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For release 10:00 a.m. (EST), Tuesday, December 5, 2017 USDL-17-1613 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Second Quarter 2017 From June 2016 to June 2017, employment increased in 318 of the 346 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. Midland, Texas, had the largest percentage increase with a gain of 7.3 percent over the year, above the national job growth rate of 1.7 percent. Within Midland, the largest employment increase occurred in natural resources and mining, which gained 3,497 jobs over the year (19.6 percent). Lucas, Ohio, had the largest over-the-year percentage decrease in employment among the largest counties in the U.S., with a loss of 1.9 percent. Within Lucas, construction had the largest decrease in employment, with a loss of 1,534 jobs (-14.2 percent). The U.S. average weekly wage increased 3.2 percent over the year, growing to $1,020 in the second quarter of 2017. New Hanover, N.C., had the largest over-the-year percentage increase in average weekly wages with a gain of 11.9 percent. Within New Hanover, an average weekly wage gain of $589 (62.7 percent) in professional and business services made the largest contribution to the county’s increase in average weekly wages. McLean, Ill., had the largest over-the-year percentage decrease in average weekly wages with a loss of 20.4 percent. Within McLean, financial activities had the largest impact on the county’s average weekly wage change with a decrease of $953 (-38.9 percent) over the year. County employment and wage data are from the Quarterly Census of Employment and Wages (QCEW) program, which provides the only detailed quarterly and annual universe count of establishments, employment, and wages at the county, metropolitan statistical area, state, and national levels by detailed industry. These data are published within 6 months following the end of each quarter. Large County Employment In June 2017, national employment was 145.2 million (as measured by the QCEW program). Over the year, employment increased 1.7 percent, or 2.4 million. In June 2017, the 346 U.S. counties with 75,000 or more jobs accounted for 72.7 percent of total U.S. employment and 77.7 percent of total wages. These 346 counties had a net job growth of 1.8 million over the year, accounting for 76.8 percent of the overall U.S. employment increase. The 5 counties with the largest increases in employment levels had a combined over-the-year employment gain of 258,900 jobs, which was 10.8 percent of the overall job increase for the U.S. (See table A.) Employment declined in 23 of the largest counties from June 2016 to June 2017. Lucas, Ohio, had the largest over-the-year percentage decrease in employment (-1.9 percent), followed by Caddo, La.; Kanawha, W.Va.; Shawnee, Kan.; and Anchorage, Alaska. (See table 1.) Table A. Large counties ranked by June 2017 employment, June 2016-17 employment increase, and June 2016-17 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- June 2017 employment | Increase in employment, | Percent increase in employment, (thousands) | June 2016-17 | June 2016-17 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 145,186.4| United States 2,407.0| United States 1.7 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,373.6| Los Angeles, Calif. 71.9| Midland, Texas 7.3 Cook, Ill. 2,598.4| Maricopa, Ariz. 61.2| Weld, Colo. 5.3 New York, N.Y. 2,469.1| King, Wash. 44.2| Utah, Utah 5.2 Harris, Texas 2,284.5| New York, N.Y. 41.1| York, S.C. 4.8 Maricopa, Ariz. 1,891.7| Dallas, Texas 40.5| Elkhart, Ind. 4.7 Dallas, Texas 1,686.9| Orange, Calif. 33.2| Davis, Utah 4.5 Orange, Calif. 1,598.1| San Diego, Calif. 28.9| Clark, Wash. 4.4 San Diego, Calif. 1,440.9| Fulton, Ga. 27.9| Deschutes, Ore. 4.3 King, Wash. 1,369.7| Clark, Nev. 26.9| Boone, Ky. 4.2 Miami-Dade, Fla. 1,111.0| Orange, Fla. 26.5| Williamson, Tenn. 4.1 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased to $1,020, a 3.2 percent increase, during the year ending in the second quarter of 2017. Among the 346 largest counties, 325 had over-the-year increases in average weekly wages. New Hanover, N.C., had the largest percentage wage increase among the largest U.S. counties (11.9 percent). (See table B.) Of the 346 largest counties, 19 experienced an over-the-year decrease in average weekly wages. McLean, Ill., had the largest percentage decrease in average weekly wages (-20.4 percent), followed by Union, N.J.; Warren, Ohio; Somerset, N.J.; Fairfield, Conn.; and Washington, Ore. (See table 1.) Table B. Large counties ranked by second quarter 2017 average weekly wages, second quarter 2016-17 increase in average weekly wages, and second quarter 2016-17 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average second quarter 2017 | wage, second quarter 2016-17 | weekly wage, second | | quarter 2016-17 -------------------------------------------------------------------------------------------------------- | | United States $1,020| United States $32| United States 3.2 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,392| San Mateo, Calif. $214| New Hanover, N.C. 11.9 San Mateo, Calif. 2,093| Santa Clara, Calif. 141| San Mateo, Calif. 11.4 San Francisco, Calif. 1,941| Midland, Texas 135| Midland, Texas 11.4 New York, N.Y. 1,907| San Francisco, Calif. 132| Kitsap, Wash. 11.0 Washington, D.C. 1,675| Morris, N.J. 102| Clackamas, Ore. 10.0 Suffolk, Mass. 1,651| Kitsap, Wash. 97| Bell, Texas 9.6 Arlington, Va. 1,609| New Hanover, N.C. 94| St. Louis, Minn. 9.5 Fairfax, Va. 1,542| Clackamas, Ore. 93| Newport News City, Va. 7.4 Morris, N.J. 1,525| King, Wash. 83| San Francisco, Calif. 