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 Series Title: Employment Level: Nonagriculture, Government Wage and Salary Workers, Mexican: 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 411068.
 2000 02 181085·
 2000 03 641043·
 2000 04 76935.
 2000 05 75916.
 2000 06 84936·
 2000 07 27915.
 2000 08 58927.
 2000 09 14982·
 2000 10 86918·
 2000 11 28952·
 2000 12 76985.
 2001 01  61081.
 2001 02  61045·
 2001 03  801046.
 2001 04  751006·
 2001 05  28911·
 2001 06  50975.
 2001 07  22981·
 2001 08  231090.
 2001 09  801075·
 2001 10  501063·
 2001 11  16996·
 2001 12  141076·
 2002 01 881067·
 2002 02 631039.
 2002 03 501122.
 2002 04 681057.
 2002 05 11016·
 2002 06 701023.
 2002 07 36979.
 2002 08 291039.
 2002 09 961054.
 2002 10 781041·
 2002 11 141062.
 2002 12 661037.
 2003 01  721083.
 2003 02  18982·
 2003 03  45941·
 2003 04  74908.
 2003 05  55977.
 2003 06  471011.
 2003 07  17968.
 2003 08  70939.
 2003 09  95964.
 2003 10  47967·
 2003 11  441009.
 2003 12  01050.
 2004 01 68969·
 2004 02 241002·
 2004 03 641031.
 2004 04 32982.
 2004 05 741022.
 2004 06 101070·
 2004 07 381047.
 2004 08 621105.
 2004 09 391160·
 2004 10 621082·
 2004 11 561072.
 2004 12 71060.
 2005 01  131027·
 2005 02  611010.
 2005 03  301144·
 2005 04  121207.
 2005 05  711193·
 2005 06  141117.
 2005 07  971107·
 2005 08  461107.
 2005 09  141093.
 2005 10  531178·
 2005 11  211173.
 2005 12  31102.
 2006 01 661123·
 2006 02 341096·
 2006 03 901165·
 2006 04 31193.
 2006 05 801183·
 2006 06 391117.
 2006 07 171126·
 2006 08 901073·
 2006 09 291121.
 2006 10 741200.
 2006 11 391266.
 2006 12 321253·
 2007 01  141178·
 2007 02  721230·
 2007 03  191141·
 2007 04  241170·
 2007 05  611291·
 2007 06  881178·
 2007 07  451212·
 2007 08  851246.
 2007 09  201208·
 2007 10  51237.
 2007 11  311215·
 2007 12  591248·
 2008 01 671178·
 2008 02 281138·
 2008 03 131168·
 2008 04 151174.
 2008 05 11318·
 2008 06 911210.
 2008 07 561203.
 2008 08 481265·
 2008 09 491183·
 2008 10 491188.
 2008 11 461220·
 2008 12 761254.
 2009 01  601181.
 2009 02  831399·
 2009 03  971392·
 2009 04  441361·
 2009 05  101389·
 2009 06  21216·
 2009 07  471223·
 2009 08  211148·
 2009 09  01150·
 2009 10  351201.
 2009 11  581287·
 2009 12  01365·
 2010 01 541412·
 2010 02 411402.
 2010 03 181391·
 2010 04 21344.
 2010 05 231331.
 2010 06 01418·
 2010 07 401265.
 2010 08 701294·
 2010 09 831333.
 2010 10 191278·
 2010 11 631260·
 2010 12 741234.
 2011 01  341224.
 2011 02  301264.
 2011 03  491284·
 2011 04  71339·
 2011 05  21271·
 2011 06  901297·
 2011 07  851232·
 2011 08  881198.
 2011 09  361240.
 2011 10  801217.
 2011 11  861180·
 2011 12  631267·
 2012 01 121257.
 2012 02 61263.
 2012 03 881300·
 2012 04 991282.
 2012 05 311334·
 2012 06 11350·
 2012 07 541278.
 2012 08 911313·
 2012 09 171348.
 2012 10 211316·
 2012 11 741264·
 2012 12 591368·
 2013 01  221353.
 2013 02  471436.
 2013 03  581462.
 2013 04  461396.
 2013 05  211478·
 2013 06  871313.
 2013 07  461396·
 2013 08  221414.
 2013 09  131423·
 2013 10  721342.
 2013 11  01260.
 2013 12  801315·
 2014 01 261396·
 2014 02 581383·
 2014 03 81429.
 2014 04 401500·
 2014 05 81404·
 2014 06 281299.
 2014 07 821363·
 2014 08 461343.
 2014 09 351339·
 2014 10 871328·
 2014 11 621315.
 2014 12 671346·
 2015 01  81343·
 2015 02  681396.
 2015 03  101418·
 2015 04  971397·
 2015 05  521458.
 2015 06  271604.
 2015 07  131517·
 2015 08  551461.
 2015 09  171425.
 2015 10  461351·
 2015 11  841257·
 2015 12  861268·
 2016 01 791262.
 2016 02 321325.
 2016 03 621389.
 2016 04 591391.
 2016 05 381533·
 2016 06 151501·
 2016 07 91375·
 2016 08 681415.
 2016 09 201551·
 2016 10 531482·
 2016 11 131518·
 2016 12 311579.
 2017 01  751521.
 2017 02  281469·
 2017 03  881546·
 2017 04  931551·
 2017 05  781493·
 2017 06  461466.


 2017 07  701446.
 2017 08  241469·
 2017 09  301484·
 2017 10  381560.
 2017 11  231560.
 2017 12  171663.
 2018 01 561594·
 2018 02 611508.
 2018 03 351480.
 2018 04 911587·
 2018 05 71662.
 2018 06 981537.
 2018 07 181495·
 2018 08 911426·
 2018 09 581458·
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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: Nonagriculture, Government Wage and Salary Workers, Mexican: 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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