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For release 10:00 a.m. (EDT), Wednesday, June 7, 2017 USDL-17-0769 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Fourth Quarter 2016 From December 2015 to December 2016, employment increased in 280 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 5.1 percent over the year, above the national job growth rate of 1.2 percent. Within Williamson, the largest employment increase occurred in professional and business services, which gained 1,995 jobs over the year (6.0 percent). Lafayette, La., had the largest over-the- year percentage decrease in employment among the largest counties in the U.S., with a loss of 5.1 percent. Within Lafayette, natural resources and mining had the largest decrease in employment, with a loss of 2,397 jobs (-19.8 percent). The U.S. average weekly wage decreased 1.5 percent over the year, declining to $1,067 in the fourth quarter of 2016. This is one of only eight declines in the history of the series, which dates back to 1978. The 1.5 percent decline in average weekly wages was the largest decline since fourth quarter 2011, when average weekly wages decreased by 1.7 percent. The most recent decline occurred in first quarter 2016, when the U.S. average weekly wage decreased 0.6 percent over the year. McLean, Ill., had the largest over-the-year percentage decrease in average weekly wages with a loss of 9.2 percent. Within McLean, an average weekly wage loss of $178 (-10.9 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 11.3 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 $265 (25.3 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 December 2016, national employment was 143.7 million (as measured by the QCEW program). Over the year, employment increased 1.2 percent, or 1.8 million. In December 2016, the 344 U.S. counties with 75,000 or more jobs accounted for 72.8 percent of total U.S. employment and 78.1 percent of total wages. These 344 counties had a net job growth of 1.4 million over the year, accounting for 80.7 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 215,600 jobs, which was 12.2 percent of the overall job increase for the U.S. (See table A.) Employment declined in 58 of the largest counties from December 2015 to December 2016. Lafayette, La., had the largest over-the-year percentage decrease in employment (-5.1 percent), followed by Gregg, Texas; Midland, Texas; Erie, Pa.; and Kanawha, W.Va. (See table 1.) Table A. Large counties ranked by December 2016 employment, December 2015-16 employment increase, and December 2015-16 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- December 2016 employment | Increase in employment, | Percent increase in employment, (thousands) | December 2015-16 | December 2015-16 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 143,749.9| United States 1,773.6| United States 1.2 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,415.7| Los Angeles, Calif. 50.2| Williamson, Tenn. 5.1 Cook, Ill. 2,590.2| Dallas, Texas 45.7| York, S.C. 4.6 New York, N.Y. 2,471.6| Maricopa, Ariz. 45.2| Williamson, Texas 4.5 Harris, Texas 2,272.0| King, Wash. 42.7| Utah, Utah 4.5 Maricopa, Ariz. 1,926.9| Orange, Calif. 31.8| Northampton, Pa. 4.4 Dallas, Texas 1,688.4| Fulton, Ga. 30.4| Brevard, Fla. 4.2 Orange, Calif. 1,588.8| Santa Clara, Calif. 25.7| Seminole, Fla. 4.2 San Diego, Calif. 1,427.5| Clark, Nev. 23.7| Galveston, Texas 4.0 King, Wash. 1,340.4| San Diego, Calif. 21.8| Thurston, Wash. 4.0 Miami-Dade, Fla. 1,132.9| Orange, Fla. 21.5| Benton, Wash. 3.8 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation declined to $1,067, a 1.5 percent decrease, during the year ending in the fourth quarter of 2016. Among the 344 largest counties, 290 had over-the-year decreases in average weekly wages. McLean, Ill., had the largest percentage wage decrease among the largest U.S. counties (-9.2 percent). (See table B.) Of the 344 largest counties, 48 experienced over-the-year increases in average weekly wages. Clayton, Ga., had the largest percentage increase in average weekly wages (11.3 percent), followed by Washington, Pa.; Marin, Calif.; Elkhart, Ind.; San Francisco, Calif.; and Champaign, Ill. (See table 1.) Table B. Large counties ranked by fourth quarter 2016 average weekly wages, fourth quarter 2015-16 decrease in average weekly wages, and fourth 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 fourth quarter 2016 | wage, fourth quarter 2015-16 | weekly wage, fourth | | quarter 2015-16 -------------------------------------------------------------------------------------------------------- | | United States $1,067| United States -$16| United States -1.5 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,365| McLean, Ill. -$93| McLean, Ill. -9.2 New York, N.Y. 2,212| Douglas, Colo. -88| Clay, Mo. -8.3 San Mateo, Calif. 2,098| Clay, Mo. -83| Lafayette, La. -8.0 San Francisco, Calif. 