7.3 Middlesex, Mass. 1,522| Bell, Texas 77| Washington, Ark. 7.2 | | Morris, N.J. 7.2 -------------------------------------------------------------------------------------------------------- Ten Largest U.S. Counties All of the largest counties had over-the-year percentage increases in employment in June 2017. King, Wash., and Maricopa, Ariz., had the largest gain (3.3 percent). Within King, trade, transportation, and utilities had the largest over-the-year employment level increase, with a gain of 16,004 jobs, or 6.4 percent. Within Maricopa, education and health services had the largest over-the-year employment level increase, with a gain of 11,768 jobs, or 4.2 percent. Cook, Ill., had the lowest percentage increase in employment among the 10 largest counties (0.3 percent). Within Cook, leisure and hospitality had the largest over-the-year employment level increase, with a gain of 7,020 jobs, or 2.4 percent. (See table 2.) Average weekly wages increased over the year in 9 of the 10 largest U.S. counties. King, Wash., experienced the largest percentage gain in average weekly wages (6.0 percent). Within King, trade, transportation, and utilities had the largest impact on the county’s average weekly wage growth. Within trade, transportation, and utilities, average weekly wages increased by $183, or 12.8 percent, over the year. Harris, Texas, had the only percent loss in average weekly wages among the 10 largest counties (-0.4 percent). Within Harris, natural resources and mining had the largest impact on the county’s average weekly wage growth with a decrease of $290 (-9.0 percent) over the year. For More Information The tables included in this release contain data for the nation and for the 346 U.S. counties with annual average employment levels of 75,000 or more in 2016. June 2017 employment and 2017 second quarter average weekly wages for all states are provided in table 3 of this release. The data are derived from reports submitted by employers who are subject to unemployment insurance (UI) laws. The 9.9 million employer reports cover 145.2 million full- and part-time workers. Data for the second quarter of 2017 will be available later at www.bls.gov/cew. Additional information about the quarterly employment and wages data is available in the Technical Note. More information about QCEW data may be obtained by calling (202) 691-6567. The most current news release on quarterly measures of gross job flows is available from QCEW Business Employment Dynamics at www.bls.gov/news.release/pdf/cewbd.pdf. Several BLS regional offices issue QCEW news releases targeted to local data users. Links to these releases are available at www.bls.gov/cew/cewregional.htm. _____________ The County Employment and Wages release for third quarter 2017 is scheduled to be released on Thursday, March 8, 2018.
Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment and total pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment insurance programs that require most employers to pay quarterly taxes based on the employment and wages of workers covered by UI. QCEW data in this release are based on the 2017 North American Industry Classification System (NAICS). Data for 2017 are preliminary and subject to revision. For purposes of this release, large counties are defined as having employment levels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of employment for the previous year. The 347 counties presented in this release were derived using 2016 preliminary annual averages of employment. For 2017 data, three counties have been added to the publication tables: Sussex, Del.; Maui + Kalawao, Hawaii; and Deschutes, Ore. These counties will be included in all 2017 quarterly releases. One county, Gregg, Texas, which was published in the 2016 releases, will be excluded from this and future 2017 releases because its 2016 annual average employment level was less than 75,000. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' continuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. 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- | 634,000 establish- | submitted by 9.9 | ministrative records| ments | million establish- | submitted by 7.9 | | ments in first | million private-sec-| | quarter of 2017 | tor employers | -----------|---------------------|----------------------|------------------------ Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal- | age, including all | ing government, pri-| ary jobs: | employers subject | vate households, and|--UI coverage, exclud- | to state and fed- | establishments with | ing agriculture, pri- | eral UI laws | zero employment | vate households, and | | | self-employed workers | | |--Other employment, in- | | | cluding railroads, | | | religious organiza- | | | tions, and other non- | | | UI-covered jobs -----------|---------------------|----------------------|------------------------ Publication|--Quarterly |--Quarterly |--Monthly frequency | -Within 6 months | -7 months after the | -Usually the 3rd Friday | after the end of | end of each quarter| after the end of the | each quarter | | week including | | | the 12th of the month -----------|---------------------|----------------------|------------------------ Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam- file | and publishes each | quarter to longitu- | pling frame and to an- | new quarter of UI | dinal database and | nually realign sample- | data | directly summarizes | based estimates to pop- | | gross job gains and | ulation counts (bench- | | losses | marking) -----------|---------------------|----------------------|------------------------ Principal |--Provides a quarter-|--Provides quarterly |--Provides current month- products | ly and annual uni- | employer dynamics | ly estimates of employ- | verse count of es- | data on establish- | ment, hours, and earn- | tablishments, em- | ment openings, clos-| ings at the MSA, state, | ployment, and wages| ings, expansions, | and national level by | at the county, MSA,| and contractions at | industry | state, and national| the national level | | levels by detailed | by NAICS supersec- | | industry | tors and by size of | | | firm, and at the | | | state private-sector| | | total level | | |--Future expansions | | | will include data | | | with greater indus- | | | try detail and data | | | at the county and | | | MSA level | -----------|---------------------|----------------------|------------------------ Principal |--Major uses include:|--Major uses include: |--Major uses include: uses | -Detailed locality | -Business cycle | -Principal federal | data | analysis | economic indicator | -Periodic universe | -Analysis of employ-| -Official time series | counts for bench- | er dynamics under- | for employment change | marking sample | lying economic ex- | measures | survey estimates | pansions and con- | -Input into other ma- | -Sample frame for | tractions | jor economic indi- | BLS establishment | -Analysis of employ-| cators | surveys | ment expansion and | | | contraction by size| | | of firm | | | | -----------|---------------------|----------------------|------------------------ Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/sae/ 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 9.7 million employer reports of employment and wages submitted by states to the BLS in 2016. These reports are based on place of employment rather than place of residence. UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became effective, expanding coverage to include most state and local government employees. In 2016, UI and UCFE programs covered workers in 141.9 million jobs. The estimated 136.6 million workers in these jobs (after adjustment for multiple jobholders) represented 96.4 percent of civilian wage and salary employment. Covered workers received $7.607 trillion in pay, representing 94.1 percent of the wage and salary component of personal income and 40.9 percent of the gross domestic product. Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected officials in most states, most employees of railroads, some domestic workers, most student workers at schools, and employees of certain small nonprofit organizations. State and federal UI laws change periodically. These changes may have an impact on the employment and wages reported by employers covered under the UI program. Coverage changes may affect the over-the-year comparisons presented in this news release. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all employees of covered firms are reported, including production and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also are included. Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and wage values. The average wage values that can be calculated using rounded data from the BLS database may differ from the averages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans such as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may reflect fluctuations in average monthly employment and/or total quarterly wages between the current quarter and prior year levels. Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individuals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. For instance, the average weekly wage of the workforce could increase significantly when there is a large decline in the number of employees that had been receiving below-average wages. Wages may include payments to workers not present in the employment counts because they did not work during the pay period including the 12th of the month. When comparing average weekly wage levels between industries, states, or quarters, these factors should be taken into consideration. Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This variability may be due to calendar effects resulting from some quarters having more pay dates than others. The effect is most visible in counties with a dominant employer. In particular, this effect has been observed in counties where government employers represent a large fraction of overall employment. Similar calendar effects can result from private sector pay practices. However, these effects are typically less pronounced for two reasons: employment is less concentrated in a single private employer, and private employers use a variety of pay period types (weekly, biweekly, semimonthly, monthly). For example, the effect on over-the-year pay comparisons can be pronounced in federal government due to the uniform nature of federal payroll processing. Most federal employees are paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates, while in other quarters there are seven pay dates. Over-the-year comparisons of average weekly wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include seven pay dates, with year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the current quarter reflecting six pay dates are compared with year-ago wages for a quarter including seven pay dates. In order to ensure the highest possible quality of data, states verify with employers and update, if necessary, the industry, location, and ownership classification of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from this process are introduced with the data reported for the first quarter of the year. Changes resulting from improved employer reporting also are introduced in the first quarter. QCEW data are not designed as a time series. QCEW data are simply the sums of individual establishment records and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can move in or out of a county or industry for a number of reasons- -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 2016 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-the-year percent change in employment and wages are not published. These adjusted prior-year levels do not match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news release. The adjusted data used to calculate the over-the-year change measures presented in this release 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 2016 edition of this publication, which was published in September 2017, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2017 version of this news release. Tables and additional content from the 2016 edition of Employment and Wages Annual Averages Online are now available at www.bls.gov/cew/cewbultn16.htm. The 2017 edition of Employment and Wages Annual Averages Online will be available in September 2018. News releases on quarterly measures of gross job flows also are available from BED at www.bls.gov/bdm, (202) 691-6467, or data.bls.gov/cgi-bin/forms/bdm. Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; TDD message referral phone number: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 347 largest counties, second quarter 2017 Employment Average weekly wage(2) Establishments, County(1) second quarter Percent Ranking Percent Ranking 2017 June change, by Second change, by (thousands) 2017 June percent quarter second percent (thousands) 2016-17(3) change 2017 quarter change 2016-17(3) United States(4)......... 