, OR.............. 7.2 83.3 3.6 44 751 0.9 102
Lane, OR................. 12.0 148.5 2.5 124 749 -0.9 220
Marion, OR............... 10.4 145.3 3.5 50 784 1.7 59
Multnomah, OR............ 33.9 487.5 3.4 55 1,065 3.7 9
Washington, OR........... 18.8 277.9 2.8 99 1,247 -2.3 284
Allegheny, PA............ 35.7 678.1 0.4 306 1,128 -6.2 337
Berks, PA................ 9.0 169.4 1.5 224 878 -0.5 207
Bucks, PA................ 19.8 255.3 1.9 176 929 -0.1 177
Butler, PA............... 5.0 84.0 1.5 224 902 -1.8 260
Chester, PA.............. 15.5 244.9 1.8 192 1,343 -2.5 293
Cumberland, PA........... 6.4 130.2 2.2 150 907 -0.7 216
Dauphin, PA.............. 7.5 177.2 1.4 235 984 -4.7 329
Delaware, PA............. 14.0 216.9 1.3 242 1,117 -1.3 239
Erie, PA................. 7.1 121.0 -1.4 334 769 -0.1 177
Lackawanna, PA........... 5.8 96.3 0.6 295 751 0.0 165
Lancaster, PA............ 13.3 230.3 2.7 109 823 1.1 88
Lehigh, PA............... 8.7 183.0 2.3 142 1,004 0.0 165
Luzerne, PA.............. 7.5 142.1 1.3 242 772 -2.4 287
Montgomery, PA........... 27.5 477.3 2.1 158 1,371 -0.3 191
Northampton, PA.......... 6.7 109.1 3.1 78 881 -0.1 177
Philadelphia, PA......... 35.1 654.2 1.5 224 1,206 -1.7 254
Washington, PA........... 5.5 84.4 -2.5 338 1,066 -12.0 342
Westmoreland, PA......... 9.3 131.3 1.0 263 791 0.1 155
York, PA................. 9.0 174.7 1.6 211 862 0.8 108
Providence, RI........... 17.5 280.7 1.5 224 1,038 -3.2 307
Charleston, SC........... 14.4 238.2 3.4 55 894 1.6 63
Greenville, SC........... 14.0 259.1 2.5 124 860 -0.1 177
Horry, SC................ 8.8 118.3 4.7 9 587 0.5 129
Lexington, SC............ 6.6 114.2 2.8 99 757 1.6 63
Richland, SC............. 9.6 214.8 1.7 200 868 0.7 114
Spartanburg, SC.......... 6.1 130.3 3.5 50 848 2.3 29
York, SC................. 5.3 85.7 2.5 124 806 0.4 137
Minnehaha, SD............ 7.0 122.4 1.3 242 881 1.7 59
Davidson, TN............. 21.2 462.0 3.9 31 1,097 1.8 47
Hamilton, TN............. 9.2 194.7 2.8 99 882 0.8 108
Knox, TN................. 11.8 233.4 2.6 116 875 2.0 38
Rutherford, TN........... 5.2 117.8 5.5 4 848 -1.1 224
Shelby, TN............... 20.1 487.2 1.6 211 991 -1.7 254
Williamson, TN........... 8.1 121.3 7.9 1 1,198 -4.9 330
Bell, TX................. 5.0 118.0 4.1 25 842 2.6 21
Bexar, TX................ 38.0 832.4 2.1 158 934 -0.3 191
Brazoria, TX............. 5.3 102.7 -0.6 324 1,065 -0.4 201
Brazos, TX............... 4.2 99.5 2.1 158 725 -0.1 177
Cameron, TX.............. 6.3 136.5 0.6 295 592 0.0 165
Collin, TX............... 22.1 370.4 3.3 63 1,272 2.3 29
Dallas, TX............... 71.8 1,614.7 3.2 69 1,291 -1.2 233
Denton, TX............... 13.3 222.1 4.2 21 923 2.1 34
El Paso, TX.............. 14.3 292.1 1.7 200 691 -0.3 191
Fort Bend, TX............ 11.8 170.7 1.4 235 982 -3.9 317
Galveston, TX............ 5.8 105.1 3.8 33 919 3.0 16
Gregg, TX................ 4.2 74.4 -4.4 341 829 -5.9 335
