An official website of the United States government
For release 10:00 a.m. (EDT), Wednesday, March 9, 2016 USDL-16-0462 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Third Quarter 2015 From September 2014 to September 2015, employment increased in 312 of the 342 largest U.S. counties (counties with 75,000 or more jobs in 2014), the U.S. Bureau of Labor Statistics reported today. Williamson, Tenn., had the largest percentage increase, with a gain of 6.5 percent over the year, above the national job growth of 1.9 percent. Within Williamson, the largest employment increase occurred in professional and business services, which gained 2,538 jobs over the year (8.8 percent). Ector, Texas, had the largest over-the-year percentage decrease in employment among the largest counties in the U.S., with a loss of 8.3 percent. Within Ector, natural resources and mining had the largest decrease in employment, with a loss of 3,752 jobs (-28.4 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 2.6 percent over the year, growing to $974 in the third quarter of 2015. Rockland, N.Y., had the largest over-the-year percentage increase in average weekly wages with a gain of 24.9 percent. Within Rockland, an average weekly wage gain of $3,170, or 220.4 percent, in manufacturing made the largest contribution to the county’s increase in average weekly wages. Midland, Texas, experienced the largest percentage decrease in average weekly wages with a loss of 6.7 percent over the year. Within Midland, natural resources and mining had the largest impact on the county’s average weekly wage decline with a decrease of $163 (-8.1 percent) over the year. Table A. Large counties ranked by September 2015 employment, September 2014-15 employment increase, and September 2014-15 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- September 2015 employment | Increase in employment, | Percent increase in employment, (thousands) | September 2014-15 | September 2014-15 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 140,442.2| United States 2,679.6| United States 1.9 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,261.8| Los Angeles, Calif. 93.0| Williamson, Tenn. 6.5 Cook, Ill. 2,535.6| Maricopa, Ariz. 65.4| Utah, Utah 6.3 New York, N.Y. 2,370.4| Dallas, Texas 62.9| Denton, Texas 6.1 Harris, Texas 2,287.6| Orange, Calif. 49.0| Chesterfield, Va. 5.7 Maricopa, Ariz. 1,824.7| New York, N.Y. 48.3| Lee, Fla. 5.5 Dallas, Texas 1,616.8| King, Wash. 42.0| Osceola, Fla. 5.4 Orange, Calif. 1,524.0| Santa Clara, Calif. 39.8| Loudoun, Va. 5.3 San Diego, Calif. 1,384.0| San Diego, Calif. 38.7| San Francisco, Calif. 5.2 King, Wash. 1,292.1| Cook, Ill. 37.8| Clay, Mo. 5.1 Miami-Dade, Fla. 1,076.1| San Francisco, Calif. 34.0| San Mateo, Calif. 5.0 -------------------------------------------------------------------------------------------------------- Large County Employment In September 2015, national employment was 140.4 million (as measured by the QCEW program). Over the year, employment increased 1.9 percent, or 2.7 million. In September 2015, the 342 U.S. counties with 75,000 or more jobs accounted for 72.2 percent of total U.S. employment and 77.3 percent of total wages. These 342 counties had a net job growth of 2.1 million over the year, accounting for 79.6 percent of the overall U.S. employment increase. Williamson, Tenn., had the largest percentage increase in employment (6.5 percent) among the largest U.S. counties. The five counties with the largest increases in employment levels were Los Angeles, Calif.; Maricopa, Ariz.; Dallas, Texas; Orange, Calif.; and New York, N.Y. These counties had a combined over-the-year employment gain of 318,600 jobs, which was 11.9 percent of the overall job increase for the U.S. (See table A.) Employment declined in 24 of the largest counties from September 2014 to September 2015. Ector, Texas, had the largest over-the-year percentage decrease in employment (-8.3 percent). Midland, Texas, had the second largest percentage decrease in employment, followed by Gregg, Texas; Lafayette, La.; and Atlantic, N.J. (See table 1.) Table B. Large counties ranked by third quarter 2015 average weekly wages, third quarter 2014-15 increase in average weekly wages, and third quarter 2014-15 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average third quarter 2015 | wage, third quarter 2014-15 | weekly wage, third | | quarter 2014-15 -------------------------------------------------------------------------------------------------------- | | United States $974| United States $25| United States 2.6 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,090| Rockland, N.Y. $233| Rockland, N.Y. 24.9 San Mateo, Calif. 1,894| Lake, Ill. 136| Lake, Ill. 11.7 New York, N.Y. 1,829| Washington, Ore. 78| Onondaga, N.Y. 6.5 San Francisco, Calif. 1,712| Marin, Calif. 68| Washington, Ore. 6.4 Washington, D.C. 1,667| Santa Clara, Calif. 65| Marin, Calif. 6.1 Arlington, Va. 1,587| San Mateo, Calif. 62| Santa Cruz, Calif. 6.1 Suffolk, Mass. 1,559| Somerset, N.J. 60| Genesee, Mich. 5.6 King, Wash. 1,463| Onondaga, N.Y. 56| Davidson, Tenn. 5.5 Fairfax, Va. 1,462| Davidson, Tenn. 54| Placer, Calif. 