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 Series Title: Employed: Wholesale and 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 34901.
 2000 02 72883·
 2000 03 56775.
 2000 04 39809.
 2000 05 99840·
 2000 06 14829.
 2000 07 65904·
 2000 08 63943·
 2000 09 99882·
 2000 10 23856·
 2000 11 44822·
 2000 12 54862·
 2001 01  85884·
 2001 02  82915·
 2001 03  5861·
 2001 04  56864·
 2001 05  31899.
 2001 06  96860·
 2001 07  86892·
 2001 08  39890·
 2001 09  39901·
 2001 10  97898.
 2001 11  51869.
 2001 12  97826·
 2002 01 33789·
 2002 02 41818.
 2002 03 88752.
 2002 04 49843·
 2002 05 64768.
 2002 06 11836.
 2002 07 8970·
 2002 08 39976·
 2002 09 8926·
 2002 10 14965·
 2002 11 50907.
 2002 12 37875.
 2003 01  65793·
 2003 02  80761·
 2003 03  78704.
 2003 04  35773.
 2003 05  60831·
 2003 06  66896.
 2003 07  78987·
 2003 08  54932·
 2003 09  43913·
 2003 10  54797.
 2003 11  63893.
 2003 12  38997·
 2004 01 91940·
 2004 02 51857.
 2004 03 73835.
 2004 04 56810·
 2004 05 2855.
 2004 06 88952.
 2004 07 68945.
 2004 08 2977.
 2004 09 4854.
 2004 10 22851·
 2004 11 38880·
 2004 12 95888·
 2005 01  46925.
 2005 02  35977.
 2005 03  961039·
 2005 04  79936·
 2005 05  3905·
 2005 06  33902·
 2005 07  78914·
 2005 08  23910.
 2005 09  76878·
 2005 10  47881·
 2005 11  49885·
 2005 12  22871·
 2006 01 91887.
 2006 02 35900.
 2006 03 95893·
 2006 04 95857.
 2006 05 97877.
 2006 06 79836·
 2006 07 30874·
 2006 08 85893·
 2006 09 71900.
 2006 10 84906.
 2006 11 92919·
 2006 12 63971.
 2007 01  12895·
 2007 02  82969·
 2007 03  43955.
 2007 04  21951·
 2007 05  46922·
 2007 06  30878·
 2007 07  72823.
 2007 08  80855·
 2007 09  23847·
 2007 10  10942·
 2007 11  921048.
 2007 12  831015·
 2008 01 19988·
 2008 02 29874.
 2008 03 39831.
 2008 04 99884·
 2008 05 33787.
 2008 06 17810·
 2008 07 78854.
 2008 08 24878·
 2008 09 93919·
 2008 10 32975·
 2008 11 951016.
 2008 12 75970·
 2009 01  47889.
 2009 02  55942.
 2009 03  5894.
 2009 04  96925.
 2009 05  72995·
 2009 06  93994·
 2009 07  44978·
 2009 08  58928.
 2009 09  67938.
 2009 10  77935.
 2009 11  77900.
 2009 12  55856·
 2010 01 58805·
 2010 02 65815.
 2010 03 89817.
 2010 04 4907·
 2010 05 95887.
 2010 06 38913.
 2010 07 961028·
 2010 08 511010·
 2010 09 55976·
 2010 10 1942·
 2010 11 33953.
 2010 12 65874·
 2011 01  36789.
 2011 02  88867·
 2011 03  65832·
 2011 04  39893.
 2011 05  66956.
 2011 06  551003·
 2011 07  79933.
 2011 08  94884·
 2011 09  93869.
 2011 10  83900.
 2011 11  88908·
 2011 12  72889·
 2012 01 931004.
 2012 02 791005.
 2012 03 30957.
 2012 04 44909.
 2012 05 67965.
 2012 06 751064.
 2012 07 681023.
 2012 08 31069·
 2012 09 411080.
 2012 10 27989·
 2012 11 90979.
 2012 12 681122·
 2013 01  641042.
 2013 02  171080.
 2013 03  8986·
 2013 04  84967.
 2013 05  121020·
 2013 06  511071.
 2013 07  611062.
 2013 08  201063·
 2013 09  851026.
 2013 10  62962·
 2013 11  111033.
 2013 12  701178·
 2014 01 91104·
 2014 02 51216·
 2014 03 461174.
 2014 04 521076·
 2014 05 341099·
 2014 06 631109.
 2014 07 801148.
 2014 08 121134·
 2014 09 351035.
 2014 10 43987·
 2014 11 491052·
 2014 12 191129·
 2015 01  141116.
 2015 02  741250·
 2015 03  181260.
 2015 04  651237.
 2015 05  951223.
 2015 06  161214·
 2015 07  731200·
 2015 08  711166·
 2015 09  281079·
 2015 10  481007·
 2015 11  281013.
 2015 12  821061·
 2016 01 891067.
 2016 02 781209·
 2016 03 211281·
 2016 04 641278.
 2016 05 791171·
 2016 06 371218·
 2016 07 141234·
 2016 08 141082·
 2016 09 231143.
 2016 10 831189.
 2016 11 961171·
 2016 12 501270·
 2017 01  711224.
 2017 02  961127.
 2017 03  791200.
 2017 04  171206·
 2017 05  981230·
 2017 06  831244.


 2017 07  11196.
 2017 08  831235.
 2017 09  61169.
 2017 10  761208.
 2017 11  261156·
 2017 12  421137.
 2018 01 191119.
 2018 02 321049·
 2018 03 401044.
 2018 04 141113.
 2018 05 881173.
 2018 06 311216·
 2018 07 571173·
 2018 08 431156·
 2018 09 881084.
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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: Wholesale and 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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