Snapshot img

US Senior Living Market Size 2025-2029

The senior living market in US size is forecast to increase by USD 30.58 billion at a CAGR of 5.9% between 2024 and 2029.

The senior living market is experiencing significant growth due to various driving factors. One of the primary factors is the aging population, as the number of seniors continues to increase, the demand for services is also rising. Another key trend is the integration of technology into senior living facilities, which enhances the quality of care and improves the overall living experience for seniors. Innovations in artificial intelligence, data analytics, predictive modeling, and personalized care plans are disrupting traditional care models and improving overall financial sustainability through cost containment and value-based care. However, affordability remains a challenge for many seniors and their families, as the cost of services can be prohibitive. This report provides a comprehensive analysis of these factors and more, offering insights into the current state and future direction of the market.

What will be the Size of the Market During the Forecast Period?

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The market encompasses a range of services designed to address the unique needs of an aging population, including long-term care, end-of-life care, palliative care, hospice care, respite care, adult day care, home health services, geriatric care, and various forms of cognitive and behavioral health support. This market is driven by demographic trends, with the global population of individuals aged 65 and above projected to reach 1.5 billion by 2050. 


Key challenges in this market include addressing cognitive decline, social isolation, fall prevention, medication management, nutritional support, mobility assistance, personal care assistance, continence management, and other aspects of daily living. Additionally, there is a growing focus on quality of life, resident satisfaction, staffing ratios, caregiver training, technology adoption, and regulatory compliance. The aging services network is evolving to provide a continuum of care, from independent living to palliative care, with a focus on evidence-based practices, industry best practices, and regulatory compliance.

How is this market segmented, and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Service TypeAssisted livingIndependent livingCCRCAge GroupAge 85 and olderAge 66-84Age 65 and underBy TypeMedical ServicesNon-Medical ServicesDistribution ChannelDirect SalesAgency ReferralsOnline PlatformsEnd-UserBaby BoomersSilent GenerationGen XGeographyUS

By Service Type Insights

The assisted living segment is estimated to witness significant growth during the forecast period. Assisted living communities cater to seniors who require assistance with daily activities but do not necessitate full-time nursing care. These residences offer a combination of personalized care, social engagement, and medical support in a secure and comfortable setting. The market is experiencing growth due to the expanding aging population, rising life expectancy, and a preference for home-like environments over traditional nursing homes. Personalized care services are a defining feature of assisted living. Residents receive aid with activities of daily living, such as bathing, dressing, grooming, medication management, and mobility assistance, based on their individual needs.
Trained staff members are available 24/7 to ensure the safety and well-being of residents. Memory care communities are a specialized segment within assisted living, designed for seniors with Alzheimer's disease and other forms of dementia. These facilities provide secure environments and specialized care techniques to address the unique needs of these residents. Independent living communities offer seniors the opportunity to live in a social, active environment while maintaining their independence. These communities provide housing solutions with minimal support services, such as meal preparation and housekeeping. Nursing care homes and skilled nursing facilities offer comprehensive care for seniors with chronic health conditions and complex care needs.

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Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of US Senior Living Market?

An aging population is the key driver of the market. The market in the US is experiencing significant grow

This dataset contains the Live Well Report monthly data that reflects wellness enrollment and engagement in the Virgin Pulse Program (corporate wellness programs that are designed to support and encourage a holistic approach to employee well-being by creating an organizational culture of health). Update Frequency : Semi-Annually

Abstract

The Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey 2002-2003 is one of the biggest national surveys carried out in accordance with an international methodology with technical and financial support from the World Bank and United Nations Development Programme.

Background This survey was developed in response to provide the picture of the current situation of poverty in Mongolia in relation to social and economic indicators and contribute toward implementation and progress on National Millennium Development Goals articulated in the National Millennium Development Report and monitoring of the Economic Growth Support and Poverty Reduction Strategy, as well as toward developing and designing future policies and actions. Also, the survey enriched the national database on poverty and contributed in improving the professional capacity of experts and professionals of the National Statistical Office of Mongolia.