5.4 Somerset, N.J. 1,447| Williamson, Tenn. 54| Williamson, Tenn. 5.2 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased to $974, a 2.6 percent increase, during the year ending in the third quarter of 2015. Among the 342 largest counties, 319 had over-the-year increases in average weekly wages. Rockland, N.Y., had the largest percentage wage increase among the largest U.S. counties (24.9 percent). Of the 342 largest counties, 20 experienced over-the-year decreases in average weekly wages. Midland, Texas, had the largest percentage decrease in average weekly wages, with a loss of 6.7 percent. Ector, Texas, had the second largest percentage decrease in average weekly wages, followed by Lafayette, La.; Stark, Ohio; and Gregg, Texas. (See table 1.) Ten Largest U.S. Counties All of the 10 largest counties had over-the-year percentage increases in employment in September 2015. Dallas, Texas, had the largest gain (4.0 percent). Within Dallas, trade, transportation, and utilities had the largest over-the-year employment level increase, with a gain of 17,638 jobs, or 5.6 percent. Harris, Texas, had the smallest percentage increase in employment (0.8 percent) among the 10 largest counties. (See table 2.) Average weekly wages increased over the year in all of the 10 largest U.S. counties. San Diego, Calif., experienced the largest percentage gain in average weekly wages (4.2 percent). Within San Diego, professional and business services had the largest impact on the county’s average weekly wage growth. Within this industry, average weekly wages increased by $120, or 8.4 percent, over the year. Harris, Texas, had the smallest percentage gain in average weekly wages (0.1 percent) among the 10 largest counties. For More Information The tables included in this release contain data for the nation and for the 342 U.S. counties with annual average employment levels of 75,000 or more in 2014. September 2015 employment and 2015 third 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 9.6 million employer reports cover 140.4 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 third quarter of 2015 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 fourth quarter 2015 is scheduled to be released on Wednesday, June 8, 2016. ---------------------------------------------------------------------------------------------------------- | | | Census Area Name Change Effective with BLS Release of Data for Fourth Quarter of 2015 | | | | On July 1, 2015, Wade Hampton, Alaska, was officially renamed Kusilvak, Alaska. This census area is | | not part of this release because it has fewer than 75,000 jobs. However, BLS does publish data for this | | census area. This name change is not reflected in this quarter’s data release. The census area name | | change will be implemented by BLS with the fourth quarter 2015 news release. The name change will | | also be retroactively implemented for the third quarter data. Data prior to third quarter 2015 will | | still be available under Wade Hampton, Alaska. | | | ----------------------------------------------------------------------------------------------------------
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 2015 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 343 counties presented in this release were derived using 2014 preliminary annual averages of employment. For 2015 data, three counties have been added to the publication tables: Butte, Calif.; Hall, Ga.; and Ector, Texas. These counties will be included in all 2015 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- | 623,000 establish- | submitted by 9.5 | ministrative records| ments | million establish- | submitted by 7.6 | | ments in first | million private-sec-| | quarter of 2015 | 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| -7 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 | | | --------------------------------------------------------------------------------- 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 9.4 million employer reports of employment and wages submitted by states to the BLS in 2014. 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 2014, UI and UCFE programs covered workers in 136.6 million jobs. The estimated 131.8 million workers in these jobs (after adjustment for multiple jobholders) represented 96.3 percent of civilian wage and salary employment. Covered workers received $7.017 trillion in pay, representing 93.8 percent of the wage and salary component of personal income and 40.5 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 2014 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 343 largest counties, third quarter 2015 Employment Average weekly wage(2) Establishments, County(1) third quarter Percent Ranking Percent Ranking 2015 September change, by Third change, by (thousands) 2015 September percent quarter third percent (thousands) 2014-15(3) change 2015 quarter change 2014-15(3) United States(4)......... 