9,922.4 145,186.4 1.7 - $1,020 3.2 - Jefferson, AL............ 18.5 345.1 1.2 212 1,008 4.3 71 Madison, AL.............. 9.6 197.0 2.7 70 1,072 2.3 220 Mobile, AL............... 10.1 170.3 -0.1 324 857 1.4 273 Montgomery, AL........... 6.4 133.2 1.1 222 840 0.2 322 Shelby, AL............... 5.8 85.2 0.6 274 948 2.6 196 Tuscaloosa, AL........... 4.6 91.7 0.7 264 850 4.8 48 Anchorage, AK............ 8.3 151.4 -1.1 342 1,064 1.0 300 Maricopa, AZ............. 96.5 1,891.7 3.3 29 986 1.6 261 Pima, AZ................. 18.8 359.5 1.8 146 861 4.2 75 Benton, AR............... 6.4 118.0 2.1 117 1,022 2.6 196 Pulaski, AR.............. 14.4 250.1 0.7 264 909 1.5 266 Washington, AR........... 6.0 106.3 1.9 134 867 7.2 10 Alameda, CA.............. 62.9 778.9 2.9 55 1,376 5.8 23 Butte, CA................ 8.5 82.8 2.2 109 771 3.4 134 Contra Costa, CA......... 32.0 370.7 1.5 183 1,240 3.9 92 Fresno, CA............... 35.1 392.9 2.2 109 805 3.9 92 Kern, CA................. 18.8 325.6 3.1 37 840 2.2 226 Los Angeles, CA.......... 483.9 4,373.6 1.7 157 1,130 3.8 102 Marin, CA................ 12.5 118.0 2.2 109 1,278 1.4 273 Merced, CA............... 6.6 80.0 1.9 134 790 3.8 102 Monterey, CA............. 13.7 205.9 0.3 304 878 4.6 57 Napa, CA................. 5.9 79.3 2.4 89 1,014 4.8 48 Orange, CA............... 119.3 1,598.1 2.1 117 1,130 2.5 207 Placer, CA............... 12.9 162.4 3.0 47 1,015 1.9 242 Riverside, CA............ 63.0 713.6 3.2 33 826 2.0 236 Sacramento, CA........... 57.4 652.4 2.6 80 1,107 3.9 92 San Bernardino, CA....... 58.1 727.0 3.3 29 863 2.6 196 San Diego, CA............ 109.8 1,440.9 2.0 125 1,101 2.8 183 San Francisco, CA........ 60.4 717.4 3.1 37 1,941 7.3 9 San Joaquin, CA.......... 17.6 248.9 3.4 24 863 4.5 60 San Luis Obispo, CA...... 10.4 120.6 3.5 21 870 3.7 114 San Mateo, CA............ 28.1 402.5 2.8 62 2,093 11.4 2 Santa Barbara, CA........ 15.5 201.7 1.8 146 988 4.4 66 Santa Clara, CA.......... 72.2 1,077.3 2.5 85 2,392 6.3 14 Santa Cruz, CA........... 9.6 110.3 1.8 146 950 5.7 25 Solano, CA............... 11.4 139.9 2.0 125 1,056 5.0 41 Sonoma, CA............... 20.0 208.4 2.5 85 973 4.6 57 Stanislaus, CA........... 15.4 189.3 2.5 85 860 5.0 41 Tulare, CA............... 10.3 169.4 3.1 37 711 0.7 309 Ventura, CA.............. 26.9 326.4 1.3 200 1,015 2.9 175 Yolo, CA................. 6.7 103.5 2.2 109 1,098 3.7 114 Adams, CO................ 10.9 206.7 3.0 47 975 2.1 231 Arapahoe, CO............. 22.0 331.5 2.3 99 1,166 4.4 66 Boulder, CO.............. 15.2 181.2 2.3 99 1,192 4.4 66 Denver, CO............... 32.1 510.0 3.1 37 1,214 3.4 134 Douglas, CO.............. 12.0 123.6 2.8 62 1,135 4.1 82 El Paso, CO.............. 19.6 274.0 3.0 47 899 2.6 196 Jefferson, CO............ 20.2 235.7 0.4 294 1,047 4.1 82 Larimer, CO.............. 12.0 160.5 2.8 62 899 3.2 148 Weld, CO................. 7.3 106.1 5.3 2 894 5.1 38 Fairfield, CT............ 35.3 429.3 -0.5 334 1,503 -1.9 341 Hartford, CT............. 27.9 514.9 0.8 252 1,214 1.6 261 New Haven, CT............ 24.0 369.2 1.2 212 1,067 2.3 220 New London, CT........... 7.5 126.7 1.9 134 1,003 -0.2 331 New Castle, DE........... 19.6 286.6 -0.3 327 1,135 3.3 140 Sussex, DE............... 6.7 84.6 3.4 24 732 2.2 226 Washington, DC........... 39.0 766.5 1.0 235 1,675 3.3 140 Alachua, FL.............. 7.2 127.3 2.8 62 845 -1.4 338 Bay, FL.................. 5.7 78.9 1.0 235 760 3.8 102 Brevard, FL.............. 15.8 206.5 2.9 55 932 6.5 13 Broward, FL.............. 69.7 793.0 2.4 89 958 3.1 156 Collier, FL.............. 13.9 135.4 2.3 99 874 0.9 303 Duval, FL................ 29.9 500.3 3.2 33 959 3.2 148 Escambia, FL............. 8.3 133.7 3.8 13 783 0.1 324 Hillsborough, FL......... 42.4 663.6 2.0 125 964 1.2 291 Lake, FL................. 8.2 92.2 3.3 29 702 3.2 148 Lee, FL.................. 22.1 248.2 3.1 37 830 2.9 175 Leon, FL................. 8.8 146.2 0.7 264 819 0.4 315 Manatee, FL.............. 10.9 116.7 3.0 47 792 1.7 253 Marion, FL............... 8.3 100.6 2.7 70 716 0.0 326 Miami-Dade, FL........... 98.8 1,111.0 1.8 146 971 1.8 247 Okaloosa, FL............. 6.4 82.7 0.9 247 868 5.7 25 Orange, FL............... 42.4 811.8 3.4 24 900 3.9 92 Osceola, FL.............. 7.0 89.1 3.0 47 716 3.6 124 Palm Beach, FL........... 56.4 593.4 2.4 89 1,002 3.7 114 Pasco, FL................ 11.0 109.5 3.2 33 749 2.3 220 Pinellas, FL............. 33.1 425.8 2.6 80 888 1.4 273 Polk, FL................. 13.3 209.9 2.3 99 773 0.8 307 Sarasota, FL............. 15.9 163.3 2.4 89 839 2.9 175 Seminole, FL............. 15.1 187.0 2.7 70 891 4.2 75 Volusia, FL.............. 14.4 167.2 2.6 80 751 2.7 189 Bibb, GA................. 4.2 83.0 0.2 311 775 -0.1 328 Chatham, GA.............. 8.2 152.7 1.5 183 857 3.5 125 Clayton, GA.............. 4.0 122.4 0.5 287 966 3.4 134 Cobb, GA................. 22.1 358.4 2.7 70 1,070 3.5 125 DeKalb, GA............... 18.0 298.6 1.0 235 1,030 1.4 273 Fulton, GA............... 43.5 854.1 3.4 24 1,329 3.4 134 Gwinnett, GA............. 25.0 352.5 1.7 157 969 0.4 315 Hall, GA................. 4.4 85.7 4.0 11 866 6.1 15 Muscogee, GA............. 4.6 93.1 0.8 252 782 1.4 273 Richmond, GA............. 4.4 104.2 0.6 274 843 2.6 196 Honolulu, HI............. 26.3 472.5 0.6 274 976 3.8 102 Maui + Kalawao, HI....... 6.2 77.3 1.6 170 840 2.8 183 Ada, ID.................. 15.4 235.8 4.0 11 884 3.3 140 Champaign, IL............ 4.4 89.6 -0.3 327 886 2.5 207 Cook, IL................. 155.4 2,598.4 0.3 304 1,179 3.1 156 DuPage, IL............... 38.6 631.9 1.1 222 1,149 3.0 168 Kane, IL................. 13.9 216.3 2.1 117 898 2.3 220 Lake, IL................. 22.6 349.0 2.7 70 1,300 2.1 231 McHenry, IL.............. 8.8 100.4 0.7 264 827 1.2 291 McLean, IL............... 3.7 83.5 -0.2 325 920 -20.4 346 Madison, IL.............. 6.0 98.1 1.3 200 791 2.2 226 Peoria, IL............... 4.6 100.3 0.3 304 907 0.8 307 St. Clair, IL............ 5.5 93.3 0.5 287 812 5.2 37 Sangamon, IL............. 5.2 129.5 0.4 294 988 0.5 312 Will, IL................. 16.4 242.3 2.3 99 886 1.3 282 Winnebago, IL............ 6.6 127.8 0.0 319 844 1.2 291 Allen, IN................ 8.8 186.6 1.5 183 829 3.2 148 Elkhart, IN.............. 4.7 135.0 4.7 5 915 3.5 125 Hamilton, IN............. 9.4 142.0 2.4 89 955 2.8 183 Lake, IN................. 10.4 188.5 0.2 311 854 1.8 247 Marion, IN............... 24.1 597.4 1.2 212 1,027 4.7 52 St. Joseph, IN........... 5.8 123.7 0.0 319 828 3.0 168 Tippecanoe, IN........... 3.4 82.4 -0.5 334 878 5.0 41 Vanderburgh, IN.......... 4.8 107.9 0.6 274 827 4.9 45 Johnson, IA.............. 4.2 84.1 1.7 157 944 3.1 156 Linn, IA................. 6.8 132.3 0.5 287 971 2.6 196 Polk, IA................. 17.3 303.5 1.9 134 1,018 4.3 71 Scott, IA................ 5.6 93.0 1.6 170 812 2.4 215 Johnson, KS.............. 23.7 342.0 1.0 235 1,031 1.2 291 Sedgwick, KS............. 12.7 247.2 -0.5 334 860 0.4 315 Shawnee, KS.............. 5.2 96.5 -1.2 343 842 4.9 45 Wyandotte, KS............ 3.5 91.0 0.7 264 987 5.9 18 Boone, KY................ 4.3 87.7 4.2 9 888 -1.8 340 Fayette, KY.............. 10.8 193.7 0.8 252 918 4.2 75 Jefferson, KY............ 24.9 468.0 1.2 212 1,014 4.3 71 Caddo, LA................ 7.3 112.5 -1.7 345 804 1.5 266 Calcasieu, LA............ 5.3 98.3 2.6 80 871 2.8 183 East Baton Rouge, LA..... 15.7 261.4 0.5 287 959 1.9 242 Jefferson, LA............ 