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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 11777.
 2000 02 62784·
 2000 03 14770·
 2000 04 95898.
 2000 05 57821.
 2000 06 53959.
 2000 07 41933·
 2000 08 5921·
 2000 09 13908·
 2000 10 88838·
 2000 11 13884.
 2000 12 58914.
 2001 01  29873.
 2001 02  45857.
 2001 03  43855.
 2001 04  98904·
 2001 05  81891·
 2001 06  34956·
 2001 07  68907·
 2001 08  93923.
 2001 09  70864·
 2001 10  84925.
 2001 11  51914·
 2001 12  38908.
 2002 01 20931.
 2002 02 54990.
 2002 03 81961.
 2002 04 521064.
 2002 05 821079.
 2002 06 24982.
 2002 07 69951.
 2002 08 79954·
 2002 09 32873·
 2002 10 0937·
 2002 11 551010·
 2002 12 89945.
 2003 01  69951.
 2003 02  16939.
 2003 03  1994·
 2003 04  751029.
 2003 05  201068·
 2003 06  271069.
 2003 07  941011·
 2003 08  561054·
 2003 09  34966.
 2003 10  15960.
 2003 11  481031·
 2003 12  771045.
 2004 01 88941.
 2004 02 86883.
 2004 03 1974.
 2004 04 71085·
 2004 05 801129.
 2004 06 731112·
 2004 07 151074.
 2004 08 401017.
 2004 09 381209·
 2004 10 321261·
 2004 11 781244·
 2004 12 951181.
 2005 01  101062·
 2005 02  441022·
 2005 03  41005·
 2005 04  161006.
 2005 05  741017·
 2005 06  16993.
 2005 07  301099·
 2005 08  721080·
 2005 09  281132.
 2005 10  811089·
 2005 11  18996·
 2005 12  581065.
 2006 01 981028.
 2006 02 931075.
 2006 03 581100·
 2006 04 931141·
 2006 05 351154.
 2006 06 361112·
 2006 07 621199·
 2006 08 211252·
 2006 09 151204.
 2006 10 161192·
 2006 11 671064·
 2006 12 101005.
 2007 01  361037.
 2007 02  641097·
 2007 03  371104·
 2007 04  561268.
 2007 05  261318.
 2007 06  371364·
 2007 07  591322.
 2007 08  761405·
 2007 09  341349.
 2007 10  161150.
 2007 11  471068.
 2007 12  171175·
 2008 01 361195.
 2008 02 91255·
 2008 03 371184.
 2008 04 451143·
 2008 05 321176·
 2008 06 471220·
 2008 07 941185·
 2008 08 571171·
 2008 09 471214.
 2008 10 871241.
 2008 11 61195·
 2008 12 701196·
 2009 01  321156·
 2009 02  91084·
 2009 03  841136.
 2009 04  291177·
 2009 05  621214·
 2009 06  221184·
 2009 07  661261·
 2009 08  31227·
 2009 09  381136·
 2009 10  561174.
 2009 11  171232·
 2009 12  861196.
 2010 01 851192·
 2010 02 681193.
 2010 03 231187.
 2010 04 981228.
 2010 05 541239·
 2010 06 401247.
 2010 07 981358·
 2010 08 821319·
 2010 09 191297.
 2010 10 791229.
 2010 11 781207·
 2010 12 81168.
 2011 01  811152.
 2011 02  151136.
 2011 03  291228.
 2011 04  691212·
 2011 05  661267·
 2011 06  71220·
 2011 07  361309·
 2011 08  861325·
 2011 09  241339·
 2011 10  341319.
 2011 11  141271.
 2011 12  11264.
 2012 01 311093·
 2012 02 51115·
 2012 03 991211·
 2012 04 851252.
 2012 05 861256·
 2012 06 501323·
 2012 07 551317.
 2012 08 311267·
 2012 09 151345.
 2012 10 271324.
 2012 11 701200·
 2012 12 591221·
 2013 01  691197.
 2013 02  171193.
 2013 03  571219.
 2013 04  121302·
 2013 05  231238·
 2013 06  261267·
 2013 07  531306·
 2013 08  601233·
 2013 09  771309.
 2013 10  861371·
 2013 11  211285.
 2013 12  961312.
 2014 01 571275.
 2014 02 541295.
 2014 03 501361·
 2014 04 411400.
 2014 05 131367.
 2014 06 121319.
 2014 07 521297·
 2014 08 381375.
 2014 09 201425·
 2014 10 651500.
 2014 11 651542·
 2014 12 571477.
 2015 01  791467·
 2015 02  471366·
 2015 03  521418.
 2015 04  901352·
 2015 05  161410.
 2015 06  131424·
 2015 07  01391.
 2015 08  591514·
 2015 09  801411·
 2015 10  451403.
 2015 11  241427·
 2015 12  61308.
 2016 01 471313.
 2016 02 491300·
 2016 03 601417.
 2016 04 11433.
 2016 05 181480.
 2016 06 581543·
 2016 07 721505·
 2016 08 381471·
 2016 09 181336·
 2016 10 281387.
 2016 11 471337.
 2016 12 361326·
 2017 01  961239·
 2017 02  411284.
 2017 03  981331·
 2017 04  401357.


 2017 05  301433·
 2017 06  21466.
 2017 07  641515.
 2017 08  181447·
 2017 09  181430·
 2017 10  451491.
 2017 11  61438·
 2017 12  641382·
 2018 01 871273·
 2018 02 251267·
 2018 03 141291.
 2018 04 991342·
 2018 05 871364.
 2018 06 251357·
 2018 07 991366.
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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

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