Person with disability means a person suffering from not less than forty percent of any disability as certified by a medical authority (any hospital or institution, specified for the purposes of this Act by notification by the appropriate Government). As per the act "Disability" means - (i) Blindness; (ii) Low vision; (iii) Leprosy-cured; (iv) Hearing impairment; (v) Loco motor disability; (vi) Mental retardation; (vii) Mental illness. Those workers who had worked for the major part of the reference period (i.e. 6 months or more) are termed as Main Workers. Those workers who had not worked for the major part of the reference period (i.e. less than 6 months) are termed as Marginal Workers. A person who did not at all work during the reference period was treated as non-worker. Get data on disabled population among main workers, marginal workers, non-workers by type of disability, age and sex.

The dataset contains survey data from a total of 226 low-income migrant workers (100 in Jalandhar and 126 in Guwahati) in India. It contains data on 60 variables, focussing on socio-economic background, migratory experience, ill-treatment and access to justice and access to basic services. Abstract of the study: Indian cities attract a considerable number of low-income migrants from marginal rural households experiencing difficult economic, political and social conditions at home who migrate in search of livelihoods and security. These migrants come from around the country as well as across the border from Nepal, Bangladesh and Myanmar to work in low-income manual occupations in a range of small-scale petty trade, service sector work, transport and construction work. Low-income migrants live and work in precarious conditions and are often denied basic amenities and fundamental rights. Poorly-paid intermittent and insecure jobs make them vulnerable to abuse, extortion or bribery. Many such migrants, both internal and international, lack documentation and proof of identity, whether for basic services such as health care and schooling or electoral voting. Their marginal position entails poorer access to health care provisions and other determinants of health than general (non-migrant) populations, thereby enhancing their vulnerability to ill-health, abuse and ill treatment whilst simultaneously compromising their ability to access protection, legal support or redress, and forms of accountability. Language, appearance and cultural differences exposes many low-income migrants from interior parts of the country or across the border to harassment and political exclusion. Moreover, despite their ubiquitous presence, their precarious livelihoods, informality and invisibility keep them unnoticed in urban planning, in the work of civil society organisations and in social science research. In this context, this collaborative project was designed to generate evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. We examined what India's urban transformation means for low-income migrants, their inclusion and social justice by exploring: 1. Low-income migrants' views on transformations in Indian cities, and the opportunities and challenges that confront them; 2. Low-income migrants perceptions of their entitlements, claim-making processes and attempts to protect their own health in a context of poor living and working conditions; 3. The prevalence of violence and extent of exclusion experienced by low-income migrants and how they protect themselves from various forms of violence; 4. The legal, developmental, humanitarian and human rights responses to low-income migrants in Indian cities. Fieldwork based in Guwahati (Assam) and Jalandhar (Punjab), two of India's fastest growing cities, aimed to enrich our understanding of access to health care, the social determinants of health, and experiences of violence, inclusion/exclusion and accessing justice, from the vantage point of diverse low-income migrant workers, from within India as well as cross-border. The project focussed on migrants' perceptions and lived experiences and will generate evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. Low-income migrants are mobile, dispersed and invisible, so they present methodological challenges, especially for creating a sampling frame or mapping in a particular locality. A distinctive strength of the project is its innovative methods for accessing these 'hard-to-reach' groups. The proposed research adopted a mixed methods approach. In order to unravel the nuances and complexities of low-income migrants' experiences and situate these within the broader processes of urban transformation in Jalandhar and Guwahati, we combined ethnographic fieldwork with in-depth interviews, a brief survey, and participatory methods such as photovoice.

