Labor Force Participation Rate in the United States decreased to 62.40 percent in May from 62.60 percent in April of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

The National Labor Force Survey aims to obtain characteristics of employment, unemployment, underemployment, and of working age population not in the labor force who are in schools, doing housekeeping, others, excludes personal activity. This survey covered all provinces in Indonesia (33 provinces). The total number of household samples were around 200 000 households that consist of 50 000 households of quarterly samples and 150 000 household of additional samples, with a 96.30 percent of response rate.

It is important to note the industrial classification applied in the survey is the Indonesian Standard Industrial Classification (KBLI) 2009 (aligned with ISIC Rev 4). The occupation classification is based on the Indonesian Classification of Occupation (KBJI) 2002, which refers to ISCO 88, which presents occupation classification in much more detail.

Sakernas is a survey which is specifically designed for labor data collection. Sakernas is relatively different compared with SP (Population Census) and Supas (Intercencal Population Cencus) which are more focused on demographic characteristics of the population. Other labor force data source is Susenas (National Social and Economic Survey) which collects data on many aspects of social and economic characteristics, such as: consumption, labor, health and household variables. The differences in the characteristics coverage of surveys/census mentioned above are contributed to the labor data quality, in which the Sakernas results are regarded better.

Geographic coverage

National coverage

Analysis unit

• Households
• Household members aged 10 and older

Universe

The August 2014 Sakernas covered all provinces in Indonesia (33 provinces). The total number of household samples were around 200 000 households that consist of 50 000 households of quarterly samples and 150 000 household of additional samples, with a 96.30 percent of response rate.

The main information collected in The National Labor Force Survey are data on individual household members covering persons aged 10 years and older. However tabulated data covers household members aged 15 years and older.

Kind of data

Sample survey data [ssd]

Sampling procedure

Total number of household samples were 200,000 with a response rate of 96.30 percent.

The difference of sample size of Sakernas compared with SP, Supas and Susenas will lead to different levels of sampling error. The smaller sample size will cause the greater sampling error of a survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires were published in Bahasa Indonesia only.

The way of structuring and wording questionnaire on labor characteristics will strongly affect the quality of census and survey data attainment. The structure and word of questionnaire plan covers producing correct sentences without ambiguous meaning, choosing appropriate word and order in the questions, and the number of variables and questions that will be asked to respondent. In the Sakernas, the questionnaire has been designed in a simple and concise way. The respondent is expected to understand and easily catch the aim of questions in the survey and avoid memory lapse and uninterested response during interview data collection. Furthermore, the design of Sakernas questionnaire remain stable in order to keep data comparison needs.

Response rate

96.30 percent.

Sampling error estimates

Population 15 Years of Age and Over Economically Active Not Economically Active Labor Force Participation Rate (%) Unemployment Rate (%) Educational Attainment Main Industry Main Employment Status

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Unemployment rate can be defined by either the national definition, the ILO harmonized definition, or the OECD harmonized definition. The OECD harmonized unemployment rate gives the number of unemployed persons as a percentage of the labor force (the total number of people employed plus unemployed). [OECD Main Economic Indicators, OECD, monthly] As defined by the International Labour Organization, unemployed workers are those who are currently not working but are willing and able to work for pay, currently available to work, and have actively searched for work. [ILO, http://www.ilo.org/public/english/bureau/stat/res/index.htm]

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show unemployment numbers and percentages by Neighborhood Statistical Areas E02 and E06 in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

Pop16P_e

# Population 16 years and over, 2017

Pop16P_m

# Population 16 years and over, 2017 (MOE)

InLabForce_e

# In labor force, 2017

InLabForce_m

# In labor force, 2017 (MOE)

pInLabForce_e

% In labor force, 2017

pInLabForce_m

% In labor force, 2017 (MOE)

CivLabForce_e

# In civilian labor force, 2017

CivLabForce_m

# In civilian labor force, 2017 (MOE)

pCivLabForce_e

% In civilian labor force, 2017

pCivLabForce_m

% In civilian labor force, 2017 (MOE)

