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 2009-2013 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..Industry codes are 4-digit codes and are based on the North American Industry Classification System (NAICS). The Census industry codes for 2013 and later years are based on the 2012 revision of the NAICS. To allow for the creation of 2009-2013 and 2011-2013 tables, industry data in the multiyear files (2009-2013 and 2011-2013) were recoded to 2013 Census industry codes. We recommend using caution when comparing data coded using 2013 Census industry codes with data coded using Census industry codes prior to 2013. For more information on the Census industry code changes, please visit our website at http://www.census.gov/people/io/methodology/..Census occupation codes are 4-digit codes and are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2010 and later years are based on the 2010 revision of the SOC. To allow for the creation of 2009-2013 tables, occupation data in the multiyear files (2009-2013) were recoded to 2013 Census occupation codes. We recommend using caution when comparing data coded using 2013 Census occupation codes with data coded using Census occupation codes prior to 2010. For more information on the Census occupation code changes, please visit our website at http://www.census.gov/people/io/methodology/..Workers include members of the Armed Forces and civilians who were at work last week..There were changes in the edit between 2009 and 2010 regarding Supplemental Security Income (SSI) and Social Security. The changes in the edit loosened restrictions on disability requirements for receipt of SSI resulting in an increase in the total number of SSI recipients in the American Community Survey. The changes also loosened restrictions on possible reported monthly amounts in Social Security income resulting in higher Social Security aggregate amounts. These results more closely match administrative counts compiled by the Social Security Administration..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 in...

Brazil Working Age Population: Labour Force: Amapá: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data was reported at 72.000 Person th in Mar 2019. This records a decrease from the previous number of 77.000 Person th for Dec 2018. Brazil Working Age Population: Labour Force: Amapá: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data is updated quarterly, averaging 75.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 90.000 Person th in Sep 2017 and a record low of 63.000 Person th in Mar 2013. Brazil Working Age Population: Labour Force: Amapá: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA011: Continuous National Household Sample Survey: Working Age Population: Labour Force: Employed: by Economic Activity.

Background
The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS) (held at the UK Data Archive under GN 33246), all of its associated LFS boosts and the APS boost. Thus, the APS combines results from five different sources: the LFS (waves 1 and 5); the English Local Labour Force Survey (LLFS), the Welsh Labour Force Survey (WLFS), the Scottish Labour Force Survey (SLFS) and the Annual Population Survey Boost Sample (APS(B) - however, this ceased to exist at the end of December 2005, so APS data from January 2006 onwards will contain all the above data apart from APS(B)). Users should note that the LLFS, WLFS, SLFS and APS(B) are not held separately at the UK Data Archive. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, selected volumes of which have been included with the APS documentation for reference purposes (see 'Documentation' table below).

The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples such as the WLFS and SLFS, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.

Secure Access APS data
Secure Access datasets for the APS include additional variables not included in the standard End User Licence (EUL) versions (see under GN 33357). Extra variables that typically can be found in the Secure Access version but not in the EUL versions relate to:

• geography
  
• types of benefits claimed
• qualifications, education and training, including level of highest qualification, qualifications below highest level, class of first degree, single subject of degree, qualifications from Government schemes, number of O-levels/GCSEs etc passed, type of 'other qualification', type of other work-related or vocational qualifications, qualifications related to work, sources of qualifications, qualifications from school, level of Welsh baccalaureate
• frequency of Welsh speaking
• casual/holiday work
• regular/normal work pattern
• reasons not in work or for leaving work, reasons not looking for work
• payment of own National Insurance and tax
• smoking habits
• single year of age
• health issues
• learning difficulty/disability
• number of bedrooms
• serving in armed forces
• marital status
• main reason for coming to the UK

The EUL version contains less detailed variables. For example, the lowest geography is Government Office Region, only banded age is available, only 3-digit SOC is available for main, second and last job, and only industry division for main, second and last job.

Prospective users of the Secure Access version of the APS will need to fulfil additional requirements, commencing with the completion of extra application forms to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access data users must also complete face-to-face training and agree to the Secure Access User Agreement and Licence Compliance Policy (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access. Further details and links to all APS studies available from the UK Data Archive can be found via the APS Key Data series webpage.

Documentation and coding frames
The APS is compiled from variables present in the LFS. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation (e.g. coding frames for education, industrial and geographic variables, which are held in LFS User Guide Vol.5, Classifications), users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Disability variables from 2013 onwards - LFS and APS
ONS have provided some information on changes since 2013 to the disability variables available on the LFS and APS. The Disability Discrimination Act (DDA) disabled (current disability) category within the historic DISCURR variable no longer corresponds with the advised legal definition of 'current disability'. DISCURR should only be available on LFS microdata from Spring 1998 to January-March 2013 (JM13); beyond that point users should ignore or delete it. In addition, the same 'DDA disabled (current disability)' category within variable DISCURR13 is also not the most appropriate variable to use because a) it is not comparable to the corresponding category in variable DISCURR due to question changes, and b) it no longer measures either the DDA definition of disability or the latest Equality Act definition of disability. However, DISCURR13 is available from the April-March 2013 quarter (AJ13) onwards and was introduced to demonstrate that the variables used to compile DISCURR had also changed from that quarter. Therefore, users are advised to use the disability variable DISEA from AJ13 onwards, which reflects the Equality Act 2010 legal definition of 'disabled', measured according to the GSS Harmonised Standard on health conditions and illnesses. The harmonised disability variables DISEA and DISCURR13 should both be present on the APS person microdata from April 2013-March 2014 (A13M14) onwards. This ensures that APS users have a complete 12 months' data on which to base analysis of the variables. DISCURR should only be present on APS microdata up to and including April 2012-March 2013 (A12M13).