9,633.8 140,442.2 1.9 - $974 2.6 - Jefferson, AL............ 17.8 338.3 0.7 266 962 0.8 311 Madison, AL.............. 9.2 186.5 1.7 186 1,053 1.5 265 Mobile, AL............... 9.7 166.6 0.4 292 835 1.7 253 Montgomery, AL........... 6.3 128.7 0.7 266 819 1.0 300 Shelby, AL............... 5.5 83.6 1.0 231 914 4.2 33 Tuscaloosa, AL........... 4.4 92.2 1.6 192 811 -0.4 326 Anchorage Borough, AK.... 8.4 156.9 0.5 284 1,084 1.6 259 Maricopa, AZ............. 95.7 1,824.7 3.7 45 929 1.4 274 Pima, AZ................. 19.1 354.7 0.6 276 816 0.0 320 Benton, AR............... 5.9 111.7 4.8 12 949 2.4 178 Pulaski, AR.............. 14.5 247.2 1.7 186 869 2.1 215 Washington, AR........... 5.8 101.3 4.2 22 786 2.3 193 Alameda, CA.............. 59.6 734.5 3.3 71 1,289 2.3 193 Butte, CA................ 8.0 79.1 1.8 172 731 3.8 53 Contra Costa, CA......... 30.9 349.0 2.4 128 1,168 3.2 91 Fresno, CA............... 32.5 375.9 2.2 143 772 3.3 82 Kern, CA................. 17.7 323.2 -0.5 325 828 -0.5 328 Los Angeles, CA.......... 457.6 4,261.8 2.2 143 1,074 3.7 60 Marin, CA................ 12.3 112.3 2.6 117 1,185 6.1 5 Monterey, CA............. 13.2 202.3 2.3 137 825 3.3 82 Orange, CA............... 112.3 1,524.0 3.3 71 1,077 2.1 215 Placer, CA............... 12.0 149.9 3.9 39 989 5.4 9 Riverside, CA............ 57.0 659.5 4.8 12 781 3.0 117 Sacramento, CA........... 54.3 629.7 2.8 104 1,068 1.6 259 San Bernardino, CA....... 53.8 686.9 3.2 81 815 3.0 117 San Diego, CA............ 104.5 1,384.0 2.9 101 1,071 4.2 33 San Francisco, CA........ 59.2 684.1 5.2 8 1,712 1.4 274 San Joaquin, CA.......... 17.1 238.6 4.2 22 834 4.0 44 San Luis Obispo, CA...... 10.1 114.6 2.9 101 814 4.1 37 San Mateo, CA............ 27.1 387.8 5.0 10 1,894 3.4 77 Santa Barbara, CA........ 14.9 197.6 1.6 192 934 3.9 47 Santa Clara, CA.......... 68.6 1,026.6 4.0 32 2,090 3.2 91 Santa Cruz, CA........... 9.4 103.4 2.3 137 888 6.1 5 Solano, CA............... 10.6 132.7 3.5 58 981 2.4 178 Sonoma, CA............... 19.3 200.6 2.8 104 935 4.7 19 Stanislaus, CA........... 14.8 183.5 3.2 81 840 4.3 30 Tulare, CA............... 9.5 158.9 1.7 186 685 3.6 64 Ventura, CA.............. 25.5 313.4 1.5 204 961 1.6 259 Yolo, CA................. 6.4 102.8 1.1 227 1,006 3.1 104 Adams, CO................ 10.1 194.1 4.1 25 952 3.0 117 Arapahoe, CO............. 21.0 318.6 2.9 101 1,117 1.3 279 Boulder, CO.............. 14.4 173.8 2.7 112 1,158 3.1 104 Denver, CO............... 29.9 483.7 3.6 49 1,194 2.0 228 Douglas, CO.............. 11.2 112.9 3.3 71 1,033 -1.0 333 El Paso, CO.............. 18.2 259.7 3.5 58 876 1.9 241 Jefferson, CO............ 19.2 230.2 2.8 104 992 4.5 25 Larimer, CO.............. 11.3 149.5 3.7 45 892 3.8 53 Weld, CO................. 6.7 101.2 -1.3 331 861 -1.4 335 Fairfield, CT............ 34.7 422.5 0.8 252 1,406 0.4 316 Hartford, CT............. 27.2 506.0 0.4 292 1,142 1.9 241 New Haven, CT............ 23.5 361.0 0.0 313 1,021 3.2 91 New London, CT........... 7.3 122.6 0.2 307 943 1.6 259 New Castle, DE........... 18.8 284.0 1.9 162 1,066 -0.7 332 Washington, DC........... 38.2 743.6 1.4 211 1,667 2.3 193 Alachua, FL.............. 6.8 124.4 1.9 162 805 2.0 228 Brevard, FL.............. 15.0 193.9 1.9 162 873 2.5 165 Broward, FL.............. 66.3 759.7 2.4 128 898 3.3 82 Collier, FL.............. 12.9 128.7 4.0 32 815 1.2 286 Duval, FL................ 27.7 474.0 3.6 49 909 2.0 228 Escambia, FL............. 8.0 126.6 1.5 204 760 3.5 72 Hillsborough, FL......... 39.4 641.6 3.6 49 914 2.0 228 Lake, FL................. 7.6 90.2 4.0 32 680 3.7 60 Lee, FL.................. 20.4 236.2 5.5 5 766 3.1 104 Leon, FL................. 8.3 142.4 0.2 307 795 2.4 178 Manatee, FL.............. 10.0 111.9 4.4 18 740 4.8 13 Marion, FL............... 8.1 96.4 1.3 217 658 2.0 228 Miami-Dade, FL........... 93.2 1,076.1 2.8 104 924 3.9 47 Okaloosa, FL............. 6.2 80.2 2.1 145 816 4.7 19 Orange, FL............... 38.7 765.8 4.0 32 854 4.1 37 Osceola, FL.............. 6.2 85.1 5.4 6 671 2.8 138 Palm Beach, FL........... 52.7 559.3 3.6 49 924 2.2 204 Pasco, FL................ 10.3 109.2 3.1 89 676 4.5 25 Pinellas, FL............. 31.4 407.8 2.8 104 846 2.3 193 Polk, FL................. 12.5 203.5 3.7 45 740 1.5 265 Sarasota, FL............. 15.2 158.1 3.6 49 777 3.2 91 Seminole, FL............. 14.1 174.9 3.6 49 803 3.2 91 Volusia, FL.............. 13.7 160.7 3.0 95 697 5.0 11 Bibb, GA................. 4.5 82.9 1.4 211 760 3.0 117 Chatham, GA.............. 8.4 146.7 3.2 81 821 2.5 165 Clayton, GA.............. 4.4 117.0 3.3 71 912 2.5 165 Cobb, GA................. 23.2 334.6 3.1 89 1,006 2.2 204 DeKalb, GA............... 19.2 291.1 2.5 124 977 3.1 104 Fulton, GA............... 45.8 796.3 3.2 81 1,266 2.3 193 Gwinnett, GA............. 26.2 335.6 2.8 104 962 2.2 204 Hall, GA................. 4.6 80.5 4.3 19 825 3.3 82 Muscogee, GA............. 4.9 93.3 -0.7 328 761 1.9 241 Richmond, GA............. 4.8 104.7 2.1 145 819 2.4 178 Honolulu, HI............. 25.2 462.1 1.1 227 932 3.1 104 Ada, ID.................. 14.1 219.2 4.1 25 841 1.1 294 Champaign, IL............ 4.6 90.3 -0.2 319 877 3.4 77 Cook, IL................. 165.4 2,535.6 1.5 204 1,108 3.4 77 DuPage, IL............... 40.2 603.6 0.6 276 1,121 4.8 13 Kane, IL................. 14.5 209.2 0.5 284 867 4.5 25 Lake, IL................. 23.8 334.6 0.7 266 1,298 11.7 2 McHenry, IL.............. 9.3 97.7 0.3 301 808 2.9 129 McLean, IL............... 4.0 84.7 0.5 284 895 0.3 317 Madison, IL.............. 6.4 98.8 -0.4 324 794 3.1 104 Peoria, IL............... 4.9 100.7 0.7 266 910 3.8 53 St. Clair, IL............ 5.8 93.3 1.0 231 787 2.3 193 Sangamon, IL............. 5.5 129.3 -0.5 325 1,001 1.1 294 Will, IL................. 17.1 224.7 1.9 162 858 2.4 178 Winnebago, IL............ 7.1 128.1 0.0 313 811 1.8 247 Allen, IN................ 8.7 183.8 2.4 128 796 2.7 147 Elkhart, IN.............. 4.7 125.0 3.2 81 790 1.2 286 Hamilton, IN............. 8.9 134.8 3.9 39 913 2.6 154 Lake, IN................. 10.3 187.8 0.2 307 841 -0.6 