For release 10:00 a.m. (EDT), Wednesday, June 17, 2015 USDL-15-1163
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Fourth Quarter 2014
From December 2013 to December 2014, employment increased in 319 of the 339 largest U.S.
counties (counties with 75,000 or more jobs in 2013), the U.S. Bureau of Labor Statistics
reported today. Weld, Colo., and Midland, Texas, had the largest percentage increases, with
gains of 8.0 percent each over the year, compared with national job growth of 2.2 percent.
Within Weld, the largest employment increase occurred in natural resources and mining, which
gained 2,074 jobs over the year (19.6 percent). Within Midland, the largest employment
increase also occurred in natural resources and mining, which gained 3,135 jobs over the year
(14.9 percent). Atlantic, N.J., had the largest over-the-year percentage decrease in employment
among the largest counties in the U.S. with a loss of 5.0 percent. County employment and wage
data are compiled under the Quarterly Census of Employment and Wages (QCEW) program, which
produces detailed information on county employment and wages within 6 months after the end of
each quarter.
The U.S. average weekly wage increased 3.5 percent over the year, growing to $1,035 in the
fourth quarter of 2014. Benton, Ark., had the largest over-the-year percentage increase in
average weekly wages with a gain of 9.9 percent. Within Benton, an average weekly wage gain
of $209, or 16.2 percent, in professional and business services made the largest contribution to
the county’s increase in average weekly wages. San Mateo, Calif., experienced the largest
percentage decrease in average weekly wages with a loss of 20.4 percent over the year.
Table A. Large counties ranked by December 2014 employment, December 2013-14 employment
increase, and December 2013-14 percent increase in employment
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Employment in large counties
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December 2014 employment | Increase in employment, | Percent increase in employment,
(thousands) | December 2013-14 | December 2013-14
| (thousands) |
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| |
United States 139,204.8| United States 3,033.7| United States 2.2
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| |
Los Angeles, Calif. 4,243.8| Harris, Texas 87.4| Weld, Colo. 8.0
New York, N.Y. 2,568.3| Los Angeles, Calif. 68.3| Midland, Texas 8.0
Cook, Ill. 2,512.5| New York, N.Y. 66.8| Adams, Colo. 6.4
Harris, Texas 2,312.2| Dallas, Texas 64.8| Lee, Fla. 6.2
Maricopa, Ariz. 1,821.9| Maricopa, Ariz. 48.4| Williamson, Tenn. 6.1
Dallas, Texas 1,591.0| Clark, Nev. 41.0| Utah, Utah 5.8
Orange, Calif. 1,506.0| King, Wash. 40.4| Denton, Texas 5.7
San Diego, Calif. 1,359.7| Cook, Ill. 39.3| Montgomery, Texas 5.7
King, Wash. 1,262.8| Orange, Calif. 38.1| Benton, Ark. 5.5
Miami-Dade, Fla. 1,082.5| Miami-Dade, Fla. 35.0| Fort Bend, Texas 5.5
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Large County Employment
In December 2014, national employment was 139.2 million (as measured by the QCEW
program). Over the year, employment increased 2.2 percent, or 3.0 million. The 339 U.S.
counties with 75,000 or more jobs accounted for 72.1 percent of total U.S. employment and 77.4
percent of total wages. These 339 counties had a net job growth of 2.2 million over the year,
accounting for 73.4 percent of the overall U.S. employment increase.
Weld, Colo., and Midland, Texas, had the largest percentage increases in employment (8.0
percent each) among the largest U.S. counties. The five counties with the largest increases in
employment levels were Harris, Texas; Los Angeles, Calif.; New York, N.Y.; Dallas, Texas; and
Maricopa, Ariz. These counties had a combined over-the-year employment gain of 335,700 jobs,
which was 11.1 percent of the overall job increase for the U.S. (See table A.)
Employment declined in 17 of the largest counties from December 2013 to December 2014.
Atlantic, N.J., had the largest over-the-year percentage decrease in employment (-5.0 percent).
Within Atlantic, leisure and hospitality had the largest decrease in employment, with a loss of
7,333 jobs (-16.8 percent). Norfolk City, Va., had the second largest percentage decrease in
employment, followed by McLean, Ill.; Peoria, Ill.; and Lake, Ill. (See table 1.)
Table B. Large counties ranked by fourth quarter 2014 average weekly wages, fourth quarter 2013-14
increase in average weekly wages, and fourth quarter 2013-14 percent increase in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Increase in average weekly | Percent increase in average
fourth quarter 2014 | wage, fourth quarter 2013-14 | weekly wage, fourth
| | quarter 2013-14
--------------------------------------------------------------------------------------------------------
| |
United States $1,035| United States $35| United States 3.5
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| |
San Mateo, Calif. $2,166| Santa Clara, Calif. $134| Benton, Ark. 9.9
New York, N.Y. 2,138| Midland, Texas 118| Washington, Pa. 9.2
Santa Clara, Calif. 2,114| Suffolk, Mass. 108| Midland, Texas 9.0
Suffolk, Mass. 1,856| Douglas, Colo. 100| Brazoria, Texas 8.9
San Francisco, Calif. 1,850| New York, N.Y. 91| Douglas, Colo. 8.8
Washington, D.C. 1,696| Washington, Pa. 91| Clayton, Ga. 7.6
Fairfield, Conn. 1,674| Benton, Ark. 90| Jefferson, Texas 7.6
Arlington, Va. 1,613| San Francisco, Calif. 87| Rockingham, N.H. 7.4
Fairfax, Va. 1,584| Brazoria, Texas 86| Yolo, Calif. 7.1
Somerset, N.J. 1,543| King, Wash. 81| Vanderburgh, Ind. 7.0
| | Atlantic, N.J. 7.0
| | Hamilton, Tenn. 7.0
| | Nueces, Texas 7.0
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $1,035, a 3.5 percent increase, during the year
ending in the fourth quarter of 2014. Among the 339 largest counties, 332 had over-the-year
increases in average weekly wages. Benton, Ark., had the largest percentage wage increase
among the largest U.S. counties (9.9 percent).
Of the 339 largest counties, 7 experienced over-the-year decreases in average weekly wages. San
Mateo, Calif., had the largest percentage decrease in average weekly wages, with a loss of 20.4
percent. Within San Mateo, information had the largest impact on the county’s average weekly
wage decrease. Within this industry, average weekly wages declined by $8,606 (-60.1 percent)
over the year. This decline in average weekly wages is partially due to wages returning to normal
after higher levels in 2012 and 2013. Olmsted, Minn., had the second largest percentage decrease
in average weekly wages, followed by Morris, N.J.; Rockland, N.Y.; Camden, N.J.; and Butler,
Pa. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in
December 2014. Dallas, Texas, had the largest gain (4.2 percent). Within Dallas, trade,
transportation, and utilities had the largest over-the-year employment level increase among all
private industry groups with a gain of 17,303 jobs, or 5.5 percent. Cook, Ill., and Los Angeles,
Calif., had the smallest percentage increases in employment (1.6 percent each) among the 10
largest counties. (See table 2.)