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files
The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.

Latest edition information:
For the thirty-second edition (August 2025), a data file for January to December 2024 has been added to the study.

Labour Force Survey 2nd Quarter 2013, Persons with Disabilities

In the second quarter of each year, the Labor Force Survey (LFS) has some additional questions about disability. The statistics provide information on the situation in the labor market for people with disabilities, and the development over time, compared with the entire population. This data set contains a supplementary survey to the Labor Force Survey (LFS) in the second quarter of 2013. Corresponding surveys have been conducted annually since 2002.

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led to some breaks in the time series in the aforementioned areas.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: "Om bruk av stikkprøver ved kontoret for intervjuundersøkelser", SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: "Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and "over employment" in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The "old" means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the "new" implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable "Labour-market status" was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. In addition, an escalation scheme to increase the sample size was started. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the...

Abstract

This data is from the first round of a unique, cross-country panel survey conducted in Uganda by the Secure Livelihoods Research Consortium (SLRC). The Overseas Development Institute (ODI) is the lead organisation of SLRC. SLRC partners who participated in the survey were: the Centre for Poverty Analysis (CEPA) in Sri Lanka, Feinstein International Center (FIC, Tufts University), the Sustainable Development Policy Institute(SDPI) in Pakistan, Humanitarian Aid and Reconstruction, based at Wageningen University (WUR) in the Netherlands, the Nepal Centre for Contemporary Research (NCCR), and the Food and Agriculture Organization (FAO).

This survey generated the first round of data on people's livelihoods, their access to and experience of basic services, and their views of governance actors. SLRC will attempt to re-interview the same respondents in 2015 to find out how the livelihoods and governance perceptions of people shift (or not) over time, and which factors may have contributed towards that change.

Geographic coverage

Regional

Analysis unit

Households

Universe

Randomly selected households in purposely sampled sites (sampling procedure varied slightly by country). Within a selected household, only one household members was interviewed about the household. Respondents were adults and we aimed to interview a fairly even share of men/ women. In some countries this was achieved, but in other countries the share of male respondents is substantially higher (e.g. Pakistan).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy was designed to select households that are relevant to the main research questions and as well as being of national relevance, while also being able to produce statistically significant conclusions at the study and village level. To meet these objectives, purposive and random sampling were combined at different stages of the sampling strategy. The first stages of the sampling process involved purposive sampling, with random sampling only utilized in the last stage of the process. Sampling locations were selected purposely (including districts and locations within districts), and then randomly households were selected within these locations. A rigorous sample is geared towards meeting the objectives of the research. The samples are not representative for the case study countries and cannot be used to represent the case study countries as a whole, nor for the districts. The samples are representative at the village level, with the exception of Uganda.

Sampling locations (sub-regions or districts, sub-districts and villages) were purposively selected, using criteria, such as levels of service provision or levels of conflict, in order to locate the specific groups of interest and to select geographical locations that are relevant to the broader SLRC research areas and of policy relevance at the national level. For instance, locations experienced high/ low levels of conflict and locations with high/ low provision of services were selected and locations that accounted for all possible combinations of selection criteria were included. Survey locations with different characteristics were chose, so that we could explore the relevance of conflict affectedness, access to services and variations in geography and livelihoods on our outcome variables. Depending on the administrative structure of the country, this process involved selecting a succession of sampling locations (at increasingly lower administrative units).

The survey did not attempt to achieve representativeness at the country /or district level, but it aimed for representativeness at the sub-district /or village level through random sampling (Households were randomly selected within villages so that the results are representative and statistically significant at the village level and so that a varied sample was captured. Households were randomly selected using a number of different tools, depending on data availability, such as random selection from vote registers (Nepal), construction of household listings (DRC) and a quasi-random household process that involved walking in a random direction for a random number of minutes (Uganda).

The samples are statistically significant at the survey level and village level (in all countries) and at the district level in Sri Lanka and sub-region level in Uganda. The sample size was calculated with the aim to achieve statistical significance at the study and village level, and to accommodate the available budget, logistical limitations, and to account for possible attrition between 2012-2015. In a number of countries estimated population data had to be used, as recent population data were not available.

The minimum overall sample size required to achieve significance at the study level, given population and average household size across districts, was calculated using a basic sample size calculator at a 95% confidence level and confidence interval of 5. The sample size at the village level was again calculated at the using a 95% confidence level and confidence interval of 5. Finally, the sample was increased by 20% to account for possible attrition between 2012 and 2015, so that the sample size in 2015 is likely to be still statistically significant. The overall sample required to achieve the sampling objectives in selected districts in each country ranged from 1,259 to 3,175 households.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

CSPro was used for data entries in most countries.

