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 Series Title: Employed: Wage and salary workers, Nondurable goods manufacturing, Women: NSA
   For this series:   Numerical Data  |  Forecast  |  GIF Chart  |  PDF Chart  |  Yearly Chart  |  Excel file  |  Self-updating Excel file
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 2000 01 742672·
 2000 02 812812.
 2000 03 352735·
 2000 04 562635.
 2000 05 912675·
 2000 06 872802·
 2000 07 932780.
 2000 08 902699.
 2000 09 382628.
 2000 10 542487.
 2000 11 152518·
 2000 12 852648·
 2001 01  632593.
 2001 02  632668.
 2001 03  142607.
 2001 04  862444·
 2001 05  442487.
 2001 06  222380.
 2001 07  262359·
 2001 08  602378.
 2001 09  792277.
 2001 10  612291.
 2001 11  162344·
 2001 12  42357·
 2002 01 952375·
 2002 02 382393.
 2002 03 702306.
 2002 04 352259.
 2002 05 412249·
 2002 06 352300·
 2002 07 22304·
 2002 08 982437·
 2002 09 942294·
 2002 10 572233.
 2002 11 502291.
 2002 12 622195·
 2003 01  532294·
 2003 02  362251·
 2003 03  772240.
 2003 04  212304·
 2003 05  442177.
 2003 06  302233.
 2003 07  712258·
 2003 08  952345.
 2003 09  182227·
 2003 10  192289·
 2003 11  432271.
 2003 12  272154·
 2004 01 492263·
 2004 02 112265.
 2004 03 332282.
 2004 04 862216·
 2004 05 392342·
 2004 06 372225.
 2004 07 32174·
 2004 08 672210·
 2004 09 122153.
 2004 10 532134.
 2004 11 422175·
 2004 12 892181.
 2005 01  182109.
 2005 02  422180·
 2005 03  612104.
 2005 04  962195·
 2005 05  352188·
 2005 06  92188.
 2005 07  992158.
 2005 08  572149·
 2005 09  612096.
 2005 10  872046.
 2005 11  192026.
 2005 12  672010.
 2006 01 222101·
 2006 02 332113.
 2006 03 92134·
 2006 04 442046.
 2006 05 682099·
 2006 06 272146.
 2006 07 812074·
 2006 08 322087.
 2006 09 752019.
 2006 10 772010·
 2006 11 742008·
 2006 12 11941.
 2007 01  442129.
 2007 02  142078.
 2007 03  502111·
 2007 04  452043.
 2007 05  992009.
 2007 06  942098.
 2007 07  972180.
 2007 08  832191·
 2007 09  922151.
 2007 10  12131.
 2007 11  282071.
 2007 12  592107·
 2008 01 962093·
 2008 02 392020.
 2008 03 812019·
 2008 04 421955.
 2008 05 142018·
 2008 06 412011·
 2008 07 202056·
 2008 08 272062·
 2008 09 701951·
 2008 10 631915·
 2008 11 301843·
 2008 12 321587·
 2009 01  141603.
 2009 02  871697.
 2009 03  271745·
 2009 04  21864·
 2009 05  221912.
 2009 06  201930·
 2009 07  471932·
 2009 08  51901·
 2009 09  951891.
 2009 10  931779.
 2009 11  541704.
 2009 12  371639.
 2010 01 581644.
 2010 02 631693.
 2010 03 351726.
 2010 04 161765·
 2010 05 601805·
 2010 06 121825·
 2010 07 31742.
 2010 08 861725.
 2010 09 791760.
 2010 10 451734.
 2010 11 971791·
 2010 12 161815.
 2011 01  131715·
 2011 02  961835.
 2011 03  781815.
 2011 04  11787.
 2011 05  881719.
 2011 06  201769.
 2011 07  351778.
 2011 08  601846·
 2011 09  561861.
 2011 10  991971.
 2011 11  811963·
 2011 12  361870.
 2012 01 901787·
 2012 02 401796.
 2012 03 541885·
 2012 04 991887.
 2012 05 921935.
 2012 06 741927.
 2012 07 161934.
 2012 08 441938.
 2012 09 871848.
 2012 10 581921.
 2012 11 561840·
 2012 12 881844.
 2013 01  251844.
 2013 02  741806·
 2013 03  871889.
 2013 04  771914·
 2013 05  821993.
 2013 06  292003.
 2013 07  451945·
 2013 08  801940·
 2013 09  571953·
 2013 10  531820.
 2013 11  951898·
 2013 12  901852·
 2014 01 781894·
 2014 02 41938·
 2014 03 231940·
 2014 04 271863.
 2014 05 931961.
 2014 06 631969.
 2014 07 441975.
 2014 08 352048.
 2014 09 782045·
 2014 10 372028·
 2014 11 941944.
 2014 12 831876.
 2015 01  341996·
 2015 02  572021.
 2015 03  371954.
 2015 04  651952.
 2015 05  861888.
 2015 06  811974·
 2015 07  612082.
 2015 08  02095·
 2015 09  852083·
 2015 10  892055.
 2015 11  781999.
 2015 12  331992·
 2016 01 52013·
 2016 02 891943·
 2016 03 271997·
 2016 04 331856·
 2016 05 481858·
 2016 06 121826.
 2016 07 691917·
 2016 08 932050.
 2016 09 382057·
 2016 10 152132.
 2016 11 942100·
 2016 12 342038·
 2017 01  262006·
 2017 02  792013.
 2017 03  142032.
 2017 04  982073·
 2017 05  871962.
 2017 06  111958.
 2017 07  671918·
 2017 08  561968·