CivEmployed_e

# Civilian employed, 2017

CivEmployed_m

# Civilian employed, 2017 (MOE)

pCivEmployed_e

% Civilian employed, 2017

pCivEmployed_m

% Civilian employed, 2017 (MOE)

Unemployed_e

# Civilian unemployed, 2017

Unemployed_m

# Civilian unemployed, 2017 (MOE)

pUnemployed_e

% Civilian unemployed, 2017

pUnemployed_m

% Civilian unemployed, 2017 (MOE)

ArmedForce_e

# In armed forces, 2017

ArmedForce_m

# In armed forces, 2017 (MOE)

pArmedForce_e

% In armed forces, 2017

pArmedForce_m

% In armed forces, 2017 (MOE)

NotLabForce_e

# Not in labor force, 2017

NotLabForce_m

# Not in labor force, 2017 (MOE)

pNotLabForce_e

% Not in labor force, 2017

pNotLabForce_m

% Not in labor force, 2017 (MOE)

pUnempOLabForce_e

% Unemployed as part of total labor force (including armed forces), 2017

pUnempOLabForce_m

% Unemployed as part of total labor force (including armed forces), 2017 (MOE)

UnempCivLabForce_e

# Civilian Unemployed, 2017

UnempCivLabForce_m

# Civilian Unemployed, 2017 (MOE)

pUnempCivLabForce_e

% Unemployment Rate, 2017

pUnempCivLabForce_m

% Unemployment Rate, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Graph and download economic data for Labor Force Participation Rate - 25-54 Yrs. (LNS11300060) from Jan 1948 to May 2025 about 25 to 54 years, participation, civilian, labor force, labor, household survey, rate, and USA.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show unemployment numbers and percentages by Strong, Prosperous, And Resilient Communities Challenge in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

Pop16P_e

# Population 16 years and over, 2017

Pop16P_m

# Population 16 years and over, 2017 (MOE)

InLabForce_e

# In labor force, 2017

InLabForce_m

# In labor force, 2017 (MOE)

pInLabForce_e

% In labor force, 2017

pInLabForce_m

% In labor force, 2017 (MOE)

CivLabForce_e

# In civilian labor force, 2017

CivLabForce_m

# In civilian labor force, 2017 (MOE)

pCivLabForce_e

% In civilian labor force, 2017

pCivLabForce_m

% In civilian labor force, 2017 (MOE)

CivEmployed_e

# Civilian employed, 2017

CivEmployed_m

# Civilian employed, 2017 (MOE)

pCivEmployed_e

% Civilian employed, 2017

pCivEmployed_m

% Civilian employed, 2017 (MOE)

Unemployed_e

# Civilian unemployed, 2017

Unemployed_m

# Civilian unemployed, 2017 (MOE)

pUnemployed_e

% Civilian unemployed, 2017

pUnemployed_m

% Civilian unemployed, 2017 (MOE)

ArmedForce_e

# In armed forces, 2017

ArmedForce_m

# In armed forces, 2017 (MOE)

pArmedForce_e

% In armed forces, 2017

pArmedForce_m

% In armed forces, 2017 (MOE)

NotLabForce_e

# Not in labor force, 2017

NotLabForce_m

# Not in labor force, 2017 (MOE)

pNotLabForce_e

% Not in labor force, 2017

pNotLabForce_m

% Not in labor force, 2017 (MOE)

pUnempOLabForce_e

% Unemployed as part of total labor force (including armed forces), 2017

pUnempOLabForce_m

% Unemployed as part of total labor force (including armed forces), 2017 (MOE)

