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