 2017 09  232077.
 2017 10  422061.
 2017 11  162092.
 2017 12  01993.
 2018 01 571934.
 2018 02 302015.
 2018 03 492012·
 2018 04 522012.
 2018 05 412006·
 2018 06 562081.
 2018 07 781985.
 2018 08 541959·
 2018 09 501983.
 2018 10 261973.
 2018 11 412046.
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US Unemployment Rate: Alternative Measures of Labor Underutilization

Charts

Unemployment Rate and Alternative Measures of Labor Underutilization
Unemployment Rate: Gender
Unemployment Rate: Black: Gender
Unemployment Rate: White: Gender
Unemployment Rate: Race
Unemployment Rate: Age: 16/25/35/45/55
Unemployment Rate: Education
Unemployment Rate: Nonagricultural Industry
Unemployment Rate: Job Category
Median Weeks Unemployed: Age
Median Weeks Unemployed: Race


The US Labor Force Dataset from the BLS contains nearly 15,000 series.
These series are subcategories of the popular series of:

Civilian Labor Force (subcategories)
Employment Level (subcategories)
Unemployment Level (subcategories)
US Unemployment Rate (subcategories)
Population (subcategories)
Employment-Population Ratio (subcategories)
Median Weeks Unemployed (subcategories)
Percent Distribution (subcategories)
Average Hours At Work (subcategories)
Of Total Unemployed (subcategories)

These basic measures are augmented by:

Population
Labor Force Participation Rate
Employment-Population Ratio
Not in Labor Force
Number at work
Average hours at work
Multiple jobholders
Average number of search methods used
Number At work part-time

These series can be subdivided by:

Sex:

Male
Female

Race and Ethnicity:

White
Black
Hispanic
Mexican
Puerto Rican
Cuban

Age:

16 years and over (default)
14_yrs._&_over
16-17_yrs.
16-19_yrs.
16-21_yrs.
16-24_yrs.
18-19_yrs.
20_yrs._&_over
20-24_yrs.
20-64_yrs.
25_yrs._&_over
25-29_yrs.
25-34_yrs.
25-54_yrs.
25-59_yrs.
30-34_yrs.
35-39_yrs.
35-44_yrs.
40-44_yrs.
45_yrs._&_over
45-49_yrs.
45-54_yrs.
50-54_yrs.
55_yrs._&_over
55-59_yrs.
55-64_yrs.
60-61_yrs.
60-64_yrs.
62-64_yrs.
65_yrs._&_over
65-69_yrs.
70_yrs._&_over

Class Of Worker:

Wage_And_Salary_Workers
Private_Wage_And_Salary_Workers
Government_Wage_And_Salary_Workers
Self-employed_Workers
Unpaid_Family_Workers