Average weekly wages increased over the year in all of the 10 largest U.S. counties. King,
Wash., experienced the largest percentage gain in average weekly wages (6.2 percent). Within
King, information had the largest impact on the county’s average weekly wage growth. Within
this industry, average weekly wages increased by $421, or 16.5 percent, over the year. Maricopa,
Ariz., had the smallest percentage increase in average weekly wages (2.2 percent) among the 10
largest counties.
For More Information
The tables included in this release contain data for the nation and for the 339 U.S. counties with
annual average employment levels of 75,000 or more in 2013. December 2014 employment and
2014 fourth quarter average weekly wages for all states are provided in table 3 of this release.
The employment and wage data by county are compiled under the QCEW program, also known
as the ES-202 program. The data are derived from reports submitted by every employer subject
to unemployment insurance (UI) laws. The 9.5 million employer reports cover 139.2 million full-
and part-time workers. The QCEW program provides a quarterly and annual universe count of
establishments, employment, and wages at the county, MSA, state, and national levels by
detailed industry. Data for the fourth quarter of 2014 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 are issuing QCEW news releases targeted to local data users. For
links to these releases, see www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for first quarter 2015 is scheduled to be
released on Thursday, September 17, 2015.
Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of
Employment and Wages (QCEW) program, also known as the ES-202 program. The data are
derived from summaries of employment and total pay of workers covered by state and federal
unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The
summaries are a result of the administration of state unemployment insurance programs that
require most employers to pay quarterly taxes based on the employment and wages of workers
covered by UI. QCEW data in this release are based on the 2012 North American Industry
Classification System. Data for 2014 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or
greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S.
averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the
basis of the preliminary annual average of employment for the previous year. The 340 counties
presented in this release were derived using 2013 preliminary annual averages of employment. For
2014 data, five counties have been added to the publication tables: Shelby, Ala.; Osceola, Fla.;
Black Hawk, Iowa; Washington, Minn.; and Cleveland, Okla. These counties will be included in
all 2014 quarterly releases. The counties in table 2 are selected and sorted each year based on the
annual average employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released by the
individual states. These potential differences result from the states' continuing receipt of UI data
over time and ongoing review and editing. The individual states determine their data release
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- | 588,000 establish-
| submitted by 9.4 | ministrative records| ments
| million establish- | submitted by 7.5 |
| ments in first | million private-sec-|
| quarter of 2014 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -6 months after the| -8 months after the | -Usually first Friday
| end of each quar- | end of each quarter| of following month
| ter | |
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly
contribution reports submitted to the SWAs by employers. For federal civilian workers covered by
the Unemployment Compensation for Federal Employees (UCFE) program, employment and
wage data are compiled from quarterly reports submitted by four major federal payroll processing
centers on behalf of all federal agencies, with the exception of a few agencies which still report
directly to the individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry of each of their
establishments. QCEW employment and wage data are derived from microdata summaries of 9.2
million employer reports of employment and wages submitted by states to the BLS in 2013. These
reports are based on place of employment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state since 1978,
when the 1976 amendments to the Federal Unemployment Tax Act became effective, expanding
coverage to include most State and local government employees. In 2013, UI and UCFE programs
covered workers in 134.0 million jobs. The estimated 128.7 million workers in these jobs (after
adjustment for multiple jobholders) represented 95.8 percent of civilian wage and salary
employment. Covered workers received $6.673 trillion in pay, representing 93.7 percent of the
wage and salary component of personal income and 39.8 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural workers on
small farms, all members of the Armed Forces, elected officials in most states, most employees of
railroads, some domestic workers, most student workers at schools, and employees of certain small
nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on the
employment and wages reported by employers covered under the UI program. Coverage changes
may affect the over-the-year comparisons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received pay for
the pay period including the 12th of the month. With few exceptions, all employees of covered
firms are reported, including production and sales workers, corporation officials, executives,
supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also
are included.
Average weekly wage values are calculated by dividing quarterly total wages by the average of the
three monthly employment levels (all employees, as described above) and dividing the result by
13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and
wage values. The average wage values that can be calculated using rounded data from the BLS
database may differ from the averages reported. Included in the quarterly wage data are non-wage
cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other
gratuities, and, in some states, employer contributions to certain deferred compensation plans such
as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may
reflect fluctuations in average monthly employment and/or total quarterly wages between the
current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as well as the
number of individuals in high-paying and low-paying occupations and the incidence of pay periods
within a quarter. For instance, the average weekly wage of the workforce could increase
significantly when there is a large decline in the number of employees that had been receiving
below-average wages. Wages may include payments to workers not present in the employment
counts because they did not work during the pay period including the 12th of the month. When
comparing average weekly wage levels between industries, states, or quarters, these factors should
be taken into consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This
variability may be due to calendar effects resulting from some quarters having more pay dates than
others. The effect is most visible in counties with a dominant employer. In particular, this effect
has been observed in counties where government employers represent a large fraction of overall
employment. Similar calendar effects can result from private sector pay practices. However, these
effects are typically less pronounced for two reasons: employment is less concentrated in a single
private employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal
government due to the uniform nature of federal payroll processing. Most federal employees are
paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates,
while in other quarters there are seven pay dates. Over-the-year comparisons of average weekly
wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in
part, to a comparison of quarterly wages for the current year, which include seven pay dates, with
year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the
current quarter reflecting six pay dates are compared with year-ago wages for a quarter including
seven pay dates.
In order to ensure the highest possible quality of data, states verify with employers and update, if
necessary, the industry, location, and ownership classification of all establishments on a 3-year
cycle. Changes in establishment classification codes resulting from this process are introduced with
the data reported for the first quarter of the year. Changes resulting from improved employer
reporting also are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of individual
establishment records and reflect the number of establishments that exist in a county or industry at
a point in time. Establishments can move in or out of a county or industry for a number of
reasons-some reflecting economic events, others reflecting administrative changes. For example,
economic change would come from a firm relocating into the county; administrative change would
come from a company correcting its county designation.
The over-the-year changes of employment and wages presented in this release have been adjusted
to account for most of the administrative corrections made to the underlying establishment reports.
This is done by modifying the prior-year levels used to calculate the over-the-year changes.
Percent changes are calculated using an adjusted version of the final 2013 quarterly data as the
base data. The adjusted prior-year levels used to calculate the over-the-year percent change in
employment and wages are not published. These adjusted prior-year levels do not match the
unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data
from the Web site, or from data published in prior BLS news releases, may differ substantially
from the over-the-year changes presented in this news release.
The adjusted data used to calculate the over-the-year change measures presented in this release
account for most of the administrative changes-those occurring when employers update the
industry, location, and ownership information of their establishments. The most common
adjustments for administrative change are the result of updated information about the county
location of individual establishments. Included in these adjustments are administrative changes
involving the classification of establishments that were previously reported in the unknown or
statewide county or unknown industry categories. 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 2013 edition
of this publication, which was published in September 2014, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2014 version of this news release. Tables and additional content from Employment and
Wages Annual Averages 2013 are now available online at
http://www.bls.gov/cew/cewbultn13.htm. The 2014 edition of Employment and Wages Annual
Averages Online will be available in September 2015.
News releases on quarterly measures of gross job flows also are available upon request from the
Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics),
telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals upon request.
Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-877-8339.