The dataset contains survey data from a total of 226 low-income migrant workers (100 in Jalandhar and 126 in Guwahati) in India. It contains data on 60 variables, focussing on socio-economic background, migratory experience, ill-treatment and access to justice and access to basic services. Abstract of the study: Indian cities attract a considerable number of low-income migrants from marginal rural households experiencing difficult economic, political and social conditions at home who migrate in search of livelihoods and security. These migrants come from around the country as well as across the border from Nepal, Bangladesh and Myanmar to work in low-income manual occupations in a range of small-scale petty trade, service sector work, transport and construction work. Low-income migrants live and work in precarious conditions and are often denied basic amenities and fundamental rights. Poorly-paid intermittent and insecure jobs make them vulnerable to abuse, extortion or bribery. Many such migrants, both internal and international, lack documentation and proof of identity, whether for basic services such as health care and schooling or electoral voting. Their marginal position entails poorer access to health care provisions and other determinants of health than general (non-migrant) populations, thereby enhancing their vulnerability to ill-health, abuse and ill treatment whilst simultaneously compromising their ability to access protection, legal support or redress, and forms of accountability. Language, appearance and cultural differences exposes many low-income migrants from interior parts of the country or across the border to harassment and political exclusion. Moreover, despite their ubiquitous presence, their precarious livelihoods, informality and invisibility keep them unnoticed in urban planning, in the work of civil society organisations and in social science research. In this context, this collaborative project was designed to generate evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. We examined what India's urban transformation means for low-income migrants, their inclusion and social justice by exploring: 1. Low-income migrants' views on transformations in Indian cities, and the opportunities and challenges that confront them; 2. Low-income migrants perceptions of their entitlements, claim-making processes and attempts to protect their own health in a context of poor living and working conditions; 3. The prevalence of violence and extent of exclusion experienced by low-income migrants and how they protect themselves from various forms of violence; 4. The legal, developmental, humanitarian and human rights responses to low-income migrants in Indian cities. Fieldwork based in Guwahati (Assam) and Jalandhar (Punjab), two of India's fastest growing cities, aimed to enrich our understanding of access to health care, the social determinants of health, and experiences of violence, inclusion/exclusion and accessing justice, from the vantage point of diverse low-income migrant workers, from within India as well as cross-border. The project focussed on migrants' perceptions and lived experiences and will generate evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. Low-income migrants are mobile, dispersed and invisible, so they present methodological challenges, especially for creating a sampling frame or mapping in a particular locality. A distinctive strength of the project is its innovative methods for accessing these 'hard-to-reach' groups. The proposed research adopted a mixed methods approach. In order to unravel the nuances and complexities of low-income migrants' experiences and situate these within the broader processes of urban transformation in Jalandhar and Guwahati, we combined ethnographic fieldwork with in-depth interviews, a brief survey, and participatory methods such as photovoice.

This table contains figures on the inflow and outflow of workers in the narrow care and welfare sector; this is an aggregation of all branches in care and welfare excluding childcare. The reference date of the figures is the last day of each quarter, with the exception of the 4th quarter. In the 4th quarter, the last Friday before Christmas is taken as the reference date. To determine inflow or outflow, the population in a quarter is compared with the population in the same quarter one year earlier. If the employee was not working in care and well-being on one of the two reference dates and was on the other, there is mobility. As a result, employees who do not work in care and well-being every month, such as employees with a flexible employment relationship or employees with a zero-hour contract, are more often classified as mobile. The figures are classified according to the Standard Business Classification 2008 (SBI 2008) of Statistics Netherlands and broken down by ASBL branches, country, district, province and RegioPlus labour market regions. The calculations relate to the main jobs of employees. The region was determined on the basis of the employee's municipality of residence. The main activity (SBI code) of the company in which an employee works was used to determine the ASBL branches. This may not be the only activity a company undertakes. For the main activity ‘Other social advice, community houses and welfare cooperation bodies’ (SBI code: 88999) the collective agreement under which the employee is employed is also taken into account. Employees covered by collective labour agreements codes 0004 to 0300 or under collective labour agreements codes 0302 to 8102 are classified under branch ‘Other Care and Welfare’. Employees covered by another collective labour agreement code are classified under ‘Social Work (Other)’. A complete overview of all CLA codes can be found under Section 3. This table presents figures on labour market flows for the narrow care and welfare sectors. This means that figures on labour market flows for the care and welfare sectors are broadly excluded (for a reference to these figures see Section 3). The narrow labour market for care and welfare means that the childcare sector is not included. As a result, the figures on labour market flows of care and well-being relate narrowly to a different population than figures on care and well-being broadly. Figures on different populations are not published in the same table. The figures on the labour market in care and welfare are presented in a separate table. This table was developed as part of the Labour Market Care and Welfare (AZW) research programme. For more trends and developments in the labour market in care and welfare, see azwstatline.cbs.nl (see section 3).

