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 Series Title: Employed: Retail trade, 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 18741.
 2000 02 55697.
 2000 03 77638.
 2000 04 95646·
 2000 05 42667·
 2000 06 51682·
 2000 07 61755.
 2000 08 38765·
 2000 09 32669.
 2000 10 46638·
 2000 11 45641.
 2000 12 80695.
 2001 01  38704·
 2001 02  20720·
 2001 03  43648.
 2001 04  65631·
 2001 05  37689.
 2001 06  82659·
 2001 07  93738·
 2001 08  61713.
 2001 09  28720·
 2001 10  62728·
 2001 11  57719·
 2001 12  64686.
 2002 01 38669·
 2002 02 17677·
 2002 03 24608.
 2002 04 92681.
 2002 05 15589·
 2002 06 29648.
 2002 07 6755·
 2002 08 89761.
 2002 09 36720.
 2002 10 4763·
 2002 11 42721·
 2002 12 25720·
 2003 01  2635.
 2003 02  74603·
 2003 03  93570.
 2003 04  93636.
 2003 05  37685.
 2003 06  95716.
 2003 07  49786.
 2003 08  13757·
 2003 09  91726.
 2003 10  50657·
 2003 11  21703.
 2003 12  29797.
 2004 01 41752.
 2004 02 45678.
 2004 03 49664.
 2004 04 10607·
 2004 05 87659·
 2004 06 11714.
 2004 07 94736·
 2004 08 51764.
 2004 09 53656.
 2004 10 5658.
 2004 11 44667.
 2004 12 69703.
 2005 01  91737.
 2005 02  9769·
 2005 03  13865.
 2005 04  10771.
 2005 05  76743.
 2005 06  30728·
 2005 07  39751.
 2005 08  43731·
 2005 09  8656.
 2005 10  98657.
 2005 11  84654·
 2005 12  62625·
 2006 01 12679·
 2006 02 72725.
 2006 03 35744.
 2006 04 85692·
 2006 05 40685·
 2006 06 31661·
 2006 07 7686.
 2006 08 58732·
 2006 09 66712.
 2006 10 84702.
 2006 11 37700.
 2006 12 49736·
 2007 01  90697.
 2007 02  61769.
 2007 03  88730.
 2007 04  52737·
 2007 05  39702·
 2007 06  20686.
 2007 07  48660·
 2007 08  86675·
 2007 09  63703·
 2007 10  41769·
 2007 11  29835.
 2007 12  96821.
 2008 01 36801·
 2008 02 33708.
 2008 03 69668·
 2008 04 51688.
 2008 05 59613.
 2008 06 25631.
 2008 07 32670.
 2008 08 45710.
 2008 09 59783·
 2008 10 75809.
 2008 11 5834·
 2008 12 97820.
 2009 01  84739·
 2009 02  65781.
 2009 03  2725.
 2009 04  51762.
 2009 05  11803.
 2009 06  0797·
 2009 07  59834.
 2009 08  77778.
 2009 09  40779.
 2009 10  14790.
 2009 11  41752·
 2009 12  67720.
 2010 01 96668.
 2010 02 62686·
 2010 03 34652·
 2010 04 43716·
 2010 05 30670·
 2010 06 26668·
 2010 07 52778·
 2010 08 47770·
 2010 09 17776.
 2010 10 13799·
 2010 11 33803·
 2010 12 49759.
 2011 01  57652.
 2011 02  53699.
 2011 03  39657·
 2011 04  43684.
 2011 05  76743.
 2011 06  5788.
 2011 07  59757.
 2011 08  48725.
 2011 09  24711.
 2011 10  50740.
 2011 11  5752.
 2011 12  9731·
 2012 01 77809.
 2012 02 94829.
 2012 03 15798·
 2012 04 94758·
 2012 05 35781·
 2012 06 69853·
 2012 07 52827.
 2012 08 97839.
 2012 09 24891·
 2012 10 31838·
 2012 11 4814·
 2012 12 39918.
 2013 01  93830.
 2013 02  62879·
 2013 03  84813·
 2013 04  62804.
 2013 05  18860·
 2013 06  69916.
 2013 07  30866·
 2013 08  36883·
 2013 09  0859.
 2013 10  84815·
 2013 11  4856·
 2013 12  55991·
 2014 01 64925·
 2014 02 631029.
 2014 03 951019.
 2014 04 39919·
 2014 05 60946.
 2014 06 87939·
 2014 07 15951.
 2014 08 31966·
 2014 09 64863·
 2014 10 64837·
 2014 11 3846.
 2014 12 1921.
 2015 01  76929.
 2015 02  251034·
 2015 03  211066.
 2015 04  421001·
 2015 05  22998·
 2015 06  271019.
 2015 07  571028.
 2015 08  74995.
 2015 09  51923·
 2015 10  50804.
 2015 11  24842.
 2015 12  41879.
 2016 01 40859.
 2016 02 3964·
 2016 03 601059.
 2016 04 131072·
 2016 05 89946·
 2016 06 171000·
 2016 07 271010·
 2016 08 82857.
 2016 09 22934.
 2016 10 47953.
 2016 11 15963.
 2016 12 691064·
 2017 01  91988.
 2017 02  77934·
 2017 03  35995·
 2017 04  18977.
 2017 05  711006.
 2017 06  111043.


 2017 07  25928·
 2017 08  97992·
 2017 09  63988·
 2017 10  841015.
 2017 11  48987.
 2017 12  1986.
 2018 01 91989.
 2018 02 49905.
 2018 03 93897·
 2018 04 49925·
 2018 05 6951·
 2018 06 91978·
 2018 07 72952.
 2018 08 85921.
 2018 09 83899.
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Similar Series

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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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