Harris, TX............... 109.3 2,256.9 -1.2 332 1,381 -5.1 331
Hidalgo, TX.............. 11.8 249.5 1.6 211 614 1.0 98
Jefferson, TX............ 5.8 122.2 -1.5 335 1,080 -0.6 211
Lubbock, TX.............. 7.2 135.3 1.7 200 759 -0.1 177
McLennan, TX............. 5.0 108.2 2.1 158 804 1.8 47
Midland, TX.............. 5.3 83.2 -9.0 343 1,261 -5.7 333
Montgomery, TX........... 10.4 167.0 1.6 211 1,025 -2.8 298
Nueces, TX............... 8.1 159.0 -2.3 337 846 -3.6 313
Potter, TX............... 3.9 78.3 0.4 306 787 -1.1 224
Smith, TX................ 5.9 100.8 2.4 130 794 -0.6 211
Tarrant, TX.............. 40.3 837.2 2.1 158 1,005 -1.6 250
Travis, TX............... 37.0 690.3 2.9 94 1,173 2.4 24
Webb, TX................. 5.0 97.1 0.8 283 650 -2.0 270
Williamson, TX........... 9.5 154.0 3.5 50 1,009 -7.8 339
Davis, UT................ 8.0 117.3 3.2 69 796 0.9 102
Salt Lake, UT............ 42.5 659.8 3.8 33 973 0.7 114
Utah, UT................. 14.8 215.2 6.7 2 794 0.8 108
Weber, UT................ 5.8 101.3 2.0 168 726 1.3 72
Chittenden, VT........... 6.6 99.7 0.1 316 954 1.4 70
Arlington, VA............ 9.5 170.9 3.1 78 1,734 -0.2 187
Chesterfield, VA......... 8.8 132.3 4.3 18 840 -2.3 284
Fairfax, VA.............. 37.8 588.1 2.2 150 1,622 -1.8 260
Henrico, VA.............. 11.5 187.6 2.6 116 1,028 -4.5 325
Loudoun, VA.............. 12.1 155.9 6.2 3 1,193 -1.1 224
Prince William, VA....... 9.2 123.7 4.4 16 838 1.2 79
Alexandria City, VA...... 6.7 93.8 0.6 295 1,400 -0.1 177
Chesapeake City, VA...... 6.1 97.3 1.9 176 763 0.1 155
Newport News City, VA.... 3.9 95.5 -1.9 336 1,016 -2.4 287
Norfolk City, VA......... 5.9 140.2 1.1 260 987 -2.0 270
Richmond City, VA........ 7.8 152.6 3.2 69 1,173 -3.0 301
Virginia Beach City, VA.. 12.1 173.0 3.0 86 765 -1.3 239
Benton, WA............... 5.6 82.2 1.9 176 986 1.8 47
Clark, WA................ 13.9 147.4 4.2 21 906 0.7 114
King, WA................. 84.6 1,294.1 3.6 44 1,456 5.1 2
Kitsap, WA............... 6.6 85.4 2.2 150 887 0.1 155
Pierce, WA............... 21.4 288.8 3.4 55 895 0.6 123
Snohomish, WA............ 20.2 280.1 2.8 99 1,124 2.0 38
Spokane, WA.............. 15.4 212.3 3.0 86 852 0.1 155
Thurston, WA............. 8.0 107.9 3.8 33 900 2.4 24
Whatcom, WA.............. 7.1 86.3 2.1 158 825 1.1 88
Yakima, WA............... 7.7 105.1 2.0 168 680 3.0 16
Kanawha, WV.............. 5.9 101.9 -0.5 322 855 -0.3 191
Brown, WI................ 6.7 151.1 1.7 200 906 1.8 47
Dane, WI................. 15.0 322.9 2.6 116 1,005 0.5 129
Milwaukee, WI............ 25.9 482.0 0.9 273 997 -2.0 270
Outagamie, WI............ 5.2 104.6 1.9 176 856 1.5 66
Waukesha, WI............. 12.9 233.9 1.3 242 1,022 -1.4 243
Winnebago, WI............ 3.7 91.1 1.8 192 991 4.2 6
San Juan, PR............. 10.8 245.1 -1.6 (6) 634 0.0 (6)
(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) Data do not meet BLS or state agency disclosure standards.