2,068| Morris, N.J. -80| Douglas, Colo. -6.8 Suffolk, Mass. 1,888| Lafayette, La. -79| Passaic, N.J. -6.0 Washington, D.C. 1,763| Washington, Ore. -75| Washington, Ore. -5.8 Arlington, Va. 1,677| Passaic, N.J. -67| Tarrant, Texas -5.7 Fairfield, Conn. 1,676| Fairfield, Conn. -66| Sedgwick, Kan. -5.5 Fairfax, Va. 1,610| Lake, Ill. -65| Harford, Md. -5.4 Somerset, N.J. 1,563| Harris, Texas -65| Fort Bend, Texas -5.2 -------------------------------------------------------------------------------------------------------- Ten Largest U.S. Counties Among the 10 largest counties, 9 had over-the-year percentage increases in employment in December 2016. King, Wash., 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 11,720 jobs, or 4.7 percent. Harris, Texas, had the only percentage decrease in employment among the 10 largest counties (-1.3 percent). Within Harris, manufacturing had the largest over-the-year employment level decrease, with a loss of 14,974 jobs, or -8.3 percent. (See table 2.) Average weekly wages decreased over the year in 9 of the 10 largest U.S. counties. Harris, Texas, experienced the largest percentage loss in average weekly wages (-4.7 percent). Within Harris, 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 $92, or -5.2 percent, over the year. King, Wash., had the only percentage gain in average weekly wages among the 10 largest counties (3.5 percent). Within King, trade, transportation, and utilities had the largest impact on the county’s average weekly wage growth with an increase of $249 (20.2 percent) over the year. 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. December 2016 employment and 2016 fourth 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 143.7 million full- and part-time workers. Data for the fourth quarter of 2016 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 first quarter 2017 is scheduled to be released on Wednesday, September 6, 2017. ---------------------------------------------------------------------------------------------------------- | | | Upcoming Industry Changes to QCEW Data | | | | Beginning with the release of first quarter 2017 data, the program will switch to the 2017 version of | | the North American Industry Classification System (NAICS) as the basis for the assignment and | | tabulation of economic data by industry. For more information on the change, please see the Federal | | Register notice at www.census.gov/eos/www/naics/federal_register_notices/notices/fr08au16.pdf. | | | ----------------------------------------------------------------------------------------------------------
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 (NAICS). 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- | 634,000 establish- | submitted by 9.7 | ministrative records| ments | million establish- | submitted by 7.7 | | 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, met-| and contractions at | industry | ropolitan statisti-| the national level | | cal area (MSA), | by NAICS supersec- | | state, and national| tors and by size of | | levels by detailed | firm, and at the | | industry | 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 2015 edition of this publication, which was published in September 2016, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2016 version of this news release. Tables and additional content from the 2015 edition of Employment and Wages Annual Averages Online are now available at www.bls.gov/cew/cewbultn15.htm. The 2016 edition of Employment and Wages Annual Averages Online will be available in September 2017. News releases on quarterly measures of gross job flows also are available from BED at www.bls.gov/bdm, (202) 691-6467, or 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: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 345 largest counties, fourth quarter 2016 Employment Average weekly wage(2) Establishments, County(1) fourth quarter Percent Ranking Percent Ranking 2016 December change, by Fourth change, by (thousands) 2016 December percent quarter fourth percent (thousands) 2015-16(3) change 2016 quarter change 2015-16(3) United States(4)......... 9,869.9 143,749.9 1.2 - $1,067 -1.5 - Jefferson, AL............ 18.5 343.9 0.5 242 1,043 -0.7 95 Madison, AL.............. 9.5 195.2 1.8 114 1,098 -4.0 311 Mobile, AL............... 10.0 169.7 -0.4 308 938 -0.5 76 Montgomery, AL........... 6.4 132.6 1.2 172 942 -0.6 84 Shelby, AL............... 5.8 84.4 -0.4 308 998 -2.2 223 Tuscaloosa, AL........... 4.5 92.5 -0.3 302 873 -1.0 120 Anchorage, AK............ 8.4 149.2 -2.1 338 1,082 -4.9 327 Maricopa, AZ............. 96.5 1,926.9 2.4 76 994 -2.3 233 Pima, AZ................. 18.8 367.2 1.3 164 860 -3.4 288 Benton, AR............... 6.2 117.5 3.1 32 1,017 -2.5 242 Pulaski, AR.............. 14.5 250.7 0.6 230 949 -2.6 252 Washington, AR........... 5.9 104.7 1.8 114 950 -0.2 60 Alameda, CA.............. 61.4 760.6 2.0 105 1,377 -1.9 191 Butte, CA................ 8.2 81.3 1.8 114 790 -1.3 144 Contra Costa, CA......... 31.7 364.3 2.0 105 1,289 0.2 39 Fresno, CA............... 33.6 371.4 1.8 114 857 1.2 16 Kern, CA................. 18.1 310.3 0.8 211 868 -2.0 198 Los Angeles, CA.......... 472.0 4,415.7 1.1 184 1,256 -0.6 84 Marin, CA................ 12.5 115.3 1.2 172 1,378 4.3 3 Merced, CA............... 6.4 75.9 3.2 28 807 1.3 15 Monterey, CA............. 13.4 170.2 2.4 76 915 -0.2 60 Napa, CA................. 5.8 73.2 0.4 250 1,065 -0.2 60 Orange, CA............... 116.3 1,588.8 2.0 105 1,200 -0.6 84 Placer, CA............... 12.5 157.4 2.9 48 1,083 2.0 10 Riverside, CA............ 60.1 707.1 3.1 32 835 -0.5 76 Sacramento, CA........... 55.8 643.7 2.0 105 1,132 -0.4 70 