Data available from: First quarter 2010

Status of figures: All figures are provisional. As long as the figures are provisional, there may be minimal differences.

Changes as of 21 May 2024: The table is supplemented by figures for Q4 2023. Previous figures have been revised as a result of the use of new, more recent sources. In a number of branches, series fluctuate due to administrative changes in the underlying source data.

When will there be new figures? This table is supplemented every quarter with new figures.

This dataset provides statistics on labour productivity for large and small regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Labour productivity is measured as gross value added per employment at place of work by main economic activity. Regional gross value added and employment data are collected from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, data in current prices are transformed into constant prices and PPP measures.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

This dataset provides statistics on labour productivity, for large regions (TL2) and small regions (TL3).

Data source and definition

Labour productivity is measured as gross value added per employment at place of work by main economic activity. Regional gross value added and employment data are collected from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, labour productivity data in current prices are transformed into constant prices and PPP measures (link).

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting, see the list of OECD metropolitan regions (xlsx) and the EU methodology (link).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

This component gives all employees a continuous overview of the main jobs in the year. This means that at any point in time, the main course of a person can be determined.

More information on how to access the data:

https://www.cbs.nl/nl-nl/onze-diensten/maatwerk-en-microdata/microdata-zelf-onderzoek-doen

Methodology

The main job is the job with the highest wage. In particular, the job with the highest & estimated monthly amount &. This estimated monthly amount is calculated by dividing the annual tax salary of the job proportionally over the calendar days of the job. This makes marginal differences in terms of the (maximum) number of calendar days in a month (28, 29, 30 or 31) & smooth coated. This means that the estimated monthly amount does not fluctuate for months worked in full. Note that when determining the main job actual wage developments within a job, such as a wage increase, are spread out over all days worked in the year.

Population

people with a job

Owning their own housing not only helps migrant workers to obtain basic public services in the places where they enter, and thereby better integrate into the urban society; moreover, in Chinese traditional culture, self-owned housing often means "home" for individuals and families. Therefore, the solution and improvement of migrant workers' housing problem will help to realize their own survival and development needs, and also help to promote the process of citizenization. China's current housing provident fund system is an important part of the current basic housing security policy. Employers and individuals make compulsory contributions in accordance with a fixed contribution ratio, thereby completing the accumulation of funds for housing provident funds. Employers and individuals who pay the provident fund can enjoy the preferential policy of deduction before income tax. At the same time, depositors can apply for a provident fund loan within a certain amount and with a loan interest rate lower than the market interest rate when buying a house, so as to improve the ability to buy a house by reducing financing costs. So, What factors will affect the behavior of migrant workers' deposit? Has the housing provident fund deposit promoted the purchase of housing by migrant workers? What is the specific impact of housing provident fund deposit on different migrant workers? This article will answer these questions one by one. On the basis of discussing the internal mechanism of the housing accumulation fund system, this paper empirically analyzes the influencing factors of the housing accumulation fund and the influence of the housing accumulation fund on the housing purchase by migrant workers. Moreover, this paper also discusses the heterogeneous impact of migrant workers who deposited. The main research conclusions are as follows: (1) The proportion of migrant workers who deposited was only 13.14%, which was very low. The characteristics of individual, employment and flow all have an important impact on the deposit behavior of migrant workers. (2) For all migrant workers, the rate of buying houses in the inflow areas is only 21.04%. The proportion of migrant workers with housing provident fund who bought houses in the inflow area was 17.39% higher than that of migrant workers who did not pay the housing provident fund. Housing provident fund deposits have significantly increased the probability of buying houses for migrant workers, with a marginal effect of approximately 5.73%. Due to problems such as self-selection and omission of variables, migrant workers' housing provident fund payment behavior is often obviously endogenous. Therefore, based on the Probit model, this paper uses two research methods of PSM and IV-Probit model to correct it. It turns out that the above conclusion is not only still valid, but the marginal effect has also increased. (3) Compared with the effect of housing provident fund on the purchase of new and old migrant workers, the housing accumulation fund deposit has a stronger effect on the new generation of migrant workers. The housing accumulation fund deposit does promote the purchase of housing by middle-income migrant workers, but it does not have a significant impact on the purchase of housing by migrant workers in the lowest income group and the highest income group, that is, it presents the institutional effect of "mending the middle at both ends ", not simply" loathing the poor and loving the rich ". At the same time, it also shows that it is difficult for low-income migrant workers to enjoy the most basic system welfare of housing accumulation fund. In addition, the impact of housing accumulation fund on the purchase of housing by migrant workers is different between different administrative levels of cities, its impact The effect is most obvious in provincial capitals and sub-provincial cities, and has no significant effect in megacities. The research conclusions show that the housing provident fund deposit can significantly increase the probability of migrant workers buying houses in the inflow areas. However, at present, the proportion of migrant workers who contribute to the housing provident fund is very low, and there are significant differences in characteristics between migrant workers who deposited and those who did not. Especially low-income migrant workers can hardly enjoy housing provident fund due to the limitation of payment conditions and loan thresholds. The most basic benefits. Therefore, To provide decision-making reference for the reform of housing accumulation fund system and the improvement of living conditions of migrant workers: on the one hand, it is necessary to improve the force to increase the support scope of the housing provident fund system; on the other hand, we should make clear the functional orientation of the housing provident fund, increase the support for public housing, scientifically and reasonably design the loan policy, relax...

