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For release 10:00 a.m. (EDT), Wednesday, June 17, 2015 USDL-15-1163 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Fourth Quarter 2014 From December 2013 to December 2014, employment increased in 319 of the 339 largest U.S. counties (counties with 75,000 or more jobs in 2013), the U.S. Bureau of Labor Statistics reported today. Weld, Colo., and Midland, Texas, had the largest percentage increases, with gains of 8.0 percent each over the year, compared with national job growth of 2.2 percent. Within Weld, the largest employment increase occurred in natural resources and mining, which gained 2,074 jobs over the year (19.6 percent). Within Midland, the largest employment increase also occurred in natural resources and mining, which gained 3,135 jobs over the year (14.9 percent). Atlantic, N.J., had the largest over-the-year percentage decrease in employment among the largest counties in the U.S. with a loss of 5.0 percent. County employment and wage data are compiled under the Quarterly Census of Employment and Wages (QCEW) program, which produces detailed information on county employment and wages within 6 months after the end of each quarter. The U.S. average weekly wage increased 3.5 percent over the year, growing to $1,035 in the fourth quarter of 2014. Benton, Ark., had the largest over-the-year percentage increase in average weekly wages with a gain of 9.9 percent. Within Benton, an average weekly wage gain of $209, or 16.2 percent, in professional and business services made the largest contribution to the county’s increase in average weekly wages. San Mateo, Calif., experienced the largest percentage decrease in average weekly wages with a loss of 20.4 percent over the year. Table A. Large counties ranked by December 2014 employment, December 2013-14 employment increase, and December 2013-14 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- December 2014 employment | Increase in employment, | Percent increase in employment, (thousands) | December 2013-14 | December 2013-14 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 139,204.8| United States 3,033.7| United States 2.2 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,243.8| Harris, Texas 87.4| Weld, Colo. 8.0 New York, N.Y. 2,568.3| Los Angeles, Calif. 68.3| Midland, Texas 8.0 Cook, Ill. 2,512.5| New York, N.Y. 66.8| Adams, Colo. 6.4 Harris, Texas 2,312.2| Dallas, Texas 64.8| Lee, Fla. 6.2 Maricopa, Ariz. 1,821.9| Maricopa, Ariz. 48.4| Williamson, Tenn. 6.1 Dallas, Texas 1,591.0| Clark, Nev. 41.0| Utah, Utah 5.8 Orange, Calif. 1,506.0| King, Wash. 40.4| Denton, Texas 5.7 San Diego, Calif. 1,359.7| Cook, Ill. 39.3| Montgomery, Texas 5.7 King, Wash. 1,262.8| Orange, Calif. 38.1| Benton, Ark. 5.5 Miami-Dade, Fla. 1,082.5| Miami-Dade, Fla. 35.0| Fort Bend, Texas 5.5 -------------------------------------------------------------------------------------------------------- Large County Employment In December 2014, national employment was 139.2 million (as measured by the QCEW program). Over the year, employment increased 2.2 percent, or 3.0 million. The 339 U.S. counties with 75,000 or more jobs accounted for 72.1 percent of total U.S. employment and 77.4 percent of total wages. These 339 counties had a net job growth of 2.2 million over the year, accounting for 73.4 percent of the overall U.S. employment increase. Weld, Colo., and Midland, Texas, had the largest percentage increases in employment (8.0 percent each) among the largest U.S. counties. The five counties with the largest increases in employment levels were Harris, Texas; Los Angeles, Calif.; New York, N.Y.; Dallas, Texas; and Maricopa, Ariz. These counties had a combined over-the-year employment gain of 335,700 jobs, which was 11.1 percent of the overall job increase for the U.S. (See table A.) Employment declined in 17 of the largest counties from December 2013 to December 2014. Atlantic, N.J., had the largest over-the-year percentage decrease in employment (-5.0 percent). Within Atlantic, leisure and hospitality had the largest decrease in employment, with a loss of 7,333 jobs (-16.8 percent). Norfolk City, Va., had the second largest percentage decrease in employment, followed by McLean, Ill.; Peoria, Ill.; and Lake, Ill. (See table 1.) Table B. Large counties ranked by fourth quarter 2014 average weekly wages, fourth quarter 2013-14 increase in average weekly wages, and fourth quarter 2013-14 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average fourth quarter 2014 | wage, fourth quarter 2013-14 | weekly wage, fourth | | quarter 2013-14 -------------------------------------------------------------------------------------------------------- | | United States $1,035| United States $35| United States 3.5 -------------------------------------------------------------------------------------------------------- | | San Mateo, Calif. $2,166| Santa Clara, Calif. $134| Benton, Ark. 9.9 New York, N.Y. 2,138| Midland, Texas 118| Washington, Pa. 9.2 Santa Clara, Calif. 2,114| Suffolk, Mass. 108| Midland, Texas 9.0 Suffolk, Mass. 1,856| Douglas, Colo. 100| Brazoria, Texas 8.9 San Francisco, Calif. 1,850| New York, N.Y. 91| Douglas, Colo. 8.8 Washington, D.C. 1,696| Washington, Pa. 91| Clayton, Ga. 7.6 Fairfield, Conn. 1,674| Benton, Ark. 90| Jefferson, Texas 7.6 Arlington, Va. 1,613| San Francisco, Calif. 87| Rockingham, N.H. 7.4 Fairfax, Va. 1,584| Brazoria, Texas 86| Yolo, Calif. 7.1 Somerset, N.J. 1,543| King, Wash. 81| Vanderburgh, Ind. 7.0 | | Atlantic, N.J. 7.0 | | Hamilton, Tenn. 7.0 | | Nueces, Texas 7.0 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased to $1,035, a 3.5 percent increase, during the year ending in the fourth quarter of 2014. Among the 339 largest counties, 332 had over-the-year increases in average weekly wages. Benton, Ark., had the largest percentage wage increase among the largest U.S. counties (9.9 percent). Of the 339 largest counties, 7 experienced over-the-year decreases in average weekly wages. San Mateo, Calif., had the largest percentage decrease in average weekly wages, with a loss of 20.4 percent. Within San Mateo, information had the largest impact on the county’s average weekly wage decrease. Within this industry, average weekly wages declined by $8,606 (-60.1 percent) over the year. This decline in average weekly wages is partially due to wages returning to normal after higher levels in 2012 and 2013. Olmsted, Minn., had the second largest percentage decrease in average weekly wages, followed by Morris, N.J.; Rockland, N.Y.; Camden, N.J.; and Butler, Pa. (See table 1.) Ten Largest U.S. Counties All of the 10 largest counties had over-the-year percentage increases in employment in December 2014. Dallas, Texas, had the largest gain (4.2 percent). Within Dallas, trade, transportation, and utilities had the largest over-the-year employment level increase among all private industry groups with a gain of 17,303 jobs, or 5.5 percent. Cook, Ill., and