San Bernardino, CA....... 56.1 725.7 -0.1 287 890 0.5 33 San Diego, CA............ 107.8 1,427.5 1.6 139 1,170 -1.5 164 San Francisco, CA........ 59.9 715.5 2.7 58 2,068 3.7 5 San Joaquin, CA.......... 17.4 242.6 3.4 20 893 -0.3 67 San Luis Obispo, CA...... 10.3 113.7 1.9 110 884 -2.5 242 San Mateo, CA............ 27.6 398.8 1.7 130 2,098 -1.5 164 Santa Barbara, CA........ 15.3 192.0 0.0 281 1,025 -1.2 138 Santa Clara, CA.......... 70.9 1,064.0 2.5 71 2,365 0.9 18 Santa Cruz, CA........... 9.5 99.4 1.6 139 933 -2.0 198 Solano, CA............... 11.0 138.2 1.9 110 1,074 -0.9 110 Sonoma, CA............... 19.6 203.5 1.5 146 1,018 -2.5 242 Stanislaus, CA........... 14.9 182.3 1.5 146 884 0.0 49 Tulare, CA............... 10.0 160.0 3.1 32 772 0.9 18 Ventura, CA.............. 26.4 322.2 -0.1 287 1,044 -1.6 168 Yolo, CA................. 6.6 98.2 0.9 205 1,106 -3.7 301 Adams, CO................ 10.5 202.0 3.6 14 1,022 -1.3 144 Arapahoe, CO............. 21.6 324.6 1.6 139 1,227 -1.8 183 Boulder, CO.............. 14.7 179.9 3.0 39 1,237 -2.4 237 Denver, CO............... 30.7 501.7 2.8 50 1,287 -0.4 70 Douglas, CO.............. 11.6 118.9 1.8 114 1,204 -6.8 341 El Paso, CO.............. 18.5 268.0 2.7 58 943 -1.4 149 Jefferson, CO............ 21.5 234.4 0.7 223 1,072 -0.9 110 Larimer, CO.............. 11.6 154.0 2.5 71 980 -0.6 84 Weld, CO................. 6.8 100.4 0.2 264 900 -2.9 268 Fairfield, CT............ 35.2 426.8 -0.9 322 1,676 -3.8 306 Hartford, CT............. 27.6 512.3 0.3 257 1,264 -3.2 282 New Haven, CT............ 23.8 368.5 0.4 250 1,094 -2.8 266 New London, CT........... 7.4 123.3 0.8 211 1,023 -3.3 286 New Castle, DE........... 19.5 291.3 -0.8 318 1,166 -2.6 252 Washington, DC........... 39.5 760.9 0.5 242 1,763 0.1 40 Alachua, FL.............. 7.1 130.3 3.0 39 864 -5.1 333 Bay, FL.................. 5.5 76.1 0.3 257 783 -0.6 84 Brevard, FL.............. 15.5 206.1 4.2 6 941 -1.6 168 Broward, FL.............. 68.7 802.5 1.8 114 1,000 -1.9 191 Collier, FL.............. 13.7 150.2 3.6 14 915 -4.5 323 Duval, FL................ 29.0 499.0 2.6 65 1,001 -1.2 138 Escambia, FL............. 8.2 131.7 2.8 50 830 -3.3 286 Hillsborough, FL......... 41.5 686.9 2.3 89 1,010 -2.6 252 Lake, FL................. 8.0 96.0 3.2 28 721 -2.4 237 Lee, FL.................. 21.7 259.8 2.5 71 844 0.4 37 Leon, FL................. 8.7 148.3 2.1 98 860 -2.6 252 Manatee, FL.............. 10.6 122.7 1.2 172 809 -0.9 110 Marion, FL............... 8.2 102.7 3.5 19 750 -0.1 55 Miami-Dade, FL........... 97.5 1,132.9 1.3 164 1,029 -2.5 242 Okaloosa, FL............. 6.3 81.4 1.9 110 869 -1.0 120 Orange, FL............... 41.2 813.7 2.7 58 940 -0.5 76 Osceola, FL.............. 6.7 90.1 2.1 98 724 -0.5 76 Palm Beach, FL........... 55.5 602.8 2.4 76 1,055 -2.4 237 Pasco, FL................ 10.7 117.1 3.0 39 738 -1.5 164 Pinellas, FL............. 32.6 428.2 2.4 76 965 -1.6 168 Polk, FL................. 13.0 215.0 2.1 98 799 -2.2 223 Sarasota, FL............. 15.7 169.1 2.9 48 902 -1.0 120 Seminole, FL............. 14.8 188.1 4.2 6 897 0.0 49 Volusia, FL.............. 14.1 170.9 3.7 11 761 0.1 40 Bibb, GA................. 4.6 83.0 1.4 156 816 -2.6 252 Chatham, GA.............. 8.8 150.4 1.7 130 886 -3.7 301 Clayton, GA.............. 4.5 124.1 2.2 90 1,006 11.3 1 Cobb, GA................. 24.2 353.4 2.6 65 1,094 -1.9 191 DeKalb, GA............... 20.0 298.7 1.2 172 1,067 0.5 33 Fulton, GA............... 47.9 845.7 3.7 11 1,387 -2.0 198 Gwinnett, GA............. 27.4 350.2 2.6 65 1,022 -1.2 138 Hall, GA................. 4.7 84.4 2.4 76 929 0.1 40 Muscogee, GA............. 5.0 94.0 0.7 223 841 -3.7 301 Richmond, GA............. 4.8 105.5 0.6 230 869 -1.8 183 Honolulu, HI............. 25.7 478.7 0.3 257 994 -1.0 120 Ada, ID.................. 15.1 230.0 3.6 14 937 -0.4 70 Champaign, IL............ 4.3 89.9 -0.8 318 946 3.7 5 Cook, IL................. 152.6 2,590.2 0.6 230 1,250 -1.6 168 DuPage, IL............... 37.9 616.7 -0.1 287 1,209 -2.6 252 Kane, IL................. 13.7 209.9 0.2 264 963 -0.9 110 Lake, IL................. 22.3 332.4 -0.3 302 1,376 -4.5 323 McHenry, IL.............. 8.7 96.7 0.1 268 891 -2.0 198 McLean, IL............... 3.8 83.8 -0.7 316 918 -9.2 344 Madison, IL.............. 6.0 100.5 1.7 130 838 -3.8 306 Peoria, IL............... 4.5 100.2 -2.1 338 990 -2.3 233 St. Clair, IL............ 5.4 94.4 -0.1 287 830 -2.0 198 Sangamon, IL............. 5.2 127.6 -1.3 327 1,024 -3.5 289 Will, IL................. 16.1 236.8 3.1 32 938 -2.5 242 Winnebago, IL............ 6.6 128.0 -1.4 330 875 -2.5 242 Allen, IN................ 8.8 185.5 0.6 230 848 -2.3 233 Elkhart, IN.............. 4.7 130.3 3.3 24 918 4.0 4 Hamilton, IN............. 9.2 138.0 2.0 105 1,018 0.1 40 Lake, IN................. 10.4 188.5 -0.1 287 910 0.1 40 Marion, IN............... 23.9 598.0 0.7 223 1,053 -0.8 104 St. Joseph, IN........... 5.7 124.3 0.8 211 861 0.6 29 Tippecanoe, IN........... 3.4 83.6 0.7 223 895 -1.1 127 Vanderburgh, IN.......... 4.8 108.1 0.6 230 873 -1.4 149 Johnson, IA.............. 4.2 84.3 2.8 50 951 0.1 40 Linn, IA................. 6.7 129.9 -0.7 316 1,057 -1.1 127 Polk, IA................. 17.2 296.5 1.8 114 1,089 -0.7 95 Scott, IA................ 5.6 91.5 0.2 264 876 -3.1 275 Johnson, KS.............. 23.7 344.2 1.4 156 1,065 -2.9 268 