329 Marion, IN............... 23.8 586.7 1.8 172 966 1.7 253 St. Joseph, IN........... 5.8 122.4 3.1 89 795 2.3 193 Tippecanoe, IN........... 3.3 82.1 0.9 243 831 3.6 64 Vanderburgh, IN.......... 4.7 106.4 0.5 284 786 3.6 64 Black Hawk, IA........... 3.9 74.0 -2.5 335 808 0.9 305 Johnson, IA.............. 4.1 82.1 1.0 231 920 3.0 117 Linn, IA................. 6.6 129.4 0.6 276 929 1.5 265 Polk, IA................. 16.8 289.6 0.8 252 981 2.4 178 Scott, IA................ 5.5 91.6 0.8 252 799 4.7 19 Johnson, KS.............. 22.4 334.6 1.9 162 968 1.3 279 Sedgwick, KS............. 12.7 247.5 1.0 231 831 0.8 311 Shawnee, KS.............. 5.0 97.5 0.5 284 783 2.1 215 Wyandotte, KS............ 3.4 90.4 2.3 137 942 3.2 91 Boone, KY................ 4.3 81.9 4.3 19 826 2.6 154 Fayette, KY.............. 10.6 190.8 2.6 117 879 4.4 29 Jefferson, KY............ 24.8 453.3 1.6 192 933 3.9 47 Caddo, LA................ 7.3 114.9 0.4 292 799 0.6 314 Calcasieu, LA............ 5.0 92.0 3.3 71 881 0.9 305 East Baton Rouge, LA..... 14.9 270.6 0.5 284 914 3.3 82 Jefferson, LA............ 13.6 192.5 -0.2 319 876 1.9 241 Lafayette, LA............ 9.3 136.7 -3.9 337 919 -3.2 339 Orleans, LA.............. 12.1 190.0 3.2 81 922 -0.2 322 St. Tammany, LA.......... 7.8 86.2 3.8 42 833 1.8 247 Cumberland, ME........... 13.1 176.9 1.0 231 857 3.1 104 Anne Arundel, MD......... 15.0 261.8 2.4 128 1,048 2.9 129 Baltimore, MD............ 21.2 371.9 1.2 221 980 1.9 241 Frederick, MD............ 6.4 99.4 2.4 128 911 0.9 305 Harford, MD.............. 5.7 90.7 1.4 211 923 2.6 154 Howard, MD............... 9.9 166.0 1.8 172 1,181 -1.3 334 Montgomery, MD........... 32.7 461.1 0.9 243 1,277 2.7 147 Prince George's, MD...... 15.7 308.6 1.0 231 1,058 2.1 215 Baltimore City, MD....... 13.6 335.1 -0.2 319 1,152 2.3 193 Barnstable, MA........... 9.3 101.0 1.7 186 808 3.3 82 Bristol, MA.............. 17.0 221.9 0.3 301 882 5.0 11 Essex, MA................ 23.7 320.9 1.0 231 1,009 0.8 311 Hampden, MA.............. 17.2 204.1 0.8 252 884 2.9 129 Middlesex, MA............ 53.3 874.9 2.0 151 1,419 2.6 154 Norfolk, MA.............. 24.7 344.2 1.4 211 1,112 2.5 165 Plymouth, MA............. 15.1 188.3 0.8 252 909 4.0 44 Suffolk, MA.............. 27.5 639.1 2.0 151 1,559 3.1 104 Worcester, MA............ 23.8 334.7 0.8 252 969 3.2 91 Genesee, MI.............. 7.0 132.7 0.0 313 816 5.6 7 Ingham, MI............... 6.0 147.3 0.4 292 898 1.8 247 Kalamazoo, MI............ 5.1 114.7 0.8 252 898 2.4 178 Kent, MI................. 14.1 373.7 3.3 71 875 3.6 64 Macomb, MI............... 17.5 317.3 2.4 128 950 1.1 294 Oakland, MI.............. 38.8 709.0 1.6 192 1,061 3.0 117 Ottawa, MI............... 5.6 122.8 3.1 89 818 2.0 228 Saginaw, MI.............. 4.0 84.8 1.0 231 777 2.4 178 Washtenaw, MI............ 8.2 202.0 0.9 243 1,052 2.7 147 Wayne, MI................ 30.6 700.9 0.9 243 1,059 3.1 104 Anoka, MN................ 6.6 119.3 0.7 266 968 3.5 72 Dakota, MN............... 9.3 184.0 0.3 301 944 2.8 138 Hennepin, MN............. 37.1 888.5 2.0 151 1,198 2.0 228 Olmsted, MN.............. 3.2 94.2 1.7 186 1,113 3.4 77 Ramsey, MN............... 12.7 330.0 1.2 221 1,073 1.7 253 St. Louis, MN............ 5.1 97.7 -0.2 319 836 1.6 259 Stearns, MN.............. 4.1 84.9 0.2 307 825 4.8 13 Washington, MN........... 5.2 79.4 2.8 104 810 3.6 64 Harrison, MS............. 4.4 83.2 0.6 276 701 1.2 286 Hinds, MS................ 5.9 120.0 0.8 252 832 2.2 204 Boone, MO................ 4.9 92.5 1.8 172 795 3.7 60 Clay, MO................. 5.4 99.2 5.1 9 856 3.0 117 Greene, MO............... 8.5 162.0 1.1 227 753 3.9 47 Jackson, MO.............. 21.0 358.0 2.0 151 989 2.6 154 St. Charles, MO.......... 8.9 141.2 4.8 12 774 1.2 286 St. Louis, MO............ 35.8 593.3 1.6 192 1,004 0.9 305 St. Louis City, MO....... 12.6 228.3 1.9 162 1,045 1.6 259 Yellowstone, MT.......... 6.4 81.5 2.4 128 845 4.7 19 Douglas, NE.............. 18.9 333.2 2.1 145 928 4.7 19 Lancaster, NE............ 10.2 167.3 1.6 192 797 3.8 53 Clark, NV................ 53.9 913.4 3.5 58 843 2.4 178 Washoe, NV............... 14.4 205.1 4.3 19 877 2.5 165 Hillsborough, NH......... 12.3 197.5 1.3 217 1,031 1.5 265 Rockingham, NH........... 10.8 146.0 2.7 112 938 2.2 204 Atlantic, NJ............. 6.5 127.8 -2.8 336 814 2.5 165 Bergen, NJ............... 32.8 444.0 0.6 276 1,135 2.9 129 Burlington, NJ........... 10.9 197.9 1.1 227 994 3.0 117 Camden, NJ............... 11.9 198.5 2.0 151 943 4.8 13 Essex, NJ................ 20.1 333.1 0.6 276 1,177 2.0 228 Gloucester, NJ........... 6.2 103.2 1.8 172 835 2.1 215 Hudson, NJ............... 14.2 244.8 3.2 81 1,280 1.0 300 Mercer, NJ............... 11.1 239.9 2.7 112 1,234 1.3 279 Middlesex, NJ............ 21.8 404.6 1.0 231 1,142 2.1 215 Monmouth, NJ............. 19.9 255.5 2.6 117 932 1.7 253 Morris, NJ............... 16.8 286.4 1.6 192 1,380 2.4 178 Ocean, NJ................ 12.8 163.4 2.7 112 766 1.7 253 Passaic, NJ.............. 12.3 164.1 -0.6 327 944 2.6 154 Somerset, NJ............. 10.0 182.4 1.2 221 1,447 4.3 30 Union, NJ................ 14.2 217.0 (5) - 1,188 (5) - Bernalillo, NM........... 18.1 320.1 1.2 221 842 1.3 279 Albany, NY............... 10.5 228.9 0.5 284 1,038 3.1 104 Bronx, NY................ 18.7 297.9 0.3 301 938 2.4 178 Broome, NY............... 4.6 86.6 -1.6 333 753 2.2 204 Dutchess, NY............. 8.5 111.4 1.6 192 926 -0.6 329 