Table 1. Covered establishments, employment, and wages in the 340 largest counties,
fourth quarter 2014
Employment Average weekly wage(2)
Establishments,
County(1) fourth quarter Percent Ranking Percent Ranking
2014 December change, by Fourth change, by
(thousands) 2014 December percent quarter fourth percent
(thousands) 2013-14(3) change 2014 quarter change
2013-14(3)
United States(4)......... 9,479.2 139,204.8 2.2 - $1,035 3.5 -
Jefferson, AL............ 17.8 342.7 0.2 311 1,026 3.3 191
Madison, AL.............. 9.1 186.4 1.4 214 1,106 2.4 258
Mobile, AL............... 9.6 167.6 1.0 249 897 3.8 133
Montgomery, AL........... 6.3 129.7 0.1 317 901 2.4 258
Shelby, AL............... 5.1 80.6 3.1 84 978 3.7 143
Tuscaloosa, AL........... 4.3 91.9 4.3 28 869 2.8 231
Anchorage Borough, AK.... 8.4 152.8 0.0 320 1,094 4.2 99
Maricopa, AZ............. 94.6 1,821.9 2.7 105 974 2.2 273
Pima, AZ................. 18.9 358.8 0.3 303 858 2.1 278
Benton, AR............... 5.8 108.3 5.5 9 996 9.9 1
Pulaski, AR.............. 14.4 245.9 0.7 272 936 3.7 143
Washington, AR........... 5.7 98.1 3.5 66 896 4.3 87
Alameda, CA.............. 58.0 708.7 2.8 104 1,319 4.4 81
Contra Costa, CA......... 30.1 344.1 1.8 174 1,215 2.1 278
Fresno, CA............... 31.3 349.4 0.6 284 808 4.9 50
Kern, CA................. 17.4 306.9 0.3 303 873 2.7 235
Los Angeles, CA.......... 447.3 4,243.8 1.6 197 1,201 3.5 168
Marin, CA................ 12.2 112.0 0.6 284 1,280 5.9 29
Monterey, CA............. 13.0 159.4 1.9 162 851 3.7 143
Orange, CA............... 109.5 1,506.0 2.6 112 1,162 4.3 87
Placer, CA............... 11.6 144.6 3.4 71 1,034 5.8 31
Riverside, CA............ 54.3 641.2 3.5 66 803 4.0 113
Sacramento, CA........... 53.3 620.7 2.2 140 1,095 2.7 235
San Bernardino, CA....... 52.0 682.3 4.4 25 852 3.5 168
San Diego, CA............ 102.1 1,359.7 1.9 162 1,138 2.6 244
San Francisco, CA........ 58.2 659.1 4.4 25 1,850 4.9 50
San Joaquin, CA.......... 16.9 217.7 2.5 118 835 2.5 248
San Luis Obispo, CA...... 9.9 109.4 1.8 174 837 3.7 143
San Mateo, CA............ 26.4 385.0 4.8 18 2,166 -20.4 339
Santa Barbara, CA........ 14.7 186.5 2.5 118 981 4.9 50
Santa Clara, CA.......... 66.9 999.3 3.6 57 2,114 6.8 15
Santa Cruz, CA........... 9.3 94.6 3.7 51 926 5.0 46
Solano, CA............... 10.4 129.6 1.7 183 1,026 0.9 322
Sonoma, CA............... 19.0 192.0 0.9 259 952 4.2 99
Stanislaus, CA........... 14.5 170.3 2.5 118 832 3.9 125
Tulare, CA............... 9.3 146.5 0.7 272 739 6.2 23
Ventura, CA.............. 25.2 317.5 0.9 259 1,025 5.0 46
Yolo, CA................. 6.2 92.3 1.2 227 1,092 7.1 10
Adams, CO................ 9.6 189.0 6.4 3 987 4.3 87
Arapahoe, CO............. 20.0 311.5 3.1 84 1,223 6.7 17
Boulder, CO.............. 13.8 172.2 2.9 97 1,213 3.1 208
Denver, CO............... 28.4 474.3 5.0 13 1,247 1.8 300
Douglas, CO.............. 10.5 110.3 3.6 57 1,240 8.8 5
El Paso, CO.............. 17.4 252.9 2.5 118 916 3.4 179
Jefferson, CO............ 18.3 226.2 3.5 66 1,042 4.0 113
Larimer, CO.............. 10.8 144.3 4.3 28 962 6.8 15
Weld, CO................. 6.4 101.6 8.0 1 922 6.0 27
Fairfield, CT............ 34.2 428.4 1.7 183 1,674 1.1 315
Hartford, CT............. 26.6 509.0 1.2 227 1,246 2.8 231
New Haven, CT............ 23.1 365.5 1.1 236 1,087 4.5 73
New London, CT........... 7.1 121.2 -0.5 330 1,013 4.3 87
New Castle, DE........... 18.4 287.7 3.1 84 1,164 0.7 326
Washington, DC........... 36.8 736.9 1.6 197 1,696 3.0 220
Alachua, FL.............. 6.9 122.6 2.7 105 888 2.7 235
Brevard, FL.............. 15.1 193.1 1.6 197 885 2.0 291
Broward, FL.............. 67.4 764.4 2.9 97 960 4.0 113
Collier, FL.............. 12.8 137.8 5.0 13 891 3.1 208
Duval, FL................ 28.0 469.1 2.5 118 988 4.3 87
Escambia, FL............. 8.2 125.2 1.6 197 817 5.3 37
Hillsborough, FL......... 40.0 643.8 3.3 75 979 2.1 278
Lake, FL................. 7.8 88.2 3.1 84 691 3.6 157
Lee, FL.................. 20.3 237.9 6.2 4 803 2.4 258
Leon, FL................. 8.4 143.6 1.4 214 842 2.7 235
Manatee, FL.............. 10.1 116.4 3.0 92 767 3.4 179
Marion, FL............... 8.2 96.6 3.1 84 707 2.5 248
Miami-Dade, FL........... 95.2 1,082.5 3.3 75 1,008 2.5 248
Okaloosa, FL............. 6.3 77.1 0.3 303 823 4.7 60
Orange, FL............... 39.0 751.4 3.4 71 895 3.8 133
Osceola, FL.............. 6.1 83.0 2.9 97 687 3.9 125
Palm Beach, FL........... 53.3 565.1 3.8 46 1,006 1.0 319
Pasco, FL................ 10.4 108.6 4.6 21 711 2.7 235
Pinellas, FL............. 31.8 405.0 2.1 151 928 1.9 297
Polk, FL................. 12.8 204.3 2.2 140 777 3.7 143
Sarasota, FL............. 15.3 158.6 5.1 12 860 3.2 199
Seminole, FL............. 14.4 173.1 3.7 51 843 3.4 179
Volusia, FL.............. 13.8 158.9 2.6 112 729 4.0 113
Bibb, GA................. 4.5 83.7 2.3 130 802 4.7 60
Chatham, GA.............. 8.3 142.4 4.6 21 871 2.7 235
Clayton, GA.............. 4.4 115.6 3.8 46 977 7.6 7
Cobb, GA................. 22.9 332.6 4.1 36 1,081 3.6 157
De Kalb, GA.............. 19.0 289.8 2.3 130 1,013 2.2 273
Fulton, GA............... 45.2 790.5 4.1 36 1,338 3.7 143
Gwinnett, GA............. 25.6 333.3 3.8 46 991 3.1 208
Muscogee, GA............. 4.8 95.1 -0.4 328 804 2.0 291
Richmond, GA............. 4.7 104.1 2.3 130 834 1.8 300
Honolulu, HI............. 24.9 466.6 0.5 291 945 4.0 113
Ada, ID.................. 14.0 213.0 1.6 197 950 5.9 29
Champaign, IL............ 4.5 89.7 0.5 291 868 5.2 41
Cook, IL................. 160.7 2,512.5 1.6 197 1,209 3.2 199
Du Page, IL.............. 39.6 608.0 1.7 183 1,178 0.3 329