14.0 192.7 -0.8 339 905 4.5 60 Lafayette, LA............ 9.6 128.7 -0.4 331 860 0.1 324 Orleans, LA.............. 12.6 191.9 0.4 294 928 0.5 312 St. Tammany, LA.......... 8.3 88.4 -0.3 327 850 2.9 175 Cumberland, ME........... 14.0 186.7 1.9 134 909 0.9 303 Anne Arundel, MD......... 15.2 274.4 1.6 170 1,089 4.1 82 Baltimore, MD............ 21.4 379.4 0.0 319 1,005 3.1 156 Frederick, MD............ 6.4 102.0 1.5 183 931 1.5 266 Harford, MD.............. 5.8 94.6 1.9 134 952 1.1 298 Howard, MD............... 10.1 172.4 0.2 311 1,220 2.1 231 Montgomery, MD........... 33.0 477.9 1.3 200 1,333 1.4 273 Prince George's, MD...... 16.0 322.4 3.5 21 1,064 3.7 114 Baltimore City, MD....... 13.7 341.5 1.5 183 1,183 4.1 82 Barnstable, MA........... 9.5 108.6 2.0 125 869 4.2 75 Bristol, MA.............. 17.5 230.5 1.3 200 950 1.3 282 Essex, MA................ 25.4 330.4 0.0 319 1,093 3.7 114 Hampden, MA.............. 18.1 209.8 0.6 274 900 1.7 253 Middlesex, MA............ 55.0 912.0 2.1 117 1,522 3.3 140 Norfolk, MA.............. 25.4 357.7 0.8 252 1,182 3.7 114 Plymouth, MA............. 15.9 198.2 1.9 134 1,000 4.8 48 Suffolk, MA.............. 29.5 677.3 2.3 99 1,651 4.4 66 Worcester, MA............ 25.2 350.0 1.1 222 1,012 2.0 236 Genesee, MI.............. 6.8 136.3 0.7 264 832 0.6 310 Ingham, MI............... 6.0 151.7 1.6 170 969 2.1 231 Kalamazoo, MI............ 5.0 119.1 1.3 200 938 2.7 189 Kent, MI................. 14.4 398.2 2.0 125 884 3.9 92 Macomb, MI............... 17.6 334.7 1.2 212 1,007 3.2 148 Oakland, MI.............. 39.2 741.5 2.1 117 1,131 3.1 156 Ottawa, MI............... 5.6 126.5 0.9 247 858 2.3 220 Saginaw, MI.............. 3.9 84.5 -0.3 327 818 3.7 114 Washtenaw, MI............ 8.2 207.1 2.0 125 1,094 2.0 236 Wayne, MI................ 30.8 725.2 1.2 212 1,111 2.5 207 Anoka, MN................ 7.1 124.4 2.4 89 980 1.9 242 Dakota, MN............... 9.9 190.3 2.1 117 998 3.1 156 Hennepin, MN............. 39.0 919.1 1.8 146 1,273 4.8 48 Olmsted, MN.............. 3.4 99.1 2.1 117 1,073 3.8 102 Ramsey, MN............... 13.3 334.8 2.2 109 1,131 1.6 261 St. Louis, MN............ 5.3 99.8 1.1 222 855 9.5 7 Stearns, MN.............. 4.4 88.1 1.9 134 831 -0.1 328 Washington, MN........... 5.5 86.5 3.0 47 882 5.9 18 Harrison, MS............. 4.6 86.9 1.8 146 718 3.0 168 Hinds, MS................ 5.8 120.7 -0.8 339 849 1.0 300 Boone, MO................ 5.1 93.5 1.2 212 822 3.8 102 Clay, MO................. 5.7 106.9 2.8 62 904 2.8 183 Greene, MO............... 9.0 166.2 1.9 134 789 2.7 189 Jackson, MO.............. 22.1 371.6 1.8 146 1,021 3.5 125 St. Charles, MO.......... 9.5 149.4 1.6 170 823 -0.4 333 St. Louis, MO............ 39.0 610.4 0.8 252 1,059 1.7 253 St. Louis City, MO....... 14.6 227.7 0.9 247 1,077 4.7 52 Yellowstone, MT.......... 6.7 82.7 0.3 304 875 2.9 175 Douglas, NE.............. 19.1 341.3 1.1 222 938 2.9 175 Lancaster, NE............ 10.3 168.8 0.3 304 820 3.8 102 Clark, NV................ 55.0 967.0 2.9 55 886 2.2 226 Washoe, NV............... 14.6 217.7 3.6 16 906 3.5 125 Hillsborough, NH......... 12.2 204.1 1.5 183 1,080 3.1 156 Merrimack, NH............ 5.2 77.8 1.1 222 944 4.1 82 Rockingham, NH........... 10.9 153.0 2.3 99 1,009 1.0 300 Atlantic, NJ............. 6.6 132.1 0.3 304 855 2.0 236 Bergen, NJ............... 33.2 453.0 0.8 252 1,179 1.3 282 Burlington, NJ........... 11.0 210.1 2.7 70 1,036 1.2 291 Camden, NJ............... 12.1 207.8 1.5 183 987 4.0 89 Essex, NJ................ 20.6 346.7 1.7 157 1,231 4.5 60 Gloucester, NJ........... 6.4 109.1 1.6 170 872 0.9 303 Hudson, NJ............... 15.2 263.9 3.8 13 1,350 3.7 114 Mercer, NJ............... 11.2 251.9 1.4 194 1,279 3.5 125 Middlesex, NJ............ 22.4 426.1 2.2 109 1,181 1.8 247 Monmouth, NJ............. 20.2 271.7 1.6 170 988 0.5 312 Morris, NJ............... 17.2 295.0 1.1 222 1,525 7.2 10 Ocean, NJ................ 13.3 176.8 2.4 89 806 1.4 273 Passaic, NJ.............. 12.7 169.9 0.4 294 992 3.1 156 Somerset, NJ............. 10.3 192.2 1.1 222 1,464 -3.4 343 Union, NJ................ 14.4 223.5 1.6 170 1,237 -3.7 345 Bernalillo, NM........... 18.4 327.1 1.1 222 865 1.3 282 Albany, NY............... 10.4 235.0 0.1 316 1,084 0.6 310 Bronx, NY................ 18.8 303.2 0.9 247 978 3.7 114 Broome, NY............... 4.5 87.6 0.4 294 817 2.1 231 Dutchess, NY............. 8.5 113.5 0.4 294 1,023 3.0 168 Erie, NY................. 24.9 474.9 0.6 274 904 2.7 189 Kings, NY................ 62.8 714.0 3.7 15 850 3.2 148 Monroe, NY............... 19.0 390.9 0.6 274 968 3.9 92 Nassau, NY............... 54.4 643.6 1.7 157 1,150 -1.5 339 New York, NY............. 129.2 2,469.1 1.7 157 1,907 2.4 215 Oneida, NY............... 5.4 106.9 0.8 252 810 3.1 156 Onondaga, NY............. 13.0 247.7 0.4 294 936 1.8 247 Orange, NY............... 10.5 145.5 1.4 194 905 2.7 189 Queens, NY............... 53.2 666.3 2.9 55 965 2.4 215 Richmond, NY............. 9.8 116.7 1.7 157 911 2.4 215 Rockland, NY............. 10.9 126.5 2.4 89 989 -0.7 336 Saratoga, NY............. 6.0 89.2 2.4 89 949 1.3 282 Suffolk, NY.............. 53.3 682.8 1.0 235 1,086 0.4 315 Westchester, NY.......... 36.6 437.6 1.3 200 1,327 2.6 196 Buncombe, NC............. 9.2 129.3 1.7 157 783 3.2 148 Catawba, NC.............. 4.4 87.9 1.8 146 793 4.1 82 Cumberland, NC........... 6.2 119.2 -0.6 337 795 5.7 25 Durham, NC............... 8.3 199.1 0.6 274 1,231 3.0 168 Forsyth, NC.............. 9.2 183.1 0.0 319 906 4.5 60 Guilford, NC............. 14.2 279.1 1.4 194 890 3.9 92 Mecklenburg, NC.......... 37.3 683.2 3.1 37 1,152 4.0 89 New Hanover, NC.......... 8.0 112.4 2.8 62 884 11.9 1 Wake, NC................. 34.0 549.7 3.1 37 1,040 4.7 52 Cass, ND................. 7.2 119.1 1.0 235 917 3.9 92 Butler, OH............... 7.8 153.8 2.9 55 901 3.0 168 Cuyahoga, OH............. 35.8 728.8 0.5 287 1,029 3.5 125 Delaware, OH............. 5.3 90.4 2.6 80 971 1.3 282 Franklin, OH............. 31.9 753.3 2.7 70 1,007 1.9 242 Hamilton, OH............. 23.8 520.9 1.2 212 1,072 2.7 189 Lake, OH................. 6.3 96.9 0.3 304 838 5.4 33 Lorain, OH............... 6.2 100.0 1.3 200 792 2.9 175 Lucas, OH................ 10.2 209.5 -1.9 346 856 -0.6 335 Mahoning, OH............. 5.9 97.2 1.1 222 720 4.3 71 Montgomery, OH........... 11.8 254.9 1.6 170 871 2.6 196 Stark, OH................ 8.5 160.7 0.7 264 762 4.7 52 Summit, OH............... 14.3 269.1 0.6 274 886 1.8 247 Warren, OH............... 4.9 94.2 1.1 222 898 -3.6 344 Cleveland, OK............ 5.8 79.7 0.2 311 749 