(6) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 344 U.S. counties comprise 72.6 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
first quarter 2016
Employment Average weekly
wage(1)
Establishments,
first quarter
County by NAICS supersector 2016 Percent Percent
(thousands) March change, First change,
2016 March quarter first
(thousands) 2015-16(2) 2016 quarter
2015-16(2)
United States(3) ............................ 9,693.5 140,070.8 2.0 $1,043 -0.5
Private industry........................... 9,394.9 118,350.0 2.1 1,049 -0.6
Natural resources and mining............. 137.5 1,768.9 -8.9 1,190 -7.9
Construction............................. 768.3 6,363.7 5.4 1,053 3.8
Manufacturing............................ 343.6 12,241.8 -0.2 1,259 -1.3
Trade, transportation, and utilities..... 1,917.9 26,541.7 1.7 858 0.1
Information.............................. 155.8 2,767.3 0.9 2,009 3.1
Financial activities..................... 853.9 7,851.0 1.7 2,111 -2.2
Professional and business services....... 1,745.3 19,626.4 2.1 1,375 -1.3
Education and health services............ 1,573.9 21,474.4 2.6 865 0.1
Leisure and hospitality.................. 813.6 15,065.3 3.2 408 2.5
Other services........................... 829.6 4,317.1 1.7 665 1.4
Government................................. 298.6 21,720.8 0.9 1,008 0.2
Los Angeles, CA.............................. 464.3 4,309.9 1.9 1,138 2.1
Private industry........................... 458.2 3,741.0 1.9 1,111 1.8
Natural resources and mining............. 0.5 9.2 -4.7 1,627 1.6
Construction............................. 13.4 130.1 6.9 1,104 3.1
Manufacturing............................ 12.3 359.3 -2.3 1,348 1.9
Trade, transportation, and utilities..... 53.0 800.5 0.6 916 2.7
Information.............................. 9.3 226.8 1.1 2,145 6.5
Financial activities..................... 24.8 215.9 1.0 2,200 -1.3
Professional and business services....... 46.5 587.8 0.5 1,363 1.7
Education and health services............ 216.4 742.0 2.6 812 1.8
Leisure and hospitality.................. 31.5 491.7 3.5 586 3.4
Other services........................... 26.8 144.1 0.2 672 2.3
Government................................. 6.1 568.9 1.6 1,324 3.6
Cook, IL..................................... 154.9 2,515.9 1.2 1,278 -0.2
Private industry........................... 153.6 2,220.1 1.4 1,294 0.2
Natural resources and mining............. 0.1 1.1 27.2 1,134 3.8
Construction............................. 12.5 67.8 5.7 1,434 6.4
Manufacturing............................ 6.4 185.1 -0.9 1,257 2.1
Trade, transportation, and utilities..... 30.3 465.1 1.4 972 0.2
Information.............................. 2.6 51.9 1.0 2,078 0.0
Financial activities..................... 15.5 189.1 0.5 3,409 -1.6
Professional and business services....... 32.7 459.2 0.8 1,566 0.8
Education and health services............ 16.5 438.7 1.2 916 1.9
Leisure and hospitality.................. 14.2 261.6 3.4 476 0.6
Other services........................... 17.5 95.3 -0.2 897 -2.0
Government................................. 1.3 295.8 0.3 1,161 -2.6
New York, NY................................. 130.3 2,396.8 1.9 2,783 -1.9
Private industry........................... 129.4 2,131.8 2.0 2,969 -2.2
Natural resources and mining............. 0.0 0.2 0.7 2,942 -3.1
Construction............................. 2.2 39.6 8.6 1,825 5.4