Sedgwick, KS............. 12.8 250.1 0.0 281 903 -5.5 337 Shawnee, KS.............. 5.2 98.5 1.2 172 843 -1.4 149 Wyandotte, KS............ 3.6 91.9 2.2 90 1,035 -0.3 67 Boone, KY................ 4.4 87.4 2.4 76 905 -2.0 198 Fayette, KY.............. 10.9 196.5 -0.3 302 968 3.5 7 Jefferson, KY............ 25.4 469.7 1.9 110 1,026 -2.2 223 Caddo, LA................ 7.3 114.6 -1.2 324 859 -1.9 191 Calcasieu, LA............ 5.2 94.7 1.0 194 922 -4.2 317 East Baton Rouge, LA..... 15.4 269.1 -0.2 296 1,005 -1.0 120 Jefferson, LA............ 13.8 194.8 -0.8 318 957 -2.2 223 Lafayette, LA............ 9.4 129.8 -5.1 344 913 -8.0 342 Orleans, LA.............. 12.4 194.5 -0.2 296 996 -2.1 213 St. Tammany, LA.......... 8.1 88.8 -0.2 296 906 -2.2 223 Cumberland, ME........... 13.9 180.4 1.1 184 981 -2.3 233 Anne Arundel, MD......... 15.1 271.8 1.4 156 1,159 0.9 18 Baltimore, MD............ 21.3 380.8 0.0 281 1,085 -0.6 84 Frederick, MD............ 6.4 101.0 0.3 257 971 -3.6 294 Harford, MD.............. 5.8 93.6 -0.4 308 982 -5.4 336 Howard, MD............... 10.0 169.1 0.7 223 1,298 -1.6 168 Montgomery, MD........... 32.9 471.7 0.8 211 1,422 -0.8 104 Prince George's, MD...... 16.0 322.1 2.4 76 1,094 -1.1 127 Baltimore City, MD....... 13.7 341.0 0.6 230 1,307 0.5 33 Barnstable, MA........... 9.5 91.0 0.9 205 940 -1.8 183 Bristol, MA.............. 17.5 228.3 1.6 139 941 -4.4 319 Essex, MA................ 24.8 324.4 0.4 250 1,124 -2.5 242 Hampden, MA.............. 18.1 210.2 1.0 194 952 -4.1 315 Middlesex, MA............ 54.3 900.3 1.2 172 1,529 -2.0 198 Norfolk, MA.............. 25.2 354.2 1.1 184 1,307 -1.4 149 Plymouth, MA............. 15.7 190.9 1.5 146 1,013 -2.1 213 Suffolk, MA.............. 28.9 669.9 2.1 98 1,888 -3.2 282 Worcester, MA............ 24.8 344.8 0.8 211 1,046 -3.6 294 Genesee, MI.............. 6.9 135.4 0.6 230 889 -3.6 294 Ingham, MI............... 6.0 151.9 2.2 90 1,032 0.1 40 Kalamazoo, MI............ 5.0 118.2 1.4 156 985 -1.4 149 Kent, MI................. 14.3 398.0 1.6 139 936 -1.4 149 Macomb, MI............... 17.6 322.8 1.0 194 1,069 -2.7 259 Oakland, MI.............. 39.2 731.9 1.5 146 1,201 -1.7 181 Ottawa, MI............... 5.6 122.5 1.7 130 952 0.4 37 Saginaw, MI.............. 3.9 85.7 -0.3 302 865 -0.9 110 Washtenaw, MI............ 8.1 211.3 1.5 146 1,100 -1.4 149 Wayne, MI................ 30.5 722.7 1.7 130 1,188 -1.8 183 Anoka, MN................ 6.8 121.9 1.3 164 988 -4.6 325 Dakota, MN............... 9.5 188.3 1.0 194 1,010 -3.9 309 Hennepin, MN............. 41.1 920.7 2.2 90 1,290 -1.1 127 Olmsted, MN.............. 3.3 96.1 1.1 184 1,073 0.9 18 Ramsey, MN............... 12.8 328.5 -0.1 287 1,166 -1.6 168 St. Louis, MN............ 5.1 96.9 -0.1 287 870 0.1 40 Stearns, MN.............. 4.2 86.1 0.5 242 868 -1.8 183 Washington, MN........... 5.2 82.1 0.5 242 899 -0.2 60 Harrison, MS............. 4.6 84.9 0.1 268 731 0.0 49 Hinds, MS................ 5.9 121.9 -0.2 296 870 -2.1 213 Boone, MO................ 4.9 93.5 0.6 230 841 1.9 11 Clay, MO................. 5.6 104.0 3.4 20 921 -8.3 343 Greene, MO............... 8.6 165.7 1.0 194 807 -1.1 127 Jackson, MO.............. 21.2 367.4 1.8 114 1,070 -2.2 223 St. Charles, MO.......... 9.1 145.6 1.3 164 839 -3.6 294 St. Louis, MO............ 36.9 604.3 0.1 268 1,131 -1.6 168 St. Louis City, MO....... 13.5 225.2 0.0 281 1,124 -1.6 168 Yellowstone, MT.......... 6.5 81.0 -0.5 312 916 -0.8 104 Douglas, NE.............. 19.6 340.7 0.7 223 986 -0.8 104 Lancaster, NE............ 10.5 169.5 0.1 268 853 0.0 49 Clark, NV................ 56.4 952.7 2.6 65 909 -1.2 138 Washoe, NV............... 15.0 215.3 3.4 20 942 -1.4 149 Hillsborough, NH......... 12.3 204.2 1.0 194 1,202 -4.9 327 Merrimack, NH............ 5.1 77.5 1.0 194 1,017 -2.7 259 Rockingham, NH........... 10.9 149.1 1.2 172 1,064 -4.9 327 Atlantic, NJ............. 6.6 122.7 -1.7 335 885 -1.3 144 Bergen, NJ............... 33.2 458.7 0.8 211 1,289 -2.7 259 Burlington, NJ........... 11.1 208.1 3.0 39 1,077 -4.2 317 Camden, NJ............... 12.2 205.5 1.7 130 1,076 -1.1 127 Essex, NJ................ 20.7 343.9 0.9 205 1,297 -0.2 60 Gloucester, NJ........... 6.4 109.6 3.0 39 918 -2.4 237 Hudson, NJ............... 15.2 260.6 3.3 24 1,355 -1.6 168 Mercer, NJ............... 11.2 252.0 0.4 250 1,346 -0.1 55 Middlesex, NJ............ 22.4 430.4 3.0 39 1,240 -2.2 223 Monmouth, NJ............. 20.2 260.2 0.6 230 1,068 -2.1 213 Morris, NJ............... 17.1 290.9 0.1 268 1,524 -5.0 332 Ocean, NJ................ 13.1 162.5 1.4 156 871 -3.1 275 Passaic, NJ.............. 12.6 170.1 1.1 184 1,042 -6.0 340 Somerset, NJ............. 10.2 187.6 1.3 164 1,563 -0.7 95 Union, NJ................ 14.5 221.9 1.1 184 1,362 -0.4 70 Bernalillo, NM........... 18.3 327.8 1.2 172 895 -1.4 149 Albany, NY............... 10.4 237.1 1.2 172 1,094 -1.5 164 Bronx, NY................ 18.8 302.7 -0.3 302 1,007 0.6 29 Broome, NY............... 4.6 88.0 0.1 268 799 -4.1 315 Dutchess, NY............. 8.5 112.7 -0.2 296 1,010 -2.7 259 Erie, NY................. 24.9 473.8 0.2 264 941 -2.1 213 Kings, NY................ 62.3 705.6 3.0 39 906 -1.4 149 Monroe, NY............... 19.1 390.0 0.5 242 973 -3.5 289 Nassau, NY............... 54.5 640.4 1.7 130 1,220 -1.4 149 New York, NY............. 129.8 2,471.6 0.7 223 2,212 -1.1 127 