Data editing took place at a number of stages throughout the processing, including: • Office editing and coding • During data entry • Structure checking and completeness • Extensive secondary editing conducted by ODI

Response rate

The required sample sizes were achieved in all countries. Response rates were extremely high, ranging from 99%-100%.

Sampling error estimates

No further estimations of sampling error was conducted beyond the sampling design stage.

Data appraisal

Done on an ad hoc basis for some countries, but not consistently across all surveys and domains.

The sociobarometer is a wide-ranging survey charting expert opinion on the welfare of Finnish citizens, and on the present state of welfare services. Themes for the 2013 survey included the public service reform, use of social security benefits, welfare and health promotion, and employing the unemployed. The survey also studied the general well-being of the Finnish population, cooperation of municipalities and organisations in the National Development Program of Health and Social Services (Kaste), and the state of services provided by the Social Insurance Institution of Finland (Kela) and employment agencies. The respondents were managers or management groups of municipal social services offices, municipal health centres (primary health care), employment offices, local offices of the Social Insurance Institution of Finland, and chairs of municipal social services boards. There were separate questionnaires for each organisational sector, with somewhat differing questions for each. The questions common to all organisations studied opinions on the general well-being of the population in the municipality/area and whether there were sufficient resources to provide the required services at the time of the survey and 12 months after the survey. Regarding service provision, the respondents were asked about cooperation and service provision strategies. The respondents were also asked to evaluate the social services provided by the municipality, and the customer orientation of these services. Furthermore, the offices of Kela were asked to evaluate how well different parts of Kela's customer service functioned. Competitive procurement of services was investigated with questions about outsourced services, their oversight, and different strategies regarding outsourced services. The respondents were also asked to assess several suggestions regarding the new national public procurement law. Views of the the health and social service reform were surveyed by asking how well-integrated the services of the area were, and which areas would benefit the most from integration of the services. Furthermore, the respondents were asked how the basic level of services in the area could be improved. Regarding long-term unemployment, the respondents were asked about the possibilities for different organisations to offer services for the long-term unemployed and how provision of certain services would affect the circumstances of the unemployed. Cooperation between social services and employment agencies was investigated. Employment agencies were also asked about the significance of vocational education in improving the employment situation and how effectively different organisations in the area could employ the long-term unemployed. Promotion of health and well-being was examined by charting, among others, the role of various institutions and organisations in promoting health and well-being. Finally, views on the use and misuse of social security benefits were investigated. The background variables included, depending on the organisation, name of the organisation, municipality/area, and statistical grouping of the municipality (urban, semi-urban, rural).

As of 2022, Indonesia’s senior population amounted to nearly ** million. The number of elderly citizens has gradually increased over the past decade. As Indonesia is entering a demographic dividend period where a working-age population dominates the country, it is expected that by the end of this phase, Indonesia will see a surge in the share of its old-age population.

Indonesia’s demographic shift Contrary to some Asian countries that are trying to accelerate their population growth, the Indonesian government has been trying to persuade its citizens to have fewer children. Many initiatives in promoting family planning and later marriages have led to a more controlled population growth. However, the country is expected to face a demographic shift in the upcoming years, with projections indicating that the elderly population will make up ** percent of the country’s population in 2045. Considering this, the Indonesian government has been reassessing its family planning initiatives to better prepare for the risks of an aging population.

Retirement readiness among the society  Indonesia still has a relatively low literacy rate for pension funds and a significantly lower inclusion rate of only slightly over five percent. In conjunction with this, only roughly ** percent of the elderly households in Indonesia were part of social security programs, and a large share of the country’s senior citizens live in three-generation households. These concerns tend to expose Indonesia to higher dependency risks among its aging population. Indeed, it is crucial to address the need to increase awareness of and enhance accessibility to pension funds or social security programs for adequate retirement planning in Indonesian society.

A broad and generalized selection of 2013-2017 US Census Bureau 2017 5-year American Community Survey income and earnings data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico counties). The selection, while not comprehensive, provides a first-level characterization of the household income, median household income by race and by age group, Social Security income, the GINI Index, per capita income, median family income, and median household earnings by age, and by education level, in New Mexico. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users. The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. As in the decennial census, strict confidentiality laws protect all information that could be used to identify individuals or households.The ACS combines population or other data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. 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. While each full Data Profile contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by New Mexico county boundaries, based on TIGER/Line Files: shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database. NOTE: A '-666666666' entry 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.

Abstract

The household registration system known as ho khau has been a part of the fabric of life in Vietnam for over 50 years. The system was used as an instrument of public security, economic planning, and control of migration, at a time when the state played a stronger role in direct management of the economy and the life of its citizens. Although the system has become less rigid over time, concerns persist that ho khau limits the rights and access to public services of those who lack permanent registration in their place of residence. Due largely to data constraints, however, previous discussions about the system have relied largely on anecdotal or partial information.