Kane, IL................. 14.3 205.6 0.3 303 912 4.5 73
Lake, IL................. 23.5 331.4 -0.6 335 1,341 2.8 231
McHenry, IL.............. 9.2 95.8 0.0 320 847 2.5 248
McLean, IL............... 4.0 84.4 -0.9 336 968 1.3 313
Madison, IL.............. 6.3 97.9 2.1 151 848 3.5 168
Peoria, IL............... 4.9 100.7 -0.9 336 954 1.8 300
St. Clair, IL............ 5.8 93.8 1.3 223 799 2.4 258
Sangamon, IL............. 5.5 129.9 2.0 158 1,019 0.8 325
Will, IL................. 16.6 219.3 1.1 236 895 3.7 143
Winnebago, IL............ 7.0 127.6 1.1 236 874 3.4 179
Allen, IN................ 8.8 181.1 1.8 174 806 4.1 109
Elkhart, IN.............. 4.7 122.6 4.3 28 837 6.6 19
Hamilton, IN............. 8.8 128.3 4.4 25 971 3.5 168
Lake, IN................. 10.2 187.9 -0.3 325 898 2.4 258
Marion, IN............... 23.5 587.9 1.6 197 1,004 3.1 208
St. Joseph, IN........... 5.8 120.0 1.7 183 807 2.5 248
Tippecanoe, IN........... 3.3 82.5 2.2 140 852 4.4 81
Vanderburgh, IN.......... 4.8 107.3 1.9 162 852 7.0 11
Black Hawk, IA........... 3.8 75.5 -0.5 330 930 3.2 199
Johnson, IA.............. 4.0 80.8 0.3 303 915 3.5 168
Linn, IA................. 6.6 130.0 1.4 214 1,018 6.3 21
Polk, IA................. 16.5 287.5 1.6 197 1,029 3.7 143
Scott, IA................ 5.5 91.0 1.0 249 857 2.4 258
Johnson, KS.............. 21.8 335.9 3.4 71 1,041 2.0 291
Sedgwick, KS............. 12.4 247.7 0.6 284 921 1.5 306
Shawnee, KS.............. 4.9 97.2 0.7 272 826 2.4 258
Wyandotte, KS............ 3.3 88.5 4.3 28 940 4.3 87
Boone, KY................ 4.2 80.4 2.3 130 890 2.9 227
Fayette, KY.............. 10.5 191.3 0.8 264 881 4.3 87
Jefferson, KY............ 24.6 451.5 2.5 118 964 3.4 179
Caddo, LA................ 7.3 117.3 1.2 227 861 3.9 125
Calcasieu, LA............ 4.9 90.9 5.4 11 908 3.9 125
East Baton Rouge, LA..... 14.6 273.5 3.2 80 975 4.5 73
Jefferson, LA............ 13.5 195.0 1.1 236 926 2.2 273
Lafayette, LA............ 9.2 144.3 1.5 209 1,033 3.6 157
Orleans, LA.............. 11.6 191.4 2.4 126 996 2.4 258
St. Tammany, LA.......... 7.6 85.6 4.2 33 892 4.6 68
Cumberland, ME........... 12.8 175.3 1.0 249 951 5.2 41
Anne Arundel, MD......... 14.7 258.6 1.0 249 1,089 2.3 272
Baltimore, MD............ 21.2 373.4 0.8 264 1,043 3.6 157
Frederick, MD............ 6.3 97.4 1.0 249 968 2.1 278
Harford, MD.............. 5.6 90.6 1.0 249 982 1.4 311
Howard, MD............... 9.5 161.8 1.1 236 1,243 3.6 157
Montgomery, MD........... 32.6 462.7 1.4 214 1,342 2.1 278
Prince Georges, MD....... 15.7 309.1 1.3 223 1,049 4.6 68
Baltimore City, MD....... 13.8 335.6 1.8 174 1,223 4.9 50
Barnstable, MA........... 9.2 88.7 2.2 140 887 3.7 143
Bristol, MA.............. 16.8 222.8 1.9 162 962 6.3 21
Essex, MA................ 23.1 318.6 1.4 214 1,096 4.3 87
Hampden, MA.............. 16.8 203.8 1.4 214 947 4.3 87
Middlesex, MA............ 52.2 875.4 2.6 112 1,482 3.6 157
Norfolk, MA.............. 24.3 344.3 1.9 162 1,254 2.6 244
Plymouth, MA............. 14.7 186.3 2.6 112 982 3.8 133
Suffolk, MA.............. 26.2 630.4 2.4 126 1,856 6.2 23
Worcester, MA............ 23.1 334.7 2.1 151 1,030 3.4 179
Genesee, MI.............. 7.0 135.8 0.9 259 837 3.1 208
Ingham, MI............... 6.1 151.4 -0.3 325 966 3.3 191
Kalamazoo, MI............ 5.1 114.6 1.4 214 934 3.7 143
Kent, MI................. 14.0 371.3 3.1 84 909 3.4 179
Macomb, MI............... 17.3 312.7 2.1 151 1,025 1.5 306
Oakland, MI.............. 38.3 704.8 1.6 197 1,164 4.0 113
Ottawa, MI............... 5.5 116.7 4.1 36 914 4.7 60
Saginaw, MI.............. 4.0 84.8 -0.1 323 818 3.0 220
Washtenaw, MI............ 8.1 203.9 1.9 162 1,069 4.2 99
Wayne, MI................ 30.5 706.5 2.2 140 1,119 3.0 220
Anoka, MN................ 7.0 118.5 1.7 183 949 5.3 37
Dakota, MN............... 9.7 183.9 1.8 174 984 5.4 35
Hennepin, MN............. 41.3 883.7 1.7 183 1,259 4.1 109
Olmsted, MN.............. 3.4 92.6 -0.3 325 1,021 -5.5 338
Ramsey, MN............... 13.3 327.2 1.4 214 1,137 3.6 157
St. Louis, MN............ 5.3 96.7 0.5 291 824 3.1 208
Stearns, MN.............. 4.3 84.1 0.7 272 835 2.2 273
Washington, MN........... 5.3 77.8 0.9 259 834 3.5 168
Harrison, MS............. 4.5 82.9 -0.4 328 714 2.9 227
Hinds, MS................ 6.0 121.1 0.8 264 871 1.0 319
Boone, MO................ 4.8 91.6 2.3 130 791 3.3 191
Clay, MO................. 5.3 95.2 4.5 23 930 5.2 41
Greene, MO............... 8.3 161.4 2.6 112 773 5.0 46
Jackson, MO.............. 20.2 354.4 0.9 259 1,031 3.0 220
St. Charles, MO.......... 8.7 135.3 0.3 303 811 5.3 37
St. Louis, MO............ 34.6 590.9 1.2 227 1,121 2.5 248
St. Louis City, MO....... 11.6 224.4 1.9 162 1,067 3.5 168
Yellowstone, MT.......... 6.3 79.6 1.6 197 900 5.0 46
Douglas, NE.............. 18.4 332.4 1.7 183 932 4.7 60
Lancaster, NE............ 9.9 164.9 0.8 264 819 3.8 133
Clark, NV................ 52.6 895.5 4.8 18 885 1.1 315
Washoe, NV............... 14.1 199.0 3.1 84 923 3.2 199
Hillsborough, NH......... 12.2 199.1 1.6 197 1,210 6.7 17
Rockingham, NH........... 10.7 142.6 1.7 183 1,060 7.4 9
Atlantic, NJ............. 6.6 124.1 -5.0 339 872 7.0 11
Bergen, NJ............... 32.8 448.4 0.7 272 1,291 4.2 99
Burlington, NJ........... 11.1 200.8 0.6 284 1,060 2.4 258
Camden, NJ............... 11.9 200.7 1.1 236 1,017 -0.8 334
Essex, NJ................ 20.4 338.7 0.4 297 1,234 0.2 331
Gloucester, NJ........... 6.2 103.1 2.3 130 909 1.5 306
Hudson, NJ............... 14.3 244.1 1.7 183 1,335 3.9 125
Mercer, NJ............... 11.0 243.8 3.7 51 1,306 1.1 315
