Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

Data on the main mode of commuting by employment income groups, age and gender.

Abstract

The startling drop in incomes and increase in inequality accompanying the transition to market economies in Eastern Europe and the former Soviet Union raise critical questions: Who is most likely to be poor? How well are existing social assistance programs reaching those who most need help? And what kind of programs would be most effective in reducing poverty? As part of a project analyzing poverty and social assistance in the transition economies, a Bank research team created a database of household expenditure and income data from recent surveys - the HEIDE database. (See the book by J. Braithwaite, Ch. Grootaert and B. Milanovic, "Poverty and social assistance in Transition Countries, St. Martin's Press, 1999" and the book by B. Milanovic, Income, inequality, and poverty during the transition from planned to market economy, World Bank, 1998.)

The HEIDE database includes four countries in both Eastern Europe and the Former Soviet Union. Latvia was then added at a later stage.

The four files are: -hhold: Household data consists of the variables in Variable List at household level. -ind: Individual data consists of the variables in Variable List at individual level. -modelh: Household data consists of the variables used in regression models. -modeli: Individual data consists of the variables used in regression models.

Prefixes are used to indicate countries for the data files, i.e. A- Rural Armenia B- Bulgaria E- Estonia H- Hungary K- Kyrgyz P- Poland R- Russia S- Slovak Y- Urban Armenia

The survey data were cleaned for possible inconsistencies and errors and adjusted for missing data and outliers. The compilation of almost 100 variables with similar definitions for the eight countries allows ready cross-country analysis and comparisons. A consistent syntax is used for the variables to enable researchers to use the same macro routines across countries. There are more than 3 million data points.

Geographic coverage

The database includes data from Armenia, Bulgaria, Estonia, Hungary, Kyrgyz Republic, Latvia, Poland, Russia, and the Slovak Republic.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The survey data were cleaned for possible inconsistencies and errors and adjusted for missing data and outliers. The compilation of almost 100 variables with similar definitions for the eight countries allows ready cross-country analysis and comparisons.

See document "Household Expenditure and Income Data for Transitional Economies (HEIDE): Data Cleaning and Rent Imputation - Appendix 1 of RAD project "Poverty and Targeting of Social Assistance in Eastern Europe and the Former Soviet Union"".

Abstract

Is global inequality (inequality among world citizens) stable, decreasing or increasing? How high it is? Is it mostly due to inequalities within nations or between nations? Is there a global middle class? See the working papers above: "True world income distribution 1988 and 1993: first calculations based on household surveys alone" no. 2244, and "Decomposing global income distribution: Does the world have a middle class?" no. 2562

Household survey data (1988-2002) used in these papers, and subsequent book "Worlds Apart: Measuring International and Global Inequality", Princeton University Press, 2005. The data are for three benchmark years: 1988, 1993 and 1998

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

From 1995, the General Household Survey (GHS) is conducted in between 2 Population Censuses as a mid-decade mini-Census.

The General Household Survey (GHS) 2015 is the third in the series of mid-decade national survey. It covers a wide range of topics and provides comprehensive data on Singapore’s population and households in between the population censuses that are conducted once in ten years.

The median annual earnings in the United Kingdom was 37,430 British pounds per year in 2024. Annual earnings varied significantly by region, ranging from 47,455 pounds in London to 32,960 pounds in the North East. Along with London, two other areas of the UK had median annual earnings above the UK average; South East England, and Scotland, at 39,038 pounds and 38,315 pounds respectively. Regional Inequality in the UK Various other indicators highlight the degree of regional inequality in the UK, especially between London and the rest of the country. Productivity in London, as measured by output per hour, was 33.2 percent higher than the UK average. By comparison, every other UK region, except the South East, fell below the UK average for productivity. In gross domestic product per head, London was also an outlier. The average GDP per head in the UK was 31,947 pounds in 2021, but for London it was 56,431 pounds. Again, the South East's GDP per head was slightly above the UK average, with every other region below it. Within London itself, there is also a great degree of inequality. In 2021, for example, the average earnings in the historic City of London borough were 1,138 pounds per week, compared with 588 pounds in Redbridge, a borough in the North East of London. Wages finally catch up with inflation in 2023 After the initial economic disruption caused by the COVID-19 pandemic subsided, wages began to steadily grow in the UK. This reached a peak in June 2021, when weekly wages for regular pay were growing at 7.3 percent, or 5.2 percent when adjusted for inflation. By that November, however, prices began to rise faster than wage growth, with inflation surging throughout 2022. In October 2022, for example, while regular pay was growing by 6.1 percent, the inflation rate had surged to 11.1 percent, Although inflation peaked in that month, it wasn't until June 2023 that wages started to outpace inflation. By this point, the damage caused by high energy and food inflation has precipitated the worst Cost of Living Crisis in the UK for a generation.