The minimum wage per day guaranteed by law in Mexico was decreed to increase by approximately 12 percent between 2024 and 2025, reaching 278.8 Mexican pesos in 2025. The Northern Free Zone located near the northern border was the exception, where the minimum daily wage increased to 419.88 Mexican pesos. Education and income disparity The income distribution is entirely a new story than minimum wages, in fact, there are many factors that influence the level of salaries for Mexican workers. One of the main differences is by the number of schooling years, someone with more than 18 years of study earns on average double than employees with seven to nine years. Moreover, the area of study, while statistics and finance mean salaries, the highest wages by degree, are above 30,000 Mexican pesos per month, others such as performing arts and theology rank as the lowest paying degrees in Mexico.
Poverty still among the main problems
Despite one of the main reasons for minimum wage increases being moving people out from poverty conditions, poverty continues to be one of the main problems Mexican society faces. The number of people living under poverty conditions has decreased by 8.54 million inhabitants from 2014 to 2022, nonetheless, the figure is still higher than 46.5 million. The poverty rate varies among states, with Chiapas leading the ranking with 67.4 percent of the population under such conditions, while both Baja California and Baja California Sur recorded less than 14 percent.

Vietnam's 50 million jobs are a cornerstone of its economic success. The transformation toward services and manufacturing, and impressive labor productivity and wage growth led to plunging poverty rates and globally enviable economic growth over the last decades. Employment rates are high and unemployment rates are low by global standards. The jobs challenge is to create more high quality and inclusive jobs. Shiny foreign factories paying above the minimum wage and offering social benefits typify, at best, only 2.1 million jobs. And registered domestic firms provide no more than 6 million jobs. Meanwhile, 38 million Vietnamese jobs are in family farming, household enterprises, or uncontracted labor. These traditional jobs tend to be characterized by low productivity, low profits, meager earnings, and few worker protections. While they have been a path out of poverty, they will not provide the means to reach the middle-class status that Vietnam's citizens aspire to. Ethnic minorities, women, and unskilled workers cluster in these jobs.

Abstract

The 2011 Fourth Quarter Labour Force Survey aims to collect information on the supply side of the labour market. It provides information on the extent of available and unused labour time and on relationships between employment and income. Thus, the data collected can be used for:

Macro-economic monitoring:- from an economic point of view, a main objective of collecting data on the economically active population is to provide basic information on the size and structure of a country's workforce. The unemployment rate in particular is widely used as an overall indicator of the current performance of a country's economy.

Human resources development: The economy is changing all the time. In order to meet the needs of the changing economy, people need to be trained. These areas of training must therefore be identified.

Employment policies: For an economy to work at its maximum potential, all persons wanting to have work should have jobs. Some persons may wish to have full-time jobs, and can only find part-time work. We need to know what proportion of the labour force these people represent in order to assess the social effects of government employment policies.