Middlesex, NJ............ 21.9 401.6 1.0 249 1,217 2.4 258
Monmouth, NJ............. 20.0 252.1 2.5 118 1,053 1.7 303
Morris, NJ............... 17.0 284.6 0.2 311 1,512 -2.9 337
Ocean, NJ................ 12.7 157.6 2.0 158 845 2.1 278
Passaic, NJ.............. 12.3 170.6 -0.5 330 1,016 2.4 258
Somerset, NJ............. 10.0 183.4 2.2 140 1,543 3.6 157
Union, NJ................ 14.3 223.5 0.5 291 1,341 4.5 73
Bernalillo, NM........... 17.8 317.6 0.7 272 873 4.4 81
Albany, NY............... 10.3 230.4 1.8 174 1,062 4.8 57
Bronx, NY................ 17.8 257.1 3.2 80 958 2.5 248
Broome, NY............... 4.6 88.7 0.8 264 786 3.1 208
Dutchess, NY............. 8.5 111.4 0.7 272 1,000 4.6 68
Erie, NY................. 24.6 466.3 0.7 272 898 4.8 57
Kings, NY................ 57.9 590.9 5.0 13 849 3.9 125
Monroe, NY............... 18.7 383.3 0.7 272 935 4.2 99
Nassau, NY............... 53.6 623.6 1.1 236 1,158 3.4 179
New York, NY............. 128.0 2,568.3 2.7 105 2,138 4.4 81
Oneida, NY............... 5.4 105.1 0.2 311 793 3.4 179
Onondaga, NY............. 13.2 246.2 0.2 311 938 3.0 220
Orange, NY............... 10.2 140.6 2.2 140 847 3.4 179
Queens, NY............... 50.0 569.4 3.9 42 974 1.7 303
Richmond, NY............. 9.6 101.4 0.8 264 888 4.6 68
Rockland, NY............. 10.3 120.2 2.3 130 1,052 -1.3 336
Saratoga, NY............. 5.9 82.6 1.9 162 908 2.5 248
Suffolk, NY.............. 52.0 646.4 0.5 291 1,125 4.7 60
Westchester, NY.......... 36.5 423.2 1.9 162 1,407 4.5 73
Buncombe, NC............. 8.3 122.9 3.4 71 797 4.9 50
Catawba, NC.............. 4.3 83.2 1.7 183 760 4.0 113
Cumberland, NC........... 6.2 118.6 0.1 317 771 0.7 326
Durham, NC............... 7.7 192.2 2.2 140 1,271 1.0 319
Forsyth, NC.............. 9.2 181.6 2.0 158 933 4.2 99
Guilford, NC............. 14.1 275.2 1.5 209 890 3.5 168
Mecklenburg, NC.......... 34.0 630.4 3.8 46 1,125 2.5 248
New Hanover, NC.......... 7.5 104.9 3.5 66 828 3.8 133
Wake, NC................. 30.9 503.3 3.9 42 1,008 2.4 258
Cass, ND................. 6.8 115.9 3.7 51 935 4.5 73
Butler, OH............... 7.6 146.9 2.9 97 875 3.1 208
Cuyahoga, OH............. 35.4 717.9 0.4 297 1,050 3.7 143
Delaware, OH............. 4.7 82.8 0.4 297 968 0.9 322
Franklin, OH............. 30.3 727.9 2.9 97 998 2.9 227
Hamilton, OH............. 23.2 505.9 1.8 174 1,139 6.1 26
Lake, OH................. 6.3 95.4 0.8 264 861 5.5 33
Lorain, OH............... 6.0 96.8 0.6 284 816 2.1 278
Lucas, OH................ 10.0 208.1 0.6 284 896 5.4 35
Mahoning, OH............. 5.9 99.8 1.0 249 734 3.8 133
Montgomery, OH........... 11.9 250.5 1.9 162 879 2.1 278
Stark, OH................ 8.7 160.3 1.5 209 789 4.0 113
Summit, OH............... 14.0 264.6 1.2 227 914 4.2 99
Warren, OH............... 4.5 82.5 2.1 151 880 5.3 37
Cleveland, OK............ 5.3 81.5 2.1 151 762 4.7 60
Oklahoma, OK............. 26.7 452.2 2.3 130 981 2.0 291
Tulsa, OK................ 21.5 350.6 2.7 105 952 0.3 329
Clackamas, OR............ 13.5 148.3 3.0 92 939 2.6 244
Jackson, OR.............. 6.9 82.6 3.6 57 747 3.3 191
Lane, OR................. 11.4 145.4 2.7 105 796 3.2 199
Marion, OR............... 9.9 140.6 3.7 51 811 4.2 99
Multnomah, OR............ 31.8 476.8 3.6 57 1,030 2.4 258
Washington, OR........... 17.5 271.0 2.6 112 1,231 6.0 27
Allegheny, PA............ 35.3 688.8 -0.1 323 1,096 2.5 248
Berks, PA................ 8.9 169.5 1.7 183 913 4.6 68
Bucks, PA................ 19.7 254.9 1.7 183 1,001 4.2 99
Butler, PA............... 5.0 85.0 0.7 272 937 -0.8 334
Chester, PA.............. 15.2 243.9 1.2 227 1,333 3.3 191
Cumberland, PA........... 6.2 129.7 2.5 118 922 3.6 157
Dauphin, PA.............. 7.3 177.2 1.2 227 996 2.6 244
Delaware, PA............. 13.9 221.1 1.7 183 1,084 1.1 315
Erie, PA................. 7.2 125.2 1.0 249 806 4.1 109
Lackawanna, PA........... 5.9 98.5 0.3 303 765 3.2 199
Lancaster, PA............ 13.0 230.2 3.0 92 853 3.0 220
Lehigh, PA............... 8.6 184.2 0.4 297 1,033 7.8 6
Luzerne, PA.............. 7.5 141.5 0.7 272 781 2.9 227
Montgomery, PA........... 27.4 481.4 1.1 236 1,262 3.8 133
Northampton, PA.......... 6.6 107.8 2.0 158 881 3.6 157
Philadelphia, PA......... 34.8 652.5 2.2 140 1,210 2.2 273
Washington, PA........... 5.4 87.7 1.5 209 1,085 9.2 2
Westmoreland, PA......... 9.3 133.4 1.0 249 829 4.1 109
York, PA................. 9.0 174.2 0.7 272 875 4.5 73
Providence, RI........... 17.4 283.5 2.1 151 1,062 4.4 81
Charleston, SC........... 12.9 232.2 4.3 28 880 4.3 87
Greenville, SC........... 13.1 254.6 5.0 13 880 2.7 235
Horry, SC................ 8.1 111.3 3.6 57 610 3.7 143
Lexington, SC............ 6.0 114.4 2.4 126 765 5.2 41
Richland, SC............. 9.4 213.2 3.2 80 862 1.9 297
Spartanburg, SC.......... 5.9 126.5 2.9 97 862 4.5 73
York, SC................. 5.1 83.1 4.9 17 806 0.9 322
Minnehaha, SD............ 6.8 122.7 2.2 140 878 3.8 133
Davidson, TN............. 20.1 467.8 3.6 57 1,076 1.5 306
Hamilton, TN............. 9.0 189.7 1.1 236 974 7.0 11
Knox, TN................. 11.4 230.2 3.0 92 923 5.2 41
Rutherford, TN........... 4.9 115.7 3.9 42 908 3.2 199
Shelby, TN............... 19.8 489.7 1.4 214 1,041 2.1 278
Williamson, TN........... 7.4 112.1 6.1 5 1,231 4.9 50
Bell, TX................. 5.0 112.9 0.5 291 812 2.7 235
Bexar, TX................ 37.4 811.5 3.2 80 910 3.4 179
Brazoria, TX............. 5.3 101.2 4.1 36 1,047 8.9 4
Brazos, TX............... 4.2 99.0 4.8 18 772 4.0 113
Cameron, TX.............. 6.4 135.6 1.6 197 621 3.8 133
Collin, TX............... 21.6 354.7 3.6 57 1,186 3.2 199