Los Angeles, Calif., had the smallest percentage increases in employment (1.6 percent each) among the 10 largest counties. (See table 2.) Average weekly wages increased over the year in all of the 10 largest U.S. counties. King, Wash., experienced the largest percentage gain in average weekly wages (6.2 percent). Within King, information had the largest impact on the county’s average weekly wage growth. Within this industry, average weekly wages increased by $421, or 16.5 percent, over the year. Maricopa, Ariz., had the smallest percentage increase in average weekly wages (2.2 percent) among the 10 largest counties. For More Information The tables included in this release contain data for the nation and for the 339 U.S. counties with annual average employment levels of 75,000 or more in 2013. December 2014 employment and 2014 fourth quarter average weekly wages for all states are provided in table 3 of this release. The employment and wage data by county are compiled under the QCEW program, also known as the ES-202 program. The data are derived from reports submitted by every employer subject to unemployment insurance (UI) laws. The 9.5 million employer reports cover 139.2 million full- and part-time workers. The QCEW program provides a quarterly and annual universe count of establishments, employment, and wages at the county, MSA, state, and national levels by detailed industry. Data for the fourth quarter of 2014 will be available electronically later at www.bls.gov/cew/. For additional information about the quarterly employment and wages data, please read the Technical Note. Additional information about the QCEW data may be obtained by calling (202) 691-6567. Several BLS regional offices are issuing QCEW news releases targeted to local data users. For links to these releases, see www.bls.gov/cew/cewregional.htm. _____________ The County Employment and Wages release for first quarter 2015 is scheduled to be released on Thursday, September 17, 2015.
Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment and total pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment insurance programs that require most employers to pay quarterly taxes based on the employment and wages of workers covered by UI. QCEW data in this release are based on the 2012 North American Industry Classification System. Data for 2014 are preliminary and subject to revision. For purposes of this release, large counties are defined as having employment levels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of employment for the previous year. The 340 counties presented in this release were derived using 2013 preliminary annual averages of employment. For 2014 data, five counties have been added to the publication tables: Shelby, Ala.; Osceola, Fla.; Black Hawk, Iowa; Washington, Minn.; and Cleveland, Okla. These counties will be included in all 2014 quarterly releases. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year. The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' continuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. Each of these measures-QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES)-makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat different universe coverage, estimation procedure, and publication product. Differences in coverage and estimation methods can result in somewhat different measures of employment change over time. It is important to understand program differences and the intended uses of the program products. (See table.) Additional information on each program can be obtained from the program Web sites shown in the table. Summary of Major Differences between QCEW, BED, and CES Employment Measures --------------------------------------------------------------------------------- | QCEW | BED | CES -----------|---------------------|----------------------|------------------------ Source |--Count of UI admini-|--Count of longitudi- |--Sample survey: | strative records | nally-linked UI ad- | 588,000 establish- | submitted by 9.4 | ministrative records| ments | million establish- | submitted by 7.5 | | ments in first | million private-sec-| | quarter of 2014 | tor employers | -----------|---------------------|----------------------|------------------------ Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal- | age, including all | ing government, pri-| ary jobs: | employers subject | vate households, and|--UI coverage, exclud- | to state and fed- | establishments with | ing agriculture, pri- | eral UI laws | zero employment | vate households, and | | | self-employed workers | | |--Other employment, in- | | | cluding railroads, | | | religious organiza- | | | tions, and other non- | | | UI-covered jobs -----------|---------------------|----------------------|------------------------ Publication|--Quarterly |--Quarterly |--Monthly frequency | -6 months after the| -8 months after the | -Usually first Friday | end of each quar- | end of each quarter| of following month | ter | | -----------|---------------------|----------------------|------------------------ Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam- file | and publishes each | quarter to longitu- | pling frame and to an- | new quarter of UI | dinal database and | nually realign sample- | data | directly summarizes | based estimates to pop- | | gross job gains and | ulation counts (bench- | | losses | marking) -----------|---------------------|----------------------|------------------------ Principal |--Provides a quarter-|--Provides quarterly |--Provides current month- products | ly and annual uni- | employer dynamics | ly estimates of employ- | verse count of es- | data on establish- | ment, hours, and earn- | tablishments, em- | ment openings, clos-| ings at the MSA, state, | ployment, and wages| ings, expansions, | and national level by | at the county, MSA,| and contractions at | industry | state, and national| the national level | | levels by detailed | by NAICS supersec- | | industry | tors and by size of | | | firm, and at the | | | state private-sector| | | total level | | |--Future expansions | | | will include data | | | with greater indus- | | | try detail and data | | | at the county and | | | MSA level | -----------|---------------------|----------------------|------------------------ Principal |--Major uses include:|--Major uses include: |--Major uses include: uses | -Detailed locality | -Business cycle | -Principal national | data | analysis | economic indicator | -Periodic universe | -Analysis of employ-| -Official time series | counts for bench- | er dynamics under- | for employment change | marking sample | lying economic ex- | measures | survey estimates | pansions and con- | -Input into other ma- | -Sample frame for | tractions | jor economic indi- | BLS establishment | -Analysis of employ-| cators | surveys | ment expansion and | | | contraction by size| | | of firm | | | | -----------|---------------------|----------------------|------------------------ Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/ Web sites | | | --------------------------------------------------------------------------------- Coverage Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports submitted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of a few agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports, employers who operate multiple establishments within a state complete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of each of their establishments. QCEW employment and wage data are derived from microdata summaries of 9.2 million employer reports of employment and wages submitted by states to the BLS in 2013. These reports are based on place of employment rather than place of residence. UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became effective, expanding coverage to include most State and local government employees. In 2013, UI and UCFE programs covered workers in 134.0 million jobs. The estimated 128.7 million workers in these jobs (after adjustment for multiple jobholders) represented 95.8 percent of civilian wage and salary employment. Covered workers received $6.673 trillion in pay, representing 93.7 percent of the wage and salary component of personal income and 39.8 percent of the gross domestic product. Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected officials in most states, most employees of railroads, some domestic workers, most student workers at schools, and employees of certain small nonprofit organizations. State and federal UI laws change periodically. These changes may have an impact on the employment and wages reported by employers covered under the UI program. Coverage changes may affect the over-the-year comparisons presented in this news release. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all employees of covered firms are reported, including production and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also are included. Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and wage values. The average wage values that can be calculated using rounded data from the BLS database may differ from the averages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans such as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may reflect fluctuations in average monthly employment and/or total quarterly wages between the current quarter and prior year levels. Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individuals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. For instance, the average weekly wage of the workforce could increase significantly when there is a large decline in the number of employees that had been receiving below-average wages. Wages may include payments to workers not present in the employment counts because they did not work during the pay period including the 12th of the month. When comparing average weekly wage levels between industries, states, or quarters, these factors should be taken into consideration. Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This variability may be due to calendar effects resulting from some quarters having more pay dates than others. The effect is most visible in counties with a dominant employer. In particular, this effect has been observed in counties where government employers represent a large fraction of overall employment. Similar calendar effects can result from private sector pay practices. However, these effects are typically less pronounced for two reasons: employment is less concentrated in a single private employer, and private employers use a variety of pay period types (weekly, biweekly, semimonthly, monthly). For example, the effect on over-the-year pay comparisons can be pronounced in federal government due to the uniform nature of federal payroll processing. Most federal employees are paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates, while in other quarters there are seven pay dates. Over-the-year comparisons of average weekly wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include seven pay dates, with year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the current quarter reflecting six pay dates are compared with year-ago wages for a quarter including seven pay dates. In order to ensure the highest possible quality of data, states verify with employers and update, if necessary, the industry, location, and ownership classification of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from this process are introduced with the data reported for the first quarter of the year. Changes resulting from improved employer reporting also are introduced in the first quarter. QCEW data are not designed as a time series. QCEW data are simply the sums of individual establishment records and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can move in or out of a county or industry for a number of reasons-some reflecting economic events, others reflecting administrative changes. For example, economic change would come from a firm relocating into the county; administrative change would come from a company correcting its county designation. The over-the-year changes of employment and wages presented in this release have been adjusted to account for most of the administrative corrections made to the underlying establishment reports. This is done by modifying the prior-year levels used to calculate the over-the-year changes. Percent changes are calculated using an adjusted version of the final 2013 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-the-year percent change in employment and wages are not published. These adjusted prior-year levels do not match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news release. The adjusted data used to calculate the over-the-year change measures presented in this release account for most of the administrative changes-those occurring when employers update the industry, location, and ownership information of their establishments. The most common adjustments for administrative change are the result of updated information about the county location of individual establishments. Included in these adjustments are administrative changes involving the classification of establishments that were previously reported in the unknown or statewide county or unknown industry categories. Adjusted data account for improvements in reporting employment and wages for individual and multi-unit establishments. To accomplish this, adjustments were implemented to account for: administrative changes caused by multi-unit employers who start reporting for each individual establishment rather than as a single entity (first quarter of 2008); selected large administrative changes in employment and wages (second quarter of 2011); and state verified improvements in reporting of employment and wages (third quarter of 2014). These adjustments allow QCEW to include county employment and wage growth rates in this news release that would otherwise not meet publication standards. The adjusted data used to calculate the over-the-year change measures presented in any County Employment and Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in that particular release. Comparisons may not be valid for any time period other than the one featured in a release even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104-106. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created. County data also are presented for the New England states for comparative purposes even though townships are the more common designation used in New England (and New Jersey). The regions referred to in this release are defined as census regions. Additional statistics and other information Employment and Wages Annual Averages Online features comprehensive information by detailed industry on establishments, employment, and wages for the nation and all states. The 2013 edition of this publication, which was published in September 2014, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2014 version of this news release. Tables and additional content from Employment and Wages Annual Averages 2013 are now available online at http://www.bls.gov/cew/cewbultn13.htm. The 2014 edition of Employment and Wages Annual Averages Online will be available in September 2015. News releases on quarterly measures of gross job flows also are available upon request from the Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov). Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-877-8339.
Table 1. Covered establishments, employment, and wages in the 340 largest counties, fourth quarter 2014 Employment Average weekly wage(2) Establishments, County(1) fourth quarter Percent Ranking Percent Ranking 2014 December change, by Fourth change, by (thousands) 2014 December percent quarter fourth percent (thousands) 2013-14(3) change 2014 quarter change 2013-14(3) United States(4)......... 9,479.2 139,204.8 2.2 - $1,035 3.5 - Jefferson, AL............ 17.8 342.7 0.2 311 1,026 3.3 191 Madison, AL.............. 9.1 186.4 1.4 214 1,106 2.4 258 Mobile, AL............... 9.6 167.6 1.0 249 897 3.8 133 Montgomery, AL........... 6.3 129.7 0.1 317 901 2.4 258 Shelby, AL............... 5.1 80.6 3.1 84 978 3.7 143 Tuscaloosa, AL........... 4.3 91.9 4.3 28 869 2.8 231 Anchorage Borough, AK.... 8.4 152.8 0.0 320 1,094 4.2 99 Maricopa, AZ............. 94.6 1,821.9 2.7 105 974 2.2 273 Pima, AZ................. 18.9 358.8 0.3 303 858 2.1 278 Benton, AR............... 5.8 108.3 5.5 9 996 9.9 1 Pulaski, AR.............. 14.4 245.9 0.7 272 936 3.7 143 Washington, AR........... 5.7 98.1 3.5 66 896 4.3 87 Alameda, CA.............. 58.0 708.7 2.8 104 1,319 4.4 81 Contra Costa, CA......... 30.1 344.1 1.8 174 1,215 2.1 278 Fresno, CA............... 31.3 349.4 0.6 284 808 4.9 50 Kern, CA................. 17.4 306.9 0.3 303 873 2.7 235 Los Angeles, CA.......... 447.3 4,243.8 1.6 197 1,201 3.5 168 Marin, CA................ 12.2 112.0 0.6 284 1,280 5.9 29 Monterey, CA............. 13.0 159.4 1.9 162 851 3.7 143 Orange, CA............... 109.5 1,506.0 2.6 112 1,162 4.3 87 Placer, CA............... 11.6 144.6 3.4 71 1,034 5.8 31 Riverside, CA............ 54.3 641.2 3.5 66 803 4.0 113 Sacramento, CA........... 53.3 620.7 2.2 140 1,095 2.7 235 San Bernardino, CA....... 52.0 682.3 4.4 25 852 3.5 168 San Diego, CA............ 102.1 1,359.7 1.9 162 1,138 2.6 244 San Francisco, CA........ 58.2 659.1 4.4 25 1,850 4.9 50 San Joaquin, CA.......... 16.9 217.7 2.5 118 835 2.5 248 San Luis Obispo, CA...... 9.9 109.4 1.8 174 837 3.7 143 San Mateo, CA............ 26.4 385.0 4.8 18 2,166 -20.4 339 Santa Barbara, CA........ 14.7 186.5 2.5 118 981 4.9 50 Santa Clara, CA.......... 66.9 999.3 3.6 57 2,114 6.8 15 Santa Cruz, CA........... 9.3 94.6 3.7 51 926 5.0 46 Solano, CA............... 10.4 129.6 1.7 183 1,026 0.9 322 Sonoma, CA............... 19.0 192.0 0.9 259 952 4.2 99 Stanislaus, CA........... 14.5 170.3 2.5 118 832 3.9 125 Tulare, CA............... 9.3 146.5 0.7 272 739 6.2 23 Ventura, CA.............. 25.2 317.5 0.9 259 1,025 5.0 46 Yolo, CA................. 6.2 92.3 1.2 227 1,092 7.1 10 Adams, CO................ 9.6 189.0 6.4 3 987 4.3 87 Arapahoe, CO............. 20.0 311.5 3.1 84 1,223 6.7 17 Boulder, CO.............. 13.8 172.2 2.9 97 1,213 3.1 208 Denver, CO............... 28.4 474.3 5.0 13 1,247 1.8 300 Douglas, CO.............. 10.5 110.3 3.6 57 1,240 8.8 5 El Paso, CO.............. 17.4 252.9 2.5 118 916 3.4 179 Jefferson, CO............ 18.3 226.2 3.5 66 1,042 4.0 113 Larimer, CO.............. 10.8 144.3 4.3 28 962 6.8 15 Weld, CO................. 6.4 101.6 8.0 1 922 6.0 27 Fairfield, CT............ 34.2 428.4 1.7 183 1,674 1.1 315 Hartford, CT............. 26.6 509.0 1.2 227 1,246 2.8 231 New Haven, CT............ 23.1 365.5 1.1 236 1,087 4.5 73 New London, CT........... 7.1 121.2 -0.5 330 1,013 4.3 87 New Castle, DE........... 18.4 287.7 3.1 84 1,164 0.7 326 Washington, DC........... 36.8 736.9 1.6 197 1,696 3.0 220 Alachua, FL.............. 6.9 122.6 2.7 105 888 2.7 235 Brevard, FL.............. 15.1 193.1 1.6 197 885 2.0 291 Broward, FL.............. 67.4 764.4 2.9 97 960 4.0 113 Collier, FL.............. 12.8 137.8 5.0 13 891 3.1 208 Duval, FL................ 28.0 469.1 2.5 118 988 4.3 87 Escambia, FL............. 8.2 125.2 1.6 197 817 5.3 37 Hillsborough, FL......... 40.0 643.8 3.3 75 979 2.1 278 Lake, FL................. 7.8 88.2 3.1 84 691 3.6 157 Lee, FL.................. 20.3 237.9 6.2 4 803 2.4 258 Leon, FL................. 8.4 143.6 1.4 214 842 2.7 235 Manatee, FL.............. 10.1 116.4 3.0 92 767 3.4 179 Marion, FL............... 8.2 96.6 3.1 84 707 2.5 248 Miami-Dade, FL........... 95.2 1,082.5 3.3 75 1,008 2.5 248 Okaloosa, FL............. 6.3 77.1 0.3 303 823 4.7 60 Orange, FL............... 39.0 751.4 3.4 71 895 3.8 133 Osceola, FL.............. 6.1 83.0 2.9 97 687 3.9 125 Palm Beach, FL........... 53.3 565.1 3.8 46 1,006 1.0 319 Pasco, FL................ 10.4 108.6 4.6 21 711 2.7 235 Pinellas, FL............. 31.8 405.0 2.1 151 928 1.9 297 Polk, FL................. 12.8 204.3 2.2 140 777 3.7 143 Sarasota, FL............. 15.3 158.6 5.1 12 860 3.2 199 Seminole, FL............. 14.4 173.1 3.7 51 843 3.4 179 Volusia, FL.............. 13.8 158.9 2.6 112 729 4.0 113 Bibb, GA................. 4.5 83.7 2.3 130 802 4.7 60 Chatham, GA.............. 8.3 142.4 4.6 21 871 2.7 235 Clayton, GA.............. 4.4 115.6 3.8 46 977 7.6 7 Cobb, GA................. 22.9 332.6 4.1 36 1,081 3.6 157 De Kalb, GA.............. 19.0 289.8 2.3 130 1,013 2.2 273 Fulton, GA............... 45.2 790.5 4.1 36 1,338 3.7 143 Gwinnett, GA............. 25.6 333.3 3.8 46 991 3.1 208 Muscogee, GA............. 4.8 95.1 -0.4 328 804 2.0 291 Richmond, GA............. 4.7 104.1 2.3 130 834 1.8 300 Honolulu, HI............. 24.9 466.6 0.5 291 945 4.0 113 Ada, ID.................. 14.0 213.0 1.6 197 950 5.9 29 Champaign, IL............ 4.5 89.7 0.5 291 868 5.2 41 Cook, IL................. 160.7 2,512.5 1.6 197 