Purpose Since the onset of the transition to a market economy of Mongolia our country the need to study changes in people's living standards in relation to household members' demographic situation, their education, health, employment and household engagement in private enterprises has become extremely important. With that purpose and with the support of the World Bank and the United Nations Development Programme, the National Statistical Office of Mongolia conducted the Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey-like features between 2002 and 2003. In conjunction with LSMS household interviews the NSO also collected a price and a community questionnaire in each selected soum. The latter collected information on the quality of infrastructure, and basic education and health services.

Main importance of the survey is to provide policy makers and decision makers with realistic information about poverty and will become a resource for experts and researchers who are interested in studying poverty as well as social and economic issues of Mongolia.

In July 2003 the Government of Mongolia completed the Economic Growth and Poverty Reduction Strategy Paper in which the Government gave high priority to the fight against poverty. As part of that commitment this paper is a study that intends to monitor poverty and understand its main causes in order to provide policy-makers with useful information to improve pro-poor policies.

Content The Integrated HIES with LSMS design has the peculiarity of being a sub-sample of a larger survey, namely the Household Income and Expenditure Survey 2002. Instead of administering an independent consumption module, the Integrated HIES with LSMS 2002-2003 depends on the HIES 2002 information on household consumption expenditure. This is why the survey is referred as Integrated HIES with LSMS 2002-2003. This survey is the only source of information of income-poverty, and the questionnaire is designed to provide poverty estimates and a set of useful social indicators that can monitor more in general human development, as well as more specific issues on key sectors, such as health, education, and energy. And, the price and social survey, in conjunction with LSMS household interviews, collected information on the quality of infrastructure, and basic education and health services of each selected soum.

HIES - food expenditure and consumption, non-food expenditure, other expense, income LSMS - general information, household roster, housing, education, employment, health, fertility, migration, agriculture, livestock, non-farm enterprises, other souces of income, savings and loans, remittances, durable goods, energy PRICE SURVEY - prices of household consumer goods and services SOCIAL SURVEY - population and households, economy and infrastructure, education, health, agriculture and livestock, and non-agricultural business

Survey results The final report of this survey has main results on key poverty indicators, used internationally, as they relate to various social sectors. Its annexes contain information regarding the consumption structure, poverty lines along with the methodology used, as well as some statistical indicators.

The main contributions of this survey report are: - new poverty estimates based on the latest available household survey, the Integrated HIES with LSMS 2002-2003 - the implementation of appropriate, and internationally accepted, methodologies in the calculation of poverty and its analysis (these methodologies may constitute a reference for the analysis of future surveys) - a 'poverty profile' that describes the main characteristics of poverty

The first section of the report provides information on the Mongolian economic background, and presents the basic poverty measures that are linked to the economic performance to offer an indication of what happened to poverty and inequality in recent years. A second section goes in much more detail in generating and describing the poverty profile, in particular looking at the geographical distribution of poverty, poverty and its correlation with household demographic characteristics, characteristics of the household head, employment, and assets. A final section looks at poverty and social sectors and investigates various aspects of education, health and safety nets. The report contains also a number of useful, but more technical appendixes with information about the HIES-LSMS 2002-2003 (sample design and data quality), on the methodology used to construct the basic welfare indicator, and set the poverty line, some sensitivity analysis, and additional statistical information.

Geographic coverage

The survey is nationally representative and covers the whole of Mongolia.

Analysis unit

• Household (defined as a group of persons who usually live and eat together)
• Household member (defined as members of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)
• Selected soums (for collecting prices of household consumer goods and services and information on quality of infrastructure, basic education, health services and so on)

Universe

The survey covered selected households and all members of the households (usual residents). And the price and social surveys covered all selected soums.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Integrated HIES with LSMS 2002-2003 households are a subset of the household interviewed for the HIES 2002. One third of the HIES 2002 households were contacted again and interviewed on the LSMS topics. The subset was equally distributed among the four quarters.