Dallas, TX............... 71.8 1,591.0 4.2 33 1,233 3.1 208
Denton, TX............... 12.8 211.4 5.7 7 938 6.6 19
El Paso, TX.............. 14.4 289.1 1.2 227 707 3.2 199
Fort Bend, TX............ 11.3 170.3 5.5 9 1,048 3.5 168
Galveston, TX............ 5.7 102.5 2.3 130 918 4.7 60
Gregg, TX................ 4.2 80.5 3.3 75 940 2.8 231
Harris, TX............... 109.5 2,312.2 3.9 42 1,373 4.3 87
Hidalgo, TX.............. 11.9 246.5 2.3 130 641 3.6 157
Jefferson, TX............ 5.8 126.4 4.5 23 1,079 7.6 7
Lubbock, TX.............. 7.3 133.6 1.9 162 803 4.4 81
McLennan, TX............. 5.0 106.7 1.7 183 832 4.0 113
Midland, TX.............. 5.4 94.7 8.0 1 1,425 9.0 3
Montgomery, TX........... 10.1 164.6 5.7 7 1,044 3.9 125
Nueces, TX............... 8.2 166.7 3.6 57 936 7.0 11
Potter, TX............... 4.0 79.1 1.2 227 830 3.5 168
Smith, TX................ 6.0 100.4 3.8 46 872 2.0 291
Tarrant, TX.............. 40.3 842.8 3.1 84 1,019 3.3 191
Travis, TX............... 35.8 669.4 4.2 33 1,170 4.7 60
Webb, TX................. 5.0 97.8 3.6 57 696 4.2 99
Williamson, TX........... 9.1 148.3 2.7 105 960 0.7 326
Davis, UT................ 7.9 115.7 3.5 66 802 4.3 87
Salt Lake, UT............ 41.6 639.7 2.4 126 983 5.5 33
Utah, UT................. 14.3 202.3 5.8 6 810 -0.5 333
Weber, UT................ 5.7 97.3 3.0 92 750 4.0 113
Chittenden, VT........... 6.4 101.6 1.3 223 1,032 3.9 125
Arlington, VA............ 8.6 165.7 0.0 320 1,613 1.5 306
Chesterfield, VA......... 8.0 129.6 1.1 236 876 0.2 331
Fairfax, VA.............. 34.6 586.8 0.2 311 1,584 2.0 291
Henrico, VA.............. 10.3 182.3 2.7 105 977 3.3 191
Loudoun, VA.............. 10.5 150.1 1.1 236 1,204 1.3 313
Prince William, VA....... 8.3 120.5 1.3 223 891 3.0 220
Alexandria City, VA...... 6.1 95.9 0.3 303 1,464 3.7 143
Chesapeake City, VA...... 5.6 97.0 0.1 317 792 2.1 278
Newport News City, VA.... 3.6 99.0 -0.5 330 960 3.4 179
Norfolk City, VA......... 5.4 135.0 -1.1 338 1,001 5.6 32
Richmond City, VA........ 7.0 149.1 0.4 297 1,101 3.5 168
Virginia Beach City, VA.. 11.1 171.2 1.1 236 809 3.3 191
Benton, WA............... 5.7 79.7 3.7 51 997 1.9 297
Clark, WA................ 14.0 142.9 4.1 36 927 3.7 143
King, WA................. 84.9 1,262.8 3.3 75 1,384 6.2 23
Kitsap, WA............... 6.7 83.5 1.8 174 870 3.1 208
Pierce, WA............... 21.8 284.1 2.9 97 887 2.1 278
Snohomish, WA............ 20.4 271.9 1.9 162 1,071 4.8 57
Spokane, WA.............. 15.8 208.5 2.2 140 839 2.1 278
Thurston, WA............. 7.9 105.2 3.3 75 876 2.1 278
Whatcom, WA.............. 7.1 84.6 1.8 174 805 2.4 258
Yakima, WA............... 8.0 99.7 4.0 41 708 3.1 208
Kanawha, WV.............. 6.0 105.0 0.2 311 868 2.7 235
Brown, WI................ 6.4 150.3 0.4 297 931 4.0 113
Dane, WI................. 14.1 319.5 1.5 209 1,020 1.6 305
Milwaukee, WI............ 25.2 484.6 0.6 284 1,010 4.9 50
Outagamie, WI............ 4.9 104.7 1.1 236 865 3.8 133
Waukesha, WI............. 12.3 234.0 0.8 264 1,026 3.1 208
Winnebago, WI............ 3.5 90.1 -0.5 330 974 1.4 311
San Juan, PR............. 11.4 262.7 -1.6 (5) 659 0.2 (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 quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(5) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 339 U.S. counties comprise 72.1 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
fourth quarter 2014
Employment Average weekly
wage(1)
Establishments,
fourth quarter
County by NAICS supersector 2014 Percent Percent
(thousands) December change, Fourth change,
2014 December quarter fourth
(thousands) 2013-14(2) 2014 quarter
2013-14(2)
United States(3) ............................ 9,479.2 139,204.8 2.2 $1,035 3.5
Private industry........................... 9,184.9 117,701.2 2.6 1,042 3.6
Natural resources and mining............. 137.6 1,989.9 3.4 1,215 5.0
Construction............................. 760.5 6,192.9 5.6 1,174 5.0
Manufacturing............................ 339.9 12,255.7 1.5 1,268 4.1
Trade, transportation, and utilities..... 1,920.3 27,247.3 2.2 863 4.0
Information.............................. 153.7 2,756.5 0.7 1,755 0.0
Financial activities..................... 841.8 7,759.4 1.2 1,664 4.7
Professional and business services....... 1,702.8 19,532.8 3.3 1,377 3.1
Education and health services............ 1,498.4 20,926.8 2.0 941 3.1
Leisure and hospitality.................. 800.1 14,502.5 2.6 438 3.8
Other services........................... 820.2 4,255.2 2.0 688 3.8
Government................................. 294.3 21,503.6 0.5 997 3.2
Los Angeles, CA.............................. 447.3 4,243.8 1.6 1,201 3.5
Private industry........................... 441.5 3,698.0 1.6 1,189 3.5
Natural resources and mining............. 0.5 9.5 -4.4 1,472 -15.8
Construction............................. 13.4 119.4 1.0 1,218 4.4
Manufacturing............................ 12.5 358.2 -2.5 1,228 4.0
Trade, transportation, and utilities..... 53.6 820.4 1.3 938 2.6
Information.............................. 9.7 198.9 0.2 2,285 4.4
Financial activities..................... 24.7 209.7 -0.8 1,850 5.5
Professional and business services....... 48.1 612.8 -0.1 1,536 6.7
Education and health services............ 204.0 724.0 2.4 898 2.4
Leisure and hospitality.................. 31.0 469.8 3.3 964 0.2
Other services........................... 27.9 146.6 2.3 704 4.1
Government................................. 5.7 545.8 1.6 1,283 4.1
New York, NY................................. 128.0 2,568.3 2.7 2,138 4.4