Income Support and social programmes: For the majority of people, employment income is their main means of support. People need not only jobs, but more importantly, productive jobs in order to receive reasonable incomes. We need to know what levels of income are being earned by different groups of persons.

Geographic coverage

National Coverage

Analysis unit

• Households;
• Individuals.

Universe

The survey covered all de jure non-institutional household members (usual residents), it focuses on the employment, unemployment and current activity or inactivity status of all persons aged 15 years and over resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Every quarter (three months) approximately 1,000 households are interviewed, there is a one third overlap between the households interviewed between each round of the survey.

The Multi-Stage sampling procedure developed for the St. Lucia MS (Master Sample) Frame is used for the execution of the labour force survey:

The two stage process of sample selection in the ST. LUCIA MS entails the selection of the PSUs within the districts. This is followed by the systematic selection of the cluster of households or USU (Ultimate Sampling Units) within the selected PSUs. The two stages in the design is elaborated as follows:

a. In the first stage, a sampling frame is constructed consisting of all of the enumeration districts from the census of 2001. The size of each enumeration district is measured in units of clusters of households. In the case of the ST. LUCIA MS, approximately seven or eight households were allocated per cluster. The clusters which are allocated to the EDs all have an equal probability of selection within the specified geographic domain in which they are allocated. In addition, the number of clusters allocated to an ED is a measure of the size of the ED. Clusters, therefore ensure the selection of EDs or Primary Sampling Units with probability proportional to the size of the ED. The ST. LUCIA MS frame consists of nine sub-samples / replicates, with each replicate selected with a probability of (1 / (16 * 9)) or 1 / 144.

b. In the second stage a non-compact cluster of households is selected within the selected PSU using systematic random sampling. There are three elements to the selection of this non-compact cluster. Firstly, there is the sample interval, which is a measure of the size of the ED in terms of the total number of households it contains. The larger the ED or PSU the larger will be the sample interval assigned and consequently the larger will be the number of clusters assigned to the ED. This approach ensures that the total number of households selected in any selected ED is approximately the same. In the case of the "Castries" in the ST. LUCIA MS frame the approximate number is five (5). Secondly, the random start is determined by use of a random number generator. With a Microsoft EXCEL spreadsheet the formulae takes the following form, =ROUND(RAND()*E1,0)+1, where E1 is the cell containing the sample interval (or total number of clusters assigned) RAND() is the function which generates the random number. The round() function is used to round the result to the nearest whole number. The third element of choosing the non compact cluster is a combination of the above. A random number (r) is choosen between 1 and the sample interval value, I, inclusive, then to this number is added the sample interval for the full list of households within the primary sample unit. Thus, the list of selected households would be r, r + I, r + 2I, r + 3I, r + 4I,……, r + (n - 1)I, where n is the cluster size assigned to the district, in the case of Castries n is five.

A. Size of the Sample

As has been explained before the decision to use a sampling fraction of 1 : 16 and to assign nine replicates to each District (the geographic domain) was based on the need to take advantage of the small size of the countries covered by this MECOVI project. This was done by increasing the "spread" of the sample across EDs and as a result improving the precision of the estimates which can be obtained from it. In addition, attention was paid to ensuring that were the CSO of ST. LUCIA to consider developing further its Integrated Household Survey Programme, the ground work would have been laid through this Master Sample Frame design for periodic, ad hoc or continuous sample surveys. The achievement of this objective has already been demonstrated through the use of this Sample Frame in the conduct of St. Lucia's continuous Labour Force Survey.

Therefore for any one sub-sample given that there are nine, the sampling fraction is 1 / 16 by 1 / 9 or 1 / 144. If a periodic, ad hoc or quarterly survey included the use of three replicates then the sampling fraction for these three replicates would be 3 / 144 or 1 /16 by 3 / 9. In both cases the resultant sampling fraction is the product of the sampling probability for the Master Sampling frame and the probability of selection of a specific number of replicates.

B. Master Sample Domains of Study and Stratification

1. Domains of Study:

The Master Sample frame was subdivided into eleven areas for the purpose of the provision of estimates from samples selected from this frame. The following list of the ten domains or sub-populations is based on the Districts which formed the basis for the collection of information on the population in the 2001 Census.

