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 Series Title: Employed: Retail trade, Asian: NSA
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 2000 01 30741.
 2000 02 43697·
 2000 03 74638.
 2000 04 99646·
 2000 05 20667.
 2000 06 42682.
 2000 07 70755·
 2000 08 41765·
 2000 09 71669.
 2000 10 33638.
 2000 11 62641·
 2000 12 76695·
 2001 01  32704.
 2001 02  98720.
 2001 03  61648·
 2001 04  48631·
 2001 05  78689.
 2001 06  44659.
 2001 07  6738.
 2001 08  25713·
 2001 09  11720·
 2001 10  1728.
 2001 11  59719.
 2001 12  2686.
 2002 01 56669·
 2002 02 28677.
 2002 03 8608.
 2002 04 98681·
 2002 05 63589.
 2002 06 4648.
 2002 07 66755.
 2002 08 57761·
 2002 09 7720.
 2002 10 38763.
 2002 11 71721.
 2002 12 49720·
 2003 01  89635·
 2003 02  61603·
 2003 03  86570·
 2003 04  67636.
 2003 05  34685·
 2003 06  29716.
 2003 07  99786.
 2003 08  30757.
 2003 09  93726·
 2003 10  24657.
 2003 11  23703.
 2003 12  83797.
 2004 01 41752.
 2004 02 52678·
 2004 03 95664.
 2004 04 95607.
 2004 05 78659.
 2004 06 46714.
 2004 07 79736.
 2004 08 60764·
 2004 09 30656.
 2004 10 62658·
 2004 11 62667·
 2004 12 97703·
 2005 01  47737.
 2005 02  81769.
 2005 03  57865·
 2005 04  50771·
 2005 05  5743.
 2005 06  10728·
 2005 07  2751.
 2005 08  89731.
 2005 09  4656·
 2005 10  82657·
 2005 11  60654.
 2005 12  7625.
 2006 01 69679·
 2006 02 51725·
 2006 03 20744·
 2006 04 24692.
 2006 05 50685.
 2006 06 1661·
 2006 07 93686.
 2006 08 83732.
 2006 09 24712.
 2006 10 38702.
 2006 11 17700·
 2006 12 55736·
 2007 01  91697.
 2007 02  93769.
 2007 03  57730·
 2007 04  32737.
 2007 05  75702.
 2007 06  23686·
 2007 07  99660·
 2007 08  35675.
 2007 09  27703.
 2007 10  9769.
 2007 11  56835·
 2007 12  7821.
 2008 01 24801.
 2008 02 59708.
 2008 03 66668·
 2008 04 18688.
 2008 05 64613·
 2008 06 4631·
 2008 07 93670·
 2008 08 61710.
 2008 09 74783·
 2008 10 1809.
 2008 11 23834·
 2008 12 56820·
 2009 01  3739.
 2009 02  31781·
 2009 03  71725.
 2009 04  47762·
 2009 05  28803.
 2009 06  0797·
 2009 07  73834.
 2009 08  4778·
 2009 09  49779.
 2009 10  42790.
 2009 11  93752.
 2009 12  19720·
 2010 01 90668·
 2010 02 11686.
 2010 03 19652.
 2010 04 13716.
 2010 05 3670.
 2010 06 90668·
 2010 07 63778.
 2010 08 29770.
 2010 09 63776.
 2010 10 85799.
 2010 11 63803.
 2010 12 37759.
 2011 01  70652.
 2011 02  29699.
 2011 03  83657.
 2011 04  30684·
 2011 05  49743.
 2011 06  77788.
 2011 07  10757·
 2011 08  29725·
 2011 09  52711.
 2011 10  12740·
 2011 11  57752.
 2011 12  76731·
 2012 01 53809.
 2012 02 40829·
 2012 03 62798.
 2012 04 42758.
 2012 05 52781.
 2012 06 92853·
 2012 07 88827·
 2012 08 20839·
 2012 09 76891.
 2012 10 26838.
 2012 11 22814·
 2012 12 30918·
 2013 01  75830.
 2013 02  22879·
 2013 03  78813·
 2013 04  15804·
 2013 05  23860·
 2013 06  88916·
 2013 07  94866·
 2013 08  45883.
 2013 09  22859.
 2013 10  56815·
 2013 11  32856.
 2013 12  14991.
 2014 01 96925.
 2014 02 811029.
 2014 03 801019.
 2014 04 40919.
 2014 05 96946.
 2014 06 46939·
 2014 07 52951.
 2014 08 70966·
 2014 09 98863.
 2014 10 56837.
 2014 11 84846.
 2014 12 70921·
 2015 01  2929.
 2015 02  451034.
 2015 03  411066·
 2015 04  361001·
 2015 05  33998·
 2015 06  681019·
 2015 07  91028.
 2015 08  86995·
 2015 09  42923.
 2015 10  7804·
 2015 11  2842·
 2015 12  65879.
 2016 01 4859.
 2016 02 68964.
 2016 03 581059·
 2016 04 421072·
 2016 05 19946.
 2016 06 841000.
 2016 07 391010.
 2016 08 11857.
 2016 09 13934·
 2016 10 11953.
 2016 11 33963·
 2016 12 921064.
 2017 01  36988·
 2017 02  31934.
 2017 03  54995.
 2017 04  27977.
 2017 05  641006.
 2017 06  501043·
 2017 07  39928·
 2017 08  87992.
 2017 09  97988·


 2017 10  991015·
 2017 11  51987·
 2017 12  66986.
 2018 01 61989.
 2018 02 37905·
 2018 03 79897.
 2018 04 82925.
 2018 05 5951.
 2018 06 43978·
 2018 07 34952.
 2018 08 80921·
 2018 09 5899·
 2018 10 25920·
 2018 11 54965·
 2018 12 29947·
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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: Retail trade, 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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