Private industry........................... 127.6 2,127.8 3.1 2,337 4.2
Natural resources and mining............. 0.0 0.2 -0.7 1,976 0.2
Construction............................. 2.2 35.2 4.2 2,230 7.1
Manufacturing............................ 2.2 25.9 -0.8 1,577 2.4
Trade, transportation, and utilities..... 20.6 279.1 2.1 1,439 2.9
Information.............................. 4.8 153.8 1.9 2,715 7.4
Financial activities..................... 19.3 363.6 1.8 4,984 4.9
Professional and business services....... 27.1 537.0 3.2 2,550 4.4
Education and health services............ 9.8 334.2 3.7 1,301 2.6
Leisure and hospitality.................. 13.7 289.6 3.9 981 6.1
Other services........................... 20.3 101.7 3.4 1,142 2.4
Government................................. 0.4 440.4 0.8 1,184 4.7
Cook, IL..................................... 160.7 2,512.5 1.6 1,209 3.2
Private industry........................... 159.4 2,215.9 1.8 1,212 3.4
Natural resources and mining............. 0.1 0.9 11.5 1,294 17.5
Construction............................. 13.3 68.9 10.1 1,606 6.0
Manufacturing............................ 6.8 186.3 -0.1 1,307 5.7
Trade, transportation, and utilities..... 31.8 478.6 2.0 937 2.2
Information.............................. 2.9 55.0 2.3 1,663 -1.5
Financial activities..................... 16.3 185.0 0.3 2,215 1.7
Professional and business services....... 34.3 460.2 1.4 1,594 4.3
Education and health services............ 16.8 428.2 1.6 1,006 5.1
Leisure and hospitality.................. 14.5 251.6 2.2 496 2.3
Other services........................... 18.2 97.5 1.9 897 3.8
Government................................. 1.3 296.6 0.3 1,187 1.2
Harris, TX................................... 109.5 2,312.2 3.9 1,373 4.3
Private industry........................... 108.9 2,045.5 4.2 1,412 4.2
Natural resources and mining............. 1.8 95.0 4.5 3,321 -1.7
Construction............................. 6.9 159.6 9.6 1,465 7.8
Manufacturing............................ 4.7 201.7 4.4 1,673 6.8
Trade, transportation, and utilities..... 24.7 488.4 4.0 1,210 4.5
Information.............................. 1.2 28.3 -3.3 1,518 5.9
Financial activities..................... 11.3 119.8 1.7 1,758 5.3
Professional and business services....... 22.1 399.9 3.2 1,788 4.4
Education and health services............ 14.9 275.9 3.9 1,056 4.6
Leisure and hospitality.................. 9.2 211.4 5.0 454 3.4
Other services........................... 11.7 64.6 4.9 816 6.5
Government................................. 0.6 266.7 1.8 1,077 4.3
Maricopa, AZ................................. 94.6 1,821.9 2.7 974 2.2
Private industry........................... 93.9 1,609.5 2.9 973 2.2
Natural resources and mining............. 0.5 8.5 3.5 918 -1.8
Construction............................. 7.3 93.5 0.4 1,072 3.5
Manufacturing............................ 3.2 114.8 0.7 1,375 5.5
Trade, transportation, and utilities..... 20.1 369.1 2.4 873 1.9
Information.............................. 1.6 34.2 5.5 1,261 2.0
Financial activities..................... 11.2 157.0 3.2 1,229 3.1
Professional and business services....... 22.2 314.5 3.4 1,090 1.3
Education and health services............ 10.8 268.6 3.8 1,007 -0.2
Leisure and hospitality.................. 7.5 197.9 3.0 463 5.7
Other services........................... 6.4 48.4 1.7 687 2.2
Government................................. 0.7 212.5 1.2 986 2.8
Dallas, TX................................... 71.8 1,591.0 4.2 1,233 3.1
Private industry........................... 71.3 1,421.2 4.5 1,250 3.1
Natural resources and mining............. 0.6 10.1 4.6 3,902 5.5
Construction............................. 4.1 78.6 7.6 1,243 6.9
Manufacturing............................ 2.7 107.8 1.1 1,445 5.1
Trade, transportation, and utilities..... 15.5 331.2 5.5 1,066 1.1
Information.............................. 1.4 49.6 0.0 1,780 0.5
Financial activities..................... 8.6 153.8 2.7 1,694 6.9
Professional and business services....... 16.2 319.1 5.7 1,496 3.0
Education and health services............ 8.8 184.4 4.1 1,071 3.0
Leisure and hospitality.................. 6.2 145.6 5.2 510 -1.0
Other services........................... 6.8 40.5 2.1 793 2.9
Government................................. 0.5 169.8 2.2 1,090 2.5
Orange, CA................................... 109.5 1,506.0 2.6 1,162 4.3
Private industry........................... 108.1 1,367.6 2.6 1,167 4.4
Natural resources and mining............. 0.2 3.0 -2.0 887 22.2
Construction............................. 6.5 83.2 5.2 1,288 4.0
Manufacturing............................ 4.9 158.8 -0.1 1,482 10.1
Trade, transportation, and utilities..... 16.7 266.9 2.0 1,030 3.7
Information.............................. 1.2 23.5 -4.4 1,825 9.0
Financial activities..................... 10.8 114.6 1.4 1,958 2.6
Professional and business services....... 20.7 282.3 2.2 1,384 4.4
Education and health services............ 27.5 188.8 2.3 990 1.6
Leisure and hospitality.................. 7.9 193.7 2.8 469 7.8
Other services........................... 6.8 42.8 2.1 710 3.3
Government................................. 1.3 138.4 2.5 1,109 3.4
San Diego, CA................................ 102.1 1,359.7 1.9 1,138 2.6
Private industry........................... 100.7 1,139.0 2.2 1,127 2.1
Natural resources and mining............. 0.7 9.5 2.6 695 5.1
Construction............................. 6.4 64.6 3.0 1,185 5.8
Manufacturing............................ 3.0 97.4 0.9 1,589 4.7
Trade, transportation, and utilities..... 14.1 223.9 0.8 838 3.8