UnempCivLabForce_e

# Civilian Unemployed, 2017

UnempCivLabForce_m

# Civilian Unemployed, 2017 (MOE)

pUnempCivLabForce_e

% Unemployment Rate, 2017

pUnempCivLabForce_m

% Unemployment Rate, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..The Census Bureau introduced an improved sequence of labor force questions in the 2008 ACS questionnaire. Accordingly, we recommend using caution when making labor force data comparisons from 2008 or later with data from prior years. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the "Evaluation Report Covering Employment Status" at http://www.census.gov/acs/www/Downloads/methodology/content_test/P6a_Employment_Status.pdf, and the "Evaluation Report Covering Weeks Worked" at http://www.census.gov/acs/www/Downloads/methodology/content_test/P6b_Weeks_Worked_Final_Report.pdf. Additional information can also be found at http://www.census.gov/people/laborforce/..By definition, a person cannot be classified as both "employed" and "did not work in the past 12 months"..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..In data year 2013, there were a series of changes to data collection operations that could have affected some estimates. These changes include the addition of Internet as a mode of data collection, the end of the content portion of Failed Edit Follow-Up interviewing, and the loss of one monthly panel due to the Federal Government shut down in October 2013. For more information, see: User Notes.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampli...

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

Graph and download economic data for Civilian Labor Force Level (CLF16OV) from Jan 1948 to May 2025 about civilian, 16 years +, labor force, labor, household survey, and USA.

Graph and download economic data for Infra-Annual Labor Statistics: Employment: Economic Activity: Industry (Including Construction): Total for G7 (G7LFEAICTTSTQ) from Q1 1998 to Q4 2024 about G7, construction, labor force, and labor.

Abstract

The main objective of the survey is to update and expand the labour force statistical baseline, fully capture and analyses the employment pattern. The survey results will provide an importance reference for policy and decision makers, business entrepreneurs, analysts and government officers to develop as well as monitor and assess the implementation of government policies and programmes on employment promotion and poverty and unemployment reduction and support the effective labour market. Accordingly, the survey aims at collecting the comprehensive set of data from households to estimate employment and - 2 -IV quarter LFS+CLS, 2006-2007 unemployment characteristics which capture the seasonal variability, location, social and economic activities in accordance with the definition and methodology of ILO. In the same time, child activities module will estimate the scope, spread, profile, causes and consequences of child work and accurately count the number of children engaged in child labour or at risk of child labour. The data of the module survey will provide an important reference for short and long term planning on child protection and elimination of child labour.

Geographic coverage

The survey is nationally and regionally (5 regions - West, Central, East, South, Ulaanbaatar) representative and covers the whole of Mongolia.

Analysis unit

• Household
• Individual

Universe

The people who are staying outside the household more than 6 months(military service, working overseas and imprisoned) are not counted by the survey. In light of incorporating the child activities module in the survey by the request of IPEC/ILO, the minimum age limit of household respondents was set at 5 and above in comformity with the international standard.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling unit of survey is a household. The sample frame of 2006-2007 has been the administration units of Mongolia and the sampling was based on 2005 household, population and work age population to improve the previous sampling design. As of 2000 Population and Housing Census, the total population amounted to 2,373,493 and household to 541,149 whereas these figures went up to 2,594,792 and 632,500 respectively by the end of 2006. In addition, the migration was on the rise. Therefore, it was necessary to redesign the sample based on the final data of the previous year. As most of enumeration areas of 2000 Population and Housing Census disappeared or significantly reduced in size, the survey had to make sampling by aimag, soum and bag frames. At the same time the sampling technique required to split some of primary sampling unit (bags in aimag centers and parts of the cities which had a large number of residents) into smaller segments. In light of this requirement and ratio between sampling units and sample households, the number of households in sample enumeration areas was set at 12 and enumeration areas at 1024.

A total of 29 strata was made comprised of 21 strata representing each of all aimags and 8 strata from 9 districts of Ulaanbaatar city(least populated two districts were put into one strata). Primary sampling units are parts of khoroos of Ulaanbaatar city and bags in aimags to make up 1024 units in total comprised of 384 parts and 640 bags. The second labour force survey adopted a multi stage stratified sampling design. Sampling was made on estimate of the weight of unemployment in total population. In addition, sampling distribution by aimag anc city districts was made in proportion to work age population. At primary sampling stage, the selection was made by probability proportional to size and at secondary stage 12 households were selected from each enumeration area using the simple random sampling procedure. To rephrase, the survey households were chosen with the method of a sub-sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey conducted a questionnaire of 9 chapters and 159 questions. Adding two more chapters and 36 more questions in the questionnaire derived from the needs which emerged in midst of analysing the data of the 1st survey to correlate the employment especially child activities with the household social and economic variables and elicit the causes of employment. Hence, in addition, the data and variables on migration, education and training and informal employment have been estimated. The second survey excels the previous one by additional data as mentioned before and wider sample frame which allows estimating the data by aimags.