The HIES 2002, and consequently the Integrated HIES with LSMS 2002-2003, used the 2000 Census as sample frame. 1,248 enumerations areas were part of the sample, which is a two-stage stratified random sample. The strata, or domains of estimation, are four: Ulaanbaatar, Aimag capitals and small towns, Soum centres, and Countryside. At a first stage a number of Primary Sampling Units (PSUs) were selected from each stratum. In the selected PSUs enumerators listed all the households residing in the area, and in a second stage households were randomly selected from the list of households identified in that PSU (10 households were selected in urban areas and 8 households in rural areas).

It should be noted that non-response case of households once selected for the survey exerts unfavorable influence on the representativeness of the survey. Therefore an enumerator should take every step to avoid that. To obtain true and timely survey results a proper agreement should be reached with a selected household before a survey starts. One of the main reasons of non-response is that an enumerator doesn't meet with the household members who are able to give the required information. An enumerator should visit a household at least 3 times within the given period to take the questionnaire.

Another common reason is that a household refuses to participate in the survey. In this case an enumerator should explain the purpose of the survey again, explain that the private data will be kept strictly confidential according to the corresponding law. If necessary an enumerator can ask local statistical division or local administration for the help. However this practice is very seldom.

If there is no possibility to take the questionnaires from the selected households due to weather conditions or disasters, reserved households with numbers 11, 12, 13 respectively from the list provided by the NSO should replace the omitted ones. However the reasons of replacements are to be declared in detail on the form.

Sampling deviation

At the planning stage the time lag between the HIES and LSMS interviews was expected to be relatively short. However, for various reasons it is on average of about 9 months, and for some households more than one year. Households interviewed in the first and second quarter of 2002 were generally re-interviewed in March and April 2003, while households of the third and fourth quarter of 2002 were re-interviewed in May, June and July of 2003. The considerable time lag between HIES and LSMS interviews was the main responsible for a considerable loss of households in the LSMS sample, households that could not be easily relocated and therefore re-interviewed. Due also to some incomplete questionnaires, the number of households that were used for the final poverty analysis is 3,308.

Mode of data collection

Face-to-face [f2f]

Research instrument

A

Abstract

The principal objective of this survey is to collect basic data reflecting the actual living conditions of the population in Tajikistan. These data will then be used for evaluating socio-economic development and formulating policies to improve living conditions.

The first assessment of living standards in Tajikistan was conducted in 1999. This assessment is bringing about data in order to update the 1999 assessment.

The survey collects information on education, health, employment and other productive activities, demographic characteristics, migration, housing conditions, expenditures and assets.

The information gathered is intended to improve economic and social policy in Tajikistan. It should enable decision-makers to 1) identify target groups for government assistance, 2) inform programs of socio-economic development, and 3) analyse the impact of decisions already made and the current economic conditions on households.

Geographic coverage

National coverage. The 2003 data are representative at the regional level (4 regions) and urban/rural.

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tajikistan Living Standards Survey (TLSS) for 2003 was based on a stratified random probability sample, with the sample stratified according to oblast and urban/rural settlements and with the share of each strata in the overall sample being in proportion to its share in the total number of households as recorded in the 2000 Census. The same approach was used in the TLSS 1999 although there were some differences in the sampling. First the share of each strata in the overall sample in 1999 was determined according to ‘best estimates’, as it was conducted prior to the 2000 Census. Second the TLSS 2003 over-sampled by 40 percent in Dushanbe, 300 percent in rural Gorno-Badakhshan Administrative Oblast (GBAO) and 600 percent in urban GBAO. Third the sample size was increased in 2003 in comparison with 1999 in order to reduce sampling error. In 2003, the overall sample size was 4,156 households compared with 2,000 households in 1999. [Note: Taken from “Republic of Tajikistan: Poverty Assessment Update”, Report No. 30853, Human Development Sector Unit, Central Asia Country Unit, Europe and Central Asia Region, World Bank, January 2005.]

In addition to the capital city of Dushanbe, the country has several oblasts (regions): (i) Khatlon (comprising Kurban-Tube and Khulyab), which is an agricultural area with most of the country’s cotton growing districts; (ii) the Rayons of Republican Subordination (RRS) with the massive aluminum smelter in the west and agricultural valleys in the east growing crops other than cotton; (iii) Sugd which is the most industrialized oblast; and (iv) Gorno-Badakhshan Administrative Oblast which is mountainous and remote with a small population.