Abstract

We share data, codes and questionnaires for the replication of the paper "Estimating the Demand for Business Training: Evidence from Jamaica." The study conducted two experiments in Jamaica using the Becker-DeGroot-Marschak mechanism and take-it-or-leave-it offers to estimate the demand for training. We found that most entrepreneurs have positive willingness to pay for training, but demand falls sharply as price increases. Offering the chance to pay in installments does not increase demand. Higher prices screen out poorer, less educated entrepreneurs with smaller firms. However, charging a higher price does increase attendance among those who pay. Finally, the paper points to the limitations of using a BDM mechanism in a context of low contract enforcement, and when payments for purchasing an intangible service do not occur immediately.

Geographic coverage

Around the capital city of Kingston; Western regions of Jamaica, including the second-largest city in Jamaica, Montego Bay

Analysis unit

Entrepreneurs Firms

Kind of data

Sample survey data [ssd]

Sampling procedure

We selected firms to take part in the BDM elicitation method from the Western regions of Jamaica, which includes the second-largest city in Jamaica, Montego Bay. While we recruited firms for the TIOLI approach, mainly around the capital city of Kingston (72%), with the remainder from Montego Bay and surrounding parishes. A variety of communication methods were used to contact entrepreneurs. These included emails to the client database of the organization providing the training; advertisements via social media, newspaper and radio; and messages from other firm support organizations. Entrepreneurs were asked to take a short baseline survey by phone, giving 1,782 eligible entrepreneurs who were then invited to come to demonstration sessions. 457 entrepreneurs came to demonstration sessions and completed the BDM elicitation method (BDM sample), and 374 entrepreneurs came to demonstration sessions and received take-it-or-leave-it offers (TIOLI sample). The dataset includes this final set of 831 observations.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following instruments were used for data collection, and they are provided for download as related materials: Baseline Survey Follow-up Survey Willingness to Pay Example (for TIOLI and BDM)

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

Abstract

The Survey on Income and Living Conditions, introduced as part of the European Union harmonisation efforts, aims to produce data on income distribution, relative poverty by income, living conditions and social exclusion comparable with European Union member states. The study which uses a panel survey method is repeated every year and monitors sample of household members for four years. Every year, the study attempts to obtain two datasets: cross-sectional and panel.

The Income and Living Conditions Survey 2011 has been conducted to provide annual and regular cross-sectional data to answer questions such as:

• How equally is the income in the country distributed and how has it changed as compared to the previous years?
• How many poor people are there in the country and how do they distribute across regions? How has this situation changed as compared to the previous years?
• Who is poor? Has there been a change over time?
• How has this gap between the poor and the rich evolved over time?
• What kind of a change or transition occurs in the incomes of individuals and households? How does the direction of this change depends on characteristics and circumstances, does it decline or grow?
• How is the income distributed across sectors, types of income and household characteristics?
• How do people's living conditions change or improve over time?
• The study also aims to provide panel data to calculate indicators such as persistent income poverty and to measure net changes over time.

The cross-sectional database 2011 is documented here.

Geographic coverage

All settlements within the borders of the Republic of Turkey have been included.

Universe

All household members living in households within the borders of the Republic of Turkey. However, the study excludes the population defined as institutional population living in hospices, elderly homes, prisons, military barracks, private hospitals and in childcare centres. Migrant population has also been excluded due to practical challenges.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method: Stratified, multi-stage, clustered sampling.

Sampling unit: Household.

Sampling framework: Sampling framework has been derived from 2 sources:

1. For the settlements with municipal status; General Building Census conducted in 2000 by TurkStat and Numbering Study (conducted in 2000) Form Population 1 data have been used.
2. For the settlements without municipal status (Villages); data of General Population Census conducted in 2000 have been used to select the blocks which constituted the sampling unit of the first stage.

Selection of sample households: for the purposes of the study which used a two-staged sampling design; entire Turkey has been divided into blocks which covered 100 households each.

• At the first stage, blocks were selected as the first stage sampling unit
• At the second stage, households were selected from among the previously selected blocks as the final sampling unit. Prior to the selection of sample households, addresses at the blocks were updated through an "address screening study"

Sample size: Annual sampling size is 13,414 households in respect of the estimation, objectives and targeted variables of the study and in consideration of the attritions in the sample.

Substitution principle: Substitution has not been used as the sample size had been calculated by taking account of non-response.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Household registry form: The form filled at the beginning of the survey provides brief information on access to the address of the household, condition of the household and of the survey. Moreover, following the first field application, modalities are identified for filling in the monitoring forms if the households included in the panel survey move home.