In pursuance with the survey objectives, the questionnaire has been designed to collect the following data by current and usual status: 1. Household social and economic characteristics (housing type, ownership, energy, fuel, water supply, livestock and land property)

2.Democraphic characteristics(relationship to household head, sex, birthdate, school attendance, highest grade achieved, literacy, marital status, disability, cause for disability)

3.Activity status in the last seven days (economic and non-economic activities, time spent on these activities, economic activity and economically inactive status, primary and secondary employment, sector of employment of enterprises, employment status by occupational category, frequency of payment in primary occupation, amount of wages and payment)

1. Unemployment (available for Availability for more work (availability or unavailability for more work, reasons, steps to find more work)

5.work, reason for economically inactive status, time spent on finding work, expected kind of work, expected daily and monthly wage, whether being registered or not with employment and welfare agency, period of registration, whether having looked for work in the last 30 days, steps taken to look for work, reason for not looking for work, duration of unemployment)

6.Activity status in the last 12 months(usual economic activity status, primary and secondary occupations in the last 12 months, employment status by occupational category, economic sector to which the employer belong, duration of unemployment, steps to find work, average wage by primary and secondary employment in the last 12 months)

1. Past employment record (past employment and occupation by industry and sector to which the employer belonged, employment status, duration of employment in last occupation, last date worked, main reason for leaving the last job, main source of income support during the period of unemployment)

8.Child activities of 5-17 ages (main types of chores performed in household, current school attendance, reasons for not attending school at full time, participation in household economic activity, age at which the child began to work, reasons for participation in economic activity, whether the child engaged in any work outside the household, whether the child satisfied with work conditions, whether the child's occupation is mentally and physically stressful, whether the child works during evenings and nights, frequency of working in evenings and nights, whether the child fell sick or was injured because of work, what sickness and injuries had, main items on which the child's earnings are spent on, the number of free hours daily available for the child).

Cleaning operations

The data processing of LFS was organized at two levels. i) Data editing and validation ii) Computer processing and preparation of tabulations being undertaken centrally at the NSO.

While manual editing and coding, key entry and verification were undertaken at the provincial level. Checking the completeness of questionnaires, coding of questionnaires, range edit checks, simple consistency edits and electronic transfer of the keyed in data to the NSO were undertaken at the provincial level. The NSO computer staff were familiar with the IMPS software developed by the US Bureau of the Census and this software had been used both in population census and survey processing. Thus, LFS data entry programmes were prepared using IMPS and for range and consistency checks the CONCOR module was used.

This map shows the percent of children who have no residential parent in the civilian labor force - neither working nor actively looking for work. For children living with one parent, this means that residential parent is not in the civilian labor force. For children living with two parents, this means that neither parent is in the civilian labor force. "Children" include biological, step, and adopted children under 18 years who are living with at least one parent (children living in group homes, juvenile halls, or other institutional facilities, as well as teens living in dorms, on their own, with roommates, or unmarried partners are not included in these percentages). Children whose parents are not in the labor force are at risk, as family economic security and child well-being are closely linked. Children who face economic hardship and prolonged poverty are at risk for poor outcomes in terms of physical and emotional health, education, and even adult employment. This map shows where to bolster opportunities to improve family economic security, either through opportunities linked to employment (Earned Income Tax Credit expansions, minimum wage increases, etc.), through safety net programs (nutritional programs such as WIC and SNAP, health care programs, etc.), or a combination of both.Map opens at tract-level in Kansas City. Use the bookmarks or the search bar to explore other cities. County- and state-level data display when zoomed out. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Number of persons in the labour force (employment and unemployment), unemployment rate, participation rate and employment rate by province, gender and age group. Data are presented for 12 months earlier, previous month and current month, as well as year-over-year and month-to-month level change and percentage change. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