Manufacturing............................ 2.1 26.8 -1.0 1,552 -3.7
Trade, transportation, and utilities..... 19.7 251.8 -2.6 1,407 4.0
Information.............................. 4.9 152.7 0.2 3,210 1.8
Financial activities..................... 19.3 370.4 2.3 8,498 -5.2
Professional and business services....... 27.5 547.2 2.8 2,598 -1.7
Education and health services............ 9.8 341.0 1.7 1,226 1.4
Leisure and hospitality.................. 13.6 287.5 1.9 828 2.9
Other services........................... 20.0 99.7 0.1 1,213 5.0
Government................................. 0.8 265.1 1.1 1,273 3.1
Harris, TX................................... 109.3 2,256.9 -1.2 1,381 -5.1
Private industry........................... 108.8 1,983.1 -1.7 1,422 -5.5
Natural resources and mining............. 1.8 79.4 -16.6 4,456 -2.5
Construction............................. 6.9 164.6 2.0 1,347 0.9
Manufacturing............................ 4.7 173.5 -12.5 1,680 -8.1
Trade, transportation, and utilities..... 24.4 463.7 0.0 1,260 -4.3
Information.............................. 1.1 26.4 -2.0 1,499 -2.2
Financial activities..................... 11.3 120.9 0.8 2,123 -4.8
Professional and business services....... 22.4 382.9 -2.8 1,686 -3.2
Education and health services............ 15.0 282.7 2.9 967 2.2
Leisure and hospitality.................. 9.3 224.8 3.3 433 1.6
Other services........................... 11.4 63.6 -1.3 772 -2.0
Government................................. 0.6 273.8 2.0 1,083 0.5
Maricopa, AZ................................. 94.8 1,864.4 3.3 972 -1.5
Private industry........................... 94.1 1,653.0 3.7 975 -2.1
Natural resources and mining............. 0.4 8.4 -1.7 1,019 -13.6
Construction............................. 6.9 99.8 5.2 974 1.0
Manufacturing............................ 3.1 115.5 1.1 1,451 -4.6
Trade, transportation, and utilities..... 18.8 362.3 2.3 903 -0.6
Information.............................. 1.5 34.7 2.0 1,351 -3.9
Financial activities..................... 10.9 164.2 4.9 1,431 -3.2
Professional and business services....... 21.0 316.2 3.2 1,057 -3.5
Education and health services............ 10.6 278.6 3.4 927 0.7
Leisure and hospitality.................. 7.4 209.4 2.7 448 -0.9
Other services........................... 6.0 50.3 1.0 658 0.3
Government................................. 0.7 211.3 0.0 946 3.2
Dallas, TX................................... 71.8 1,614.7 3.2 1,291 -1.2
Private industry........................... 71.3 1,440.3 3.2 1,315 -1.4
Natural resources and mining............. 0.6 8.6 -9.9 4,945 0.7
Construction............................. 4.1 81.2 3.9 1,130 3.0
Manufacturing............................ 2.7 108.2 0.0 1,690 -2.2
Trade, transportation, and utilities..... 15.4 327.5 3.8 1,073 -2.5
Information.............................. 1.3 47.5 0.0 2,440 1.5
Financial activities..................... 8.8 154.0 2.8 2,146 -0.4
Professional and business services....... 16.2 326.7 2.9 1,450 0.3
Education and health services............ 8.9 191.3 4.7 1,018 -2.5
Leisure and hospitality.................. 6.2 153.7 5.5 497 -1.0
Other services........................... 6.7 41.0 -0.2 774 -1.3
Government................................. 0.5 174.4 3.1 1,097 0.4
Orange, CA................................... 113.9 1,545.7 2.4 1,143 -6.4
Private industry........................... 112.4 1,392.0 2.4 1,119 -7.4