Drawing from historical roots as well as the similar model of China’s hukou, the ho khau system was established in Vietnam in 1964. The 1964 law established the basic parameters of the system: every citizen was to be registered as a resident in one and only household at the place of permanent residence, and movements could take place only with the permission of authorities. Controlling migration to cities was part of the system’s early motivation, and the system’s ties to rationing, public services, and employment made it an effective check on unsanctioned migration. Transfer of one’s ho khau from one place to another was possible in principle but challenging in practice.

The force of the system has diminished since the launch of Doi Moi as well as a series of reforms starting in 2006. Most critically, it is no longer necessary to obtain permission from the local authorities in the place of departure to register in a new location. Additionally, obtaining temporary registration status in a new location is no longer difficult. However, in recent years the direction of policy changes regarding ho khau has been varied. A 2013 law explicitly recognized the authority of local authorities to set their own policies regarding registration, and some cities have tightened the requirements for obtaining permanent status.

Understanding of the system has been hampered by the fact that those without permanent registration have not appeared in most conventional sources of socioeconomic data. To gather data for this project, a survey of 5000 respondents in five provinces was done in June-July 2015. The samples are representative of the population in 5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong. Those five provinces/cities are among the provinces with the highest rate of migration as estimated using data from Population Census 2009.

Geographic coverage

5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling for the Household Registration Survey was conducted in two stages. The two stages were selection of 250 enumeration areas (50 EAs in each of 5 provinces) and then selection of 20 households in each selected EA, resulting in a total sample size of 5000 households. The EAs were selected using Probability Proportional to Size (PPS) method based on the square number of migrants in each EA, with the aim to increase the probability of being selected for EAs with higher number of migrants. “Migrants” were defined using the census data as those who lived in a different province five years previous to the census. The 2009 Population Census data was used as the sample frame for the selection of EAs. To make sure the sampling frame was accurate and up to date, EA leaders of the sampled EAs were asked to collection information of all households regardless of registration status at their ward a month before the actual fieldwork. Information collected include name of head of household, address, gender, age of household’s head, household phone number, residence registration status of household, and place of their registration 5 years ago. All households on the resulting lists were found to have either temporary or permanent registration in their current place of residence.

Using these lists, selection of survey households was stratified at the EA level to ensure a substantial surveyed population of households without permanent registration. In each EA random selection was conducted of 12 households with temporary registration status and 8 households with permanent registration status. For EAs where the number of temporary registration households was less than 12, all of the temporary registration households were selected and additional permanent registration households were selected to ensure that each EA had 20 survey households. Sampling weights were calculated taking into the account the selection rules for the first and second stages of the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was mostly adapted from the Vietnam Household Living Standard Survey (VHLSS), and the Urban Poverty Survey (UPS) with appropriate adjustment and supplement of a number of questions to follow closely the objectives of this survey. The household questionnaire consists of a set of questions on the following contents:

• Demographic characteristics of household members with emphasis on their residence status in terms of both administrative management (permanent/temporary residence book) and real residential situation. • Education of household members. Beside information on education level, the respondents are asked whether a household member attend school as “trai-tuyen” , how much “trai-tuyen” fee/enrolment fee, and difficulty in attending schools without permanent residence status. • Health and health care, collecting information on medical status and health insurance card of household members. • Labour and employment, asking household member’s employment status in the last 30 days; their most and second-most time-consuming employment during the last 30 days; and whether they had been asked about residence status when looking for job. • Assets and housing conditions. This section collects information on household’s living conditions such as assets, housing types and areas, electricity, water and energy. • Income and expenditure of households. • Social inclusion and protection. The respondents are asked whether their household members participate in social organizations, activities, services, contribution; whether they benefit from any social project/policy; do they have any loans within the last 12 months; and to provide information about five of their friends at their residential area. • Knowledge on the Law of Residence, current regulations on conditions for obtaining permanent residence, experience dealing with residence issues, and opinion on current household registration system of the respondents.

Cleaning operations

Managing and Cleaning the Data

Data were managed and cleaned each day immediately upon being received, which occurred at the same time as the fieldwork surveys. At the end of each workday, the survey teams were required to review all of the interviews conducted and transfer collected data to the server. The data received by the main server were downloaded and monitored by MDRI staff.

At this stage, MDRI assigned a technical team to work on the data. First, the team listened to interview records and used an application to detect enumerators’ errors. In this way, MDRI quickly identified and corrected the mistakes of the interviewers. Then the technical team proceeded with data cleaning by questionnaire, based on the following quantity and quality checking criteria.

• Quantity checking criteria: The number of questionnaires must be matched with the completed interviews and the questionnaires assigned to each individual in the field. According to the plan, each survey team conducted 20 household questionnaires in each village. All questionnaires were checked to ensure that they contained all essential information, and duplicated entries were eliminated. • Quality checking criteria: Our staff performed a thorough examination of the practicality and logic of the data. If there was any suspicious or inconsistent information, the data management team re – listened to the records or contacted the respondents and survey teams for clarification via phone call. Necessary revisions would then be made.

Data cleaning was implemented by the following stages: 1. Identification of illogical values; 2. Software – based detection of errors for clarification and revision; 3. Information re-checking with respondents and/or enumerators via phone or through looking at the records; 4. Development and implementation of errors correction algorithms; The list of detected and adjusted errors is attached in Annex 6.