1,209 3.2 199 Du Page, IL.............. 39.6 608.0 1.7 183 1,178 0.3 329 Kane, IL................. 14.3 205.6 0.3 303 912 4.5 73 Lake, IL................. 23.5 331.4 -0.6 335 1,341 2.8 231 McHenry, IL.............. 9.2 95.8 0.0 320 847 2.5 248 McLean, IL............... 4.0 84.4 -0.9 336 968 1.3 313 Madison, IL.............. 6.3 97.9 2.1 151 848 3.5 168 Peoria, IL............... 4.9 100.7 -0.9 336 954 1.8 300 St. Clair, IL............ 5.8 93.8 1.3 223 799 2.4 258 Sangamon, IL............. 5.5 129.9 2.0 158 1,019 0.8 325 Will, IL................. 16.6 219.3 1.1 236 895 3.7 143 Winnebago, IL............ 7.0 127.6 1.1 236 874 3.4 179 Allen, IN................ 8.8 181.1 1.8 174 806 4.1 109 Elkhart, IN.............. 4.7 122.6 4.3 28 837 6.6 19 Hamilton, IN............. 8.8 128.3 4.4 25 971 3.5 168 Lake, IN................. 10.2 187.9 -0.3 325 898 2.4 258 Marion, IN............... 23.5 587.9 1.6 197 1,004 3.1 208 St. Joseph, IN........... 5.8 120.0 1.7 183 807 2.5 248 Tippecanoe, IN........... 3.3 82.5 2.2 140 852 4.4 81 Vanderburgh, IN.......... 4.8 107.3 1.9 162 852 7.0 11 Black Hawk, IA........... 3.8 75.5 -0.5 330 930 3.2 199 Johnson, IA.............. 4.0 80.8 0.3 303 915 3.5 168 Linn, IA................. 6.6 130.0 1.4 214 1,018 6.3 21 Polk, IA................. 16.5 287.5 1.6 197 1,029 3.7 143 Scott, IA................ 5.5 91.0 1.0 249 857 2.4 258 Johnson, KS.............. 21.8 335.9 3.4 71 1,041 2.0 291 Sedgwick, KS............. 12.4 247.7 0.6 284 921 1.5 306 Shawnee, KS.............. 4.9 97.2 0.7 272 826 2.4 258 Wyandotte, KS............ 3.3 88.5 4.3 28 940 4.3 87 Boone, KY................ 4.2 80.4 2.3 130 890 2.9 227 Fayette, KY.............. 10.5 191.3 0.8 264 881 4.3 87 Jefferson, KY............ 24.6 451.5 2.5 118 964 3.4 179 Caddo, LA................ 7.3 117.3 1.2 227 861 3.9 125 Calcasieu, LA............ 4.9 90.9 5.4 11 908 3.9 125 East Baton Rouge, LA..... 14.6 273.5 3.2 80 975 4.5 73 Jefferson, LA............ 13.5 195.0 1.1 236 926 2.2 273 Lafayette, LA............ 9.2 144.3 1.5 209 1,033 3.6 157 Orleans, LA.............. 11.6 191.4 2.4 126 996 2.4 258 St. Tammany, LA.......... 7.6 85.6 4.2 33 892 4.6 68 Cumberland, ME........... 12.8 175.3 1.0 249 951 5.2 41 Anne Arundel, MD......... 14.7 258.6 1.0 249 1,089 2.3 272 Baltimore, MD............ 21.2 373.4 0.8 264 1,043 3.6 157 Frederick, MD............ 6.3 97.4 1.0 249 968 2.1 278 Harford, MD.............. 5.6 90.6 1.0 249 982 1.4 311 Howard, MD............... 9.5 161.8 1.1 236 1,243 3.6 157 Montgomery, MD........... 32.6 462.7 1.4 214 1,342 2.1 278 Prince Georges, MD....... 15.7 309.1 1.3 223 1,049 4.6 68 Baltimore City, MD....... 13.8 335.6 1.8 174 1,223 4.9 50 Barnstable, MA........... 9.2 88.7 2.2 140 887 3.7 143 Bristol, MA.............. 16.8 222.8 1.9 162 962 6.3 21 Essex, MA................ 23.1 318.6 1.4 214 1,096 4.3 87 Hampden, MA.............. 16.8 203.8 1.4 214 947 4.3 87 Middlesex, MA............ 52.2 875.4 2.6 112 1,482 3.6 157 Norfolk, MA.............. 24.3 344.3 1.9 162 1,254 2.6 244 Plymouth, MA............. 14.7 186.3 2.6 112 982 3.8 133 Suffolk, MA.............. 26.2 630.4 2.4 126 1,856 6.2 23 Worcester, MA............ 23.1 334.7 2.1 151 1,030 3.4 179 Genesee, MI.............. 7.0 135.8 0.9 259 837 3.1 208 Ingham, MI............... 6.1 151.4 -0.3 325 966 3.3 191 Kalamazoo, MI............ 5.1 114.6 1.4 214 934 3.7 143 Kent, MI................. 14.0 371.3 3.1 84 909 3.4 179 Macomb, MI............... 17.3 312.7 2.1 151 1,025 1.5 306 Oakland, MI.............. 38.3 704.8 1.6 197 1,164 4.0 113 Ottawa, MI............... 5.5 116.7 4.1 36 914 4.7 60 Saginaw, MI.............. 4.0 84.8 -0.1 323 818 3.0 220 Washtenaw, MI............ 8.1 203.9 1.9 162 1,069 4.2 99 Wayne, MI................ 30.5 706.5 2.2 140 1,119 3.0 220 Anoka, MN................ 7.0 118.5 1.7 183 949 5.3 37 Dakota, MN............... 9.7 183.9 1.8 174 984 5.4 35 Hennepin, MN............. 41.3 883.7 1.7 183 1,259 4.1 109 Olmsted, MN.............. 3.4 92.6 -0.3 325 1,021 -5.5 338 Ramsey, MN............... 13.3 327.2 1.4 214 1,137 3.6 157 St. Louis, MN............ 5.3 96.7 0.5 291 824 3.1 208 Stearns, MN.............. 4.3 84.1 0.7 272 835 2.2 273 Washington, MN........... 5.3 77.8 0.9 259 834 3.5 168 Harrison, MS............. 4.5 82.9 -0.4 328 714 2.9 227 Hinds, MS................ 6.0 121.1 0.8 264 871 1.0 319 Boone, MO................ 4.8 91.6 2.3 130 791 3.3 191 Clay, MO................. 5.3 95.2 4.5 23 930 5.2 41 Greene, MO............... 8.3 161.4 2.6 112 773 5.0 46 Jackson, MO.............. 20.2 354.4 0.9 259 1,031 3.0 220 St. Charles, MO.......... 8.7 135.3 0.3 303 811 5.3 37 St. Louis, MO............ 34.6 590.9 1.2 227 1,121 2.5 248 St. Louis City, MO....... 11.6 224.4 1.9 162 1,067 3.5 168 Yellowstone, MT.......... 6.3 79.6 1.6 197 900 5.0 46 Douglas, NE.............. 18.4 332.4 1.7 183 932 4.7 60 Lancaster, NE............ 9.9 164.9 0.8 264 819 3.8 133 Clark, NV................ 52.6 895.5 4.8 18 885 1.1 315 Washoe, NV............... 14.1 199.0 3.1 84 923 3.2 199 Hillsborough, NH......... 12.2 199.1 1.6 197 1,210 6.7 17 Rockingham, NH........... 10.7 142.6 1.7 183 1,060 7.4 9 Atlantic, NJ............. 6.6 124.1 -5.0 339 872 7.0 11 Bergen, NJ............... 32.8 448.4 0.7 272 1,291 4.2 99 Burlington, NJ........... 11.1 200.8 0.6 284 1,060 2.4 258 Camden, NJ............... 11.9 200.7 1.1 236 1,017 -0.8 334 Essex, NJ................ 20.4 338.7 0.4 297 1,234 0.2 331 Gloucester, NJ........... 6.2 103.1 2.3 130 909 1.5 306 Hudson, NJ............... 14.3 244.1 1.7 183 1,335 3.9 125 Mercer, NJ............... 11.0 243.8 3.7 51 1,306 1.1 315 Middlesex, NJ............ 21.9 401.6 1.0 249 1,217 2.4 258 Monmouth, NJ............. 20.0 252.1 2.5 118 1,053 1.7 303 Morris, NJ............... 17.0 284.6 0.2 311 1,512 -2.9 337 Ocean, NJ................ 12.7 157.6 2.0 158 845 2.1 278 Passaic, NJ.............. 12.3 170.6 -0.5 330 1,016 2.4 258 Somerset, NJ............. 10.0 183.4 2.2 140 1,543 3.6 157 Union, NJ................ 14.3 223.5 0.5 291 1,341 4.5 73 Bernalillo, NM........... 17.8 317.6 0.7 272 873 4.4 81 Albany, NY............... 10.3 230.4 1.8 174 1,062 4.8 57 Bronx, NY................ 17.8 257.1 3.2 80 958 2.5 248 Broome, NY............... 4.6 88.7 0.8 264 786 3.1 208 Dutchess, NY............. 8.5 111.4 0.7 272 1,000 4.6 68 Erie, NY................. 24.6 466.3 0.7 272 898 4.8 57 Kings, NY................ 57.9 590.9 5.0 13 849 3.9 125 Monroe, NY............... 18.7 383.3 0.7 272 935 4.2 99 Nassau, NY............... 53.6 623.6 1.1 236 1,158 3.4 179 New York, NY............. 128.0 2,568.3 2.7 105 2,138 4.4 81 Oneida, NY............... 5.4 105.1 0.2 311 793 3.4 179 Onondaga, NY............. 13.2 246.2 0.2 311 938 3.0 220 Orange, NY............... 10.2 140.6 2.2 140 847 3.4 179 Queens, NY............... 50.0 569.4 3.9 42 974 1.7 303 Richmond, NY............. 9.6 101.4 0.8 264 888 4.6 68 Rockland, NY............. 10.3 120.2 2.3 130 1,052 -1.3 336 Saratoga, NY............. 5.9 82.6 1.9 162 908 2.5 248 Suffolk, NY.............. 52.0 646.4 0.5 291 1,125 