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 Series Title: Employed: Wage and salary workers, 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 436268.
 2000 02 956320.
 2000 03 976334.
 2000 04 526244.
 2000 05 866128·
 2000 06 296320.
 2000 07 106137.
 2000 08 926102·
 2000 09 156224.
 2000 10 806022·
 2000 11 436065.
 2000 12 496186.
 2001 01  805996.
 2001 02  296063.
 2001 03  85941·
 2001 04  685803.
 2001 05  555766·
 2001 06  855640.
 2001 07  445412·
 2001 08  185402·
 2001 09  425338·
 2001 10  775448·
 2001 11  135378·
 2001 12  925327·
 2002 01 655282·
 2002 02 635270·
 2002 03 35235·
 2002 04 105078.
 2002 05 755033.
 2002 06 255033·
 2002 07 275051·
 2002 08 175221.
 2002 09 484964.
 2002 10 685100.
 2002 11 235106.
 2002 12 815079·
 2003 01  835211.
 2003 02  265164.
 2003 03  545081.
 2003 04  965108·
 2003 05  74963.
 2003 06  535051·
 2003 07  84987·
 2003 08  815102·
 2003 09  694936.
 2003 10  335021.
 2003 11  985056·
 2003 12  824959·
 2004 01 464971·
 2004 02 715037·
 2004 03 614983.
 2004 04 664932.
 2004 05 375026·
 2004 06 04822.
 2004 07 614785·
 2004 08 284908.
 2004 09 674749·
 2004 10 754734·
 2004 11 854921·
 2004 12 874888.
 2005 01  884848·
 2005 02  794848·
 2005 03  464690.
 2005 04  884853·
 2005 05  744814·
 2005 06  194790·
 2005 07  824699.
 2005 08  994725.
 2005 09  264743·
 2005 10  254656·
 2005 11  564711.
 2005 12  144722.
 2006 01 704737·
 2006 02 544828·
 2006 03 574981·
 2006 04 534778.
 2006 05 554806.
 2006 06 894863.
 2006 07 964652·
 2006 08 604697.
 2006 09 574620.
 2006 10 354618.
 2006 11 124591.
 2006 12 44501.
 2007 01  774682.
 2007 02  254785·
 2007 03  674833·
 2007 04  784759·
 2007 05  404739·
 2007 06  154626·
 2007 07  504710·
 2007 08  34790.
 2007 09  964712·
 2007 10  864818.
 2007 11  734767.
 2007 12  234750.
 2008 01 214741.
 2008 02 464560·
 2008 03 744593.
 2008 04 64508·
 2008 05 144516.
 2008 06 254666.
 2008 07 174598.
 2008 08 464600·
 2008 09 34527·
 2008 10 824506.
 2008 11 884500.
 2008 12 644115·
 2009 01  363984.
 2009 02  963965·
 2009 03  23885.
 2009 04  24021.
 2009 05  894040·
 2009 06  764038.
 2009 07  954075·
 2009 08  314018·
 2009 09  634072.
 2009 10  523937.
 2009 11  313775·
 2009 12  433595.
 2010 01 833528.
 2010 02 813603·
 2010 03 983720·
 2010 04 593822·
 2010 05 03914·
 2010 06 453995·
 2010 07 83947.
 2010 08 234002·
 2010 09 883991.
 2010 10 503933.
 2010 11 03896·
 2010 12 33922.
 2011 01  493819.
 2011 02  283904.
 2011 03  354014·
 2011 04  944056.
 2011 05  73859·
 2011 06  363905·
 2011 07  33968·
 2011 08  144012.
 2011 09  134152·
 2011 10  564242.
 2011 11  164159.
 2011 12  94061.
 2012 01 423932·
 2012 02 933972·
 2012 03 124206·
 2012 04 474247·
 2012 05 954213·
 2012 06 804231·
 2012 07 544200·
 2012 08 234192·
 2012 09 984151.
 2012 10 744244·
 2012 11 304119·
 2012 12 543973.
 2013 01  994026.
 2013 02  554047·
 2013 03  524154·
 2013 04  874124·
 2013 05  764207.
 2013 06  314236.
 2013 07  584217.
 2013 08  894238·
 2013 09  284260.
 2013 10  334113·
 2013 11  124209.
 2013 12  554174·
 2014 01 814216.
 2014 02 184353.
 2014 03 24228.
 2014 04 44244.
 2014 05 854371·
 2014 06 674310·
 2014 07 394357.
 2014 08 814509.
 2014 09 164411.
 2014 10 884383.
 2014 11 634224·
 2014 12 534139·
 2015 01  734279.
 2015 02  114305.
 2015 03  354318·
 2015 04  164242·
 2015 05  834241·
 2015 06  944348.
 2015 07  444455.
 2015 08  894462·
 2015 09  954454.
 2015 10  184423.
 2015 11  34289·
 2015 12  154303·
 2016 01 854307.
 2016 02 894302.
 2016 03 504390.
 2016 04 654225·
 2016 05 544190·
 2016 06 404119·
 2016 07 474243.
 2016 08 314402.
 2016 09 264513.
 2016 10 264669·
 2016 11 834526·
 2016 12 344423·
 2017 01  534227.
 2017 02  314418.
 2017 03  704467.
 2017 04  164654·
 2017 05  104452.
 2017 06  594357·


 2017 07  464346·
 2017 08  494311.
 2017 09  964495·
 2017 10  444501.
 2017 11  744598·
 2017 12  244357.
 2018 01 384290.
 2018 02 414331·
 2018 03 424375·
 2018 04 634473·
 2018 05 864336·
 2018 06 324358·
 2018 07 634369·
 2018 08 84404·
 2018 09 154498.
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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, 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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