Industry:

Agriculture_Service
Furniture_And_Fixtures
Motor_Vehicles_&_Motor_Vehicle_Equipment
Aircraft
Tobacco
Eating_And_Drinking
Private_Households
Hospital
Postal
Federal
State
Local
Goods_Producing
Service_Producing
Agriculture_(Excludes_Forestry_And_Fisheries)
Forestry_And_Fisheries
Nonagricultural
Mining
Construction
Manufacturing
Manufacturing,_Durables
Lumber_And_Wood_Products,_Except_Furniture
Stone,_Clay,_And_Glass_Products
Primary_Metal
Fabricated_Metal
Machinery
Electrical_machinery,_equipment,_&_supplies
Transportation_Equipment
Professional_&_photographic_equipment_&_watches
Other_transportation_equipment
Fabricated_Metals
Manufacturing,_Nondurables
Food_And_Kindred_Products
Textile_Mill_Products
Apparel_And_Other_Fabricated_Textile_Products
Paper_And_Allied_Products
Printing,_Publishing,_And_Allied_Industries
Chemicals_And_Allied_Products
Petroleum_And_Coal_Products
Rubber_And_Miscellaneous_Plastic_Products
Leather_And_Leather_Products
Transportation,_Communication,_And_Other_Public_Utilities
Transportation
Transportation_Other_Than_Railroad_And_Railway_Express
Communications_And_Other_Public_Utilities
Communications
Trade
Wholesale_Trade
Retail_Trade
Service_Industries
Finance,_Insurance,_And_Real_Estate
Insurance_And_Real_Estate
Banking_And_Other_Financial_Organizations
Business_And_Repair_Services
Businesses
Repair_Services
Personal_Services
Entertainment_And_Recreation_Services
Professional_And_Related_Services
Medical
Educational_Services
Welfare_And_Religious_Services
Public_Administration

Occupation:

Managerial_&_professional_specialty_occupation
Executive,_administration,_&_managerial
Officials_&_administration,_public_administation
Management_Related_Occupations
Professional_Specialty_Occupations
Engineers
Math_&_Computer_Scientists
Natural_scientists
Health_Diagnosing_Occupations
Health_Assessment_&_Treating
Teachers,_college_&_university
Lawyers_&_Judges
Technical,_sales,_administration_support
Technicians_&_related_support_occupations
Health_technologists_&_technicians
Engineering_&_science_technicians
Technicians
Sales_Occupations
Supervisors_&_Proprietors
Sales_reps,_finance_&_business_services
Sales_reps,_commodities
Sales,_retail_&_personal
Sales_Related_Occupations
Administrative_support
Supervisors
Computer_Equipment_Operators
Secretaries,_stenographers,_&_typists
Financial_Records_Processing
Mail_&_Message_Distributing
Service_Occupation
Private_Household
Protective_Services
Food_Service
Health_Service
Cleaning_&_Building_Service
Personal_Service
Precision_Production,_Craft,_Repair
Mechanics_&_Repairers
Construction_Trades
Operators,_Fabricators,_Laborers
Machine_operators,_assemblers,_inspectors
Operators_&_tenders
Fabricators,_assemblers,_inspectors,_samplers
Transportation,_material_moving
Motor_vehicle_operators
Handlers,_equipment_cleaners,_helpers,_laborers
Construction_Laborers
Freight,_stock_material_handlers
Farming,_Forestry,_Fishing
Farm_Operators_&_Managers
Farm_workers_&_related_occupations
Forestry_&_fishing_occupations

Input your search text below.
or alter the examples after this blank template.







      Example 1: Get Unemployment Rates for people in Computer jobs







      Example 2: Get Employment Data for Workers in the "Retail Trade" Industry







      Example 3: Get Population Level Data for Workers





 Series Title: Employed: Wage and salary workers, Nondurable goods manufacturing, Women: NSA
   For this series:   Numerical Data  |  Forecast  |  GIF Chart  |  PDF Chart  |  Excel file  |  Self-updating Excel file
   Transform this series  |  Display series in COPY/PASTE format  |  More series like this one

Unemployment data for every county in the U.S..
Click for Illinois counties, then follow the example for your state

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