Information.............................. 1.2 23.9 -3.9 1,677 -1.5
Financial activities..................... 9.5 70.5 -0.9 1,493 8.7
Professional and business services....... 18.3 231.2 1.3 1,784 -1.1
Education and health services............ 28.2 185.9 3.0 979 2.6
Leisure and hospitality.................. 7.6 175.4 2.6 460 4.3
Other services........................... 7.3 48.7 3.7 606 4.1
Government................................. 1.4 220.7 0.6 1,193 5.5
King, WA..................................... 84.9 1,262.8 3.3 1,384 6.2
Private industry........................... 84.4 1,100.7 3.6 1,402 6.4
Natural resources and mining............. 0.4 2.5 2.5 1,417 4.6
Construction............................. 6.1 60.6 13.1 1,315 3.7
Manufacturing............................ 2.3 105.8 0.2 1,640 6.8
Trade, transportation, and utilities..... 14.9 241.7 4.3 1,170 5.1
Information.............................. 2.0 85.0 2.0 2,974 16.5
Financial activities..................... 6.5 65.9 1.3 1,766 10.8
Professional and business services....... 16.1 211.6 4.8 1,766 3.3
Education and health services............ 20.5 162.6 2.9 970 2.1
Leisure and hospitality.................. 6.9 123.7 2.5 544 2.1
Other services........................... 8.6 41.3 2.7 822 2.9
Government................................. 0.6 162.1 1.4 1,267 5.0
Miami-Dade, FL............................... 95.2 1,082.5 3.3 1,008 2.5
Private industry........................... 94.9 946.2 4.0 986 2.4
Natural resources and mining............. 0.5 9.3 0.2 593 8.2
Construction............................. 5.5 37.8 11.1 986 4.4
Manufacturing............................ 2.7 37.5 2.2 989 3.9
Trade, transportation, and utilities..... 27.5 284.2 3.4 884 -0.6
Information.............................. 1.6 18.3 0.5 1,532 3.0
Financial activities..................... 10.0 73.8 5.1 1,583 4.4
Professional and business services....... 20.0 147.7 5.3 1,312 2.3
Education and health services............ 10.1 164.7 2.4 980 4.0
Leisure and hospitality.................. 7.2 132.1 3.4 569 2.5
Other services........................... 8.2 38.8 4.5 616 2.8
Government................................. 0.3 136.3 -1.2 1,162 4.5
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Counties selected are based on 2013 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
fourth quarter 2014
Employment Average weekly
wage(1)
Establishments,
fourth quarter
State 2014 Percent Percent
(thousands) December change, Fourth change,
2014 December quarter fourth
(thousands) 2013-14 2014 quarter
2013-14
United States(2)........... 9,479.2 139,204.8 2.2 $1,035 3.5
Alabama.................... 118.0 1,891.4 1.3 881 3.5
Alaska..................... 22.4 317.6 0.8 1,063 4.0
Arizona.................... 148.9 2,630.8 2.2 926 2.3
Arkansas................... 87.4 1,180.5 2.2 807 4.5
California................. 1,403.6 16,068.5 2.6 1,209 2.9
Colorado................... 180.7 2,478.0 3.9 1,066 4.1
Connecticut................ 114.7 1,681.2 1.2 1,278 2.7
Delaware................... 30.0 433.0 2.9 1,049 1.5
District of Columbia....... 36.8 736.9 0.9 1,696 3.7
Florida.................... 649.0 8,009.6 3.5 911 3.1
Georgia.................... 286.3 4,131.9 3.7 958 3.8
Hawaii..................... 39.3 638.3 0.7 908 4.2
Idaho...................... 55.2 650.7 2.5 782 4.0
Illinois................... 421.9 5,844.1 1.4 1,089 2.8
Indiana.................... 159.2 2,946.5 1.7 846 3.9
Iowa....................... 100.3 1,527.6 1.1 870 4.3
Kansas..................... 86.2 1,377.2 1.3 855 2.6
Kentucky................... 121.6 1,852.2 1.8 836 4.1
Louisiana.................. 126.5 1,954.0 2.1 923 3.8
Maine...................... 49.6 592.7 0.9 826 5.1
Maryland................... 166.1 2,590.3 1.3 1,113 3.5
Massachusetts.............. 236.1 3,415.6 2.2 1,315 4.5
Michigan................... 237.6 4,158.9 2.1 984 3.3
Minnesota.................. 167.0 2,762.9 1.4 1,024 3.6
Mississippi................ 72.1 1,118.6 1.0 747 2.3
Missouri................... 187.9 2,709.8 1.5 891 3.4
Montana.................... 44.5 442.2 0.5 794 4.5
Nebraska................... 70.4 958.1 1.4 837 5.2
Nevada..................... 77.2 1,229.6 4.2 899 1.6
New Hampshire.............. 50.8 638.0 1.4 1,081 6.3
New Jersey................. 266.1 3,933.6 1.3 1,211 2.0
New Mexico................. 56.1 808.4 1.3 850 4.4
New York................... 627.6 9,067.6 2.0 1,321 4.3
North Carolina............. 261.7 4,141.8 2.4 890 3.4
North Dakota............... 32.1 454.8 4.5 1,050 7.1
Ohio....................... 289.4 5,264.3 1.6 922 3.9
Oklahoma................... 107.9 1,614.3 2.1 876 2.8
Oregon..................... 140.0 1,755.4 3.2 928 3.8
Pennsylvania............... 351.2 5,716.5 1.2 1,013 3.7
Rhode Island............... 36.0 471.5 1.9 1,003 4.5
South Carolina............. 120.1 1,931.4 2.9 817 3.2
South Dakota............... 32.2 412.5 1.3 791 4.2
Tennessee.................. 147.9 2,822.1 2.4 927 3.5
Texas...................... 627.9 11,662.7 3.7 1,070 4.3
Utah....................... 92.6 1,324.2 3.0 872 4.3
Vermont.................... 24.6 311.0 0.7 882 4.1
Virginia................... 237.5 3,691.4 0.6 1,057 2.8
Washington................. 238.1 3,069.7 3.2 1,082 4.5
West Virginia.............. 50.0 712.0 0.1 818 3.3
Wisconsin.................. 167.5 2,789.3 1.3 894 3.4
Wyoming.................... 25.5 283.6 1.5 952 3.9
Puerto Rico................ 49.0 944.2 -1.5 556 0.7
Virgin Islands............. 3.5 38.5 -0.3 746 -1.2
(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