• Personal registry form: These forms aim to identify basic demographic characteristics of the household members, changes that occur in the status of household membership of the individuals included in the panel survey, reasons for their leaving the household, the date of their departure etc. as well as individuals who join the household.

• Household and personal follow-up form: There is need for following up the households which have moved home and the sample individuals who have left the household to join or found another one. Household and personal follow-up forms are used to identify their new addresses and access their contact information.

• Household questionnaire: These forms attempt to collect information on the type of the occupied dwelling, status of ownership, information relating to the dwelling (number of rooms, the space actually used, heating system, dwelling facilities, goods owned etc), problems of the dwelling of the neighbourhood, status of indebtedness, rent payments, expenditures for the dwelling, the extent to which households are able to meet their general economic and basic needs and incomes earned at household level.

• Personal questionnaire: These forms attempt to collect information on education, health, employment and marital status of the household members aged 15 and over, as well as the dates of employment and incomes earned during the reference year.

Abstract

Since 2014, UNHCR has undertaken a comprehensive revision of the framework for monitoring UNHCR Livelihoods and Economic Inclusion programs. Since 2017, mobile data collection (survey) tools have been rolled out globally, including in Malawi. The participating operations conducted a household survey to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline (87 observations) and endline data (78 observations) from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.

Geographic coverage

Dzaleka

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for this dataset is: Baseline data : 87 Endline data : 78 Total : 165

The sampling was conducted by each participating operation based on general sampling guidance provided as the following;

• At least 100 randomly selected beneficiaries for each project
• Representativeness of sub-groups (gender, camp, etc.) should be kept as much as possible
• Baseline and endline beneficiaries should be the same

Sampling deviation

Some operations may deviate from the sampling guidance due to local constraints such as logistical and security obstacles.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire used to collect the survey consists of five sections: Partner Information, General Information on Beneficiary, Access to Agricultural Production Enabled and Enhanced, Access to Self-Employment/ Business Facilitated, and Access to Wage Employment Facilitated.

Cleaning operations

The dataset presented here has undergone light checking, cleaning, harmonisation of localised information, and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, and grouping values of select variables). Empty values can occur for several reasons (e.g. no occurrence of agricultural interventions among the beneficiaries will result in empty variables for the agricultural module).

Response rate

Information not available

Abstract

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were: Wealth creation in terms of income and expenditure dynamics and asset endowments; Demographic dynamics as these relate to household composition and migration; Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments;
Access to cash transfers and social services

Wave 1 of the survey, conducted in 2008, collected the detailed information for the national sample. In 2010/2011 Wave 2 of NIDS re-interviewed these people, gathering information on developments in their lives since they were interviewed first in 2008. As such, the comparison of Wave 1 and Wave 2 information provides a detailed picture of how South Africans have fared over two years of very difficult socio-economic circumstances.

This administrative dataset is for schools attended by NIDS respondents. The dataset was created by matching the names of schools with Department of Education (DoE) registered lists of schools in South Africa. A detailed description of the matching process is provided in the user manual, which includes a description of the inherent limitations associated with conducting such an exercise.

Geographic coverage

The survey had national coverage

Analysis unit

The units of analysis in the dataset are schools

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

The Annual Survey of Hours and Earnings (ASHE) is the most comprehensive source of earnings information in the United Kingdom. It provides information about the levels, distribution and make-up of earnings and hours paid for employees by gender and full-time/part-time working. Estimates are available for various breakdowns including industries, occupations, geographies and age-groups within the UK. ASHE is used to produce hours and earnings statistics for a range of weekly, annual and hourly measures. ASHE is the official source of estimates for the number of jobs paid below the national minimum wage. ASHE is also used to produce estimates of the proportions of jobs within workplace pension categories. ASHE is based on a one per cent sample of employee jobs taken from HM Revenue & Customs (HMRC) PAYE records. Information on earnings and hours is obtained from employers and treated confidentially. ASHE does not cover the self-employed nor does it cover employees not paid during the reference period. The datasets used for the 2011 datasets are the Revised datasets and are published a year after the provisional. The dataset is split into several different categories including all employees, all male employees and all female employees. Datasets are further categorised by mode of working i.e. Full or Part Time. Full datasets are avaiable from The Office for National Statistics. 2014-06-13T14:25 Licence: None

Abstract

Since 2014, UNHCR has undertaken a comprehensive revision of the framework for monitoring UNHCR Livelihoods and Economic Inclusion programs. Since 2017, mobile data collection (survey) tools have been rolled out globally, including in Chad. The participating operations conducted a household survey to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline (331 observations) and endline data (308 observations) from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.