Erie, NY................. 24.8 466.1 0.7 266 856 2.4 178 Kings, NY................ 60.5 658.2 3.4 66 835 2.1 215 Monroe, NY............... 18.8 379.9 0.9 243 929 2.5 165 Nassau, NY............... 54.2 615.4 1.4 211 1,063 3.6 64 New York, NY............. 129.9 2,370.4 2.1 145 1,829 2.5 165 Oneida, NY............... 5.4 104.5 0.7 266 740 -0.3 323 Onondaga, NY............. 13.1 243.9 0.4 292 913 6.5 3 Orange, NY............... 10.3 139.7 0.9 243 807 3.9 47 Queens, NY............... 51.6 637.6 3.5 58 930 3.3 82 Richmond, NY............. 9.8 113.1 2.0 151 877 4.3 30 Rockland, NY............. 10.5 119.7 2.6 117 1,168 24.9 1 Saratoga, NY............. 5.9 83.4 2.4 128 864 2.0 228 Suffolk, NY.............. 52.7 651.7 1.6 192 1,053 2.1 215 Westchester, NY.......... 36.7 421.4 2.0 151 1,222 1.3 279 Buncombe, NC............. 8.6 125.2 3.4 66 760 4.0 44 Catawba, NC.............. 4.3 83.5 1.8 172 746 4.2 33 Cumberland, NC........... 6.3 116.8 0.1 311 767 2.7 147 Durham, NC............... 8.0 190.5 1.7 186 1,231 2.9 129 Forsyth, NC.............. 9.4 181.9 1.8 172 886 -0.3 323 Guilford, NC............. 14.3 277.3 2.5 124 857 1.5 265 Mecklenburg, NC.......... 35.5 638.2 3.7 45 1,119 4.2 33 New Hanover, NC.......... 7.7 107.7 3.8 42 769 2.8 138 Wake, NC................. 32.0 514.6 4.0 32 983 2.5 165 Cass, ND................. 6.9 116.9 0.8 252 910 1.3 279 Butler, OH............... 7.6 146.4 2.3 137 850 1.8 247 Cuyahoga, OH............. 35.5 713.1 0.7 266 985 1.1 294 Delaware, OH............. 4.9 84.8 0.8 252 929 1.0 300 Franklin, OH............. 30.7 722.9 2.1 145 982 3.5 72 Hamilton, OH............. 23.4 509.0 1.9 162 1,055 2.2 204 Lake, OH................. 6.2 94.7 0.3 301 795 1.5 265 Lorain, OH............... 6.1 96.4 -0.9 329 775 1.4 274 Lucas, OH................ 10.1 208.7 1.8 172 839 1.2 286 Mahoning, OH............. 5.9 98.0 -1.6 333 707 3.8 53 Montgomery, OH........... 11.9 249.6 1.4 211 837 3.0 117 Stark, OH................ 8.6 158.4 0.3 301 740 -2.1 338 Summit, OH............... 14.1 264.7 0.6 276 876 3.2 91 Warren, OH............... 4.7 88.6 3.1 89 854 3.4 77 Cleveland, OK............ 5.5 82.0 2.0 151 719 1.1 294 Oklahoma, OK............. 27.2 451.6 1.0 231 934 -1.4 335 Tulsa, OK................ 22.1 347.9 0.8 252 904 1.2 286 Clackamas, OR............ 14.1 153.0 3.0 95 926 4.8 13 Jackson, OR.............. 7.0 84.8 3.5 58 764 4.1 37 Lane, OR................. 11.7 147.7 2.6 117 775 2.9 129 Marion, OR............... 10.1 150.2 3.6 49 788 3.7 60 Multnomah, OR............ 32.7 481.4 3.4 66 1,006 2.9 129 Washington, OR........... 18.3 275.3 2.5 124 1,288 6.4 4 Allegheny, PA............ 35.5 687.5 0.4 292 1,051 2.6 154 Berks, PA................ 8.9 171.3 1.8 172 866 1.5 265 Bucks, PA................ 19.8 255.6 0.7 266 909 1.8 247 Butler, PA............... 5.0 85.6 0.4 292 920 3.5 72 Chester, PA.............. 15.3 244.7 1.2 221 1,208 4.1 37 Cumberland, PA........... 6.3 131.2 2.7 112 883 2.0 228 Dauphin, PA.............. 7.4 178.0 0.5 284 962 2.4 178 Delaware, PA............. 13.9 218.0 0.8 252 1,010 2.0 228 Erie, PA................. 7.1 126.0 0.9 243 775 2.8 138 Lackawanna, PA........... 5.8 97.3 -0.2 319 749 1.9 241 Lancaster, PA............ 13.2 230.9 2.0 151 815 3.2 91 Lehigh, PA............... 8.6 185.2 1.5 204 938 1.4 274 Luzerne, PA.............. 7.5 142.9 -0.1 317 779 2.6 154 Montgomery, PA........... 27.4 479.5 1.8 172 1,158 2.0 228 Northampton, PA.......... 6.7 108.5 1.9 162 849 3.2 91 Philadelphia, PA......... 34.9 651.7 0.9 243 1,160 3.0 117 Washington, PA........... 5.5 87.3 -1.3 331 948 0.9 305 Westmoreland, PA......... 9.3 134.1 0.6 276 785 2.1 215 York, PA................. 9.0 176.2 2.0 151 849 3.2 91 Providence, RI........... 17.5 283.8 1.0 231 961 2.6 154 Charleston, SC........... 13.8 235.9 3.4 66 873 4.1 37 Greenville, SC........... 13.8 257.7 3.5 58 859 2.4 178 Horry, SC................ 8.6 121.1 3.0 95 598 3.6 64 Lexington, SC............ 6.4 112.8 4.1 25 741 2.1 215 Richland, SC............. 9.6 214.1 2.1 145 833 2.3 193 Spartanburg, SC.......... 6.0 128.1 3.0 95 814 2.8 138 York, SC................. 5.1 84.9 4.1 25 763 0.5 315 Minnehaha, SD............ 7.0 123.5 1.5 204 850 3.3 82 Davidson, TN............. 20.7 459.2 3.3 71 1,030 5.5 8 Hamilton, TN............. 9.2 194.7 3.0 95 865 4.5 25 Knox, TN................. 11.6 233.2 1.6 192 834 2.3 193 Rutherford, TN........... 5.1 117.2 3.9 39 843 2.1 215 Shelby, TN............... 19.9 483.8 1.5 204 979 1.5 265 Williamson, TN........... 7.8 116.9 6.5 1 1,101 5.2 10 Bell, TX................. 5.1 116.2 4.2 22 823 2.6 154 Bexar, TX................ 38.2 821.4 3.3 71 874 2.2 204 Brazoria, TX............. 5.4 103.4 4.0 32 992 2.8 138 Brazos, TX............... 4.3 99.8 4.5 16 734 -0.4 326 Cameron, TX.............. 6.4 135.7 1.2 221 615 2.2 204 Collin, TX............... 22.4 366.9 4.9 11 1,126 2.5 165 Dallas, TX............... 73.2 1,616.8 4.0 32 1,157 1.4 274 Denton, TX............... 13.4 221.4 6.1 3 885 3.0 117 Ector, TX................ 4.0 72.0 -8.3 340 1,037 -4.9 340 El Paso, TX.............. 14.6 292.0 3.1 89 698 2.6 154 Fort Bend, TX............ 11.9 170.6 3.6 49 949 -0.3 323 Galveston, TX............ 5.8 102.8 3.5 58 853 3.5 72 Gregg, TX................ 4.3 76.1 -4.2 338 846 -1.5 337 Harris, TX............... 111.2 2,287.6 0.8 252 1,240 0.1 319 Hidalgo, TX.............. 11.9 243.9 2.5 124 624 1.0 300 Jefferson, TX............ 5.9 