Natural resources and mining............. 0.2 3.4 6.2 919 -4.1
Construction............................. 6.5 93.1 6.1 1,234 4.1
Manufacturing............................ 4.9 153.6 -1.0 1,413 0.4
Trade, transportation, and utilities..... 16.7 253.1 -0.5 1,010 -3.1
Information.............................. 1.2 25.3 2.2 2,013 -0.5
Financial activities..................... 10.8 113.9 2.3 1,903 -2.4
Professional and business services....... 20.1 289.1 0.8 1,341 -22.4
Education and health services............ 29.8 197.6 3.6 888 1.6
Leisure and hospitality.................. 8.3 207.3 4.4 460 0.4
Other services........................... 6.8 45.1 3.2 677 3.8
Government................................. 1.5 153.7 2.5 1,355 1.3
San Diego, CA................................ 105.9 1,388.4 2.3 1,108 -2.0
Private industry........................... 104.0 1,157.8 2.4 1,086 -2.2
Natural resources and mining............. 0.7 9.4 0.0 616 1.8
Construction............................. 6.4 73.3 9.3 1,109 1.4
Manufacturing............................ 3.1 106.2 0.5 1,612 -7.5
Trade, transportation, and utilities..... 14.1 214.2 0.0 896 2.5
Information.............................. 1.1 23.0 -3.9 1,803 6.9
Financial activities..................... 9.6 70.5 1.8 1,585 -2.9
Professional and business services....... 17.7 228.8 1.8 1,586 -5.3
Education and health services............ 29.7 190.7 2.7 877 -0.2
Leisure and hospitality.................. 7.9 183.3 2.5 464 3.1
Other services........................... 7.4 49.4 1.0 576 0.9
Government................................. 1.9 230.7 1.8 1,223 -0.9
King, WA..................................... 84.6 1,294.1 3.6 1,456 5.1
Private industry........................... 84.1 1,127.5 3.7 1,488 5.5
Natural resources and mining............. 0.4 3.0 17.6 2,762 95.7
Construction............................. 6.3 65.1 7.2 1,247 3.9
Manufacturing............................ 2.4 105.0 -1.8 1,716 -4.2
Trade, transportation, and utilities..... 14.5 243.8 3.8 1,358 10.8
Information.............................. 2.1 92.7 8.1 3,464 14.2
Financial activities..................... 6.5 66.3 2.7 2,013 0.1
Professional and business services....... 16.7 216.3 4.4 1,699 2.6
Education and health services............ 19.5 164.3 3.2 943 0.5
Leisure and hospitality.................. 7.0 128.5 3.9 503 2.4
Other services........................... 8.8 42.7 2.5 846 4.2
Government................................. 0.5 166.6 2.4 1,237 1.6
Miami-Dade, FL............................... 95.9 1,107.3 2.7 972 -0.3
Private industry........................... 95.5 969.9 3.0 956 -0.4
Natural resources and mining............. 0.5 10.0 2.6 518 1.6
Construction............................. 6.0 42.3 10.8 930 3.4
Manufacturing............................ 2.8 40.2 4.8 894 -0.9
Trade, transportation, and utilities..... 26.5 277.5 0.5 884 0.1
Information.............................. 1.5 17.9 -0.1 1,750 7.5
Financial activities..................... 10.4 74.0 1.8 1,852 -0.8
Professional and business services....... 21.0 152.6 3.7 1,131 -1.4
Education and health services............ 10.2 172.3 3.6 901 -3.1
Leisure and hospitality.................. 7.2 142.1 4.6 568 4.0
Other services........................... 8.2 40.3 4.1 586 0.2
Government................................. 0.3 137.4 0.7 1,087 0.8
