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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 826268.
 2000 02 346320·
 2000 03 816334.
 2000 04 516244.
 2000 05 956128·
 2000 06 686320.
 2000 07 266137.
 2000 08 536102.
 2000 09 186224.
 2000 10 446022·
 2000 11 156065·
 2000 12 996186.
 2001 01  25996·
 2001 02  446063·
 2001 03  625941·
 2001 04  305803.
 2001 05  215766.
 2001 06  755640·
 2001 07  45412·
 2001 08  185402·
 2001 09  775338.
 2001 10  685448·
 2001 11  45378·
 2001 12  555327.
 2002 01 945282.
 2002 02 795270.
 2002 03 515235·
 2002 04 615078·
 2002 05 245033·
 2002 06 915033.
 2002 07 575051.
 2002 08 815221.
 2002 09 414964.
 2002 10 105100·
 2002 11 605106.
 2002 12 825079.
 2003 01  455211.
 2003 02  685164·
 2003 03  235081.
 2003 04  605108·
 2003 05  634963.
 2003 06  215051.
 2003 07  334987·
 2003 08  345102·
 2003 09  454936.
 2003 10  515021.
 2003 11  55056.
 2003 12  474959.
 2004 01 654971.
 2004 02 655037·
 2004 03 744983.
 2004 04 694932.
 2004 05 255026·
 2004 06 944822·
 2004 07 64785·
 2004 08 944908·
 2004 09 504749.
 2004 10 954734.
 2004 11 864921.
 2004 12 414888.
 2005 01  894848.
 2005 02  494848.
 2005 03  294690·
 2005 04  554853·
 2005 05  204814.
 2005 06  644790·
 2005 07  724699.
 2005 08  34725·
 2005 09  414743·
 2005 10  704656.
 2005 11  784711·
 2005 12  824722·
 2006 01 794737.
 2006 02 254828.
 2006 03 354981.
 2006 04 84778.
 2006 05 834806.
 2006 06 104863.
 2006 07 564652.
 2006 08 284697·
 2006 09 304620.
 2006 10 324618.
 2006 11 784591·
 2006 12 104501·
 2007 01  194682·
 2007 02  224785·
 2007 03  654833.
 2007 04  484759·
 2007 05  764739·
 2007 06  374626·
 2007 07  44710.
 2007 08  614790·
 2007 09  514712·
 2007 10  594818.
 2007 11  94767·
 2007 12  934750.
 2008 01 204741·
 2008 02 944560·
 2008 03 174593·
 2008 04 144508·
 2008 05 834516.
 2008 06 744666.
 2008 07 924598·
 2008 08 174600·
 2008 09 654527.
 2008 10 564506·
 2008 11 204500.
 2008 12 234115·
 2009 01  173984·
 2009 02  213965.
 2009 03  153885.
 2009 04  744021.
 2009 05  154040·
 2009 06  764038·
 2009 07  154075.
 2009 08  404018.
 2009 09  584072·
 2009 10  523937.
 2009 11  833775.
 2009 12  163595·
 2010 01 723528.
 2010 02 63603.
 2010 03 63720.
 2010 04 193822·
 2010 05 83914·
 2010 06 213995·
 2010 07 983947.
 2010 08 964002.
 2010 09 403991·
 2010 10 13933·
 2010 11 963896.
 2010 12 143922·
 2011 01  703819·
 2011 02  803904·
 2011 03  634014·
 2011 04  684056.
 2011 05  223859.
 2011 06  143905.
 2011 07  883968.
 2011 08  764012.
 2011 09  474152·
 2011 10  834242.
 2011 11  824159.
 2011 12  844061.
 2012 01 213932·
 2012 02 963972·
 2012 03 854206.
 2012 04 604247.
 2012 05 434213·
 2012 06 844231·
 2012 07 584200.
 2012 08 984192.
 2012 09 404151·
 2012 10 34244·
 2012 11 914119·
 2012 12 183973.
 2013 01  614026·
 2013 02  344047·
 2013 03  944154·
 2013 04  634124·
 2013 05  824207.
 2013 06  864236.
 2013 07  84217·
 2013 08  34238.
 2013 09  34260·
 2013 10  884113·
 2013 11  934209·
 2013 12  324174.
 2014 01 504216·
 2014 02 984353.
 2014 03 394228.
 2014 04 694244·
 2014 05 734371·
 2014 06 984310.
 2014 07 584357.
 2014 08 584509·
 2014 09 474411.
 2014 10 944383.
 2014 11 884224.
 2014 12 694139·
 2015 01  744279·
 2015 02  184305.
 2015 03  924318.
 2015 04  954242·
 2015 05  784241.
 2015 06  264348.
 2015 07  584455·
 2015 08  564462·
 2015 09  94454·
 2015 10  04423·
 2015 11  994289.
 2015 12  954303.
 2016 01 444307.
 2016 02 474302.
 2016 03 524390·
 2016 04 824225.
 2016 05 814190·
 2016 06 984119·
 2016 07 814243.
 2016 08 114402.
 2016 09 484513.
 2016 10 104669.
 2016 11 744526.
 2016 12 24423·
 2017 01  944227.
 2017 02  654418·


 2017 03  424467·
 2017 04  974654·
 2017 05  954452·
 2017 06  694357.
 2017 07  254346·
 2017 08  684311·
 2017 09  504495.
 2017 10  544501·
 2017 11  854598·
 2017 12  64357·
 2018 01 864290·
 2018 02 764331.
 2018 03 104375·
 2018 04 334473.
 2018 05 534336.
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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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