123.1 0.4 292 1,003 2.7 147 Lubbock, TX.............. 7.4 135.0 2.4 128 779 2.1 215 McLennan, TX............. 5.1 108.1 1.9 162 792 2.2 204 Midland, TX.............. 5.4 86.8 -7.3 339 1,177 -6.7 341 Montgomery, TX........... 10.5 165.3 3.2 81 957 0.0 320 Nueces, TX............... 8.2 163.0 0.8 252 861 1.2 286 Potter, TX............... 4.0 79.1 1.6 192 804 0.2 318 Smith, TX................ 6.1 100.2 4.1 25 810 -0.6 329 Tarrant, TX.............. 41.1 844.9 2.6 117 967 2.5 165 Travis, TX............... 37.3 692.4 4.6 15 1,122 3.9 47 Webb, TX................. 5.1 97.7 2.6 117 658 0.9 305 Williamson, TX........... 9.5 150.8 4.5 16 937 1.7 253 Davis, UT................ 8.0 119.8 3.5 58 785 2.7 147 Salt Lake, UT............ 42.4 649.8 3.6 49 933 4.1 37 Utah, UT................. 14.6 211.7 6.3 2 767 2.8 138 Weber, UT................ 5.8 98.7 3.3 71 744 3.2 91 Chittenden, VT........... 6.6 101.7 0.9 243 928 1.8 247 Arlington, VA............ 9.4 171.3 3.0 95 1,587 1.5 265 Chesterfield, VA......... 8.6 131.8 5.7 4 833 1.1 294 Fairfax, VA.............. 37.1 589.0 2.0 151 1,462 1.2 286 Henrico, VA.............. 11.2 188.3 4.1 25 945 2.5 165 Loudoun, VA.............. 11.6 156.0 5.3 7 1,126 2.0 228 Prince William, VA....... 9.1 123.3 4.1 25 860 2.1 215 Alexandria City, VA...... 6.7 96.4 1.6 192 1,372 2.2 204 Chesapeake City, VA...... 6.0 97.3 0.4 292 766 2.8 138 Newport News City, VA.... 3.8 97.3 -0.1 317 957 3.1 104 Norfolk City, VA......... 5.8 140.1 0.1 311 1,002 2.9 129 Richmond City, VA........ 7.5 150.9 1.8 172 1,089 4.1 37 Virginia Beach City, VA.. 11.9 174.0 1.5 204 767 2.5 165 Benton, WA............... 5.6 84.5 3.3 71 965 3.1 104 Clark, WA................ 13.9 147.9 3.8 42 915 3.0 117 King, WA................. 84.3 1,292.1 3.4 66 1,463 1.0 300 Kitsap, WA............... 6.6 85.6 2.3 137 921 2.4 178 Pierce, WA............... 21.5 288.5 1.9 162 898 3.6 64 Snohomish, WA............ 20.1 277.8 2.8 104 1,050 3.2 91 Spokane, WA.............. 15.5 211.6 1.8 172 842 2.3 193 Thurston, WA............. 7.8 107.1 2.3 137 919 4.8 13 Whatcom, WA.............. 7.1 84.9 1.8 172 801 2.7 147 Yakima, WA............... 7.7 121.3 (5) - 679 2.9 129 Kanawha, WV.............. 5.9 102.6 -1.2 330 839 1.3 279 Brown, WI................ 6.7 152.3 1.0 231 856 3.8 53 Dane, WI................. 14.7 322.8 1.8 172 938 4.6 24 Milwaukee, WI............ 25.7 484.9 0.0 313 925 2.8 138 Outagamie, WI............ 5.1 105.4 1.3 217 835 3.3 82 Waukesha, WI............. 12.6 237.0 1.3 217 953 3.8 53 Winnebago, WI............ 3.7 90.6 0.7 266 888 3.1 104 San Juan, PR............. 10.6 250.4 0.4 (6) 614 1.7 (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 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) 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 342 U.S. counties comprise 72.2 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, third quarter 2015 Employment Average weekly wage(1) Establishments, third quarter County by NAICS supersector 2015 Percent Percent (thousands) September change, Third change, 2015 September quarter third (thousands) 2014-15(2) 2015 quarter 2014-15(2) United States(3) ............................ 9,633.8 140,442.2 1.9 $974 2.6 Private industry........................... 9,335.3 119,146.1 2.2 965 2.7 Natural resources and mining............. 138.2 2,071.5 -6.3 1,024 -4.7 Construction............................. 770.3 6,645.3 4.0 1,084 4.2 Manufacturing............................ 343.0 12,338.5 0.7 1,170 1.8 Trade, transportation, and utilities..... 1,927.7 26,664.0 2.1 823 2.6 Information.............................. 154.0 2,743.1 1.1 1,783 2.7 Financial activities..................... 849.6 7,846.0 1.9 1,444 3.7 Professional and business services....... 1,739.8 19,704.3 2.3 1,241 3.0 Education and health services............ 1,539.9 21,123.8 2.3 905 2.7 Leisure and hospitality.................. 810.2 15,377.8 3.0 413 3.5 Other services........................... 829.3 4,303.4 1.4 666 3.7 Government................................. 298.5 21,296.1 0.5 1,029 2.5 Los Angeles, CA.............................. 457.6 4,261.8 2.2 1,074 3.7 Private industry........................... 451.6 3,707.3 2.3 1,035 3.4 Natural resources and mining............. 0.5 8.6 -2.1 1,344 -0.7 Construction............................. 13.6 127.9 6.1 1,129 6.1 Manufacturing............................ 12.4 357.2 -1.5 1,167 2.8 Trade, transportation, and utilities..... 53.7 803.0 1.8 876 2.5 Information.............................. 9.8 202.6 0.9 1,957 5.8 Financial activities..................... 24.9 212.8 1.3 1,717 4.0 Professional and business services....... 48.0 595.8 0.8 1,317 5.5 Education and health services............ 211.3 731.3 2.7 821 2.5 Leisure and hospitality.................. 31.6 488.8 2.9 591 2.6 Other services........................... 27.8 147.2 0.9 691 5.8 Government................................. 6.0 554.4 1.5 1,348 5.2 New York, NY................................. 129.9 2,370.4 2.1 1,829 2.5 Private industry........................... 129.0 2,107.5 2.2 1,892 2.4 Natural resources and mining............. 0.0 0.2 -4.3 1,928 -40.9 Construction............................. 2.2 38.0 5.4 1,789 4.7 Manufacturing............................ 2.2 27.3 2.1 1,346 7.4 Trade, transportation, and utilities..... 20.2 257.3 -0.9 1,276 1.4 Information.............................. 