Oneida, NY............... 5.4 105.7 1.6 139 811 -3.0 272 Onondaga, NY............. 13.1 248.1 0.5 242 972 -2.1 213 Orange, NY............... 10.5 144.1 1.8 114 886 -2.5 242 Queens, NY............... 53.0 664.0 2.4 76 1,019 -0.9 110 Richmond, NY............. 9.8 118.3 2.2 90 940 -2.4 237 Rockland, NY............. 10.9 124.1 2.8 50 1,037 -3.2 282 Saratoga, NY............. 6.0 84.5 -0.1 287 945 -2.9 268 Suffolk, NY.............. 53.3 661.4 0.9 205 1,147 -3.5 289 Westchester, NY.......... 36.8 431.1 1.2 172 1,395 -3.7 301 Buncombe, NC............. 9.1 130.3 3.1 32 837 -0.7 95 Catawba, NC.............. 4.4 87.3 3.1 32 818 -1.3 144 Cumberland, NC........... 6.2 120.4 0.1 268 799 -1.8 183 Durham, NC............... 8.2 198.7 1.2 172 1,254 -1.6 168 Forsyth, NC.............. 9.2 184.8 0.4 250 953 -2.2 223 Guilford, NC............. 14.3 283.9 0.8 211 898 -3.1 275 Mecklenburg, NC.......... 37.3 674.2 2.1 98 1,193 -0.7 95 New Hanover, NC.......... 8.0 110.5 2.7 58 865 -0.2 60 Wake, NC................. 33.7 541.5 3.2 28 1,085 0.7 25 Cass, ND................. 7.2 117.8 0.6 230 961 -1.7 181 Butler, OH............... 7.6 154.1 1.1 184 925 -2.5 242 Cuyahoga, OH............. 35.8 723.3 0.1 268 1,088 -0.7 95 Delaware, OH............. 5.1 86.2 2.6 65 996 -0.6 84 Franklin, OH............. 31.7 759.2 2.8 50 1,023 -4.4 319 Hamilton, OH............. 23.8 514.8 1.4 156 1,119 -2.0 198 Lake, OH................. 6.3 94.2 -1.0 323 865 -2.9 268 Lorain, OH............... 6.2 97.8 0.8 211 825 -2.5 242 Lucas, OH................ 10.1 211.0 -0.4 308 903 -4.0 311 Mahoning, OH............. 5.9 98.7 -0.5 312 746 -2.2 223 Montgomery, OH........... 11.9 255.6 0.3 257 896 -3.0 272 Stark, OH................ 8.6 158.7 0.1 268 795 -3.2 282 Summit, OH............... 14.3 268.6 0.6 230 944 -1.4 149 Warren, OH............... 4.8 89.8 1.5 146 946 -1.3 144 Cleveland, OK............ 5.6 80.5 -1.2 324 766 -1.9 191 Oklahoma, OK............. 27.8 449.7 -1.4 330 975 -4.9 327 Tulsa, OK................ 22.1 353.4 -0.6 314 942 -3.6 294 Clackamas, OR............ 14.6 159.6 2.4 76 987 -1.0 120 Jackson, OR.............. 7.3 87.3 2.4 76 803 1.5 12 Lane, OR................. 11.9 153.9 2.4 76 845 0.8 24 Marion, OR............... 10.4 149.4 2.8 50 861 0.7 25 Multnomah, OR............ 34.2 498.8 1.8 114 1,099 -0.1 55 Washington, OR........... 19.0 288.2 2.8 50 1,209 -5.8 339 Allegheny, PA............ 35.8 693.9 0.3 257 1,140 -1.1 127 Berks, PA................ 9.0 172.6 0.5 242 941 -3.1 275 Bucks, PA................ 20.0 263.1 1.5 146 1,021 -1.8 183 Butler, PA............... 5.0 85.3 -0.3 302 1,004 0.7 25 Chester, PA.............. 15.5 251.2 0.8 211 1,308 -3.5 289 Cumberland, PA........... 6.5 134.0 0.5 242 921 -3.6 294 Dauphin, PA.............. 7.5 180.6 0.4 250 1,030 -4.9 327 Delaware, PA............. 14.1 224.1 1.1 184 1,111 -3.0 272 Erie, PA................. 7.0 121.8 -2.2 340 809 -4.0 311 Lackawanna, PA........... 5.8 99.1 1.0 194 797 -1.6 168 Lancaster, PA............ 13.4 237.2 1.5 146 877 -3.1 275 Lehigh, PA............... 8.8 189.1 -0.2 296 1,051 -2.1 213 Luzerne, PA.............. 7.5 144.6 -1.9 337 816 -0.5 76 Montgomery, PA........... 27.7 493.5 1.3 164 1,288 -3.1 275 Northampton, PA.......... 6.8 115.4 4.4 5 896 -3.7 301 Philadelphia, PA......... 35.1 676.1 2.1 98 1,235 -3.9 309 Washington, PA........... 5.5 85.3 -1.3 327 1,110 4.9 2 Westmoreland, PA......... 9.3 133.3 -1.6 334 833 -4.0 311 York, PA................. 9.1 178.5 0.8 211 909 -2.0 198 Providence, RI........... 18.0 287.7 0.1 268 1,077 -2.2 223 Charleston, SC........... 14.9 245.0 1.7 130 937 0.9 18 Greenville, SC........... 13.9 266.6 1.3 164 932 -0.5 76 Horry, SC................ 8.7 117.9 3.1 32 654 0.0 49 Lexington, SC............ 6.3 119.3 1.8 114 794 -0.4 70 Richland, SC............. 10.1 219.7 0.9 205 885 -2.6 252 Spartanburg, SC.......... 6.1 136.7 3.3 24 877 -2.1 213 York, SC................. 5.4 91.7 4.6 2 851 0.5 33 Minnehaha, SD............ 7.2 126.1 1.8 114 921 -0.9 110 Davidson, TN............. 21.8 481.3 3.0 39 1,163 -0.9 110 Hamilton, TN............. 9.4 199.6 1.3 164 1,004 -2.7 259 Knox, TN................. 12.0 239.6 1.5 146 959 -2.0 198 Rutherford, TN........... 5.4 121.0 2.6 65 947 -0.6 84 Shelby, TN............... 20.3 500.3 0.0 281 1,087 -0.8 104 Williamson, TN........... 8.5 127.8 5.1 1 1,208 -2.0 198 Bell, TX................. 5.2 118.0 0.1 268 883 -0.5 76 Bexar, TX................ 40.4 855.8 2.2 90 956 -1.1 127 Brazoria, TX............. 5.6 107.3 1.7 130 1,049 -3.8 306 Brazos, TX............... 4.5 102.5 1.5 146 785 0.9 18 Cameron, TX.............. 6.5 140.4 1.2 172 640 -2.0 198 Collin, TX............... 23.9 389.5 3.4 20 1,222 -0.7 95 Dallas, TX............... 75.6 1,688.4 2.8 50 1,279 -0.9 110 Denton, TX............... 14.4 232.4 3.7 11 969 -0.9 110 El Paso, TX.............. 14.8 302.3 1.8 114 729 -2.0 198 Fort Bend, TX............ 12.7 177.2 1.8 114 976 -5.2 335 Galveston, TX............ 6.2 110.0 4.0 8 918 -1.6 168 Gregg, TX................ 4.3 74.1 -3.5 343 862 -5.1 333 Harris, TX............... 113.7 2,272.0 -1.3 327 1,319 -4.7 326 Hidalgo, TX.............. 12.2 256.1 3.0 39 648 -2.0 198 Jefferson, TX............ 5.8 121.4 -1.5 332 1,081 -2.8 266 Lubbock, TX.............. 7.5 140.1 2.2 90 835 -0.4 70 McLennan, TX............. 5.2 112.8 2.1 98 859 -1.0 120 Midland, TX.............. 