Outlier detection methods The data team applied a popular non - parametric method for outlier detection, which can be done with the following procedure: 1. Identify the first quartile Q1 (the 25th percentile data point) 2. Identify the third quartile Q3 (the 75th percentile data point) 3. Identify the inter-quartile range(IQR): IQR=Q3-Q1 4. Calculate lower limits (L) and upper limits (U) by the following formulas: o L=Q1-1.5*IQR o U=Q3+1.5*IQR 5. Detect outliers by the rule: An observation is an outlier if it lies below the lower bound or beyond the upper bound (i.e. less than L or greater than U)

Data Structure The completed dataset for the “Household registration survey 2015” includes 9 files in STATA format (.dta): • hrs_maindata: Information on the households, including: assets, housing, income, expenditures, social inclusion and social protection issues, household registration procedures • hrs_muc1: Basic information on the

The percentage of the population in the poorest quintile covered by social protection and labor programs (SPL). The indicator is estimated by dividing the number of SPL beneficiaries in the poorest quintile by the number of the population in the poorest quintile, multiplied by 100. It includes direct and indirect beneficiaries (all members of the household where at least one member receives an SPL program. The poorest quintile is generated using pretransfer per capita welfare (income or consumption); the pre-transfer welfare is generated by subtracting 50 percent of social insurance and labor market transfers and 100 percent of social assistance transfers. SPL includes social assistance programs (conditional and unconditional cash transfers, social pensions, public works programs, fee waivers and targeted subsidies, school feeding program, in-kind transfers, and other social assistance), social insurance (contributory pensions and other social insurance), and labor market programs (passive and active measures). The indicator is generated by country and then aggregated at the regional and income group levels. The source of the indicator is the Atlas of Social Protection: Indicators of Resilience and Equity (ASPIRE). ASPIRE is the World Bank's premier compilation of indicators to analyze the scope and performance of social protection programs. Developed by the Social Protection and Jobs (SPJ) Global Practice, ASPIRE provides indicators for 129 countries on social assistance, social insurance and labor market programs based on both program-level administrative data and national household survey data. ASPIRE is an ongoing project that aims to improve SPL data quality, comparability, and availability to better inform SPL policies and programs. ASPIRE also produces SPL coverage indicators for the total population and for different population groups (pre- and post-transfer income/consumption quintiles, urban and rural populations, and poor and non-poor defined by the relative and international poverty lines). These indicators are used for each SPL program captured in the survey, and for 12 standardized SPL program categories and three SPL areas. The social protection (SP) coverage indicator has the following features: - It is generated using official nationally representative household surveys. - Its target population is World Bank client countries. - Actual country coverage is 101 countries out of 144 World Bank client countries. - For a country data point, it uses the most recent survey year within a 10-year time window (2013-2022). - The indicators do not rely on imputation for missing values, nor inter- or extrapolation. - It provides the best information available despite caveats.

Abstract

This data is from the first round of a unique, cross-country panel survey conducted in Uganda by the Secure Livelihoods Research Consortium (SLRC). The Overseas Development Institute (ODI) is the lead organisation of SLRC. SLRC partners who participated in the survey were: the Centre for Poverty Analysis (CEPA) in Sri Lanka, Feinstein International Center (FIC, Tufts University), the Sustainable Development Policy Institute(SDPI) in Pakistan, Humanitarian Aid and Reconstruction, based at Wageningen University (WUR) in the Netherlands, the Nepal Centre for Contemporary Research (NCCR), and the Food and Agriculture Organization (FAO).

This survey generated the first round of data on people's livelihoods, their access to and experience of basic services, and their views of governance actors. SLRC will attempt to re-interview the same respondents in 2015 to find out how the livelihoods and governance perceptions of people shift (or not) over time, and which factors may have contributed towards that change.

Geographic coverage

Uganda: Acholi and Lango sub-region Rural and urban

Analysis unit

Some questions are at the level of individuals in household (e.g. livelihood activities, education levels); other questions are at the household level (e.g. assets). A sizeable share of the questionnaire is devoted to perceptions based questions, which are at the individual (respondent) level.

Universe

Randomly selected households in purposely sampled sites (sampling procedure varied slightly by country).

Within a selected household, only one household members was interviewed about the household. Respondents were adults and we aimed to interview a fairly even share of men/ women. In some countries this was achieved, but in other countries the share of male respondents is substantially higher (e.g. Pakistan).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy was designed to select households that are relevant to the main research questions and as well as being of national relevance, while also being able to produce statistically significant conclusions at the study and village level. To meet these objectives, purposive and random sampling were combined at different stages of the sampling strategy. The first stages of the sampling process involved purposive sampling, with random sampling only utilized in the last stage of the process. Sampling locations were selected purposely (including districts and locations within districts), and then randomly households were selected within these locations. A rigorous sample is geared towards meeting the objectives of the research. The samples are not representative for the case study countries and cannot be used to represent the case study countries as a whole, nor for the districts. The samples are representative at the village level, with the exception of Uganda.