The 2003 data are representative at the regional level (4 regions) and urban/rural.

Mode of data collection

Face-to-face [f2f]

Statistics Norway has carried out level of living surveys since 1973, and from 1996 surveys have been carried out annually. The Level of Living Survey on the topic of working conditions had previously been carried out in 1996, 2000, 2003, 2006, 2009, 2013, 2016 and 2019. The Level of Living Survey on Working Conditions aims to map different working environment conditions among employees in Norway. The survey now takes place every 3 years, and covers topics such as attachment to the workplace, physical, chemical and ergonomic working environment, psychosocial working environment, work-related health problems and sickness absence and requirements and opportunities for self-determination at work.

For the Level of Living Survey on Working Conditions 2022, a nationally representative sample of approximately 35 000 people aged 18-66 was drawn. Data was collected using telephone interviews and a self-administered web form in the period August 2022 to April 2023. In Statistics Norway, the Division for income and living conditions statistics has the professional responsibility for the survey, the data collection is carried out by the Division for personal surveys.

This is the main file for the Level of Living Survey on Working Conditions 2022.

The NLSS 1995/96 is basically limited to the living standards of households. The basic objectives of this survey was to provide information required for monitoring the progress in improving national living standards and to evaluate the impact of various government policies and program on living condition of the population. This survey captured comprehensive set of data on different aspects of households welfare like consumption, income, housing, labour markets, education, health etc.

Office of Child Support Enforecment (OCSE) Story Behind the Numbers - Child Support Fact Sheet #3. This fact sheet focuses on data reported in a recent U.S. Census Bureau report, Custodial Mothers and Fathers and Their Child Support: 2011. The data reported are estimated based on a biennial survey of custodial parents, the Child Support Supplement to the Current Population Survey, March/April 2012, co-sponsored by the Office of Child Support Enforcement. The proportion of custodial parents living below poverty line continues to increase in 2011. The report found that 4.2 million custodial parents lived in poverty in 2011, representing 29 percent of all custodial parents, about twice the poverty rate for the total population. These statistics reinforce the essential role that child support services can play in helping low-income families, especially during an economic downturn.

Abstract

The principal objective of the VNLSS is to collect basic data reflescting the actual living standard of the population. These data then be used for evaluating socio-economic development and formulationg policies to improve living standard. Followings are the main goals by the year of 2000. - Reduce the population growth rate less than 2 % peryear - Reduce the infant mortaility (under 5 years old) 0,81% (1990) to 0,55%; and from 0,46% (1990) to 0,3% (under one year old) - Reduce the mortality rate of women concerning the pregnancy and maternity - Reduce the malnutrition of children under 5years old from 51,5% at present to 40% in 1995 and under 30% by the year of 2000. Heavy malnutrition should not be existed by the year of 2000. - Population can access to safe water resources from 43% (1990) to 82% of which 40% to 80% in rural areas. Population use sanitary latrine from 22% (1990) to 65% of which in rural areas from 15% to 60% - 90 percent of children complete the endeavor universal first level education before the age of 15, and the rest should complete the third grade. By the year of 2000 no children at the age of 15 will be illiterate - Improve the cultural, spiritual life of the children, to ensure that 30% of communes (by the year of 1995) and 50% of communes (by the year of 2000) have entertaining place for children

The main information collected by the survey includes: - Household income and expenditures - Health and education - Employment and other productive and activities - Demographic characteristics and migration - Housing conditions

In addition, the information gatherd is intended to improve planning of economic and social policies in Vietnam and to assist in evaluating the impact of the policies. It should enable decision makers to: - indentify target groups for government assistance - Construct models of socio-economic development policies, both overall and on individuals groups - Analyze the impact of decisions available and of the current economic situation on living condition of household

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample covers 4800 households from all areas of Viet Nam. The sample design was self-weighted, which means that each household in Viet Nam had the same probability of being selected. The overall sampling frame was stratified into two groups urban and rural, with sampling was carried out separately in each group (strata). About 20% of Vietnamese households live in urban areas, so the sample stratification ensures that 20% of selected households also come from urban areas. Within urban and rural areas, two lists of all communes was drawn up (one of urban communes and another of rural ones), province by province, in "serpentine" order. 2 The selection of communes within each list was done to ensure that they were spread out evenly among all provinces in Viet Nam.

