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 Series Title: Employment Level: Mexican, Building and Grounds Cleaning and Maintenance Occupations: 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 61777.
 2000 02 37784.
 2000 03 40770·
 2000 04 29898·
 2000 05 86821·
 2000 06 97959·
 2000 07 49933·
 2000 08 60921·
 2000 09 27908.
 2000 10 5838.
 2000 11 9884.
 2000 12 40914·
 2001 01  34873.
 2001 02  43857·
 2001 03  33855·
 2001 04  13904.
 2001 05  84891·
 2001 06  18956·
 2001 07  59907·
 2001 08  48923.
 2001 09  76864.
 2001 10  61925·
 2001 11  49914·
 2001 12  39908.
 2002 01 36931·
 2002 02 30990·
 2002 03 91961·
 2002 04 51064·
 2002 05 141079.
 2002 06 4982.
 2002 07 99951.
 2002 08 90954.
 2002 09 86873·
 2002 10 93937.
 2002 11 111010.
 2002 12 64945.
 2003 01  52951.
 2003 02  26939·
 2003 03  88994.
 2003 04  211029.
 2003 05  381068·
 2003 06  611069·
 2003 07  191011·
 2003 08  791054·
 2003 09  93966.
 2003 10  79960.
 2003 11  181031.
 2003 12  831045.
 2004 01 94941·
 2004 02 87883·
 2004 03 21974·
 2004 04 31085·
 2004 05 401129.
 2004 06 161112·
 2004 07 911074.
 2004 08 311017·
 2004 09 581209.
 2004 10 241261·
 2004 11 181244·
 2004 12 881181.
 2005 01  961062·
 2005 02  911022.
 2005 03  131005·
 2005 04  181006.
 2005 05  321017.
 2005 06  25993.
 2005 07  141099.
 2005 08  891080.
 2005 09  651132·
 2005 10  571089·
 2005 11  85996·
 2005 12  961065.
 2006 01 201028.
 2006 02 441075.
 2006 03 121100·
 2006 04 581141·
 2006 05 481154.
 2006 06 671112.
 2006 07 101199.
 2006 08 191252.
 2006 09 251204·
 2006 10 321192.
 2006 11 161064.
 2006 12 481005·
 2007 01  501037·
 2007 02  731097.
 2007 03  921104·
 2007 04  391268·
 2007 05  501318·
 2007 06  451364.
 2007 07  281322·
 2007 08  121405·
 2007 09  741349·
 2007 10  301150·
 2007 11  341068.
 2007 12  101175·
 2008 01 981195.
 2008 02 341255.
 2008 03 511184.
 2008 04 31143·
 2008 05 231176.
 2008 06 901220.
 2008 07 401185·
 2008 08 861171.
 2008 09 731214·
 2008 10 531241·
 2008 11 891195·
 2008 12 761196·
 2009 01  991156·
 2009 02  61084·
 2009 03  111136·
 2009 04  181177·
 2009 05  771214·
 2009 06  551184·
 2009 07  441261.
 2009 08  821227.
 2009 09  61136·
 2009 10  321174.
 2009 11  511232·
 2009 12  611196·
 2010 01 131192·
 2010 02 931193.
 2010 03 201187.
 2010 04 751228.
 2010 05 481239.
 2010 06 201247·
 2010 07 641358·
 2010 08 671319.
 2010 09 851297·
 2010 10 201229.
 2010 11 211207·
 2010 12 71168.
 2011 01  261152.
 2011 02  251136.
 2011 03  531228.
 2011 04  901212·
 2011 05  961267.
 2011 06  901220·
 2011 07  481309.
 2011 08  331325.
 2011 09  451339.
 2011 10  981319·
 2011 11  201271.
 2011 12  821264.
 2012 01 491093.
 2012 02 551115·
 2012 03 61211.
 2012 04 51252.
 2012 05 81256·
 2012 06 931323·
 2012 07 691317·
 2012 08 971267·
 2012 09 471345·
 2012 10 681324·
 2012 11 961200.
 2012 12 771221.
 2013 01  171197·
 2013 02  631193.
 2013 03  21219.
 2013 04  881302.
 2013 05  931238·
 2013 06  841267·
 2013 07  241306.
 2013 08  11233.
 2013 09  761309·
 2013 10  611371.
 2013 11  241285·
 2013 12  81312.
 2014 01 901275.
 2014 02 531295.
 2014 03 911361·
 2014 04 301400·
 2014 05 871367.
 2014 06 411319.
 2014 07 371297.
 2014 08 101375·
 2014 09 421425.
 2014 10 311500.
 2014 11 01542·
 2014 12 181477.
 2015 01  551467.
 2015 02  561366.
 2015 03  441418·
 2015 04  481352.
 2015 05  511410·
 2015 06  701424·
 2015 07  561391.
 2015 08  181514·
 2015 09  821411·
 2015 10  581403·
 2015 11  481427·
 2015 12  741308.
 2016 01 211313.
 2016 02 241300·
 2016 03 601417·
 2016 04 831433·
 2016 05 711480.
 2016 06 01543·
 2016 07 541505.
 2016 08 411471.
 2016 09 551336.
 2016 10 441387.
 2016 11 171337·
 2016 12 661326.
 2017 01  251239·
 2017 02  371284.
 2017 03  621331·
 2017 04  31357·
 2017 05  561433.
 2017 06  851466.
 2017 07  281515·
 2017 08  211447·


 2017 09  481430·
 2017 10  201491.
 2017 11  781438.
 2017 12  131382.
 2018 01 261273.
 2018 02 991267.
 2018 03 981291.
 2018 04 991342.
 2018 05 421364.
 2018 06 41357·
 2018 07 31366.
 2018 08 241480·
 2018 09 701489.
 2018 10 411617.
 2018 11 301552·
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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: Employment Level: Mexican, Building and Grounds Cleaning and Maintenance Occupations: 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

Unemployment data for every county in the U.S..
Click for Illinois counties, then follow the example for your state

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