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Counties selected are based on 2015 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
first quarter 2016
Employment Average weekly
wage(1)
Establishments,
first quarter
State 2016 Percent Percent
(thousands) March change, First change,
2016 March quarter first
(thousands) 2015-16 2016 quarter
2015-16
United States(2)........... 9,693.5 140,070.8 2.0 $1,043 -0.5
Alabama.................... 121.3 1,902.6 1.6 842 -0.2
Alaska..................... 22.2 317.6 -1.4 1,028 -2.0
Arizona.................... 152.6 2,679.8 2.8 918 -0.8
Arkansas................... 88.7 1,191.1 2.1 793 0.5
California................. 1,458.8 16,455.5 2.6 1,206 0.0
Colorado................... 190.2 2,514.6 2.4 1,057 -1.3
Connecticut................ 116.8 1,650.6 0.6 1,362 -1.4
Delaware................... 31.0 429.7 1.5 1,072 -3.0
District of Columbia....... 38.7 749.6 2.0 1,766 0.4
Florida.................... 659.1 8,301.8 3.5 887 0.2
Georgia.................... 297.3 4,215.1 3.0 1,008 1.9
Hawaii..................... 40.1 645.1 1.4 896 1.7
Idaho...................... 56.9 670.4 3.5 725 -1.5
Illinois................... 408.8 5,800.6 1.2 1,126 -0.5
Indiana.................... 162.2 2,949.5 1.9 853 -0.5
Iowa....................... 101.2 1,518.2 0.9 844 -0.4
Kansas..................... 89.9 1,362.3 0.4 833 -2.0
Kentucky................... 122.5 1,843.9 1.9 823 0.1
Louisiana.................. 127.5 1,910.5 -0.8 860 -2.6
Maine...................... 52.3 580.5 1.8 804 1.1
Maryland................... 169.2 2,591.7 1.9 1,103 -0.8
Massachusetts.............. 242.7 3,414.8 2.1 1,327 -1.0
Michigan................... 240.2 4,163.7 2.1 976 0.7
Minnesota.................. 160.1 2,750.1 1.5 1,065 -1.2
Mississippi................ 72.7 1,121.0 1.7 713 0.4
Missouri................... 193.2 2,729.5 1.9 879 -0.3
Montana.................... 46.5 447.8 1.8 751 0.3
Nebraska................... 71.5 956.6 1.4 817 0.0
Nevada..................... 81.4 1,264.1 3.0 875 1.2
New Hampshire.............. 50.9 635.1 1.9 998 1.6
New Jersey................. 269.7 3,909.7 2.4 1,268 -1.7
New Mexico................. 57.9 800.4 0.0 792 -1.6
New York................... 642.1 9,042.2 2.0 1,456 -0.3
North Carolina............. 272.5 4,220.3 3.0 928 -0.2
North Dakota............... 31.9 409.4 -6.2 908 -7.6
Ohio....................... 293.0 5,236.2 1.8 913 -0.8
Oklahoma................... 109.1 1,578.6 -0.9 833 -4.1
Oregon..................... 148.6 1,808.2 3.2 929 1.2
Pennsylvania............... 355.2 5,662.2 1.1 1,012 -1.9
Rhode Island............... 36.6 464.6 1.9 985 -2.2
South Carolina............. 125.6 1,974.6 2.7 806 0.8
South Dakota............... 32.7 410.5 0.9 771 1.2
Tennessee.................. 152.9 2,859.2 3.3 887 0.3
Texas...................... 630.8 11,638.7 0.7 1,066 -2.1
Utah....................... 94.4 1,369.2 3.8 849 0.6
Vermont.................... 24.7 304.6 0.1 832 1.0
Virginia................... 263.7 3,748.1 2.6 1,057 -1.2
Washington................. 239.2 3,147.7 3.1 1,121 3.0
West Virginia.............. 50.1 683.9 -1.2 782 -1.3
Wisconsin.................. 170.0 2,771.4 1.3 875 -0.2
Wyoming.................... 26.0 267.9 -3.7 850 -4.7
Puerto Rico................ 46.2 895.2 -1.2 520 -0.4
Virgin Islands............. 3.3 38.6 0.4 769 2.9
(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