Sampling locations (sub-regions or districts, sub-districts and villages) were purposively selected, using criteria, such as levels of service provision or levels of conflict, in order to locate the specific groups of interest and to select geographical locations that are relevant to the broader SLRC research areas and of policy relevance at the national level. For instance, locations experienced high/ low levels of conflict and locations with high/ low provision of services were selected and locations that accounted for all possible combinations of selection criteria were included. Survey locations with different characteristics were chose, so that we could explore the relevance of conflict affectedness, access to services and variations in geography and livelihoods on our outcome variables. Depending on the administrative structure of the country, this process involved selecting a succession of sampling locations (at increasingly lower administrative units).

The survey did not attempt to achieve representativeness at the country /or district level, but it aimed for representativeness at the sub-district /or village level through random sampling (Households were randomly selected within villages so that the results are representative and statistically significant at the village level and so that a varied sample was captured. Households were randomly selected using a number of different tools, depending on data availability, such as random selection from vote registers (Nepal), construction of household listings (DRC) and a quasi-random household process that involved walking in a random direction for a random number of minutes (Uganda).

The samples are statistically significant at the survey level and village level (in all countries) and at the district level in Sri Lanka and sub-region level in Uganda. The sample size was calculated with the aim to achieve statistical significance at the study and village level, and to accommodate the available budget, logistical limitations, and to account for possible attrition between 2012-2015. In a number of countries estimated population data had to be used, as recent population data were not available.

The minimum overall sample size required to achieve significance at the study level, given population and average household size across districts, was calculated using a basic sample size calculator at a 95% confidence level and confidence interval of 5. The sample size at the village level was again calculated at the using a 95% confidence level and confidence interval of 5. . Finally, the sample was increased by 20% to account for possible attrition between 2012 and 2015, so that the sample size in 2015 is likely to be still statistically significant.

The overall sample required to achieve the sampling objectives in selected districts in each country ranged from 1,259 to 3,175 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire per country that includes household level, individual level and respondent level perceptions based questions.

The general structure and content of the questionnaire is similar across all five countries, with about 80% of questions similar, but tailored to the country-specific process. Country-specific surveys were tailored on the basis of a generic survey instrument that was developed by ODI specifically for this survey.

The questionnaires are published in English.

Cleaning operations

CSPro was used for data entries in most countries.

Data editing took place at a number of stages throughout the processing, including: • Office editing and coding • During data entry • Structure checking and completeness • Extensive secondary editing conducted by ODI

Response rate

The required sample sizes were achieved in all countries. Response rates were extremely high, ranging from 99%-100%.

Sampling error estimates

No further estimations of sampling error was conducted beyond the sampling design stage.

Data appraisal

Done on an ad hoc basis for some countries, but not consistently across all surveys and domains.

Abstract

Bangladesh Bureau of Statistics (BBS), the National Statistical Organization of the country, has been conducting Labour Force Survey (LFS) since 1980 and repeated it every three/four year until 2013. The surveys could not be held at uniform time intervals due to resource constraint and other reasons. Finally, from July 2015, BBS has undertaken a development project and started implementation of quarterly labour force survey to provide labour market indicators. Gender disaggregated data on labour force, employment, unemployment, underemployment, not in labour force, hours worked, earnings, informal employment. Non-economic activities, volunteer activities are available in this report. The survey found that around half (51.2 per cent) of the 30.5 million employed persons worked more than 48 hours per week. By sex, the proportion of male workers working more than 48 hours (60.9 per cent) was higher than that of female workers (28.4 per cent). By industry, the highest rates of persons in excessive hours were in the Accommodation and food service activities (78.4 per cent), wholesale and retail trade sector (72.9 per cent), manufacturing (69.3 per cent), and households (61.5 per cent).

The primary objective of the survey was to collect comprehensive data on the Labour Force, employment and unemployment of the population aged 15 or older for use by the Government, international organizations, NGOs, researchers and others to efficiently provide targeted interventions. Specific objectives of the survey:

• Provide relevant information regarding the characteristics of the population and household that relate to housing, household size, female-headed households;

• Provide detailed information on education and training, such as literacy, educational attainment and vocational training;

• Provide relevant information on economic activities and the labour force regarding the working-age population, economic activity status and Labour Force participation;

• Provide detailed information on employment and informal employment by occupation and industry, education level and status in employment;

• Provide relevant information on unemployment, the youth labour force participation, youth employment, and youth unemployment;

• Provide other information on decent work regarding earnings from employment, working hours and time-related underemployment, quality and stability of employment, social security coverage, and safety at work, equal opportunities;

• Provide relevant information on non-economic activities, volunteer activities etc.

Geographic coverage

National coverage.