5.4 84.8 -2.9 342 1,297 2.4 9 Montgomery, TX........... 10.9 171.3 1.1 184 1,024 -1.8 183 Nueces, TX............... 8.3 162.6 0.1 268 901 -2.7 259 Potter, TX............... 4.0 79.8 0.0 281 874 0.0 49 Smith, TX................ 6.1 103.1 0.8 211 865 -2.0 198 Tarrant, TX.............. 42.3 876.2 2.5 71 1,027 -5.7 338 Travis, TX............... 39.5 717.2 2.4 76 1,244 1.1 17 Webb, TX................. 5.3 100.0 1.4 156 683 -3.5 289 Williamson, TX........... 10.2 162.5 4.5 3 1,007 -0.7 95 Davis, UT................ 8.3 122.8 3.6 14 862 -0.2 60 Salt Lake, UT............ 44.3 682.1 2.7 58 1,028 -0.7 95 Utah, UT................. 15.6 225.1 4.5 3 858 -1.2 138 Weber, UT................ 5.9 103.2 1.4 156 791 0.6 29 Chittenden, VT........... 6.8 102.0 -0.6 314 1,033 -3.6 294 Arlington, VA............ 9.4 174.3 0.8 211 1,677 -1.4 149 Chesterfield, VA......... 9.1 138.7 -1.5 332 901 0.7 25 Fairfax, VA.............. 37.9 604.5 1.0 194 1,610 -0.6 84 Henrico, VA.............. 11.7 192.6 0.6 230 1,009 -1.1 127 Loudoun, VA.............. 12.2 161.8 2.2 90 1,233 -0.1 55 Prince William, VA....... 9.4 128.4 1.8 114 931 -0.5 76 Alexandria City, VA...... 6.6 94.9 -1.2 324 1,497 -0.8 104 Chesapeake City, VA...... 6.1 100.2 1.0 194 808 -2.1 213 Newport News City, VA.... 3.9 97.3 -1.7 335 1,017 0.6 29 Norfolk City, VA......... 5.9 141.8 -0.8 318 1,072 -3.1 275 Richmond City, VA........ 7.8 155.1 2.4 76 1,139 -1.1 127 Virginia Beach City, VA.. 12.3 176.2 0.3 257 836 -1.9 191 Benton, WA............... 5.7 84.3 3.8 10 1,013 -4.4 319 Clark, WA................ 14.5 151.5 3.2 28 985 1.4 13 King, WA................. 86.4 1,340.4 3.3 24 1,479 3.5 7 Kitsap, WA............... 6.6 87.1 0.9 205 969 -1.4 149 Pierce, WA............... 21.9 301.3 3.6 14 932 -1.2 138 Snohomish, WA............ 20.8 285.1 1.1 184 1,114 -1.9 191 Spokane, WA.............. 15.7 217.6 2.7 58 882 -0.3 67 Thurston, WA............. 8.2 111.8 4.0 8 934 1.4 13 Whatcom, WA.............. 7.3 88.4 2.5 71 852 0.1 40 Yakima, WA............... 7.8 102.3 2.7 58 736 -0.1 55 Kanawha, WV.............. 5.8 101.7 -2.2 340 881 -1.6 168 Brown, WI................ 6.8 155.7 1.0 194 956 -2.1 213 Dane, WI................. 15.2 334.0 1.8 114 1,033 -4.4 319 Milwaukee, WI............ 26.1 487.8 -0.1 287 1,041 -0.6 84 Outagamie, WI............ 5.2 107.3 0.4 250 920 -0.6 84 Waukesha, WI............. 12.9 239.6 0.1 268 1,073 -1.4 149 Winnebago, WI............ 3.7 93.9 1.6 139 1,005 -2.7 259 San Juan, PR............. 10.7 255.8 -0.2 (5) 672 -0.7 (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 344 U.S. counties comprise 72.8 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, fourth quarter 2016 Employment Average weekly wage(1) Establishments, fourth quarter County by NAICS supersector 2016 Percent Percent (thousands) December change, Fourth change, 2016 December quarter fourth (thousands) 2015-16(2) 2016 quarter 2015-16(2) United States(3) ............................ 9,869.9 143,749.9 1.2 $1,067 -1.5 Private industry........................... 9,570.8 121,881.5 1.3 1,070 -1.7 Natural resources and mining............. 138.1 1,741.7 -4.7 1,106 -5.1 Construction............................. 784.5 6,677.4 2.0 1,234 0.1 Manufacturing............................ 345.7 12,298.6 -0.4 1,285 -3.1 Trade, transportation, and utilities..... 1,924.2 27,968.1 1.1 880 -2.2 Information.............................. 159.2 2,809.0 0.0 1,884 -1.5 Financial activities..................... 865.0 8,034.0 1.5 1,706 -0.4 Professional and business services....... 1,779.7 20,259.2 1.0 1,419 -1.4 Education and health services............ 1,626.7 21,994.4 2.2 973 -2.0 Leisure and hospitality.................. 827.9 15,365.3 1.8 461 -0.2 Other services........................... 840.2 4,387.5 1.1 719 -0.7 Government................................. 299.1 21,868.4 0.8 1,049 -0.2 Los Angeles, CA.............................. 472.0 4,415.7 1.1 1,256 -0.6 Private industry........................... 465.8 3,838.1 1.1 1,246 -0.8 Natural resources and mining............. 0.5 8.7 0.3 1,411 -7.0 Construction............................. 13.9 133.2 1.5 1,276 0.7 Manufacturing............................ 12.3 350.9 -3.3 1,385 1.2 Trade, transportation, and utilities..... 53.3 844.4 0.9 961 -0.1 Information.............................. 9.6 225.9 -0.3 2,306 -8.5 Financial activities..................... 25.3 219.9 0.5 1,932 0.8 Professional and business services....... 47.9 605.5 -0.1 1,631 0.9 Education and health services............ 218.5 758.7 1.9 929 -0.1 Leisure and hospitality.................. 32.6 512.7 2.8 986 0.4 Other services........................... 26.8 148.0 0.8 736 -0.1 Government................................. 6.2 577.6 1.6 1,325 0.1 Cook, IL..................................... 152.6 2,590.2 0.6 1,250 -1.6 Private industry........................... 151.3 2,289.0 0.6 1,255 -1.7 Natural resources and mining............. 0.1 1.0 -7.9 1,324 -1.3 Construction............................. 12.2 71.5 0.4 1,625 -0.1 Manufacturing............................ 6.3 185.5 -1.0 1,355 -3.6 Trade, transportation, and utilities..... 29.7 491.9 0.6 980 0.4 Information.............................. 2.7 55.7 4.6 1,705 -5.3 Financial activities..................... 15.1 193.2 0.4 2,284 -0.8 Professional and business services....... 