The VNLSS sample design is the following. Within each province in Viet Nam, rural areas can be broken down into districts, and districts in turn are divided into communes (Xa). Urban areas in all provinces consist of centers/towns, which are divided into quarters (Quai), and then divided further into communes (Phuong). The number of communes in all of Viet Nam, both urban and rural, is about 10,000, and the average population in each is about 6,500. As explained in Section 4, each survey team covers 32 households in 4 weeks, 16 households in one area, and 16 in another area. For convenience all 32 households (i.e. both sets of 16 household) were selected from the same commune. This implied that 150 communes needed to be randomly selected (32x150=4800), 30 in urban areas and 120 in urban areas. Within urban areas communes can be further divided into clusters (Cum), two of which were selected from which to draw two "workloads" of 16 households (16 from each of the two clusters). The same was done in rural areas, where each commune is divided into several villages (Thon). The average size of urban clusters and rural villages is somewhat less than 1000 households.

The VNLSS sample was drawn in three stages. Because the General Statistical Office in Hanoi knows the current population of each commune in Viet Nam (but not of each cluster or village within each commune), 150 communes were selected out of the 10,000 in all of Viet Nam with the probability of selection proportional to their population size. At the second stage, information was gathered from the 150 selected communes on the population of each cluster (in urban areas) or villages (in rural areas), and two clusters or villages were randomly drawn with probability proportional to their population size. Finally, the third stage involved random selection of 20 households (16 for the sample plus four "extras" to serve as replacements if some of the 16 "originals" could not be interviewed) within each cluster or village from a list of all households within each cluster or village. Note that the first stage of the sample is based on information from the 1989 Census, but the second and third stages use updated information available from the communes. The first and second stage samples were drawn in Hanoi, while the third stage was drawn in the field (see Section 4.3 below for more details).

Implementation

The attached map shows the commune number and approximate location of the 150 communes selected in Viet Nam. Of the 150 communes chosen, one was in a very remote and inaccessible area near the Chinese border and was replaced by another not quite as inaccessible. The actual interview schedule went smoothly. In one instance (commune 68) one of the selected villages was replaced because when the survey team arrived in the village it discovered that most of the adults were away from the village and thus could not be interviewed. In each cluster or village interviews were completed for 16 households, thus the 4800 household target sample was fully achieved. About 3% of the households (155) were replaced; the main reason for replacement was that their occupants were not at home. Only four households refused to participate. Community questionnaires were completed for all 120 rural communes. Price questionnaires were completed for 118 of 120 communes (the exceptions were communes 62 and 63), and comparable price data were collected from existing sources for all 30 urban areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

HOUSEHOLD QUESTIONNAIRE

The household questionnaire contains modules (sections) to collect data on household demographic structure, education, health, employment, migration, housing conditions, fertility, agricultural activities, household non-agricultural businesses, food expenditures, non-food expenditures, remittances and other income sources, savings and loans, and anthropometric (height and weight) measures.

For some sections (survey information, housing, and respondents for second round) the individual designated by the household members as the household head provided responses. For some others (agro-pastoral activities, non-farm self employment, food expenditures, non-food expenditures) a member identified as most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (education, health, employment, migration, and fertility) each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. In the case of the employment and fertility sections it is possible that the information was not provided by the relevant person; variables in these sections indicate when this is the case. The household questionnaire was completed in two interviews two weeks apart: Sections 0-8, were conducted in the first interview, sections 9-14 were conducted in the second interview, and section 15 was administered in both interviews. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each module is briefly described below.

