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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 71038·
 2000 02 591054·
 2000 03 181029.
 2000 04 48999·
 2000 05 41974.
 2000 06 861012.
 2000 07 771046.
 2000 08 381060·
 2000 09 721001.
 2000 10 421041.
 2000 11 681051.
 2000 12 571003.
 2001 01  521039·
 2001 02  101051·
 2001 03  51096.
 2001 04  501044·
 2001 05  541053·
 2001 06  961027.
 2001 07  241051.
 2001 08  871014·
 2001 09  81981.
 2001 10  31950.
 2001 11  89955.
 2001 12  60943·
 2002 01 57890·
 2002 02 73900·
 2002 03 88906.
 2002 04 91888·
 2002 05 32790·
 2002 06 3926.
 2002 07 34894·
 2002 08 21938.
 2002 09 821013.
 2002 10 27966·
 2002 11 64907.
 2002 12 22971·
 2003 01  8839.
 2003 02  78826.
 2003 03  41860.
 2003 04  87869.
 2003 05  34921·
 2003 06  11872.
 2003 07  54838.
 2003 08  34852.
 2003 09  12881.
 2003 10  9841·
 2003 11  15896.
 2003 12  9922.
 2004 01 15815·
 2004 02 78820·
 2004 03 62818·
 2004 04 80796.
 2004 05 46792.
 2004 06 50824·
 2004 07 58837·
 2004 08 42791.
 2004 09 29815.
 2004 10 81892·
 2004 11 90886·
 2004 12 13932·
 2005 01  15885.
 2005 02  53786·
 2005 03  73808.
 2005 04  38838.
 2005 05  79809·
 2005 06  73895.
 2005 07  43808·
 2005 08  85785·
 2005 09  21815·
 2005 10  72855.
 2005 11  64852·
 2005 12  50877·
 2006 01 67840.
 2006 02 7819.
 2006 03 23963·
 2006 04 51926.
 2006 05 67892·
 2006 06 60874·
 2006 07 33813·
 2006 08 32812.
 2006 09 97839.
 2006 10 37867.
 2006 11 33764.
 2006 12 63788.
 2007 01  57830.
 2007 02  70826·
 2007 03  84898.
 2007 04  37858·
 2007 05  72846.
 2007 06  15867·
 2007 07  76859.
 2007 08  74816.
 2007 09  94819·
 2007 10  55836·
 2007 11  20866·
 2007 12  18913.
 2008 01 96825·
 2008 02 95926.
 2008 03 55970.
 2008 04 38931·
 2008 05 801034·
 2008 06 681030.
 2008 07 44988.
 2008 08 28976.
 2008 09 70898.
 2008 10 30890·
 2008 11 75892.
 2008 12 7836·
 2009 01  19797·
 2009 02  63746.
 2009 03  59733·
 2009 04  20836·
 2009 05  0875.
 2009 06  73876.
 2009 07  37876·
 2009 08  35855·
 2009 09  14724.
 2009 10  92685·
 2009 11  69717.
 2009 12  47695·
 2010 01 86692.
 2010 02 77770·
 2010 03 44797·
 2010 04 75856·
 2010 05 68833·
 2010 06 9757.
 2010 07 76749.
 2010 08 63765.
 2010 09 34847.
 2010 10 95871·
 2010 11 1836·
 2010 12 80892·
 2011 01  84960·
 2011 02  66941.
 2011 03  83951·
 2011 04  20858.
 2011 05  45810.
 2011 06  80794.
 2011 07  10855.
 2011 08  3819.
 2011 09  66887.
 2011 10  18866·
 2011 11  94777.
 2011 12  72843.
 2012 01 23861·
 2012 02 86821·
 2012 03 74868.
 2012 04 97947·
 2012 05 16967·
 2012 06 16936.
 2012 07 121020·
 2012 08 72892.
 2012 09 71911.
 2012 10 65925·
 2012 11 96881·
 2012 12 16916.
 2013 01  38845.
 2013 02  15866.
 2013 03  40853·
 2013 04  73920.
 2013 05  921114·
 2013 06  711145·
 2013 07  801131·
 2013 08  121159·
 2013 09  341151.
 2013 10  661021·
 2013 11  801005·
 2013 12  27983.
 2014 01 90992·
 2014 02 57895·
 2014 03 28921.
 2014 04 38971·
 2014 05 91018.
 2014 06 251037.
 2014 07 371101.
 2014 08 891131·
 2014 09 671050.
 2014 10 761041.
 2014 11 44928.
 2014 12 49931.
 2015 01  64996.
 2015 02  421020.
 2015 03  131012·
 2015 04  11959·
 2015 05  261039·
 2015 06  811057.
 2015 07  311002·
 2015 08  961093.
 2015 09  31031·
 2015 10  72962·
 2015 11  121008.
 2015 12  47955.
 2016 01 71964.
 2016 02 25962.
 2016 03 921015·
 2016 04 331038.
 2016 05 301051·
 2016 06 681039.
 2016 07 361044.
 2016 08 491063.
 2016 09 51093·
 2016 10 611077.
 2016 11 301068·
 2016 12 131096·
 2017 01  471043.
 2017 02  831136·
 2017 03  881116.
 2017 04  701151·
 2017 05  311190·


 2017 06  431157·
 2017 07  481111·
 2017 08  891123·
 2017 09  581008.
 2017 10  701055.
 2017 11  731017.
 2017 12  651056·
 2018 01 521062.
 2018 02 231119.
 2018 03 501085.
 2018 04 151136·
 2018 05 651175·
 2018 06 821113.
 2018 07 921060·
 2018 08 851114·
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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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