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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 731038·
 2000 02 61054.
 2000 03 351029·
 2000 04 85999·
 2000 05 11974.
 2000 06 651012·
 2000 07 41046·
 2000 08 131060·
 2000 09 591001·
 2000 10 671041.
 2000 11 181051·
 2000 12 831003.
 2001 01  681039·
 2001 02  921051.
 2001 03  901096.
 2001 04  401044.
 2001 05  121053·
 2001 06  471027.
 2001 07  571051.
 2001 08  531014.
 2001 09  93981·
 2001 10  65950.
 2001 11  83955.
 2001 12  15943·
 2002 01 35890·
 2002 02 2900.
 2002 03 55906·
 2002 04 46888·
 2002 05 90790·
 2002 06 39926.
 2002 07 86894.
 2002 08 90938·
 2002 09 321013·
 2002 10 83966.
 2002 11 13907·
 2002 12 17971·
 2003 01  61839·
 2003 02  65826·
 2003 03  1860·
 2003 04  20869.
 2003 05  4921·
 2003 06  43872·
 2003 07  37838·
 2003 08  19852·
 2003 09  11881.
 2003 10  99841·
 2003 11  33896.
 2003 12  57922·
 2004 01 56815·
 2004 02 37820.
 2004 03 28818·
 2004 04 4796·
 2004 05 41792.
 2004 06 91824·
 2004 07 84837.
 2004 08 54791·
 2004 09 91815.
 2004 10 17892·
 2004 11 90886.
 2004 12 26932·
 2005 01  55885·
 2005 02  96786·
 2005 03  58808·
 2005 04  64838.
 2005 05  11809·
 2005 06  36895.
 2005 07  26808·
 2005 08  38785.
 2005 09  57815·
 2005 10  67855·
 2005 11  57852.
 2005 12  41877.
 2006 01 20840·
 2006 02 47819.
 2006 03 38963.
 2006 04 93926·
 2006 05 49892·
 2006 06 57874.
 2006 07 84813.
 2006 08 38812·
 2006 09 21839·
 2006 10 4867.
 2006 11 32764.
 2006 12 45788.
 2007 01  35830.
 2007 02  19826.
 2007 03  44898·
 2007 04  7858.
 2007 05  17846.
 2007 06  59867·
 2007 07  8859.
 2007 08  45816·
 2007 09  17819·
 2007 10  6836.
 2007 11  38866.
 2007 12  3913.
 2008 01 11825.
 2008 02 66926·
 2008 03 71970.
 2008 04 49931.
 2008 05 991034·
 2008 06 711030·
 2008 07 70988.
 2008 08 77976·
 2008 09 6898.
 2008 10 78890·
 2008 11 23892.
 2008 12 7836.
 2009 01  83797.
 2009 02  68746.
 2009 03  43733·
 2009 04  84836·
 2009 05  6875.
 2009 06  48876.
 2009 07  68876·
 2009 08  75855·
 2009 09  74724·
 2009 10  49685.
 2009 11  61717·
 2009 12  75695·
 2010 01 13692·
 2010 02 29770.
 2010 03 9797.
 2010 04 4856·
 2010 05 74833.
 2010 06 68757·
 2010 07 26749·
 2010 08 6765.
 2010 09 31847.
 2010 10 74871.
 2010 11 49836·
 2010 12 49892.
 2011 01  86960.
 2011 02  77941.
 2011 03  42951·
 2011 04  27858.
 2011 05  63810.
 2011 06  36794.
 2011 07  91855.
 2011 08  61819.
 2011 09  13887·
 2011 10  31866.
 2011 11  11777·
 2011 12  94843.
 2012 01 12861.
 2012 02 69821.
 2012 03 39868·
 2012 04 95947.
 2012 05 59967.
 2012 06 81936.
 2012 07 371020.
 2012 08 58892·
 2012 09 81911.
 2012 10 25925·
 2012 11 94881·
 2012 12 3916·
 2013 01  27845·
 2013 02  82866·
 2013 03  23853·
 2013 04  73920.
 2013 05  731114·
 2013 06  581145·
 2013 07  611131.
 2013 08  381159·
 2013 09  191151.
 2013 10  851021·
 2013 11  721005·
 2013 12  92983.
 2014 01 91992·
 2014 02 21895.
 2014 03 43921.
 2014 04 54971·
 2014 05 391018.
 2014 06 451037·
 2014 07 871101·
 2014 08 681131·
 2014 09 381050·
 2014 10 661041·
 2014 11 42928·
 2014 12 4931·
 2015 01  90996.
 2015 02  151020.
 2015 03  711012.
 2015 04  55959·
 2015 05  191039.
 2015 06  481057·
 2015 07  621002·
 2015 08  401093·
 2015 09  751031·
 2015 10  47962·
 2015 11  281008.
 2015 12  5955·
 2016 01 35964·
 2016 02 19962.
 2016 03 61015.
 2016 04 171038·
 2016 05 971051.
 2016 06 951039·
 2016 07 601044·
 2016 08 711063.
 2016 09 891093·
 2016 10 321077.
 2016 11 771068.
 2016 12 861096·
 2017 01  601043·
 2017 02  121136.


 2017 03  711116·
 2017 04  71151.
 2017 05  861190.
 2017 06  411157.
 2017 07  781111.
 2017 08  21123.
 2017 09  661008·
 2017 10  511055·
 2017 11  631017.
 2017 12  661056·
 2018 01 271062·
 2018 02 981119.
 2018 03 131085·
 2018 04 171136.
 2018 05 191175.
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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

New data set: Quarterly Financial Reporting: Manufacturing, Mining, Trade, and Selected Service Industries

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