In this table you will find a historical series of annual figures from 1800 on the composition of the Dutch working population aged 15 to 65 by gender and a number of other personal characteristics. The data on the registered unemployed by migration background and level of education are only available from 1996. The data on occupational prestige, socio-economic status and social class are only available from 1975 and refer only to the total employed labor force. Until 1987, the definition of the employed labor force differs slightly from the current definition. It concerns persons who work at least 15 hours instead of 12 hours. Data available from 1800 to 2013 Status of the figures: On 26 February 2015, new revised tables on the labor force were published. This revision of labor force statistics has two parts. The definitions have been adapted to the internationally agreed definitions and the data collection has been improved by being the first statistics office in Europe to conduct surveys via the internet. The figures in this table have not yet been revised and therefore deviate from other data on the labor force on StatLine. For more information about the revision, see the link to the press release in section 3. Changes as of June 29, 2018 None, this table has been discontinued When will new figures be released? Not applicable anymore.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show unemployment numbers and percentages by Zip Code Tabulation Area in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

Pop16P_e

# Population 16 years and over, 2017

Pop16P_m

# Population 16 years and over, 2017 (MOE)

InLabForce_e

# In labor force, 2017

InLabForce_m

# In labor force, 2017 (MOE)

pInLabForce_e

% In labor force, 2017

pInLabForce_m

% In labor force, 2017 (MOE)

CivLabForce_e

# In civilian labor force, 2017

CivLabForce_m

# In civilian labor force, 2017 (MOE)

pCivLabForce_e

% In civilian labor force, 2017

pCivLabForce_m

% In civilian labor force, 2017 (MOE)

CivEmployed_e

# Civilian employed, 2017

CivEmployed_m

# Civilian employed, 2017 (MOE)

pCivEmployed_e

% Civilian employed, 2017

pCivEmployed_m

% Civilian employed, 2017 (MOE)

Unemployed_e

# Civilian unemployed, 2017

Unemployed_m

# Civilian unemployed, 2017 (MOE)

pUnemployed_e

% Civilian unemployed, 2017

pUnemployed_m

% Civilian unemployed, 2017 (MOE)

ArmedForce_e

# In armed forces, 2017

ArmedForce_m

# In armed forces, 2017 (MOE)

pArmedForce_e

% In armed forces, 2017

pArmedForce_m

% In armed forces, 2017 (MOE)

NotLabForce_e

# Not in labor force, 2017

NotLabForce_m

# Not in labor force, 2017 (MOE)

pNotLabForce_e

% Not in labor force, 2017

pNotLabForce_m

% Not in labor force, 2017 (MOE)

pUnempOLabForce_e

% Unemployed as part of total labor force (including armed forces), 2017

pUnempOLabForce_m

% Unemployed as part of total labor force (including armed forces), 2017 (MOE)

UnempCivLabForce_e

# Civilian Unemployed, 2017

UnempCivLabForce_m

# Civilian Unemployed, 2017 (MOE)

pUnempCivLabForce_e

% Unemployment Rate, 2017

pUnempCivLabForce_m

% Unemployment Rate, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This table contains quarterly and yearly figures on labour participation in the Netherlands. The population of 15 to 74 years of age (excluding the institutionalized population) is divided into the employed labour force, the unemployed labour force and those not in the labour force. The employed labour force is subdivided on the basis of the professional status, and the average working hours. A division by sex, age and level of education is available.

Data available from: 2013

Status of the figures: The figures in this table are final.

Changes as of April 30, 2025: The figures for the 1st quarter 2025 have been added.

Changes as of November 14, 2024: The figures for 3rd quarter 2024 are added. Figures have been added on labor participation based on whether or not the state pension age has been reached.