4.7 60 Westchester, NY.......... 36.5 423.2 1.9 162 1,407 4.5 73 Buncombe, NC............. 8.3 122.9 3.4 71 797 4.9 50 Catawba, NC.............. 4.3 83.2 1.7 183 760 4.0 113 Cumberland, NC........... 6.2 118.6 0.1 317 771 0.7 326 Durham, NC............... 7.7 192.2 2.2 140 1,271 1.0 319 Forsyth, NC.............. 9.2 181.6 2.0 158 933 4.2 99 Guilford, NC............. 14.1 275.2 1.5 209 890 3.5 168 Mecklenburg, NC.......... 34.0 630.4 3.8 46 1,125 2.5 248 New Hanover, NC.......... 7.5 104.9 3.5 66 828 3.8 133 Wake, NC................. 30.9 503.3 3.9 42 1,008 2.4 258 Cass, ND................. 6.8 115.9 3.7 51 935 4.5 73 Butler, OH............... 7.6 146.9 2.9 97 875 3.1 208 Cuyahoga, OH............. 35.4 717.9 0.4 297 1,050 3.7 143 Delaware, OH............. 4.7 82.8 0.4 297 968 0.9 322 Franklin, OH............. 30.3 727.9 2.9 97 998 2.9 227 Hamilton, OH............. 23.2 505.9 1.8 174 1,139 6.1 26 Lake, OH................. 6.3 95.4 0.8 264 861 5.5 33 Lorain, OH............... 6.0 96.8 0.6 284 816 2.1 278 Lucas, OH................ 10.0 208.1 0.6 284 896 5.4 35 Mahoning, OH............. 5.9 99.8 1.0 249 734 3.8 133 Montgomery, OH........... 11.9 250.5 1.9 162 879 2.1 278 Stark, OH................ 8.7 160.3 1.5 209 789 4.0 113 Summit, OH............... 14.0 264.6 1.2 227 914 4.2 99 Warren, OH............... 4.5 82.5 2.1 151 880 5.3 37 Cleveland, OK............ 5.3 81.5 2.1 151 762 4.7 60 Oklahoma, OK............. 26.7 452.2 2.3 130 981 2.0 291 Tulsa, OK................ 21.5 350.6 2.7 105 952 0.3 329 Clackamas, OR............ 13.5 148.3 3.0 92 939 2.6 244 Jackson, OR.............. 6.9 82.6 3.6 57 747 3.3 191 Lane, OR................. 11.4 145.4 2.7 105 796 3.2 199 Marion, OR............... 9.9 140.6 3.7 51 811 4.2 99 Multnomah, OR............ 31.8 476.8 3.6 57 1,030 2.4 258 Washington, OR........... 17.5 271.0 2.6 112 1,231 6.0 27 Allegheny, PA............ 35.3 688.8 -0.1 323 1,096 2.5 248 Berks, PA................ 8.9 169.5 1.7 183 913 4.6 68 Bucks, PA................ 19.7 254.9 1.7 183 1,001 4.2 99 Butler, PA............... 5.0 85.0 0.7 272 937 -0.8 334 Chester, PA.............. 15.2 243.9 1.2 227 1,333 3.3 191 Cumberland, PA........... 6.2 129.7 2.5 118 922 3.6 157 Dauphin, PA.............. 7.3 177.2 1.2 227 996 2.6 244 Delaware, PA............. 13.9 221.1 1.7 183 1,084 1.1 315 Erie, PA................. 7.2 125.2 1.0 249 806 4.1 109 Lackawanna, PA........... 5.9 98.5 0.3 303 765 3.2 199 Lancaster, PA............ 13.0 230.2 3.0 92 853 3.0 220 Lehigh, PA............... 8.6 184.2 0.4 297 1,033 7.8 6 Luzerne, PA.............. 7.5 141.5 0.7 272 781 2.9 227 Montgomery, PA........... 27.4 481.4 1.1 236 1,262 3.8 133 Northampton, PA.......... 6.6 107.8 2.0 158 881 3.6 157 Philadelphia, PA......... 34.8 652.5 2.2 140 1,210 2.2 273 Washington, PA........... 5.4 87.7 1.5 209 1,085 9.2 2 Westmoreland, PA......... 9.3 133.4 1.0 249 829 4.1 109 York, PA................. 9.0 174.2 0.7 272 875 4.5 73 Providence, RI........... 17.4 283.5 2.1 151 1,062 4.4 81 Charleston, SC........... 12.9 232.2 4.3 28 880 4.3 87 Greenville, SC........... 13.1 254.6 5.0 13 880 2.7 235 Horry, SC................ 8.1 111.3 3.6 57 610 3.7 143 Lexington, SC............ 6.0 114.4 2.4 126 765 5.2 41 Richland, SC............. 9.4 213.2 3.2 80 862 1.9 297 Spartanburg, SC.......... 5.9 126.5 2.9 97 862 4.5 73 York, SC................. 5.1 83.1 4.9 17 806 0.9 322 Minnehaha, SD............ 6.8 122.7 2.2 140 878 3.8 133 Davidson, TN............. 20.1 467.8 3.6 57 1,076 1.5 306 Hamilton, TN............. 9.0 189.7 1.1 236 974 7.0 11 Knox, TN................. 11.4 230.2 3.0 92 923 5.2 41 Rutherford, TN........... 4.9 115.7 3.9 42 908 3.2 199 Shelby, TN............... 19.8 489.7 1.4 214 1,041 2.1 278 Williamson, TN........... 7.4 112.1 6.1 5 1,231 4.9 50 Bell, TX................. 5.0 112.9 0.5 291 812 2.7 235 Bexar, TX................ 37.4 811.5 3.2 80 910 3.4 179 Brazoria, TX............. 5.3 101.2 4.1 36 1,047 8.9 4 Brazos, TX............... 4.2 99.0 4.8 18 772 4.0 113 Cameron, TX.............. 6.4 135.6 1.6 197 621 3.8 133 Collin, TX............... 21.6 354.7 3.6 57 1,186 3.2 199 Dallas, TX............... 71.8 1,591.0 4.2 33 1,233 3.1 208 Denton, TX............... 12.8 211.4 5.7 7 938 6.6 19 El Paso, TX.............. 14.4 289.1 1.2 227 707 3.2 199 Fort Bend, TX............ 11.3 170.3 5.5 9 1,048 3.5 168 Galveston, TX............ 5.7 102.5 2.3 130 918 4.7 60 Gregg, TX................ 4.2 80.5 3.3 75 940 2.8 231 Harris, TX............... 109.5 2,312.2 3.9 42 1,373 4.3 87 Hidalgo, TX.............. 11.9 246.5 2.3 130 641 3.6 157 Jefferson, TX............ 5.8 126.4 4.5 23 1,079 7.6 7 Lubbock, TX.............. 7.3 133.6 1.9 162 803 4.4 81 McLennan, TX............. 5.0 106.7 1.7 183 832 4.0 113 Midland, TX.............. 5.4 94.7 8.0 1 1,425 9.0 3 Montgomery, TX........... 10.1 164.6 5.7 7 1,044 3.9 125 Nueces, TX............... 8.2 166.7 3.6 57 936 7.0 11 Potter, TX............... 4.0 79.1 1.2 227 830 3.5 168 Smith, TX................ 6.0 100.4 3.8 46 872 2.0 291 Tarrant, TX.............. 40.3 842.8 3.1 84 1,019 3.3 191 Travis, TX............... 35.8 669.4 4.2 33 1,170 4.7 60 Webb, TX................. 5.0 97.8 3.6 57 696 4.2 99 Williamson, TX........... 9.1 148.3 2.7 105 960 0.7 326 Davis, UT................ 7.9 115.7 3.5 66 802 4.3 87 Salt Lake, UT............ 41.6 639.7 2.4 126 983 5.5 33 Utah, UT................. 14.3 202.3 5.8 6 810 -0.5 333 Weber, UT................ 5.7 97.3 3.0 92 750 4.0 113 Chittenden, VT........... 6.4 101.6 1.3 223 1,032 3.9 125 Arlington, VA............ 8.6 165.7 0.0 320 1,613 1.5 306 Chesterfield, VA......... 8.0 129.6 1.1 236 876 0.2 331 Fairfax, VA.............. 34.6 586.8 0.2 311 1,584 2.0 291 Henrico, VA.............. 10.3 182.3 2.7 105 977 3.3 191 Loudoun, VA.............. 10.5 150.1 1.1 236 1,204 1.3 313 Prince William, VA....... 8.3 120.5 1.3 223 891 3.0 220 Alexandria City, VA...... 6.1 95.9 0.3 303 1,464 3.7 143 Chesapeake City, VA...... 5.6 97.0 0.1 317 792 2.1 278 Newport News City, VA.... 3.6 99.0 -0.5 330 960 3.4 179 Norfolk City, VA......... 5.4 135.0 -1.1 338 1,001 5.6 32 Richmond City, VA........ 7.0 149.1 0.4 297 1,101 3.5 168 Virginia Beach City, VA.. 11.1 171.2 1.1 236 809 3.3 191 Benton, WA............... 5.7 79.7 3.7 51 997 1.9 297 Clark, WA................ 14.0 142.9 4.1 36 927 3.7 143 King, WA................. 84.9 1,262.8 3.3 75 1,384 6.2 23 Kitsap, WA............... 6.7 83.5 1.8 174 870 3.1 208 Pierce, WA............... 21.8 284.1 2.9 97 887 2.1 278 Snohomish, WA............ 20.4 271.9 1.9 162 1,071 4.8 57 Spokane, WA.............. 15.8 208.5 2.2 140 839 2.1 278 Thurston, WA............. 7.9 105.2 3.3 75 876 2.1 278 Whatcom, WA.............. 7.1 84.6 1.8 174 805 2.4 258 Yakima, WA............... 8.0 99.7 4.0 41 708 3.1 208 Kanawha, WV.............. 6.0 105.0 0.2 311 868 2.7 235 Brown, WI................ 6.4 150.3 0.4 297 931 4.0 113 Dane, WI................. 14.1 319.5 1.5 209 1,020 1.6 305 Milwaukee, WI............ 25.2 484.6 0.6 284 1,010 4.9 50 Outagamie, WI............ 4.9 104.7 1.1 236 865 3.8 133 Waukesha, WI............. 12.3 234.0 0.8 264 1,026 3.1 208 Winnebago, WI............ 3.5 90.1 -0.5 330 974 1.4 311 San Juan, PR............. 11.4 262.7 -1.6 (5) 659 0.2 (5) (1) Includes areas not officially designated as counties. See Technical Note. (2) Average weekly wages were calculated using unrounded data. (3) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. (5) This county was not included in the U.S. rankings. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 339 U.S. counties comprise 72.1 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, fourth quarter 2014 Employment Average weekly wage(1) Establishments, fourth quarter County by NAICS supersector 2014 Percent Percent (thousands) December change, Fourth change, 2014 December quarter fourth (thousands) 2013-14(2) 2014 quarter 2013-14(2) United States(3) ............................ 9,479.2 139,204.8 2.2 $1,035 3.5 Private industry........................... 9,184.9 117,701.2 2.6 1,042 3.6 Natural resources and mining............. 137.6 1,989.9 3.4 1,215 5.0 Construction............................. 760.5 6,192.9 5.6 1,174 5.0 Manufacturing............................ 339.9 12,255.7 1.5 1,268 4.1 Trade, transportation, and utilities..... 1,920.3 27,247.3 2.2 863 4.0 Information.............................. 153.7 2,756.5 0.7 1,755 0.0 Financial activities..................... 841.8 7,759.4 1.2 1,664 4.7 Professional and business services....... 1,702.8 19,532.8 3.3 1,377 3.1 Education and health services............ 1,498.4 20,926.8 2.0 941 3.1 Leisure and hospitality.................. 800.1 14,502.5 2.6 438 3.8 Other services........................... 820.2 4,255.2 2.0 688 3.8 Government................................. 294.3 21,503.6 0.5 997 3.2 Los Angeles, CA.............................. 447.3 4,243.8 1.6 1,201 3.5 Private industry........................... 441.5 3,698.0 1.6 1,189 3.5 Natural resources and mining............. 0.5 9.5 -4.4 1,472 -15.8 Construction............................. 13.4 119.4 1.0 1,218 4.4 Manufacturing............................ 12.5 358.2 -2.5 1,228 4.0 Trade, transportation, and utilities..... 53.6 820.4 1.3 938 2.6 Information.............................. 9.7 198.9 0.2 2,285 4.4 Financial activities..................... 24.7 209.7 -0.8 1,850 5.5 Professional and business services....... 48.1 612.8 -0.1 1,536 6.7 Education and health services............ 204.0 724.0 2.4 898 2.4 Leisure and hospitality.................. 31.0 469.8 3.3 964 0.2 Other services........................... 27.9 146.6 2.3 704 4.1 Government................................. 5.7 545.8 1.6 1,283 4.1 New York, NY................................. 128.0 2,568.3 2.7 2,138 4.4 Private industry........................... 127.6 2,127.8 3.1 2,337 4.2 Natural resources and mining............. 0.0 0.2 -0.7 1,976 0.2 Construction............................. 2.2 35.2 4.2 2,230 7.1 Manufacturing............................ 2.2 25.9 -0.8 1,577 2.4 Trade, transportation, and utilities..... 20.6 279.1 2.1 1,439 2.9 Information.............................. 4.8 153.8 1.9 2,715 7.4 Financial activities..................... 19.3 363.6 1.8 4,984 4.9 Professional and business services....... 27.1 537.0 3.2 2,550 4.4 Education and health services............ 9.8 334.2 3.7 1,301 2.6 Leisure and hospitality.................. 13.7 289.6 3.9 981 6.1 Other services........................... 20.3 101.7 3.4 1,142 2.4 Government................................. 0.4 440.4 0.8 1,184 4.7 Cook, IL..................................... 160.7 2,512.5 1.6 1,209 3.2 Private industry........................... 159.4 2,215.9 1.8 1,212 3.4 Natural resources and mining............. 0.1 0.9 11.5 1,294 17.5 Construction............................. 13.3 68.9 10.1 1,606 6.0 Manufacturing............................ 6.8 186.3 -0.1 1,307 5.7 Trade, transportation, and utilities..... 31.8 478.6 2.0 937 2.2 Information.............................. 2.9 55.0 2.3 1,663 -1.5 Financial activities..................... 16.3 185.0 0.3 2,215 1.7 Professional and business services....... 34.3 460.2 1.4 1,594 4.3 Education and health services............ 16.8 428.2 1.6 1,006 5.1 Leisure and hospitality.................. 14.5 251.6 2.2 496 2.3 Other services........................... 18.2 97.5 1.9 897 3.8 Government................................. 1.3 296.6 0.3 1,187 1.2 Harris, TX................................... 109.5 2,312.2 3.9 1,373 4.3 Private industry........................... 108.9 2,045.5 4.2 1,412 4.2 Natural resources and mining............. 1.8 95.0 4.5 3,321 -1.7 Construction............................. 6.9 159.6 9.6 1,465 7.8 Manufacturing............................ 4.7 201.7 4.4 1,673 6.8 Trade, transportation, and utilities..... 24.7 488.4 4.0 1,210 4.5 Information.............................. 1.2 28.3 -3.3 1,518 5.9 Financial activities..................... 11.3 119.8 1.7 1,758 5.3 Professional and business services....... 22.1 399.9 3.2 1,788 4.4 Education and health services............ 14.9 275.9 3.9 1,056 4.6 Leisure and hospitality.................. 9.2 211.4 5.0 454 3.4 Other services........................... 11.7 64.6 4.9 816 6.5 Government................................. 0.6 266.7 1.8 1,077 4.3 Maricopa, AZ................................. 94.6 1,821.9 2.7 974 2.2 Private industry........................... 93.9 1,609.5 2.9 973 2.2 Natural resources and mining............. 0.5 8.5 3.5 918 -1.8 Construction............................. 7.3 93.5 0.4 1,072 3.5 Manufacturing............................ 3.2 114.8 0.7 1,375 5.5 Trade, transportation, and utilities..... 20.1 369.1 2.4 873 1.9 Information.............................. 1.6 34.2 5.5 1,261 2.0 Financial activities..................... 11.2 157.0 3.2 1,229 3.1 Professional and business services....... 22.2 314.5 3.4 1,090 1.3 Education and health services............ 10.8 268.6 3.8 1,007 -0.2 Leisure and hospitality.................. 7.5 197.9 3.0 463 5.7 Other services........................... 6.4 48.4 1.7 687 2.2 Government................................. 0.7 212.5 1.2 986 2.8 Dallas, TX................................... 71.8 1,591.0 4.2 1,233 3.1 Private industry........................... 71.3 1,421.2 4.5 1,250 3.1 Natural resources and mining............. 0.6 10.1 4.6 3,902 5.5 Construction............................. 4.1 78.6 7.6 1,243 6.9 Manufacturing............................ 2.7 107.8 1.1 1,445 5.1 Trade, transportation, and utilities..... 15.5 331.2 5.5 1,066 1.1 Information.............................. 1.4 49.6 0.0 1,780 0.5 Financial activities..................... 8.6 153.8 2.7 1,694 6.9 Professional and business services....... 16.2 319.1 5.7 1,496 3.0 Education and health services............ 8.8 184.4 4.1 1,071 3.0 Leisure and hospitality.................. 6.2 145.6 5.2 510 -1.0 Other services........................... 6.8 40.5 2.1 793 2.9 Government................................. 