Analysis unit

• Individuals

• Household

Universe

Age is a strong determinant of labour market so a common age cut-off and categories are important. The labour related questions of the survey refer to the population of 15 years old and over. The following age ranges is used in presenting the statistics: 15–24; 25–34; 35–44; 45–54; 55–64; and 65 and over. Besides, LMI is provided separately for youths as the youths are more prone to unstable transition to labour market. However, in setting the minimum LFS coverage age is the fact that the Government of Bangladesh, being aware that many young people, who are unable to continue with higher schooling, enter the labour market instead, has set the legal age for admission to employment at 14 completed years. Given that, inclusion of persons aged 15 years and over may result in the undercount of persons employed or unemployed in the country.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used for the selection of sample for the survey was based on the Population and Housing Census 2011. Sampling Frame which was made up of preparing of PSUs that is consists of collapsing one or more Enumeration Area (EAs) that was created for the Population and Housing Census 2011. EAs is geographical contiguous areas of land with identifiable boundaries. On average, each PSUs has 225 households. All the Enumeration areas of the country was identified into three segments viz. Strong, Semi-strong and not-strong based on the housing materials. The frame has 1284 PSUs/EAs spread all over the country, and covers all socio-economic classes and hence able to get a suitable and representative sample of the population. The survey was distributed into twenty-one domains viz. Rural, Urban and City corporations of seven administrative divisions.

From each selected PSUs/EAs, an equal number of 24 households were selected systematically, with a random start. The systematic sampling method was adopted as it enables the distribution of the sample across the cluster evenly and yields good estimates for the population parameters. Selection of the households was done at the HQ and assigned to the Enumerators, with strictly no allowance for replacement of non-responding households. The Bangladesh Quarterly Labor Force Survey (QLFS) sample will be selected in two stages, with small area units called Primary Sampling Units (PSUs) in the first stage and a cluster of 24 households per PSU in the second stage. Both stages are random selections. The survey will implement a rotational panel strategy, in which some of the households in each cluster will be replaced by new households each quarter. The survey launched in July 2015, with a total sample size of about 30,800 households (1,284 PSUs) in each quarter and 123634 in the year 2015-16, intended to deliver reliable quarterly estimates of unemployment and other relevant labor force indicators for of the country's seven divisions and locality viz. national level estimates with disaggregation by City Corporations, Rural and Urban.

The survey involved a sample of 30816 households from 1284 PSUs/sample enumeration areas distributed across all the 64 Districts for each quarter and the ultimate sample households for the year 2015-16 was 126000 in total. The survey covered both urban and rural areas and dwelling households, including one person households. The institutional households, that is, those living in hostels, hotels, hospitals, old homes, military and police barracks, prisons, welfare homes and other institutions were excluded from the coverage of the survey.

Sampling deviation

Most BBS household surveys use a two-stage sampling strategy similar to that of the QLFS, and most of them share a common set of PSUs – the Integrated Multi-Purpose Sample (IMPS) – as a basis for their first sampling stage. However, the QLFS, given the specificities of its rotational strategy, has opted for choosing an independent set of PSUs for this purpose. The first stage sample frame of the QLFS was developed on the basis of the list of Enumeration Areas (EAs) generated by the 2011 Census. Some of the original 293,093 EAs were deemed too small to support the adopted rotational panel strategy, and were joined to neighboring EAs in order to create 146,576 PSUs of more adequate size: most of the resulting PSUs have between 150 and 300 households, with an average of 217. Whenever possible, the EAs with less than 150 households were appended to EAs from the same village, although in the most sparsely populated areas it was sometimes necessary to append entire villages to neighboring villages within the same mauza or mahalla (the lower level administrative division of the country.)2 Entire mauzas or mahallas were never appended to neighboring areas, even if they were too small – they remained as individual PSUs in the sample frame. The second stage sample frame will be a full listing of all households in the selected PSUs. The listings were completed between February and March 2015. If the survey indeed becomes a regular exercise, they should be permanently updated so that they are never older than two years.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Quarterly Labour Force Survey 2015-16 questionnaire comprised 14 sections, as follows:

• Section 1. Household basic information

• Section 2. Household roster (members’ basic information)

• Section 3. General education (for persons aged 5 years or older) & vocational training (for persons aged 15 years or older)

• Section 4. Working status (for persons aged 15 years or older)

• Section 5. Main activities (for persons aged 15 years or older)

• Section 6. Secondary activities (for employed persons aged 15 years or older)

• Section 7. Occupational safety and health within the previous 12 months (for persons aged 15 years or older)

• Section 8. Underemployment (for employed persons aged 15 years or older)

• Section 9. Unemployment (for not employed persons aged 15 years or older)

• Section 10. Own use production of goods (for persons aged 15 years or older)

• Section 11. Own use provision of services (for persons aged 15 years or older)

• Section 12. Unpaid trainee/apprentice work (for persons aged 15 years or older)

• Section 13. Volunteer work (for persons aged 15 years or older)

• Section 14. Migration (for persons aged 15 years or older)

Cleaning operations

With regard to editing and processing errors, several consistency checks were done, both manually and computerized programme using CSPro; batch editing was done using Stata, to ensure the quality and acceptability of the data produced. The Non-sampling error is to ensure high quality data, several steps were taken to minimize non-sampling errors. Unlike sampling errors, these errors cannot be measured and can only be overcome through several administrative procedures. These errors can arise as a result of incomplete survey coverage, frame defect, response error, non-response and

In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the 21st round of the survey (June 2022) include self-reported health status, health insurance coverage, access to health care, disability, COVID-19, awareness of the Medicaid continuous coverage requirement, past-due medical debt, unfair treatment in health care settings, food security, and access to transportation. Additional information collected by the survey includes age, gender, sexual orientation, marital status, education, race and ethnicity, United States citizenship, housing type, home ownership, internet access, income, and employment status.