Changes as of August 17, 2022: None, this is a new table. This table has been compiled on the basis of the Labor Force Survey (LFS). Due to changes in the research design and the questionnaire of the LFS, the figures for 2021 are not automatically comparable with the figures up to and including 2020. The key figures in this table have therefore been made consistent with the (non-seasonally adjusted) figures in the table Arbeidsdeelname, kerncijfers seizoengecorrigeerd (see section 4), in which the outcomes for the period 2013-2020 have been recalculated to align with the outcomes from 2021. When further detailing the outcomes according to job and personal characteristics, there may nevertheless be differences from 2020 to 2021 as a result of the new method.

When will new figures be released? New figures will be published in July 2025.

Abstract

The major aim of the 1998/99 Labour Force Survey in Nepal was to collect a set of comprehensive statistics on employment, unemployment and underemployment. The results from the survey provide information required for skill development and planning, for employment generation, for improving the status of women and children, for assessing the role and importance of the informal sector, and for identifying the number and characteristics of the unemployed and underemployed.

The survey asked detailed questions about economic activity, on both a current and a usual basis. It covered a large national representative sample of more than 14,000 households, with data collection spread over a complete 12-month period so as to reflect any seasonal variations in activity. Finally, and most importantly, the survey adopted the current international standards for measuring economic activity, as recommended by the ILO.

Geographic coverage

National coverage Urban/ rural areas Development regions Ecological belt

Analysis unit

Individiual, household

Universe

The survey covered the whole country, and no geographical areas were excluded. All permanent residents of Nepal (including foreign nationals) were considered eligible for inclusion in the survey, but households of diplomatic missions were excluded. As is normal in household surveys, the homeless and those people living for six months or more away from the household or in institutions such as school hostels, prisons, army camps and hospitals were also excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total sample of 14,400 households was selected for this survey, half of it in urban and half in rural areas. The sampling frame was based on the listing of enumeration areas from the 1991 census, but with certain modifications. In particular, the elements making up those new municipalities that had been created since the 1991 census were transferred from the rural frame to the urban frame. The sample design involved a two-stage probability proportional to size (PPS) selection process. First, wards were selected with PPS, where the number of households at the time of the census provided the measure of size. Then within the selected primary sampling unit (PSU), consisting of the ward or in some cases a sub-ward or an amalgamation of small wards, all households were to be listed in the field and 20 households selected by systematic sampling.

Annex B of the NLFS report provides a detailed description of the sample design and its implementation.

Mode of data collection

Face-to-face [f2f]

Research instrument

An initial NLFS survey questionnaire was developed by the CBS on the basis of an ILO manual 3 and subsequently modified, taking account of the advice received from the Steering Committee and ILO technical advisers as well as of the experience gained in several small pre-tests.

Some particular aspects of the questionnaire are worth noting. Great care was taken to ensure that the terminology complied with international recommendations. The lower age cut-off point was set at 5 years, to enable the collection of data on the economic activities of children. In an attempt to make the questionnaire more gender sensitive, the section on current activities was expanded. Information was collected not just on those activities which count as 'work' under the international definitions but also on those activities (such as cooking, cleaning and child minding) which are performed without pay for the household, mainly by women.

The design of the part of the questionnaire dealing with usual activity proved particularly difficult. In an early pre-test, an attempt was made to collect details of the number of weeks in the past year that the person had spent in three different categories of economic activity (working, not working but available for work, and not working and not available). This did not work well. In Nepal the use of a 'week' in measuring economic activity is not easy to apply, since the public appears to have difficulty with this idea. Days and months are easier concepts to work with.

Consideration was therefore given to alternatives, based on the advice given in the ILO manual and the patterns used in various recent LFS questionnaires in other countries. One method (the Canadian method) involves obtaining broad estimates of the amount of work done each month, but this was considered too complicated for use in Nepal. Instead, a method based on days was used, where respondents were asked to state, for each of the last 12 months, the approximate number of days spent in each of the three economic activity categories. To simplify the recording of this information, it was assumed that each month consisted of 30 days, making a nominal year of 360 days.