Geographic coverage

Amboko Amnabak Belom Djabal Doholo Dosseye Gondje Koloma Moyo

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for this dataset is: Baseline data : 331 Endline data : 308 Total : 639

The sampling was conducted by each participating operation based on general sampling guidance provided as the following;

• At least 100 randomly selected beneficiaries for each project
• Representativeness of sub-groups (gender, camp, etc.) should be kept as much as possible
• Baseline and endline beneficiaries should be the same

Sampling deviation

Some operations may deviate from the sampling guidance due to local constraints such as logistical and security obstacles.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire used to collect the survey consists of five sections: Partner Information, General Information on Beneficiary, Access to Agricultural Production Enabled and Enhanced, Access to Self-Employment/ Business Facilitated, and Access to Wage Employment Facilitated.

Cleaning operations

The dataset presented here has undergone light checking, cleaning, harmonization of localized information, and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, and grouping values of select variables). Empty values can occur for several reasons (e.g. no occurrence of agricultural interventions among the beneficiaries will result in empty variables for the agricultural module). Local suppression did not lead to empty variables.

Response rate

Information not available

Russia Purchasing Capacity: Avg Monthly Household Income per Capita: Female Model Shoes: Genuine Leather data was reported at 7.700 Pair in 2017. This records a decrease from the previous number of 8.000 Pair for 2016. Russia Purchasing Capacity: Avg Monthly Household Income per Capita: Female Model Shoes: Genuine Leather data is updated yearly, averaging 6.050 Pair from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 9.100 Pair in 2014 and a record low of 2.400 Pair in 1999. Russia Purchasing Capacity: Avg Monthly Household Income per Capita: Female Model Shoes: Genuine Leather data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HD002: Purchasing Capacity: Average Household Income per Capita: Annual.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Fire Safety (module 33): Awareness of Fire Safety Week, knowledge of facts about fire safety and precautions taken. Alcohol and Tobacco from the EU (Module 64): alcohol and/or tobacco products brought back from European Union Countries during previous two months; quantity bought. Head of Household/Highest Income Earner Information (Module 70): occupation and supervisory status of head of household and highest income earner. GP Accidents (Module 78): accidents in previous three months that resulted in seeing a doctor or going to hospital; where accident happened; whether saw a GP or went straight to hospital. Telephones (Module 96): ownership of private telephone; whether has had difficulty in paying or is behind with telephone bills; reasons for not owning a telephone; preferred method of payment for calls; access to telephone; preferred method of keeping in touch with friends and relatives; ownership of consumer goods; whether behind with any household or credit payments; household's financial situation and income. Multi-stage stratified random sample Face-to-face interview

This data set provides a rate of trips taken by individuals sampled in obtaining data for the ACT Household Travel Survey. The data is categorised by age, gender and income. A trip is defined as the travel between two main activities, where a stop may constitute a change in transport mode. As an example: driving from home to a park and ride facility, then catching a bus to an interchange, then walking to a shop to purchase an item and finally walking to work is comprised of 4 ‘stops’ and two …Show full descriptionThis data set provides a rate of trips taken by individuals sampled in obtaining data for the ACT Household Travel Survey. The data is categorised by age, gender and income. A trip is defined as the travel between two main activities, where a stop may constitute a change in transport mode. As an example: driving from home to a park and ride facility, then catching a bus to an interchange, then walking to a shop to purchase an item and finally walking to work is comprised of 4 ‘stops’ and two ‘trips’. Note: This data represents travel and activity on an average weekday. Total trip rate includes ‘other/not stated’ gender respondents.

Source: SINGAPORE DEPARTMENT OF STATISTICS

Data Last Updated: 29/03/2016

Update Frequency: 10 years

Survey period: General Household Survey 2005

Footnotes: Household income from work includes employer CPF contributions.

Adapted from: https://tablebuilder.singstat.gov.sg/table/CT/8843

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Abstract

The Survey of Income and Living Conditions (EU-SILC) is the European Union reference source for comparative statistics on income distribution and social exclusion at the European level, particularly in the context of the 'Programme of Community action to encourage cooperation between Member States to combat social exclusion' and for producing key policy indicators on social cohesion for the follow up of the EU2020 main target on poverty and social inclusion and flagship initiatives in related domains, e.g. in the context of the European Semester. It provides two types of annual data: Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions, and Longitudinal data pertaining to individual-level changes over time, observed periodically over a four-year period. The first priority is to be given to the delivery of comparable, timely and high quality data. The cross-sectional data is collected in two stages: An early subset of variables collected by register or interview to assess as early as possible poverty trends. A full set of variables provided along with the longitudinal data to produce main key policy indicators on social cohesion.

Geographic coverage

National

Universe

The reference population of EU-SILC is all private households and their current members residing in the territory of the Member States (MS) at the time of data collection. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements: For all components of EU-SILC (whether survey or register based), the cross-sectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.

Mode of data collection

Face-to-face [f2f]