The total number of PSUs in the ST. LUCIA MS is 401, a breakdown of the number of PSUs by District is shown in the table above. The average size of the PSUs was 118 approximately with a standard deviation of approximately 47. This configuration does not in the near term present a major problem for sample implementation, since the EDs/PSUs size does not exceed 100 by too great an extent, in addition, while consideration must be given to splitting EDs which have grown in size to over 200, there are not as exist in the case of St. Vincent and the Grenadines a significant number of excessively large EDs. Continuous maintenance of this situation is required and can be done by splitting all EDs over 200 in size into smaller ones of approximate size 100. The main objective of controlling the size of the PSUs, is to reduce variability and thereby improve the precision of estimates from the sample. The more equal the sizes of the PSUs the more likely the variance of characteristics between PSUs will be minimized and inversely the precision of the samples derived from the estimates from the Master Sample Frame increased.

1. Stratification

As shown in the table above each of the domains of study was stratified according to specific criteria. In the more urban domains the criteria used was the percentage of Managers, professional, sub-professionals in the population. The PSUs or EDs were therefore arranged in descending order of the proportion of this group in the population of the ED. In the rural domains the PSUs were arranged in descending order of the proportion of agriculture workers in the population of the ED. In the case of Canaries and Anse-la-Raye, the sizes of the populations in these domains mandated a joining of the two to allow for the creation of a large enough domain for reporting purposes.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is administered to all members of the household. Questions 1 through 6 are to be completed for all members of the household, these questions cover age, sex, relation to head of household, country of birth etc. All subsequent questions refer to persons 15 year of age and older. The questionnaire is divided into five parts:

PART 1:For all members of the household (regardless of age) - Demographic and emigration questions
PART 2: To be completed for persons 15 years and older - Education, Training, activities during the reference week or month, working at a job, on vacation, methods of seeking work, availability for employment PART 3: For persons employed during the reference week - Number of actual hours of work, number of usual hours of work, seeking additional work, status in employment, industry and occupation of employment PART 3A: For persons holding more than one job during the reference week - Number of actual hours of work, number of usual hours of work, seeking additional work, status in employment, industry and occupation of employment
PART 4: For persons unemployed

In 2023, the hours worked by employees on the main job in Germany remained nearly unchanged at around 34.25 hours per week per person. Yet 2023 saw the lowest hours worked in this industry in Germany with 34.25 hours per week per person. Nevertheless, the hours worked has been subject to fluctuation over the observed period.

Graph and download economic data for Infra-Annual Labor Statistics: Working-Age Population Total: From 15 to 64 Years for United States (LFWA64TTUSM647S) from Jan 1977 to Jun 2025 about working-age, 15 to 64 years, population, and USA.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm). There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following sections: - Particulars of each person in the household - Economic activities in the last week for persons aged 15 years - Unemployment and economic inactivity for persons aged 15 years - Main work activity in the last week for persons aged 15 years - Earnings in the main job for employees, employers and own-account workers aged 15 years - Migration for all persons aged 15 years

Data appraisal

The statistical release notes that missing values were "generally imputed" for item non-response but provides no detail on how Statistics SA did so.

As a part of China’s social security system, the housing provident fund (HPF) plays an important role in solving housing problems. This paper analyzes the internal mechanism of how housing provident funds influence migrant workers’house purchase, and then uses the dy- namic monitoring survey data of the floating population in 2016 to conduct relevant empirical re- search. Based on the probit model, it is shown that migrant workers’participation in the HPF system has a significant positive impact on the purchase of housing, with a marginal effect of approximately 5．73%. After using the propensity score matching method and instrumental variable method to control the endogeneity, the conclusion above not only remains robust but the marginal effect also increases． The sub-sample results point out that the effect of HPF on house purchase is most pronounced among new generations of migrant workers, and those who are employed in provincial capitals and sub-provincial cities. Besides, the HPF does promote the house purchase of middle - income migrant workers, but it does not significantly support the lowest and highest income groups in buying houses. That means a system characteristic of “Both ends subsidize the middle” exists. Finally, the paper discusses the relevant policy implications.

