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 Series Title: Employed: Manufacturing, Asian: 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 871038.
 2000 02 511054·
 2000 03 951029·
 2000 04 95999·
 2000 05 55974·
 2000 06 781012.
 2000 07 701046·
 2000 08 661060.
 2000 09 841001·
 2000 10 971041·
 2000 11 391051·
 2000 12 291003·
 2001 01  231039.
 2001 02  901051.
 2001 03  171096·
 2001 04  291044.
 2001 05  481053·
 2001 06  641027·
 2001 07  391051.
 2001 08  341014.
 2001 09  59981·
 2001 10  50950·
 2001 11  70955·
 2001 12  39943·
 2002 01 82890.
 2002 02 4900·
 2002 03 97906.
 2002 04 64888.
 2002 05 67790.
 2002 06 9926·
 2002 07 34894·
 2002 08 51938·
 2002 09 891013·
 2002 10 71966·
 2002 11 17907·
 2002 12 71971.
 2003 01  49839·
 2003 02  22826.
 2003 03  10860·
 2003 04  17869.
 2003 05  72921·
 2003 06  18872·
 2003 07  1838·
 2003 08  8852.
 2003 09  9881.
 2003 10  41841.
 2003 11  3896·
 2003 12  89922·
 2004 01 35815.
 2004 02 97820·
 2004 03 53818·
 2004 04 29796·
 2004 05 51792.
 2004 06 19824.
 2004 07 15837·
 2004 08 22791.
 2004 09 81815·
 2004 10 63892·
 2004 11 76886·
 2004 12 87932.
 2005 01  76885.
 2005 02  11786.
 2005 03  50808.
 2005 04  69838.
 2005 05  12809·
 2005 06  68895.
 2005 07  73808.
 2005 08  35785.
 2005 09  89815.
 2005 10  67855.
 2005 11  9852.
 2005 12  82877.
 2006 01 32840.
 2006 02 65819.
 2006 03 47963.
 2006 04 27926.
 2006 05 92892·
 2006 06 43874.
 2006 07 98813.
 2006 08 14812.
 2006 09 44839.
 2006 10 8867.
 2006 11 1764.
 2006 12 14788·
 2007 01  88830·
 2007 02  91826.
 2007 03  18898·
 2007 04  20858.
 2007 05  50846.
 2007 06  49867·
 2007 07  38859.
 2007 08  51816·
 2007 09  81819.
 2007 10  64836·
 2007 11  74866·
 2007 12  51913·
 2008 01 55825.
 2008 02 42926.
 2008 03 62970·
 2008 04 90931·
 2008 05 741034.
 2008 06 151030·
 2008 07 67988·
 2008 08 61976·
 2008 09 62898.
 2008 10 40890·
 2008 11 75892·
 2008 12 84836.
 2009 01  70797.
 2009 02  98746.
 2009 03  1733·
 2009 04  4836.
 2009 05  73875·
 2009 06  96876.
 2009 07  82876.
 2009 08  88855.
 2009 09  71724.
 2009 10  65685·
 2009 11  80717·
 2009 12  31695·
 2010 01 91692·
 2010 02 62770.
 2010 03 83797.
 2010 04 53856.
 2010 05 82833·
 2010 06 69757·
 2010 07 6749.
 2010 08 97765·
 2010 09 20847.
 2010 10 93871.
 2010 11 84836.
 2010 12 33892.
 2011 01  5960.
 2011 02  65941.
 2011 03  82951.
 2011 04  55858.
 2011 05  63810·
 2011 06  41794.
 2011 07  87855·
 2011 08  82819.
 2011 09  43887.
 2011 10  4866.
 2011 11  12777·
 2011 12  57843.
 2012 01 22861.
 2012 02 60821.
 2012 03 95868.
 2012 04 51947·
 2012 05 0967.
 2012 06 40936.
 2012 07 991020.
 2012 08 57892.
 2012 09 2911·
 2012 10 2925·
 2012 11 12881·
 2012 12 95916·
 2013 01  78845·
 2013 02  27866.
 2013 03  46853·
 2013 04  8920·
 2013 05  51114.
 2013 06  211145·
 2013 07  401131.
 2013 08  671159·
 2013 09  821151·
 2013 10  171021·
 2013 11  561005.
 2013 12  63983.
 2014 01 34992.
 2014 02 70895·
 2014 03 82921·
 2014 04 18971.
 2014 05 131018·
 2014 06 631037·
 2014 07 321101.
 2014 08 761131·
 2014 09 931050·
 2014 10 11041.
 2014 11 42928·
 2014 12 4931.
 2015 01  75996.
 2015 02  181020.
 2015 03  771012.
 2015 04  98959.
 2015 05  841039.
 2015 06  691057·
 2015 07  241002.
 2015 08  771093.
 2015 09  711031.
 2015 10  36962.
 2015 11  951008.
 2015 12  84955·
 2016 01 11964·
 2016 02 26962.
 2016 03 281015·
 2016 04 891038·
 2016 05 41051·
 2016 06 701039·
 2016 07 881044·
 2016 08 861063·
 2016 09 761093·
 2016 10 251077·
 2016 11 611068.
 2016 12 951096.
 2017 01  781043.
 2017 02  731136.
 2017 03  601116.
 2017 04  581151.
 2017 05  841190.
 2017 06  201157.
 2017 07  231111.
 2017 08  461123.


 2017 09  951008.
 2017 10  981055·
 2017 11  131017·
 2017 12  531056.
 2018 01 361062.
 2018 02 631119.
 2018 03 181085.
 2018 04 981136·
 2018 05 591175·
 2018 06 981113·
 2018 07 511060·
 2018 08 241114.
 2018 09 37992·
 2018 10 8979·
 2018 11 501023.
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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: Manufacturing, Asian: 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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