I. FIRST INTERVIEW

Section 0 SURVEY INFORMATION 0A HOUSEHOLD HEAD AND RESPONDENT INFORMATION 0B SUMMARY OF SURVEY RESULTS 0C OBSERVATIONS AND COMMENTS

The date of the interview, the religion, ethnic group of the household head, the language used by the respondent and other technical information related to the interview are noted. Section 0B summarizes the results of the survey visits, i.e. whether a section was completed on the first visit or the second visit. Section 0C, not entered into the computer, contains remarks of the interviewer and the supervisor. Since the data in Section 0C are retained only on the questionnaires, researchers cannot gain access to them without checking the original questionnaires at the General Statistical Office in Hanoi.

Section 1 HOUSEHOLD MEMBERSHIP 1A HOUSEHOLD ROSTER 1B INFORMATION ON PARENTS OF HOUSEHOLD MEMBERS 1C CHILDREN RESIDING ELSEWHERE

The roster in Section 1A lists the age, sex, marital status and relation to household head of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include "all the people who normally live and eat their meals together in this dwelling. Those who were absent more than nine of the last twelve months were excluded, except for the head of the household and infants less than three months old. A lunar calendar is provided in the

Snapshot img

Senior Living Market Size 2025-2029

The senior living market size is forecast to increase by USD 130.9 billion, at a CAGR of 5.8% between 2024 and 2029.

The market is experiencing significant growth and transformation, driven primarily by the aging baby boomer population. This demographic cohort, the largest in history, is entering the age bracket requiring senior living solutions. The increasing prevalence of age-related health issues necessitates specialized care and accommodation, creating a burgeoning demand for senior living facilities. However, this market is not without challenges. Technological advances in long-term healthcare are transforming the senior living landscape, necessitating significant investments in infrastructure and staff training. These advancements include telehealth, remote monitoring, and automated systems, which aim to enhance care quality and efficiency.
Moreover, staffing and workplace challenges persist as the senior living industry grapples with attracting and retaining skilled workers. The physical and emotional demands of caregiving, coupled with low wages and long hours, make it a challenging profession. Addressing these staffing issues through competitive compensation, benefits, and training programs is crucial for providers seeking to maintain high-quality care and operational excellence.

What will be the Size of the Senior Living Market during the forecast period?

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The market continues to evolve, with dynamic market activities unfolding across various sectors. Community outings remain a crucial aspect of senior living, providing opportunities for social engagement and enrichment. Nursing homes and residential care facilities offer essential services for those requiring round-the-clock care, while continuing care communities cater to the diverse needs of seniors as they age. Senior living communities, including those specializing in Alzheimer's care and memory care, prioritize resident safety through rigorous regulatory compliance and advanced health information technology. Personal care and rehabilitation services help seniors maintain their independence and improve their quality of life. Capital expenditures for skilled nursing and retirement homes remain a significant focus, with ongoing investments in caregiver training, emergency response systems, and electronic health records.

Long-term care insurance plays a vital role in financing these services, ensuring seniors receive the care they need. Life enrichment programs, such as fitness centers, wellness programs, and volunteer opportunities, promote overall well-being and help seniors stay active and engaged. Continuous innovation in areas like smart homes, universal design, and hospice care further enhances the senior living experience. Operating costs, including staffing ratios, medication management, and infection control, are critical considerations for senior living providers. Ongoing regulatory compliance and the integration of technology help mitigate these costs while maintaining high-quality care. In the ever-changing senior living landscape, providers must remain agile and adapt to the evolving needs of their residents.

From independent living to post-acute care, the focus remains on enhancing the quality of life for seniors through personalized care, community engagement, and ongoing innovation.

How is this Senior Living Industry segmented?

The senior living industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Assisted living
  Independent living
  CCRC


Services

  Healthcare Services
  Lifestyle and Wellness Programs
  Dining Services


Technology Integration

  Smart Home Systems
  Health Monitoring Devices
  Safety and Security Systems


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Service Insights

The assisted living segment is estimated to witness significant growth during the forecast period.