4.9 151.6 0.6 2,571 7.6 Financial activities..................... 19.2 367.7 1.6 3,292 0.2 Professional and business services....... 27.4 542.5 3.3 2,122 2.6 Education and health services............ 9.8 326.8 1.8 1,309 3.9 Leisure and hospitality.................. 13.7 284.4 2.3 854 4.0 Other services........................... 20.4 99.7 0.7 1,098 4.6 Government................................. 0.8 263.0 1.2 1,312 1.8 Cook, IL..................................... 165.4 2,535.6 1.5 1,108 3.4 Private industry........................... 164.1 2,240.3 1.7 1,111 3.6 Natural resources and mining............. 0.1 1.1 18.4 1,222 10.3 Construction............................. 13.6 73.3 2.4 1,430 3.4 Manufacturing............................ 6.8 186.6 0.4 1,155 1.9 Trade, transportation, and utilities..... 32.5 467.5 1.9 898 3.2 Information.............................. 2.8 55.0 2.2 1,640 0.8 Financial activities..................... 16.5 188.0 0.6 1,923 4.6 Professional and business services....... 35.2 465.6 1.3 1,425 5.4 Education and health services............ 17.1 432.6 1.5 939 1.8 Leisure and hospitality.................. 15.0 270.1 3.1 512 6.7 Other services........................... 18.8 95.0 -0.1 876 3.7 Government................................. 1.3 295.3 0.5 1,084 0.6 Harris, TX................................... 111.2 2,287.6 0.8 1,240 0.1 Private industry........................... 110.7 2,023.3 0.6 1,252 -0.2 Natural resources and mining............. 1.8 84.1 -11.5 2,990 -3.4 Construction............................. 7.1 164.9 3.2 1,302 3.7 Manufacturing............................ 4.8 185.5 -7.3 1,459 -2.1 Trade, transportation, and utilities..... 24.9 474.9 1.7 1,109 0.5 Information.............................. 1.2 27.4 0.5 1,393 1.0 Financial activities..................... 11.5 120.4 0.6 1,570 4.7 Professional and business services....... 22.5 394.5 -0.6 1,527 1.3 Education and health services............ 15.3 283.0 4.7 1,017 4.6 Leisure and hospitality.................. 9.4 222.4 5.3 440 5.0 Other services........................... 11.7 65.3 2.6 800 6.4 Government................................. 0.6 264.3 2.3 1,147 2.5 Maricopa, AZ................................. 95.7 1,824.7 3.7 929 1.4 Private industry........................... 95.0 1,614.6 4.2 922 1.4 Natural resources and mining............. 0.4 7.5 7.0 926 0.2 Construction............................. 7.1 97.1 3.6 966 2.8 Manufacturing............................ 3.2 115.8 0.5 1,307 0.0 Trade, transportation, and utilities..... 19.7 361.0 3.8 856 2.9 Information.............................. 1.5 34.2 2.8 1,226 -0.6 Financial activities..................... 11.0 160.6 4.9 1,190 4.1 Professional and business services....... 21.7 307.4 3.5 1,002 0.2 Education and health services............ 10.8 273.5 4.1 948 1.4 Leisure and hospitality.................. 7.5 199.3 4.4 435 0.5 Other services........................... 6.1 49.0 1.9 670 3.4 Government................................. 0.7 210.2 0.4 989 1.7 Dallas, TX................................... 73.2 1,616.8 4.0 1,157 1.4 Private industry........................... 72.7 1,445.7 4.2 1,161 1.5 Natural resources and mining............. 0.6 9.2 -2.8 3,478 -9.2 Construction............................. 4.2 82.0 6.2 1,141 5.0 Manufacturing............................ 2.7 105.9 -0.4 1,256 -0.6 Trade, transportation, and utilities..... 15.8 330.0 5.6 1,051 2.1 Information.............................. 1.4 48.5 1.0 1,752 2.0 Financial activities..................... 8.9 157.7 2.7 1,563 2.2 Professional and business services....... 16.4 328.5 4.5 1,338 3.4 Education and health services............ 9.0 187.8 4.2 1,048 1.4 Leisure and hospitality.................. 6.3 154.4 6.6 477 -0.6 Other services........................... 6.9 41.0 2.0 760 -0.8 Government................................. 0.5 171.1 2.4 1,126 1.1 Orange, CA................................... 112.3 1,524.0 3.3 1,077 2.1 Private industry........................... 110.9 1,384.4 3.3 1,062 2.0 Natural resources and mining............. 0.2 3.0 -8.9 831 1.7 Construction............................. 6.6 92.1 8.9 1,221 4.6 Manufacturing............................ 4.9 155.4 0.1 1,321 1.9 Trade, transportation, and utilities..... 16.8 255.4 1.2 943 1.3 Information.............................. 1.3 25.0 0.6 1,654 1.2 Financial activities..................... 10.9 117.0 3.6 1,666 5.2 Professional and business services....... 20.5 282.5 1.5 1,284 0.8 Education and health services............ 29.0 193.7 4.2 907 2.6 Leisure and hospitality.................. 8.1 205.0 4.0 468 2.9 Other services........................... 7.0 44.5 2.4 674 4.3 Government................................. 1.4 139.6 3.7 1,237 2.9 San Diego, CA................................ 104.5 1,384.0 2.9 1,071 4.2 Private industry........................... 102.7 1,157.0 3.2 1,031 4.7 Natural resources and mining............. 0.7 9.7 -7.3 630 1.3 Construction............................. 6.5 71.5 9.5 1,115 3.6 Manufacturing............................ 3.1 104.8 2.6 1,404 -0.1 Trade, transportation, and utilities..... 14.3 215.7 1.2 813 3.7 Information.............................. 1.2 23.5 -4.4 1,773 1.6 Financial activities..................... 9.6 70.9 2.9 1,343 6.3 Professional and business services....... 18.2 229.5 2.4 1,541 8.4 Education and health services............ 