32.4 473.4 -0.3 1,669 -1.6 Education and health services............ 16.3 441.1 1.2 1,028 -2.0 Leisure and hospitality.................. 14.2 273.5 0.9 526 -2.6 Other services........................... 17.3 96.3 -0.4 955 -1.1 Government................................. 1.3 301.3 0.8 1,207 -1.1 New York, NY................................. 129.8 2,471.6 0.7 2,212 -1.1 Private industry........................... 128.9 2,202.4 0.8 2,319 -1.4 Natural resources and mining............. 0.0 0.2 9.1 2,094 4.8 Construction............................. 2.2 40.2 -1.0 2,343 1.1 Manufacturing............................ 2.1 26.3 -5.0 1,649 -1.1 Trade, transportation, and utilities..... 19.3 265.9 -1.7 1,474 -1.3 Information.............................. 4.8 162.9 0.4 2,808 0.5 Financial activities..................... 19.2 371.5 -0.2 4,587 -0.4 Professional and business services....... 27.4 564.9 1.4 2,599 -3.3 Education and health services............ 9.9 346.8 0.3 1,390 0.8 Leisure and hospitality.................. 13.7 303.8 1.4 1,014 -1.6 Other services........................... 20.3 103.3 0.5 1,195 -1.0 Government................................. 0.8 269.2 0.5 1,335 2.6 Harris, TX................................... 113.7 2,272.0 -1.3 1,319 -4.7 Private industry........................... 113.2 1,993.6 -1.8 1,344 -5.1 Natural resources and mining............. 1.8 72.9 -13.4 3,416 -3.3 Construction............................. 7.2 155.9 -4.1 1,477 -2.6 Manufacturing............................ 4.8 166.3 -8.3 1,652 -2.3 Trade, transportation, and utilities..... 25.0 477.2 -0.8 1,121 -5.5 Information.............................. 1.2 27.3 0.7 1,478 -2.4 Financial activities..................... 11.9 124.5 1.4 1,768 -3.3 Professional and business services....... 23.2 384.1 -3.7 1,693 -5.2 Education and health services............ 15.9 292.3 2.7 1,080 -1.3 Leisure and hospitality.................. 9.8 226.8 2.1 470 -0.6 Other services........................... 11.7 64.8 -0.5 822 -1.8 Government................................. 0.6 278.4 2.4 1,142 -0.5 Maricopa, AZ................................. 96.5 1,926.9 2.4 994 -2.3 Private industry........................... 95.7 1,713.5 2.6 995 -2.1 Natural resources and mining............. 0.4 8.3 0.4 935 -1.6 Construction............................. 6.9 103.1 4.0 1,116 -0.7 Manufacturing............................ 3.1 115.7 -1.3 1,375 -5.0 Trade, transportation, and utilities..... 18.5 386.8 2.5 893 -2.3 Information.............................. 1.5 34.4 -1.1 1,386 1.5 Financial activities..................... 10.8 173.2 5.7 1,316 0.5 Professional and business services....... 20.9 331.7 1.0 1,108 -1.3 Education and health services............ 10.7 289.8 3.0 993 -4.2 Leisure and hospitality.................. 7.6 207.9 2.0 477 -2.3 Other services........................... 6.0 49.6 -0.5 722 0.7 Government................................. 0.7 213.4 0.7 985 -4.1 Dallas, TX................................... 75.6 1,688.4 2.8 1,279 -0.9 Private industry........................... 75.0 1,514.5 3.1 1,290 -1.3 Natural resources and mining............. 0.6 8.6 -6.4 4,042 13.5 Construction............................. 4.5 86.3 5.1 1,368 2.5 Manufacturing............................ 2.7 110.0 1.1 1,420 -6.2 Trade, transportation, and utilities..... 16.0 355.8 3.2 1,079 -3.1 Information.............................. 1.4 48.9 0.9 1,821 -0.1 Financial activities..................... 9.3 161.1 4.4 1,759 -0.7 Professional and business services....... 17.0 342.1 2.7 1,574 0.2 Education and health services............ 9.4 197.5 2.5 1,153 -1.1 Leisure and hospitality.................. 6.7 160.3 4.3 542 -1.8 Other services........................... 7.0 42.7 2.1 808 -1.3 Government................................. 0.6 174.0 0.3 1,184 2.7 Orange, CA................................... 116.3 1,588.8 2.0 1,200 -0.6 Private industry........................... 114.9 1,442.2 2.2 1,201 -0.8 Natural resources and mining............. 0.2 2.7 3.8 965 4.2 Construction............................. 6.7 96.5 1.7 1,390 0.9 Manufacturing............................ 4.9 157.1 -0.1 1,493 -0.9 Trade, transportation, and utilities..... 16.8 265.6 -0.6 1,044 -0.5 Information.............................. 1.3 25.8 2.8 2,069 1.6 Financial activities..................... 10.9 118.1 0.9 2,038 1.8 Professional and business services....... 20.6 304.7 4.0 1,407 -3.8 Education and health services............ 30.5 204.6 3.1 996 -3.7 Leisure and hospitality.................. 8.5 210.9 2.1 529 7.5 Other services........................... 6.8 46.5 2.7 734 0.1 Government................................. 1.5 146.6 1.0 1,189 1.4 San Diego, CA................................ 107.8 1,427.5 1.6 1,170 -1.5 Private industry........................... 105.9 1,194.0 1.5 1,145 -2.2 Natural resources and mining............. 0.6 7.9 -8.3 744 1.6 Construction............................. 6.7 77.1 5.4 1,272 0.7 Manufacturing............................ 3.2 106.5 -0.9 1,586 -10.4 Trade, transportation, and utilities..... 14.1 229.0 0.3 846 -2.5 Information.............................. 1.1 23.5 -1.5 1,759 0.6 Financial activities..................... 9.7 72.8 1.7 1,548 0.2 Professional and business services....... 