Assisted living arrangements provide apartment-style dwellings for aging adults who require assistance with activities of daily living, such as bathing, doing laundry, and managing medications. These communities offer various levels of care, including memory care units for individuals with cognitive impairments, which may include increased security measures and restricted kitchen access for safety reasons. The demand for specialized memory care units is growing as the population ages and the prevalence of conditions l

Approximately 81 percent of people in the Republic of Ireland thought that the state of the global economy was the main contributing factor to the rising cost of living in the country. By contrast, just 49 percent of people in Ireland believed that workers demanding pay rises was the main reason.

Estimates of how the coronavirus (COVID-19) has impacted income and affordability in Great Britain. Data are from the Survey on Living Conditions (SLC).

About this dataset

Context

The dataset presents median household incomes for various household sizes in Live Oak, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in Live Oak. Across the different household sizes in Live Oak the mean income is $78,053, and the standard deviation is $32,385. The coefficient of variation (CV) is 41.49%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $29,580. It then further increased to $127,405 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/live-oak-ca-median-household-income-by-household-size.jpeg" alt="Live Oak, CA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Live Oak median household income. You can refer the same here

The assisted living sector is navigating a complex landscape shaped by economic pressures, regulatory changes and demographic shifts. Recent trends show facilities facing challenges because of wage pressures. Employee shortages drive up wages, straining budgets and reducing funds for upgrades. Proposed federal and state funding caps at the start of 2025 create financial uncertainty and reduce revenue sources for assisted living retirement communities. Despite these challenges, the aging population is boosting demand for senior living options, providing a counterbalance by expanding the potential resident base. Facilities increasingly opt for efficiency and sustainability by consolidating spaces and focusing on tailored services, aligning with evolving resident preferences and enhancing overall service offerings. Revenue is expected to climb at a CAGR of 1.7% to $96.8 billion through the end of 2025, with a healthy boost of 4.4% in 2025 alone.

Over the past five years, various cost-related factors have pressured profitability. Rising wages, driven by employee shortages, force facilities to enhance compensation packages and benefits, pressing profit. Regulations mandating staff-to-patient ratios often require hiring costly temporary staff, further heightening expenses. Communities are adjusting pricing structures and optimizing staffing solutions to counter these challenges, balancing the rising costs to sustain financial stability. Downsizing has eased rent costs, permitting reallocations toward wages or tech upgrades.

The industry anticipates reshaping through technology integration and service diversification in the later years. Incorporating virtual reality, telehealth and wearable devices promises transformative impacts on resident care, enhancing engagement and health management. Larger organizations with robust resources are poised to lead with specialized memory care and holistic wellness programs, attracting private payees. The consolidation trend could lead to economies of scale and increased profitability, especially as cuts in government funding threaten smaller entities. With an aging population delaying retirement, facilities have a unique opportunity to expand amenities and cater to diverse needs, positioning themselves for a promising future amid demographic and technological trends. Industry revenue is expected to strengthen at a CAGR of 3.4% to $114.5 billion through the end of 2030.

Abstract

South Africa's first Living Conditions Survey (LCS) was conducted by Statistics South Africa over a period of one year between 13 October 2014 and 25 October 2015. The main aim of this survey is to provide data that will contribute to a better understanding of living conditions and poverty in South Africa for monitoring levels of poverty over time. Data was collected from 27 527 households across the country. The survey used a combination of the diary and recall methods. Households were asked to record their daily acquisitions in diaries provided by Statistics SA for a period of a month. The survey also employed a household questionnaire to collect data on household expenditure, subjective poverty, and income.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The sample for the survey included all domestic households, holiday homes and all households in workers' residences, such as mining hostels and dormitories for workers, but excludes institutions such as hospitals, prisons, old-age homes, student hostels, and dormitories for scholars, boarding houses, hotels, lodges and guesthouses.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Living Conditions Survey 2014-2015 sample was based on the LCS 2008-2009 master sample of 3 080 PSUs. However, there were 40 PSUs with no DU sample, thus the sample of 30 818 DUs was selected from only 3 040 PSUs. Amongst the PSUs with no DU sample, 25 PSUs were non-respondent because 19 PSUs were not captured on the dwelling frame, and 6 PSUs had an insufficient DU count. The remaining 15 PSUs were vacant and therefore out-of-scope. Among the PSUs with a DU sample, 2 974 PSUs were respondent, 50 PSUs were non-respondent and 16 PSUs were out-of-scope. The scope of the Master Sample (MS) is national coverage of all households in South Africa. It was designed to cover all households living in private dwelling units and workers living in workers' quarters in the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Living Conditions Survey 2014-2015 used three data collection instruments, namely a household questionnaire, a weekly diary, and the summary questionnaire. The household questionnaire was a booklet of questions administered to respondents during the course of the survey month. The weekly diary was a booklet that was left with the responding household to track all acquisitions made by the household during the survey month. The household (after being trained by the Interviewer) was responsible for recording all their daily acquisitions, as well as information about where they purchased the item and the purpose of the item. A household completed a different diary for each of the four weeks of the survey month. Interviewers then assigned codes for the classification of individual consumption according to purpose (COICOP) to items recorded in the weekly diary, using a code list provided to them.