29.0 187.4 3.5 900 3.1 Leisure and hospitality.................. 7.8 185.3 2.6 482 5.9 Other services........................... 7.4 50.3 1.8 582 2.6 Government................................. 1.8 227.0 1.4 1,287 2.4 King, WA..................................... 84.3 1,292.1 3.4 1,463 1.0 Private industry........................... 83.8 1,129.2 3.4 1,487 0.9 Natural resources and mining............. 0.4 3.1 18.1 1,196 -5.2 Construction............................. 6.2 64.8 7.0 1,263 4.2 Manufacturing............................ 2.4 107.0 -0.1 1,568 1.9 Trade, transportation, and utilities..... 14.6 243.4 4.2 1,186 5.3 Information.............................. 2.1 91.3 3.8 4,798 -5.6 Financial activities..................... 6.5 66.7 1.8 1,556 4.1 Professional and business services....... 16.4 216.6 5.2 1,538 2.1 Education and health services............ 19.4 161.5 (4) 964 (4) Leisure and hospitality.................. 6.9 132.1 4.4 545 6.0 Other services........................... 8.9 42.7 3.0 821 4.5 Government................................. 0.5 162.9 3.2 1,299 2.9 Miami-Dade, FL............................... 93.2 1,076.1 2.8 924 3.9 Private industry........................... 92.8 940.9 3.2 905 3.9 Natural resources and mining............. 0.5 7.0 -6.2 572 0.0 Construction............................. 5.7 40.4 9.3 937 6.4 Manufacturing............................ 2.7 39.1 3.2 860 4.2 Trade, transportation, and utilities..... 25.9 274.1 2.0 838 4.2 Information.............................. 1.4 17.6 -2.6 1,444 2.8 Financial activities..................... 9.9 73.7 3.5 1,421 3.9 Professional and business services....... 20.0 147.7 5.0 1,080 2.8 Education and health services............ 9.9 168.7 2.5 953 4.4 Leisure and hospitality.................. 6.8 131.0 2.6 548 5.0 Other services........................... 8.0 39.8 5.1 586 1.9 Government................................. 0.3 135.2 -0.1 1,058 4.1 (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. (4) Data do not meet BLS or state agency disclosure standards. Note: Data are preliminary. Counties selected are based on 2014 annual average employment. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state, third quarter 2015 Employment Average weekly wage(1) Establishments, third quarter State 2015 Percent Percent (thousands) September change, Third change, 2015 September quarter third (thousands) 2014-15 2015 quarter 2014-15 United States(2)........... 9,633.8 140,442.2 1.9 $974 2.6 Alabama.................... 119.6 1,893.6 1.2 830 1.8 Alaska..................... 22.5 346.4 0.4 1,041 2.2 Arizona.................... 152.6 2,613.9 2.9 889 1.5 Arkansas................... 88.3 1,193.4 1.9 756 2.6 California................. 1,436.2 16,474.4 3.0 1,134 3.4 Colorado................... 187.9 2,513.0 2.9 1,006 2.4 Connecticut................ 116.5 1,668.3 0.2 1,147 2.0 Delaware................... 30.6 436.3 2.1 963 0.3 District of Columbia....... 38.2 743.6 1.4 1,667 2.3 Florida.................... 644.2 8,023.2 3.5 852 3.1 Georgia.................... 291.9 4,171.1 2.8 916 2.8 Hawaii..................... 39.8 635.4 1.4 896 3.1 Idaho...................... 55.9 680.3 3.3 736 2.1 Illinois................... 432.2 5,888.6 1.3 1,020 3.9 Indiana.................... 160.3 2,971.7 1.6 818 2.4 Iowa....................... 101.2 1,535.9 0.4 823 3.0 Kansas..................... 87.6 1,370.9 0.6 809 1.8 Kentucky................... 122.3 1,852.5 1.4 804 2.9 Louisiana.................. 127.4 1,926.3 -0.2 858 0.7 Maine...................... 51.3 609.7 0.7 779 3.3 Maryland................... 167.6 2,607.8 1.3 1,067 2.4 Massachusetts.............. 241.6 3,446.9 1.4 1,197 3.0 Michigan................... 242.0 4,203.0 1.6 921 2.7 Minnesota.................. 159.4 2,800.7 1.4 990 2.6 Mississippi................ 72.1 1,118.9 1.2 706 1.3 Missouri................... 193.3 2,737.9 1.9 846 2.2 Montana.................... 45.5 457.9 1.9 759 3.7 Nebraska................... 72.6 964.0 1.4 811 4.2 Nevada..................... 79.0 1,254.5 3.2 862 2.5 New Hampshire.............. 51.3 642.8 1.5 952 2.7 New Jersey................. 265.4 3,933.9 1.4 1,116 2.6 New Mexico................. 57.0 809.2 0.6 798 1.3 New York................... 639.5 9,065.4 1.8 1,180 3.1 North Carolina............. 267.8 4,194.1 2.5 863 3.0 North Dakota............... 32.3 438.0 -3.8 956 -2.3 Ohio....................... 291.4 5,282.7 1.2 878 1.9 Oklahoma................... 109.4 1,598.0 0.2 825 0.0 Oregon..................... 144.8 1,812.8 3.0 924 4.4 Pennsylvania............... 353.4 5,722.1 0.8 961 2.5 Rhode Island............... 36.6 477.4 1.2 919 2.6 South Carolina............. 123.5 1,959.7 2.9 788 2.6 South Dakota............... 32.6 419.5 0.9 756 3.1 Tennessee.................. 150.8 2,850.6 2.7 864 3.2 Texas...................... 640.7 11,681.0 2.1 999 1.1 Utah....................... 94.1 1,353.9 3.7 829 3.2 Vermont.................... 24.8 308.2 0.5 829 3.0 Virginia................... 258.8 3,759.7 2.5 1,014 2.5 Washington................. 235.4 3,187.6 2.5 1,111 2.2 West Virginia.............. 50.1 702.4 -1.1 785 0.9 Wisconsin.................. 168.5 2,815.7 0.9 834 3.5 Wyoming.................... 26.2 287.4 -1.5 866 -1.1 Puerto Rico................ 45.4 891.1 -0.7 512 1.4 Virgin Islands............. 3.4 36.8 -2.1 738 2.1 (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.