Abstract

The Nigeria Labour Force Survey (NLFS) is a statistical survey designed to collect comprehensive data on employment, unemployment, and other characteristics of the population labour force. It provides quarterly estimates of the main labour force statistics with sufficient precision at the national level. This report contains findings from the Nigeria Labour Force Survey (NLFS) for the second quarter of 2024. The statistics are measured based on the labour force framework as guided by the international standard for labour market statistics for international comparability and the specific data requirements for the country.

The main objective of the NLFS is to collect basic statistics on the labour market situation in Nigeria and make labour statistics available to support government policies and programmes for effective planning, and for the private sector to support investment decision-making aimed at improving the employment situation in the country. The Labour Force Survey also serves as a tool for monitoring progress towards national goals and global commitments with an overarching goal of promotingthe welfare of the Nigerian population while ensuring the availability of labour market statistics to feed into the global sustainable development goals agenda. Labour is often one of the most important factors of production and is a major determinant of the economic system globally. Therefore, it is imperative to know whether people are working or not, how long they work, and the nature of the jobs they are engaged in.

The NLFS enables key labour market statistics and the employment situation to be monitored periodically in Nigeria. The indicators include the labour force participation rate, employment-to-population ratio, unemployment rate, time-related underemployment, self-employment, labour underutilisation, and other key job characteristics.

Geographic coverage

National Zone State Sector

Analysis unit

Individual

Universe

Household Members

Kind of data

Sample survey data [ssd]

Sampling procedure

The target sample for the entire year is 35,520 households divided across 12 months, meaning the target sample for each quarter is 8,880 households. After small levels of non-response and replacement, the final sample for Q1 2024 is 8,836 households across the 36 states including the FCT.

Sampling deviation

No Deviations

Mode of data collection

Face-to-face [f2f]

Research instrument

A structured questionnaire was used for NLFS. A household questionnaire was administered in each household, which collected various information on Identification, Demographic Characteristics(inclusion of disability questions for 5 years or older), Education, Employed at work,Temporarily absence, Agricultural work and Market Orientation, Characteristics of main and secondary job, Unemployent and out of labour.Some of the questions were administered at household level while others were at individual level.

Cleaning operations

Real - Time data editing took place at different stages throughout the processing which includes: 1) Data editing and cleaning 2) Structure checking and completeness 3) Secondary editing 4) Structural checking of data files

Response rate

The household response rate is 100%.

Sampling error estimates

The margin of error of each quarter is 1% for national estimates.

Data appraisal

A series of data quality tables and graphs are available in the reports.

Intervention effectiveness on primary outcomes in different domains as unadjusted estimated marginal means of change from baseline.

Logistic regression calculated as positive Coccidioides detection using the CocciEnv qPCR assay, as a function of sampling site and sampling month. Only significant contrasts are shown. p-values are Tukey adjusted to total contrasts performed. Total month contrasts = 66. Total site contrasts = 10.

The main purpose of this study was to investigate the validity of the Polish version of the Workplace PERMA-Profiler (WPP). Our work was guided by Martin Seligman’s perspective on positive psychology, emphasizing well-being as its central theme, flourishing as the gold standard for measuring well-being, and the ultimate goal of increasing flourishing. According to his PERMA model, flourishing encompasses five key elements: positive emotions, engagement, relationships, meaning, and accomplishment. The WPP is a tool specifically designed to measure this construct in the workplace context. Polish workers completed online surveys at the initial measurement (N = 1070). In addition, a group of working adults (N = 66) took part in a survey with a repeated measure diagnosing measurement stability. Flourishing, perceived stress, and work satisfaction were measured for comparisons of convergent validity, while zero-sum belief was measured for divergent validity. The reliability indices of the Polish version of the WPP met the minimum reliability requirements, and the Confirmatory Factor Analysis indicated that the five-factor model (contrasted to the single-factor model) achieved the desired goodness-of-fit properties. The WPP also demonstrated convergent validity through strong interrelationships with related constructs and strong stability. Obtaining satisfactory psychometric properties of the Polish version of the WPP enabled the conduction of additional type of exploratory analyzes focused on the relationship between workplace flourishing and the efforts aimed at building employee well-being undertaken by the organization where the respondents work. The study revealed a positive, moderate relationship between employees’ flourishing and the evaluation of organizational practices for increasing employee well-being.

Covariate means for main regression specifications.