18.0 232.9 -0.7 1,764 0.3 Education and health services............ 30.0 195.1 2.0 1,007 -2.8 Leisure and hospitality.................. 8.1 190.2 3.6 513 3.2 Other services........................... 7.3 50.5 1.0 647 1.6 Government................................. 1.9 233.5 1.8 1,295 1.3 King, WA..................................... 86.4 1,340.4 3.3 1,479 3.5 Private industry........................... 85.9 1,171.5 3.4 1,501 3.9 Natural resources and mining............. 0.4 2.9 -3.0 1,208 -9.3 Construction............................. 6.5 68.2 6.2 1,386 0.3 Manufacturing............................ 2.5 102.0 -3.9 1,662 0.0 Trade, transportation, and utilities..... 14.5 261.7 4.7 1,484 20.2 Information.............................. 2.2 99.8 9.2 2,865 -1.7 Financial activities..................... 6.6 67.9 3.0 1,773 2.0 Professional and business services....... 17.5 220.5 1.7 1,828 0.2 Education and health services............ 19.5 169.7 4.1 1,055 -0.3 Leisure and hospitality.................. 7.2 134.5 3.8 572 0.5 Other services........................... 9.1 44.4 3.6 858 1.7 Government................................. 0.5 168.9 2.4 1,326 0.8 Miami-Dade, FL............................... 97.5 1,132.9 1.3 1,029 -2.5 Private industry........................... 97.2 993.1 1.2 1,016 -1.7 Natural resources and mining............. 0.5 9.0 -6.7 684 4.7 Construction............................. 6.4 44.6 7.0 986 -1.9 Manufacturing............................ 2.9 40.5 1.2 968 -2.0 Trade, transportation, and utilities..... 26.4 289.3 -0.1 911 -2.4 Information.............................. 1.5 18.1 2.1 1,682 0.2 Financial activities..................... 10.6 75.3 0.5 1,636 -1.2 Professional and business services....... 21.5 158.1 1.8 1,314 -1.2 Education and health services............ 10.4 176.0 2.4 1,036 -2.1 Leisure and hospitality.................. 7.2 141.0 0.5 606 -2.1 Other services........................... 8.3 40.1 1.3 634 -2.3 Government................................. 0.3 139.8 1.8 1,119 -7.3 (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, fourth quarter 2016 Employment Average weekly wage(1) Establishments, fourth quarter State 2016 Percent Percent (thousands) December change, Fourth change, 2016 December quarter fourth (thousands) 2015-16 2016 quarter 2015-16 United States(2)........... 9,869.9 143,749.9 1.2 $1,067 -1.5 Alabama.................... 123.6 1,932.6 0.7 901 -1.3 Alaska..................... 22.3 310.0 -1.9 1,038 -5.2 Arizona.................... 156.9 2,760.1 2.1 945 -2.2 Arkansas................... 89.4 1,205.4 0.4 827 -1.4 California................. 1,509.9 16,923.3 1.9 1,271 -0.3 Colorado................... 192.6 2,588.6 2.0 1,086 -1.5 Connecticut................ 117.7 1,685.5 0.0 1,289 -3.4 Delaware................... 31.5 441.2 -0.1 1,055 -2.9 District of Columbia....... 39.5 760.9 0.5 1,763 0.6 Florida.................... 673.4 8,538.9 2.7 942 -1.8 Georgia.................... 305.5 4,349.3 2.4 993 -0.9 Hawaii..................... 40.7 658.3 0.7 954 -0.3 Idaho...................... 59.7 691.6 3.2 800 -0.4 Illinois................... 404.3 5,947.6 0.4 1,122 -2.0 Indiana.................... 162.7 3,021.7 0.9 883 -0.9 Iowa....................... 101.7 1,542.0 0.1 911 -1.0 Kansas..................... 90.8 1,384.5 0.1 877 -2.2 Kentucky................... 123.8 1,894.2 0.6 874 -1.4 Louisiana.................. 129.7 1,907.4 -1.6 914 -2.9 Maine...................... 54.1 602.6 0.8 855 -2.1 Maryland................... 170.5 2,666.7 1.0 1,169 -0.4 Massachusetts.............. 249.2 3,530.4 1.3 1,352 -2.4 Michigan................... 242.0 4,283.0 1.5 1,026 -1.6 Minnesota.................. 164.2 2,839.7 1.2 1,062 -1.1 Mississippi................ 74.4 1,134.0 0.0 756 -1.8 Missouri................... 196.4 2,783.2 0.9 918 -1.7 Montana.................... 46.6 456.5 0.7 822 0.5 Nebraska................... 74.2 972.4 0.0 876 -0.5 Nevada..................... 82.7 1,307.8 2.7 924 -1.2 New Hampshire.............. 52.3 656.9 1.3 1,092 -4.1 New Jersey................. 271.6 4,042.1 1.4 1,239 -1.9 New Mexico................. 58.3 811.4 0.0 844 -2.5 New York................... 647.2 9,332.5 1.2 1,342 -2.3 North Carolina............. 269.9 4,326.3 1.8 932 -0.7 North Dakota............... 32.2 414.4 -3.2 978 -4.2 Ohio....................... 294.0 5,365.6 0.7 943 -2.3 Oklahoma................... 109.7 1,587.7 -1.2 864 -3.5 Oregon..................... 149.2 1,860.7 2.4 970 -1.0 Pennsylvania............... 356.9 5,799.8 0.7 1,039 -2.3 Rhode Island............... 37.1 478.3 0.0 1,027 -1.6 South Carolina............. 126.7 2,024.3 1.8 855 -0.6 South Dakota............... 33.2 419.9 0.5 828 -0.5 Tennessee.................. 155.5 2,947.5 1.8 970 -1.1 Texas...................... 662.5 11,974.7 1.2 1,072 -2.5 Utah....................... 98.4 1,415.1 2.9 910 -0.3 Vermont.................... 25.3 312.6 0.1 897 -2.4 Virginia................... 270.2 3,831.6 0.6 1,091 -0.3 Washington................. 240.2 3,227.9 2.8 1,150 1.7 West Virginia.............. 50.7 693.1 -1.6 809 -2.5 Wisconsin.................. 172.7 2,842.4 0.5 924 -2.0 Wyoming.................... 26.1 265.8 -3.9 894 -4.7 Puerto Rico................ 45.7 928.2 -0.3 555 -1.9 Virgin Islands............. 3.4 38.5 0.2 769 -1.8 (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.