The survey “Health on equal terms?” is a national survey, conducted every year since 2004. The Public Health Agency is responsible for the survey and Statistics Sweden (SCB) is responsible for the distribution, collection and registration. During the data collection in 2013, the City of Gothenburg ordered an additional sample for Gothenburg.

The additional sample in 2013 included all individuals aged 16-84 years living in Gothenburg and consisted of 2,648 individuals. The total sample of Gothenburg, including the national sample, was 3,740 individuals. The questionnaire was answered by 1,687 individuals from Gothenburg, representing a response rate of 45,2 percent.

Including the national sample produced by Statistics Sweden (SCB), a total of 2,810 individuals participated from Västra Götaland County.

The national questionnaire 2013 consisted a total of 145 questions covering e.g. issues of health, lifestyle, economic conditions, work and employment, and security and social relations. In addition to data collected through the questionnaires, registry data were also included.

Purpose:

The aim of the national survey was to investigate the health in the population and to show changes in the population's health over time as a follow up of the national health politics. The aim of the additional sample in Gothenburg was to investigate the health in the population living in Gothenburg.

In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a quarterly survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the first quarter 2015 survey (the ninth round of the HRMS) include self-reported health status, awareness of key provisions of the ACA, sources of information about the health plans offered in the ACA marketplace, whether health insurance was purchased through the ACA marketplace, difficulties with access to health care and paying for medical bills and housing costs, out-of-pocket health care costs, type of health insurance coverage if any, and reasons for not having health insurance. Respondents who enrolled in a health insurance plan through the ACA marketplace in 2014 were asked if and why they renewed or changed their plan in 2015. Additional information collected by the survey includes age, gender, sexual orientation, marital status, family size, education, race, Hispanic origin, United States citizenship, housing type, home ownership, internet access, income, employment status, and employer size. The data file also records whether the respondent reported an ambulatory care sensitive condition or a mental or behavioral health condition and whether the respondent or a family member received Social Security, Supplemental Security Income, unemployment insurance benefits or benefits though the Supplement Nutrition Assistance Program, Earned Income Tax Credit, Temporary Assistance for Needy Families, or child care services or child care assistance from a local welfare agency or case manager.

Brazil Working Age Population: Labour Force: Northeast: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data was reported at 3,939.000 Person th in Mar 2019. This records a decrease from the previous number of 4,001.000 Person th for Dec 2018. Brazil Working Age Population: Labour Force: Northeast: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data is updated quarterly, averaging 3,764.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 4,036.000 Person th in Sep 2018 and a record low of 3,405.000 Person th in Mar 2013. Brazil Working Age Population: Labour Force: Northeast: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA011: Continuous National Household Sample Survey: Working Age Population: Labour Force: Employed: by Economic Activity.

Brazil Working Age Population: Labour Force: Central West: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data was reported at 1,471.000 Person th in Mar 2019. This records a decrease from the previous number of 1,499.000 Person th for Dec 2018. Brazil Working Age Population: Labour Force: Central West: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data is updated quarterly, averaging 1,322.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,499.000 Person th in Dec 2018 and a record low of 1,251.000 Person th in Mar 2013. Brazil Working Age Population: Labour Force: Central West: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA011: Continuous National Household Sample Survey: Working Age Population: Labour Force: Employed: by Economic Activity.

Brazil Working Age Population: Labour Force: Pernambuco: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data was reported at 683.000 Person th in Mar 2019. This records an increase from the previous number of 680.000 Person th for Dec 2018. Brazil Working Age Population: Labour Force: Pernambuco: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data is updated quarterly, averaging 615.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 702.000 Person th in Jun 2018 and a record low of 563.000 Person th in Mar 2013. Brazil Working Age Population: Labour Force: Pernambuco: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA011: Continuous National Household Sample Survey: Working Age Population: Labour Force: Employed: by Economic Activity.

Brazil Working Age Population: Labour Force: Maranhão: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data was reported at 432.000 Person th in Mar 2019. This records a decrease from the previous number of 479.000 Person th for Dec 2018. Brazil Working Age Population: Labour Force: Maranhão: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data is updated quarterly, averaging 432.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 479.000 Person th in Dec 2018 and a record low of 374.000 Person th in Mar 2013. Brazil Working Age Population: Labour Force: Maranhão: Employed: by Economic Activity: Public Administration, Defense, Social Security, Health & Social Services data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA011: Continuous National Household Sample Survey: Working Age Population: Labour Force: Employed: by Economic Activity.

Cambodia KH: Coverage: Social Insurance Programs: Richest Quintile: % of Population data was reported at 3.241 % in 2013. This records a decrease from the previous number of 4.756 % for 2008. Cambodia KH: Coverage: Social Insurance Programs: Richest Quintile: % of Population data is updated yearly, averaging 3.999 % from Dec 2008 (Median) to 2013, with 2 observations. The data reached an all-time high of 4.756 % in 2008 and a record low of 3.241 % in 2013. Cambodia KH: Coverage: Social Insurance Programs: Richest Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cambodia – Table KH.World Bank.WDI: Social: Social Protection and Insurance. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;