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 Series Title: Employment Level: Agriculture and Related Industries, Mexican Women: NSA
   For this series:   Numerical Data  |  Forecast  |  GIF Chart  |  PDF Chart  |  Excel file  |  Self-updating Excel file
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 2012 04 9344·
 2012 03 3265·
 2012 02 9991.
 2012 01 1084.
 2011 12  584.
 2011 11  2466·
 2011 10  1190·
 2011 09  39112.
 2011 08  64136.
 2011 07  95145.
 2011 06  4124·
 2011 05  4898·
 2011 04  2063·
 2011 03  2859·
 2011 02  9385·
 2011 01  8181·
 2010 12 5173·
 2010 11 2898.
 2010 10 15115.
 2010 09 8109·
 2010 08 54117.
 2010 07 29120·
 2010 06 32118·
 2010 05 21115·
 2010 04 5853·
 2010 03 8651.
 2010 02 3460·
 2010 01 4267.
 2009 12  1057·
 2009 11  8066·
 2009 10  3267·
 2009 09  1279.
 2009 08  8896.
 2009 07  1999·
 2009 06  6496.
 2009 05  384.
 2009 04  9173.
 2009 03  3545·
 2009 02  9460·
 2009 01  782·
 2008 12 4464·
 2008 11 8365.
 2008 10 9101·
 2008 09 4582.
 2008 08 9989·
 2008 07 4573·
 2008 06 2263.
 2008 05 5961·
 2008 04 3071·
 2008 03 2963.
 2008 02 7147.
 2008 01 2064.
 2007 12  3867.
 2007 11  6649.
 2007 10  2062·
 2007 09  2168·
 2007 08  4954.
 2007 07  1282.
 2007 06  5590.
 2007 05  8257·
 2007 04  1263·
 2007 03  4972.
 2007 02  9065.
 2007 01  4745.
 2006 12 352·
 2006 11 8448.
 2006 10 6262.
 2006 09 8575.
 2006 08 22112.
 2006 07 48100.
 2006 06 35118.
 2006 05 8980.
 2006 04 9681.
 2006 03 9844·
 2006 02 9651.
 2006 01 8053·
 2005 12  7447·
 2005 11  1574·
 2005 10  5976.
 2005 09  188·
 2005 08  3197.
 2005 07  774·
 2005 06  6172·
 2005 05  2266.
 2005 04  6848.
 2005 03  556·
 2005 02  6543.
 2005 01  3649·
 2004 12 1144.
 2004 11 6593.
 2004 10 5997.
 2004 09 63101.
 2004 08 9117.
 2004 07 7076.
 2004 06 5677·
 2004 05 4675.
 2004 04 3969·
 2004 03 2548.
 2004 02 9349·
 2004 01 5669.
 2003 12  081.
 2003 11  6390.
 2003 10  35120.
 2003 09  86114.
 2003 08  18114.
 2003 07  3982·
 2003 06  6783.
 2003 05  5584.
 2003 04  4472·
 2003 03  3780.
 2003 02  467·
 2003 01  858.
 2002 12 5257.
 2002 11 6263·
 2002 10 84106·
 2002 09 1397.
 2002 08 4372·
 2002 07 46116.
 2002 06 6985.
 2002 05 6599·
 2002 04 4367.
 2002 03 4561.
 2002 02 4671.
 2002 01 8384.
 2001 12  256.
 2001 11  4269.
 2001 10  3085.
 2001 09  1064.
 2001 08  9568.
 2001 07  4349·
 2001 06  5866.
 2001 05  17104.
 2001 04  2367.
 2001 03  7955.
 2001 02  9259·
 2001 01  9786·
 2000 12 1164·
 2000 11 863.
 2000 10 2164.
 2000 09 7054.
 2000 08 990.
 2000 07 1077.
 2000 06 5898·
 2000 05 7979·
 2000 04 9483·
 2000 03 9375.
 2000 02 4082·
 2000 01 9490·
 1999 12  6497.
 1999 11  8967·
 1999 10  3078·
 1999 09  896·
 1999 08  47108.
 1999 07  52108·
 1999 06  17123·
 1999 05  4277.
 1999 04  9175·
 1999 03  6751·
 1999 02  1363.
 1999 01  3867·
 1998 12 7240.
 1998 11 3858·
 1998 10 5589.
 1998 09 67128.
 1998 08 92146·
 1998 07 9884·
 1998 06 865·
 1998 05 2386.
 1998 04 7575·
 1998 03 470·
 1998 02 5886.
 1998 01 2468.
 1997 12  7169·
 1997 11  3675·
 1997 10  2079·
 1997 09  14112.
 1997 08  56115·
 1997 07  2115·
 1997 06  2984·
 1997 05  6482·
 1997 04  3179·
 1997 03  3343·
 1997 02  5047·
 1997 01  6660.
 1996 12 2144.
 1996 11 8766·
 1996 10 7161·
 1996 09 4065.
 1996 08 7576.
 1996 07 4380·
 1996 06 384·
 1996 05 3156.
 1996 04 7055.
 1996 03 1070·
 1996 02 8673.
 1996 01 750.
 1995 12  756.
 1995 11  1961.
 1995 10  8278.
 1995 09  6382.
 1995 08  986·
 1995 07  2685·
 1995 06  7993·
 1995 05  1787.
 1995 04  2861·
 1995 03  1461·
 1995 02  8937·
 1995 01  1432.
 1994 12 4047·
 1994 11 8849·
 1994 10 5255.
 1994 09 2371.
 1994 08 4398·
 1994 07 8085·
 1994 06 2877.
 1994 05 3868.
 1994 04 6551·
 1994 03 4841.
 1994 02 847.
 1994 01 7947·
 1993 12  5551.
 1993 11  7264.
 1993 10  248.
 1993 09  8861.
 1993 08  8959·
 1993 07  5960.
 1993 06  5056·
 1993 05  3649.
 1993 04  6746·
 1993 03  4239.
 1993 02  1428·
 1993 01  1742.
 1992 12 1441·
 1992 11 7646.
 1992 10 8237·
 1992 09 5066·
 1992 08 8161.
 1992 07 4359·
 1992 06 6777·
 1992 05 7953·
 1992 04 4457.
 1992 03 5058·
 1992 02 740·
 1992 01 7231·
 1991 12  227·
 1991 11  2939.
 1991 10  6570·
 1991 09  2569.
 1991 08  8192.
 1991 07  3658·
 1991 06  2896·
 1991 05  5659·
 1991 04  3130·
 1991 03  5241.
 1991 02  1048.
 1991 01  1035.
 1990 12 2834·
 1990 11 8533.
 1990 10 4867.
 1990 09 5584·
 1990 08 7388.
 1990 07 6677·
 1990 06 591.
 1990 05 8653·
 1990 04 9062·
 1990 03 5249.
 1990 02 6927·
 1990 01 2245·
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US Unemployment Rate: Alternative Measures of Labor Underutilization

Charts

Unemployment Rate: US Unemployment Rate: 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: Agriculture and Related Industries, Mexican Women: 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

Make charts for Okun's Law and Phillip's Curve

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