0.5 169.8 2.2 1,090 2.5 Orange, CA................................... 109.5 1,506.0 2.6 1,162 4.3 Private industry........................... 108.1 1,367.6 2.6 1,167 4.4 Natural resources and mining............. 0.2 3.0 -2.0 887 22.2 Construction............................. 6.5 83.2 5.2 1,288 4.0 Manufacturing............................ 4.9 158.8 -0.1 1,482 10.1 Trade, transportation, and utilities..... 16.7 266.9 2.0 1,030 3.7 Information.............................. 1.2 23.5 -4.4 1,825 9.0 Financial activities..................... 10.8 114.6 1.4 1,958 2.6 Professional and business services....... 20.7 282.3 2.2 1,384 4.4 Education and health services............ 27.5 188.8 2.3 990 1.6 Leisure and hospitality.................. 7.9 193.7 2.8 469 7.8 Other services........................... 6.8 42.8 2.1 710 3.3 Government................................. 1.3 138.4 2.5 1,109 3.4 San Diego, CA................................ 102.1 1,359.7 1.9 1,138 2.6 Private industry........................... 100.7 1,139.0 2.2 1,127 2.1 Natural resources and mining............. 0.7 9.5 2.6 695 5.1 Construction............................. 6.4 64.6 3.0 1,185 5.8 Manufacturing............................ 3.0 97.4 0.9 1,589 4.7 Trade, transportation, and utilities..... 14.1 223.9 0.8 838 3.8 Information.............................. 1.2 23.9 -3.9 1,677 -1.5 Financial activities..................... 9.5 70.5 -0.9 1,493 8.7 Professional and business services....... 18.3 231.2 1.3 1,784 -1.1 Education and health services............ 28.2 185.9 3.0 979 2.6 Leisure and hospitality.................. 7.6 175.4 2.6 460 4.3 Other services........................... 7.3 48.7 3.7 606 4.1 Government................................. 1.4 220.7 0.6 1,193 5.5 King, WA..................................... 84.9 1,262.8 3.3 1,384 6.2 Private industry........................... 84.4 1,100.7 3.6 1,402 6.4 Natural resources and mining............. 0.4 2.5 2.5 1,417 4.6 Construction............................. 6.1 60.6 13.1 1,315 3.7 Manufacturing............................ 2.3 105.8 0.2 1,640 6.8 Trade, transportation, and utilities..... 14.9 241.7 4.3 1,170 5.1 Information.............................. 2.0 85.0 2.0 2,974 16.5 Financial activities..................... 6.5 65.9 1.3 1,766 10.8 Professional and business services....... 16.1 211.6 4.8 1,766 3.3 Education and health services............ 20.5 162.6 2.9 970 2.1 Leisure and hospitality.................. 6.9 123.7 2.5 544 2.1 Other services........................... 8.6 41.3 2.7 822 2.9 Government................................. 0.6 162.1 1.4 1,267 5.0 Miami-Dade, FL............................... 95.2 1,082.5 3.3 1,008 2.5 Private industry........................... 94.9 946.2 4.0 986 2.4 Natural resources and mining............. 0.5 9.3 0.2 593 8.2 Construction............................. 5.5 37.8 11.1 986 4.4 Manufacturing............................ 2.7 37.5 2.2 989 3.9 Trade, transportation, and utilities..... 27.5 284.2 3.4 884 -0.6 Information.............................. 1.6 18.3 0.5 1,532 3.0 Financial activities..................... 10.0 73.8 5.1 1,583 4.4 Professional and business services....... 20.0 147.7 5.3 1,312 2.3 Education and health services............ 10.1 164.7 2.4 980 4.0 Leisure and hospitality.................. 7.2 132.1 3.4 569 2.5 Other services........................... 8.2 38.8 4.5 616 2.8 Government................................. 0.3 136.3 -1.2 1,162 4.5 (1) Average weekly wages were calculated using unrounded data. (2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. Note: Data are preliminary. Counties selected are based on 2013 annual average employment. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state, fourth quarter 2014 Employment Average weekly wage(1) Establishments, fourth quarter State 2014 Percent Percent (thousands) December change, Fourth change, 2014 December quarter fourth (thousands) 2013-14 2014 quarter 2013-14 United States(2)........... 9,479.2 139,204.8 2.2 $1,035 3.5 Alabama.................... 118.0 1,891.4 1.3 881 3.5 Alaska..................... 22.4 317.6 0.8 1,063 4.0 Arizona.................... 148.9 2,630.8 2.2 926 2.3 Arkansas................... 87.4 1,180.5 2.2 807 4.5 California................. 1,403.6 16,068.5 2.6 1,209 2.9 Colorado................... 180.7 2,478.0 3.9 1,066 4.1 Connecticut................ 114.7 1,681.2 1.2 1,278 2.7 Delaware................... 30.0 433.0 2.9 1,049 1.5 District of Columbia....... 36.8 736.9 0.9 1,696 3.7 Florida.................... 649.0 8,009.6 3.5 911 3.1 Georgia.................... 286.3 4,131.9 3.7 958 3.8 Hawaii..................... 39.3 638.3 0.7 908 4.2 Idaho...................... 55.2 650.7 2.5 782 4.0 Illinois................... 421.9 5,844.1 1.4 1,089 2.8 Indiana.................... 159.2 2,946.5 1.7 846 3.9 Iowa....................... 100.3 1,527.6 1.1 870 4.3 Kansas..................... 86.2 1,377.2 1.3 855 2.6 Kentucky................... 121.6 1,852.2 1.8 836 4.1 Louisiana.................. 126.5 1,954.0 2.1 923 3.8 Maine...................... 49.6 592.7 0.9 826 5.1 Maryland................... 166.1 2,590.3 1.3 1,113 3.5 Massachusetts.............. 236.1 3,415.6 2.2 1,315 4.5 Michigan................... 237.6 4,158.9 2.1 984 3.3 Minnesota.................. 167.0 2,762.9 1.4 1,024 3.6 Mississippi................ 72.1 1,118.6 1.0 747 2.3 Missouri................... 187.9 2,709.8 1.5 891 3.4 Montana.................... 44.5 442.2 0.5 794 4.5 Nebraska................... 70.4 958.1 1.4 837 5.2 Nevada..................... 77.2 1,229.6 4.2 899 1.6 New Hampshire.............. 50.8 638.0 1.4 1,081 6.3 New Jersey................. 266.1 3,933.6 1.3 1,211 2.0 New Mexico................. 56.1 808.4 1.3 850 4.4 New York................... 627.6 9,067.6 2.0 1,321 4.3 North Carolina............. 261.7 4,141.8 2.4 890 3.4 North Dakota............... 32.1 454.8 4.5 1,050 7.1 Ohio....................... 289.4 5,264.3 1.6 922 3.9 Oklahoma................... 107.9 1,614.3 2.1 876 2.8 Oregon..................... 140.0 1,755.4 3.2 928 3.8 Pennsylvania............... 351.2 5,716.5 1.2 1,013 3.7 Rhode Island............... 36.0 471.5 1.9 1,003 4.5 South Carolina............. 120.1 1,931.4 2.9 817 3.2 South Dakota............... 32.2 412.5 1.3 791 4.2 Tennessee.................. 147.9 2,822.1 2.4 927 3.5 Texas...................... 627.9 11,662.7 3.7 1,070 4.3 Utah....................... 92.6 1,324.2 3.0 872 4.3 Vermont.................... 24.6 311.0 0.7 882 4.1 Virginia................... 237.5 3,691.4 0.6 1,057 2.8 Washington................. 238.1 3,069.7 3.2 1,082 4.5 West Virginia.............. 50.0 712.0 0.1 818 3.3 Wisconsin.................. 167.5 2,789.3 1.3 894 3.4 Wyoming.................... 25.5 283.6 1.5 952 3.9 Puerto Rico................ 49.0 944.2 -1.5 556 0.7 Virgin Islands............. 3.5 38.5 -0.3 746 -1.2 (1) Average weekly wages were calculated using unrounded data. (2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.