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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 452672·
 2000 02 622812.
 2000 03 372735·
 2000 04 252635·
 2000 05 12675.
 2000 06 442802.
 2000 07 372780.
 2000 08 892699·
 2000 09 912628.
 2000 10 952487.
 2000 11 62518.
 2000 12 202648.
 2001 01  682593.
 2001 02  932668·
 2001 03  582607.
 2001 04  632444·
 2001 05  52487·
 2001 06  482380.
 2001 07  302359·
 2001 08  872378.
 2001 09  32277.
 2001 10  902291.
 2001 11  862344.
 2001 12  862357·
 2002 01 882375·
 2002 02 752393·
 2002 03 132306·
 2002 04 782259·
 2002 05 102249·
 2002 06 862300.
 2002 07 242304.
 2002 08 92437.
 2002 09 442294·
 2002 10 292233.
 2002 11 572291·
 2002 12 472195·
 2003 01  922294·
 2003 02  352251.
 2003 03  752240·
 2003 04  862304.
 2003 05  772177·
 2003 06  622233.
 2003 07  652258.
 2003 08  462345·
 2003 09  512227·
 2003 10  912289.
 2003 11  422271.
 2003 12  712154.
 2004 01 892263·
 2004 02 282265.
 2004 03 412282·
 2004 04 292216·
 2004 05 922342.
 2004 06 62225·
 2004 07 282174.
 2004 08 152210.
 2004 09 842153.
 2004 10 812134·
 2004 11 882175·
 2004 12 492181·
 2005 01  162109.
 2005 02  342180·
 2005 03  362104.
 2005 04  732195.
 2005 05  642188·
 2005 06  732188·
 2005 07  92158·
 2005 08  582149.
 2005 09  282096·
 2005 10  282046.
 2005 11  702026·
 2005 12  982010.
 2006 01 332101.
 2006 02 572113.
 2006 03 72134.
 2006 04 112046·
 2006 05 302099.
 2006 06 402146.
 2006 07 332074·
 2006 08 422087.
 2006 09 942019.
 2006 10 862010.
 2006 11 862008·
 2006 12 601941.
 2007 01  262129·
 2007 02  252078·
 2007 03  282111.
 2007 04  592043.
 2007 05  502009.
 2007 06  142098.
 2007 07  472180.
 2007 08  592191.
 2007 09  182151·
 2007 10  352131·
 2007 11  572071.
 2007 12  22107·
 2008 01 52093·
 2008 02 932020.
 2008 03 952019.
 2008 04 601955.
 2008 05 102018·
 2008 06 222011.
 2008 07 252056.
 2008 08 242062·
 2008 09 601951·
 2008 10 781915·
 2008 11 331843.
 2008 12 161587.
 2009 01  51603.
 2009 02  421697.
 2009 03  331745.
 2009 04  791864.
 2009 05  201912.
 2009 06  201930·
 2009 07  631932·
 2009 08  811901·
 2009 09  61891·
 2009 10  451779.
 2009 11  861704.
 2009 12  181639·
 2010 01 881644.
 2010 02 741693·
 2010 03 891726·
 2010 04 81765.
 2010 05 161805·
 2010 06 871825.
 2010 07 861742.
 2010 08 961725·
 2010 09 661760.
 2010 10 911734.
 2010 11 141791·
 2010 12 501815·
 2011 01  381715·
 2011 02  191835.
 2011 03  371815.
 2011 04  651787.
 2011 05  581719·
 2011 06  261769.
 2011 07  421778·
 2011 08  601846.
 2011 09  751861·
 2011 10  951971.
 2011 11  411963·
 2011 12  791870·
 2012 01 671787·
 2012 02 321796.
 2012 03 741885.
 2012 04 911887.
 2012 05 321935·
 2012 06 681927.
 2012 07 21934.
 2012 08 911938·
 2012 09 961848.
 2012 10 611921.
 2012 11 151840·
 2012 12 971844·
 2013 01  91844·
 2013 02  911806.
 2013 03  241889.
 2013 04  541914·
 2013 05  481993·
 2013 06  662003·
 2013 07  671945·
 2013 08  301940·
 2013 09  121953.
 2013 10  171820.
 2013 11  81898·
 2013 12  201852.
 2014 01 261894·
 2014 02 21938.
 2014 03 341940·
 2014 04 441863·
 2014 05 421961.
 2014 06 01969·
 2014 07 581975·
 2014 08 442048.
 2014 09 342045·
 2014 10 202028·
 2014 11 181944.
 2014 12 911876·
 2015 01  31996.
 2015 02  922021.
 2015 03  771954·
 2015 04  791952·
 2015 05  741888.
 2015 06  741974·
 2015 07  232082.
 2015 08  252095·
 2015 09  982083·
 2015 10  472055.
 2015 11  691999·
 2015 12  761992.
 2016 01 502013.
 2016 02 181943·
 2016 03 521997.
 2016 04 01856.
 2016 05 311858·
 2016 06 181826.
 2016 07 361917.
 2016 08 902050.
 2016 09 652057.
 2016 10 42132.
 2016 11 92100·
 2016 12 672038·
 2017 01  42006.
 2017 02  572013·


 2017 03  822032.
 2017 04  742073·
 2017 05  221962·
 2017 06  851958.
 2017 07  951918.
 2017 08  801968.
 2017 09  202077·
 2017 10  192061·
 2017 11  102092·
 2017 12  801993·
 2018 01 241934·
 2018 02 152015·
 2018 03 152012·
 2018 04 932012.
 2018 05 832006.
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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

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