0.9 303 Oklahoma, OK............. 28.0 450.0 0.4 294 943 2.5 207 Tulsa, OK................ 22.4 353.0 1.1 222 914 2.5 207 Clackamas, OR............ 14.8 165.0 2.7 70 1,027 10.0 5 Deschutes, OR............ 8.3 81.9 4.3 8 844 5.9 18 Jackson, OR.............. 7.3 88.1 3.0 47 791 5.6 29 Lane, OR................. 12.0 156.3 2.4 89 816 4.1 82 Marion, OR............... 10.6 155.6 1.5 183 854 4.0 89 Multnomah, OR............ 34.5 505.1 2.0 125 1,071 5.7 25 Washington, OR........... 19.1 293.7 2.8 62 1,264 -1.9 341 Allegheny, PA............ 35.8 703.6 0.6 274 1,082 3.8 102 Berks, PA................ 9.0 172.2 0.8 252 929 3.3 140 Bucks, PA................ 20.0 269.2 1.8 146 949 1.5 266 Butler, PA............... 5.1 86.2 0.2 311 949 4.5 60 Chester, PA.............. 15.5 252.6 1.5 183 1,322 5.1 38 Cumberland, PA........... 6.5 133.6 0.7 264 931 3.9 92 Dauphin, PA.............. 7.6 185.4 0.6 274 997 4.9 45 Delaware, PA............. 14.3 223.9 1.0 235 1,063 -0.7 336 Erie, PA................. 7.1 123.4 -0.4 331 771 0.0 326 Lackawanna, PA........... 5.7 97.7 0.6 274 777 2.5 207 Lancaster, PA............ 13.5 239.7 1.2 212 840 2.2 226 Lehigh, PA............... 8.9 191.5 1.4 194 977 -0.3 332 Luzerne, PA.............. 7.5 146.3 0.7 264 797 3.5 125 Montgomery, PA........... 27.8 498.8 1.7 157 1,205 0.2 322 Northampton, PA.......... 6.8 114.7 1.7 157 878 3.8 102 Philadelphia, PA......... 35.5 671.5 1.9 134 1,170 1.7 253 Washington, PA........... 5.5 89.3 3.1 37 990 4.2 75 Westmoreland, PA......... 9.3 136.2 0.8 252 829 5.9 18 York, PA................. 9.2 177.8 0.1 316 896 5.5 32 Providence, RI........... 18.2 287.2 0.5 287 1,016 1.5 266 Charleston, SC........... 14.9 249.2 1.7 157 915 4.2 75 Greenville, SC........... 13.7 267.8 1.9 134 903 5.4 33 Horry, SC................ 8.6 136.2 3.4 24 622 3.8 102 Lexington, SC............ 6.5 117.5 1.4 194 775 2.9 175 Richland, SC............. 10.1 220.0 1.3 200 854 0.4 315 Spartanburg, SC.......... 6.2 137.3 2.9 55 888 1.3 282 York, SC................. 5.6 94.3 4.8 4 824 4.7 52 Minnehaha, SD............ 7.2 127.5 1.3 200 876 3.4 134 Davidson, TN............. 22.3 485.4 3.6 16 1,053 3.7 114 Hamilton, TN............. 9.6 201.9 1.6 170 893 1.8 247 Knox, TN................. 12.2 236.6 0.8 252 877 3.1 156 Rutherford, TN........... 5.5 124.6 3.6 16 927 2.0 236 Shelby, TN............... 20.4 495.2 1.0 235 1,008 3.8 102 Williamson, TN........... 8.6 130.1 4.1 10 1,124 2.8 183 Bell, TX................. 5.4 119.1 1.5 183 883 9.6 6 Bexar, TX................ 40.7 853.6 1.8 146 914 4.6 57 Brazoria, TX............. 5.7 106.0 -0.2 325 1,030 1.7 253 Brazos, TX............... 4.5 97.7 1.0 235 763 5.0 41 Cameron, TX.............. 6.5 140.2 1.3 200 615 2.3 220 Collin, TX............... 24.5 398.6 3.6 16 1,169 1.7 253 Dallas, TX............... 76.1 1,686.9 2.5 85 1,213 2.6 196 Denton, TX............... 14.7 240.2 3.6 16 933 4.4 66 El Paso, TX.............. 15.0 299.6 1.7 157 717 3.3 140 Fort Bend, TX............ 13.1 179.7 1.6 170 936 0.4 315 Galveston, TX............ 6.2 111.3 1.7 157 904 3.1 156 Harris, TX............... 114.2 2,284.5 0.7 264 1,231 -0.4 333 Hidalgo, TX.............. 12.3 254.8 2.7 70 632 1.1 298 Jefferson, TX............ 5.9 123.4 0.4 294 1,026 1.2 291 Lubbock, TX.............. 7.5 138.7 1.3 200 801 5.1 38 McLennan, TX............. 5.2 113.4 2.0 125 830 1.6 261 Midland, TX.............. 5.4 89.3 7.3 1 1,321 11.4 2 Montgomery, TX........... 11.2 175.2 3.2 33 1,008 2.0 236 Nueces, TX............... 8.3 164.5 1.3 200 861 1.4 273 Potter, TX............... 4.0 78.6 -0.4 331 832 6.1 15 Smith, TX................ 6.2 103.4 0.8 252 823 1.5 266 Tarrant, TX.............. 42.8 877.0 2.7 70 1,011 3.9 92 Travis, TX............... 40.2 728.7 3.1 37 1,186 5.6 29 Webb, TX................. 5.4 99.9 2.2 109 667 1.4 273 Williamson, TX........... 10.6 166.8 3.1 37 992 5.6 29 Davis, UT................ 8.3 128.1 4.5 6 837 5.4 33 Salt Lake, UT............ 44.0 687.6 2.8 62 967 2.7 189 Utah, UT................. 15.8 232.4 5.2 3 814 1.6 261 Weber, UT................ 6.0 103.9 2.3 99 762 1.7 253 Chittenden, VT........... 6.9 103.5 0.8 252 978 0.3 321 Arlington, VA............ 9.2 178.7 2.3 99 1,609 3.3 140 Chesterfield, VA......... 9.0 137.1 1.4 194 862 2.5 207 Fairfax, VA.............. 37.3 610.3 1.2 212 1,542 3.5 125 Henrico, VA.............. 11.6 195.4 1.6 170 972 1.7 253 Loudoun, VA.............. 12.2 168.2 2.9 55 1,165 2.6 196 Prince William, VA....... 9.3 130.7 1.5 183 880 2.4 215 Alexandria City, VA...... 6.5 94.8 -0.9 341 1,389 3.1 156 Chesapeake City, VA...... 6.1 100.0 1.0 235 806 1.9 242 Newport News City, VA.... 3.9 97.8 0.9 247 975 7.4 8 Norfolk City, VA......... 5.9 142.4 2.1 117 1,029 5.8 23 Richmond City, VA........ 7.7 154.1 1.6 170 1,087 3.3 140 Virginia Beach City, VA.. 12.2 183.8 0.6 274 786 3.8 102 Benton, WA............... 5.7 93.1 3.0 47 1,010 1.3 282 Clark, WA................ 14.4 157.8 4.4 7 954 5.9 18 King, WA................. 86.1 1,369.7 3.3 29 1,472 6.0 17 Kitsap, WA............... 6.7 88.6 2.0 125 978 11.0 4 Pierce, WA............... 21.7 303.9 1.8 146 934 3.4 134 Snohomish, WA............ 20.7 286.2 0.1 316 1,106 3.0 168 Spokane, WA.............. 15.6 222.0 2.3 99 868 4.5 60 Thurston, WA............. 8.2 113.4 3.5 21 934 5.3 36 Whatcom, WA.............. 7.3 90.4 1.9 134 860 6.8 12 Yakima, WA............... 7.7 121.6 -0.6 337 716 4.2 75 Kanawha, WV.............. 5.7 100.7 -1.5 344 876 1.5 266 Brown, WI................ 6.8 159.3 2.2 109 868 1.2 291 Dane, WI................. 15.1 334.2 1.0 235 1,004 -0.1 328 Milwaukee, WI............ 25.7 488.8 0.4 294 970 2.6 196 Outagamie, WI............ 5.2 110.2 1.0 235 860 3.2 148 Waukesha, WI............. 12.8 246.6 0.5 287 996 1.3 282 Winnebago, WI............ 3.7 94.8 1.1 222 928 2.5 207 San Juan, PR............. 11.0 242.0 -0.9 (5) 622 2.6 (5) (1) Includes areas not officially designated as counties. See Technical Note. (2) Average weekly wages were calculated using unrounded data. (3) Percent changes were computed from employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. (5) This county was not included in the U.S. rankings. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 346 U.S. counties comprise 72.7 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, second quarter 2017 Employment Average weekly wage(1) Establishments, second quarter County by NAICS supersector 2017 Percent Percent (thousands) June change, Second change, 2017 June quarter second (thousands) 2016-17(2) 2017 quarter 2016-17(2) United States(3) ............................ 