At the final stage of questionnaire preparation, the English questionnaire was translated into Nepali, and then back-translated independently into English. Differences in the two English versions were noted, and attempts made to clarify the Nepali version of the questionnaire so that there would be less chance of misunderstandings about the intended meaning of each question.

Cleaning operations

A centralised processing system was used for this survey. The Integrated Microcomputer Processing System (IMPS) package was used for processing the NLFS. This package, developed by the U.S. Bureau of the Census, is widely used in national statistical offices around the world. It is easy to use, and contains programs covering all phases of data processing, from data entry through to tabulation and the calculation of sampling errors. Some of the NLFS staff had previous experience of using another package (STATA) for preparing output from the NLSS. Where IMPS did not provide a convenient method for producing output (as for instance in the case of calculating averages), the required tables were produced using STATA.

The NLFS report mentions some issues worth knowing about data entry and processing of the NLFS dataset.

Response rate

There was very little non-response on the survey, with only 45 households lost out of 14,400 yielding a response rate of 99.997 percent. Twenty of these households are accounted for by one PSU in the Far-western mountains. This PSU could not be covered in the third season because it could not be reached in the time available. The weights for the two other PSUs in the area were therefore increased at the analysis stage to try to compensate for the 20 missing households.

Sampling error estimates

In a survey of this size, the robustness of the sample design means that the sampling errors for statistics at the national level are likely to be fairly small. Non-sampling errors are likely to be the major source of concern, and every effort has been made at all stages of the survey to try to minimise these non-sampling errors.

Data are available giving an indication of the likely sampling errors for some of the key aggregates measured in this survey. These sampling errors have been calculated by means of the CENVAR module in the IMPS package that was used for processing this survey. In order to derive these estimates of sampling error, account was taken of the structural design of the survey, with PSUs being assigned to either the urban or the rural stratum, and with different sampling fractions being used in each stratum.

However the resulting sampling errors probably substantially overstate the width of the true confidence intervals, since they take no account of the very strong implicit stratification by region and ecological zone incorporated into the design. The true confidence intervals for sampling errors will therefore be much narrower.

All the estimates can be found in the appropriate section of the survey report. In the case of estimate for the total currently active population, the 95 percent lower and upper bounds for this estimate are 9.410 and 9.873 million respectively. This means that we can be 95 percent confident that the number of people currently economically active lies within this range. Put another way, we can say that we are 95 percent confident that the true value lies within the range 9.641 million plus or minus 231 thousand.

Labour Force Survey summary data, including employment, unemployment and economic inactivity levels and rates, UK, rolling three-monthly figures published monthly, non-seasonally adjusted. These are official statistics in development.

The data in this table are based on the Labor Force Survey (EBB). The EBB is a survey conducted by Statistics Netherlands to collect information about the relationship between people and the labor market. Characteristics of persons are related to their current or future position on the labor market. This table contains data on the labor force according to the international definition. The Dutch definition of the labor force deviates from the definition that applies as an international standard: that of the International Labor Organization (ILO). As a result, the size and composition of the labor force differs. Firstly, the Dutch definition uses a threshold value of twelve hours for the number of hours per week that someone works or wants to work. This is not the case in the international definition. Second, the unemployed labor force is defined differently. According to the international definition, someone should be able to start a job within two weeks. In the Dutch definition, in certain cases, a period of three months is used for the period in which someone can start working or has developed search activities. Due to a new weighting method of the EBB, all EBB tables have been discontinued and moved to the archive. Instead, new tables are created. In these new tables, the figures have been corrected up to and including 2001 using a new weighting method. From 2001 it is also possible to publish quarterly figures for a limited set of variables. The years prior to 2001 have not been corrected and are the previously published figures. A detailed description of the new weighing method of the EBB can be found on the theme page. Data available from: 2000 Status of the figures Figures based on the EBB are always final. Change as of January 10, 2017: Table has been discontinued. When will new numbers come out? Discontinued.