9,922.4 145,186.4 1.7 $1,020 3.2 Private industry........................... 9,624.1 123,579.7 1.9 1,010 3.2 Natural resources and mining............. 136.9 2,001.8 1.9 1,017 2.2 Construction............................. 791.8 7,102.1 3.6 1,119 4.0 Manufacturing............................ 348.4 12,484.2 0.7 1,239 3.0 Trade, transportation, and utilities..... 1,925.9 27,199.7 1.0 863 3.1 Information.............................. 162.6 2,794.8 0.5 1,880 5.6 Financial activities..................... 871.5 8,131.0 1.7 1,537 3.0 Professional and business services....... 1,789.3 20,439.6 1.9 1,318 2.7 Education and health services............ 1,640.5 22,056.4 2.5 929 3.0 Leisure and hospitality.................. 840.1 16,514.3 2.1 431 3.9 Other services........................... 847.2 4,514.9 1.4 701 3.9 Government................................. 298.3 21,606.6 0.7 1,075 3.4 Los Angeles, CA.............................. 483.9 4,373.6 1.7 1,130 3.8 Private industry........................... 477.5 3,791.8 1.7 1,099 4.0 Natural resources and mining............. 0.5 8.6 7.0 1,082 2.5 Construction............................. 14.1 138.4 4.6 1,187 5.0 Manufacturing............................ 12.2 347.5 -3.3 1,307 6.4 Trade, transportation, and utilities..... 54.0 818.4 0.8 924 3.8 Information.............................. 10.1 185.8 -0.2 2,192 4.5 Financial activities..................... 25.7 219.2 0.9 1,782 3.2 Professional and business services....... 48.0 598.6 1.0 1,406 5.3 Education and health services............ 228.1 773.2 3.2 862 1.8 Leisure and hospitality.................. 33.4 525.9 3.1 625 3.6 Other services........................... 26.6 148.8 0.9 753 10.1 Government................................. 6.3 581.8 1.2 1,334 3.0 Cook, IL..................................... 155.4 2,598.4 0.3 1,179 3.1 Private industry........................... 154.1 2,299.4 0.4 1,166 3.1 Natural resources and mining............. 0.1 1.3 5.2 1,182 4.9 Construction............................. 12.5 76.3 1.2 1,436 2.9 Manufacturing............................ 6.3 186.1 0.0 1,232 3.4 Trade, transportation, and utilities..... 30.1 469.1 -0.3 973 5.2 Information.............................. 2.7 51.4 0.5 1,778 -0.4 Financial activities..................... 15.3 194.7 0.3 2,051 2.4 Professional and business services....... 32.8 475.4 -0.6 1,495 4.2 Education and health services............ 16.5 443.0 0.5 962 1.7 Leisure and hospitality.................. 14.6 297.5 2.4 539 3.5 Other services........................... 17.7 98.4 0.5 947 6.2 Government................................. 1.3 299.0 -0.7 1,276 2.8 New York, NY................................. 129.2 2,469.1 1.7 1,907 2.4 Private industry........................... 128.4 2,206.4 1.8 1,976 2.2 Natural resources and mining............. 0.0 0.2 7.0 2,059 4.4 Construction............................. 2.3 40.4 -2.4 1,865 3.8 Manufacturing............................ 2.1 25.5 -3.8 1,418 2.5 Trade, transportation, and utilities..... 19.5 253.3 -0.5 1,367 0.2 Information.............................. 5.0 164.2 5.4 2,509 0.4 Financial activities..................... 19.5 377.1 0.0 3,591 2.0 Professional and business services....... 27.0 578.4 2.1 2,201 1.5 Education and health services............ 10.0 342.7 1.0 1,334 7.8 Leisure and hospitality.................. 14.1 303.0 2.3 875 3.3 Other services........................... 20.5 104.4 1.1 1,253 7.4 Government................................. 0.8 262.7 0.7 1,339 5.1 Harris, TX................................... 114.2 2,284.5 0.7 1,231 -0.4 Private industry........................... 113.7 2,007.4 0.6 1,247 -0.6 Natural resources and mining............. 1.6 66.7 -2.3 2,940 -9.0 Construction............................. 7.4 158.9 -1.4 1,328 2.5 Manufacturing............................ 4.8 169.5 -1.1 1,558 1.3 Trade, transportation, and utilities..... 25.0 468.0 0.1 1,118 1.4 Information.............................. 1.2 26.9 -4.2 1,400 -2.1 Financial activities..................... 12.1 126.2 2.1 1,633 2.6 Professional and business services....... 23.1 394.6 0.6 1,529 -3.0 Education and health services............ 15.9 291.2 2.8 1,034 3.1 Leisure and hospitality.................. 10.0 236.5 1.7 451 4.6 Other services........................... 11.6 66.4 1.5 800 3.0 Government................................. 0.6 277.1 1.5 1,113 1.1 Maricopa, AZ................................. 96.5 1,891.7 3.3 986 1.6 Private industry........................... 95.8 1,705.9 3.5 975 1.8 Natural resources and mining............. 0.4 8.6 2.3 908 5.5 Construction............................. 6.9 110.6 7.1 1,038 4.2 Manufacturing............................ 3.1 117.9 1.0 1,432 -1.1 Trade, transportation, and utilities..... 18.2 370.5 2.1 899 2.5 Information.............................. 1.5 34.7 -0.5 1,376 -0.7 Financial activities..................... 10.7 174.5 5.2 1,258 0.2 Professional and business services....... 20.6 323.8 2.0 1,065 2.1 Education and health services............ 10.6 289.1 4.2 978 2.5 Leisure and hospitality.................. 7.6 211.6 3.7 477 5.8 Other services........................... 5.9 50.6 -2.3 718 4.8 Government................................. 0.7 185.7 1.9 1,075 0.4 Dallas, TX................................... 76.1 1,686.9 2.5 1,213 2.6 Private industry........................... 75.5 1,514.5 2.7 1,218 2.4 Natural resources and mining............. 0.5 8.7 5.1 3,279 -4.9 Construction............................. 4.5 89.0 4.1 1,222 7.6 Manufacturing............................ 2.8 112.7 0.9 1,440 -1.2 Trade, transportation, and utilities..... 15.9 342.8 3.2 1,046 0.6 Information.............................. 1.4 48.6 -1.3 1,818 -1.7 Financial activities..................... 9.4 165.4 4.2 1,704 3.1 Professional and business services....... 17.1 340.5 2.5 1,417 3.5 Education and health services............ 9.5 197.2 2.9 1,105 5.6 Leisure and hospitality.................. 6.8 164.1 2.4 489 2.5 Other services........................... 7.0 43.9 -0.6 803 6.2 Government................................. 0.6 172.4 0.4 1,170 5.0 Orange, CA................................... 119.3 1,598.1 2.1 1,130 2.5 Private industry........................... 117.8 1,443.1 2.3 1,115 2.6 Natural resources and mining............. 0.2 2.9 -1.0 899 6.8 Construction............................. 6.7 101.0 3.4 1,320 6.2 Manufacturing............................ 4.9 158.0 -0.4 1,397 2.0 Trade, transportation, and utilities..... 16.9 258.5 1.3 1,003 1.4 Information.............................. 1.3 27.0 1.2 1,930 9.4 Financial activities..................... 11.1 117.9 1.3 1,724 1.6 Professional and business services....... 20.4 294.6 1.2 1,344 3.9 Education and health services............ 32.9 208.2 4.0 911 -0.4 Leisure and hospitality.................. 8.7 219.9 3.2 502 6.1 Other services........................... 6.8 45.9 1.1 725 5.7 Government................................. 1.5 155.0 0.6 1,267 2.2 San Diego, CA................................ 109.8 1,440.9 2.0 1,101 2.8 Private industry........................... 107.9 1,205.0 2.0 1,058 1.5 Natural resources and mining............. 0.6 9.0 -8.3 720 2.3 Construction............................. 6.8 79.2 4.2 1,181 3.3 Manufacturing............................ 3.2 107.9 0.7 1,504 3.0 Trade, transportation, and utilities..... 14.2 224.1 0.9 873 0.3 Information.............................. 1.2 24.2 0.3 1,890 5.8 Financial activities..................... 9.9 73.3 0.9 1,446 4.1 Professional and business services....... 18.0 229.2 0.5 1,481 0.1 Education and health services............ 31.3 198.4 3.0 933 0.5 Leisure and hospitality.................. 8.3 200.0 2.8 504 5.7 Other services........................... 7.2 51.9 2.4 625 4.3 Government................................. 1.9 235.9 2.1 1,317 8.2 King, WA..................................... 86.1 1,369.7 3.3 1,472 6.0 Private industry........................... 85.6 1,198.8 3.6 1,495 6.6 Natural resources and mining............. 0.4 3.1 1.1 1,240 1.2 Construction............................. 6.8 71.1 5.9 1,334 3.3 Manufacturing............................ 2.5 102.6 -2.7 1,617 -1.8 Trade, transportation, and utilities..... 14.5 265.8 6.4 1,618 12.8 Information.............................. 2.2 103.4 5.9 2,991 8.6 Financial activities..................... 6.7 67.9 2.7 1,650 3.8 Professional and business services....... 17.9 224.5 2.6 1,668 5.0 Education and health services............ 18.0 170.8 2.5 1,040 4.6 Leisure and hospitality.................. 7.3 143.8 4.5 578 4.1 Other services........................... 9.3 45.7 1.6 882 5.6 Government................................. 0.5 171.0 1.7 1,318 1.7 Miami-Dade, FL............................... 98.8 1,111.0 1.8 971 1.8 Private industry........................... 98.5 985.9 1.8 949 3.3 Natural resources and mining............. 0.5 8.1 4.4 627 1.6 Construction............................. 6.7 46.0 5.6 926 1.6 Manufacturing............................ 2.9 41.4 2.7 879 1.9 Trade, transportation, and utilities..... 25.9 280.3 0.5 898 3.8 Information.............................. 1.6 17.9 -0.8 1,696 8.0 Financial activities..................... 10.6 75.5 1.4 1,498 3.7 Professional and business services....... 22.1 157.2 2.9 1,128 2.8 Education and health services............ 10.5 176.8 2.1 972 2.3 Leisure and hospitality.................. 7.3 141.5 1.8 581 3.8 Other services........................... 8.4 39.6 -0.4 621 2.5 Government................................. 0.3 125.2 1.3 1,125 -6.9 (1) Average weekly wages were calculated using unrounded data. (2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. Note: Data are preliminary. Counties selected are based on 2016 annual average employment. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state, second quarter 2017 Employment Average weekly wage(1) Establishments, second quarter State 2017 Percent Percent (thousands) June change, Second change, 2017 June quarter second (thousands) 2016-17 2017 quarter 2016-17 United States(2)........... 9,922.4 145,186.4 1.7 $1,020 3.2 Alabama.................... 124.2 1,946.4 1.2 858 2.8 Alaska..................... 22.1 338.4 -0.7 1,005 -0.5 Arizona.................... 158.2 2,699.6 2.9 943 2.5 Arkansas................... 89.5 1,206.0 0.7 810 3.2 California................. 1,522.5 17,150.9 2.2 1,210 4.7 Colorado................... 198.4 2,638.8 2.5 1,042 4.2 Connecticut................ 118.6 1,701.2 0.6 1,216 0.4 Delaware................... 31.7 446.6 0.6 1,012 2.2 District of Columbia....... 39.0 766.5 1.0 1,675 3.3 Florida.................... 684.9 8,390.6 2.8 905 2.5 Georgia.................... 278.1 4,357.8 2.1 956 2.9 Hawaii..................... 41.7 653.0 1.0 935 3.5 Idaho...................... 61.0 723.5 3.4 765 3.4 Illinois................... 412.2 6,006.6 0.9 1,062 2.4 Indiana.................... 164.3 3,041.0 1.5 859 3.7 Iowa....................... 101.7 1,571.4 0.4 853 3.3 Kansas..................... 90.3 1,377.8 -0.1 849 2.4 Kentucky................... 120.8 1,889.4 0.8 862 2.9 Louisiana.................. 131.4 1,907.7 0.0 869 2.0 Maine...................... 54.4 629.1 0.9 814 2.5 Maryland................... 171.8 2,694.8 1.4 1,103 3.1 Massachusetts.............. 252.3 3,604.5 1.6 1,278 3.6 Michigan................... 242.9 4,365.3 1.6 969 2.9 Minnesota.................. 169.0 2,902.1 2.0 1,037 3.9 Mississippi................ 73.4 1,128.9 0.7 732 0.8 Missouri................... 206.6 2,818.7 1.2 889 3.0 Montana.................... 48.3 473.6 1.3 797 3.9 Nebraska................... 72.6 984.0 0.4 833 3.5 Nevada..................... 80.7 1,333.5 3.4 900 2.9 New Hampshire.............. 52.1 665.4 1.6 1,015 1.2 New Jersey................. 272.8 4,123.5 1.8 1,173 2.3 New Mexico................. 58.5 815.4 0.7 823 1.5 New York................... 648.6 9,417.4 1.6 1,237 2.2 North Carolina............. 272.0 4,361.4 1.8 902 4.3 North Dakota............... 31.9 422.7 -0.2 953 5.0 Ohio....................... 295.2 5,422.8 1.2 912 3.3 Oklahoma................... 110.4 1,583.8 0.8 845 2.5 Oregon..................... 150.2 1,912.6 2.2 967 3.8 Pennsylvania............... 360.1 5,859.4 1.3 1,000 3.0 Rhode Island............... 37.3 487.3 1.0 980 2.6 South Carolina............. 128.1 2,053.9 2.0 834 3.6 South Dakota............... 33.2 435.5 0.6 785 3.4 Tennessee.................. 157.2 2,948.1 1.8 906 3.5 Texas...................... 671.5 12,059.6 2.1 1,027 2.7 Utah....................... 98.5 1,440.3 3.4 862 2.6 Vermont.................... 25.6 314.2 1.0 870 2.1 Virginia................... 269.6 3,886.6 1.5 1,047 3.7 Washington................. 239.2 3,352.5 2.2 1,141 5.6 West Virginia.............. 50.1 690.9 -0.3 828 3.4 Wisconsin.................. 171.7 2,905.3 1.1 876 2.3 Wyoming.................... 26.1 280.2 -0.7 875 3.1 Puerto Rico................ 46.9 873.6 -1.0 515 1.2 Virgin Islands............. 3.3 38.6 0.4 762 2.6 (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.