Data appraisal

Anthropometric data collected during the survey are not included in the dataset.

Assisted Living Software Market Outlook

The global assisted living software market size was valued at approximately USD 3.5 billion in 2023 and is poised to reach a remarkable USD 7.9 billion by 2032, expanding at a compound annual growth rate (CAGR) of 9.5% during the forecast period. This impressive growth trajectory is primarily driven by increasing demand for efficient management systems in assisted living facilities, which cater to the growing elderly population. Rising awareness about the benefits of digital transformation in healthcare management, coupled with technological advancements in software solutions, are key factors fueling this market's growth. The introduction of innovative software that enhances operational efficiencies while ensuring compliance with regulatory standards also plays a crucial role in bolstering market expansion.

The surge in the elderly population across the globe, particularly in developed regions, is a major driver for the assisted living software market. As the demographic shift leans towards an aging population, there is a growing need for assisted living facilities that provide tailored care and services. This demographic trend is not just limited to North America and Europe but is also becoming prevalent in Asia Pacific and other regions, thereby creating significant opportunities for market players. Additionally, the increasing prevalence of chronic diseases among the elderly has necessitated the adoption of sophisticated management software that can streamline operations, manage patient data efficiently, and ensure high levels of resident care. The ability of such software to integrate various functions like billing, compliance, and scheduling makes it indispensable for modern healthcare facilities.

Furthermore, the integration of advanced technologies such as artificial intelligence, IoT, and big data analytics into assisted living software is transforming the way these facilities operate. These technologies enhance predictive analytics, enabling facilities to anticipate and manage the needs of their residents more effectively. AI-driven solutions can help in personalizing resident care plans, optimizing staffing requirements, and improving resource allocation. IoT devices, integrated with software platforms, enable real-time monitoring of residents' health conditions, ensuring timely interventions and reducing the risk of emergencies. This technological revolution not only improves the quality of care but also significantly reduces operational costs, thereby providing a substantial impetus to the market's growth.

In terms of regional outlook, North America currently dominates the assisted living software market, owing to well-established healthcare infrastructure and early adoption of advanced technologies. The U.S., in particular, is a lucrative market due to its large elderly population and a strong focus on enhancing healthcare services. Europe follows closely, with countries like Germany and the U.K. investing heavily in digital healthcare solutions. The Asia Pacific region is expected to exhibit the highest CAGR during the forecast period, driven by rapid urbanization, increasing disposable incomes, and a burgeoning elderly population. Governments in countries like China and India are also investing in healthcare infrastructure, further propelling market growth in this region.

In the realm of assisted living, Intelligent Care Support systems are revolutionizing the way facilities manage and deliver care. These systems leverage advanced technologies such as AI and machine learning to provide personalized care plans tailored to individual resident needs. By analyzing data from various sources, Intelligent Care Support can predict potential health issues, optimize staff schedules, and enhance overall operational efficiency. This not only improves the quality of care provided but also ensures that resources are used effectively, reducing costs and improving resident satisfaction. As the demand for more personalized and efficient care solutions grows, the integration of Intelligent Care Support in assisted living facilities is becoming increasingly essential.

Component Analysis

The assisted living software market is segmented by component into software and services, each playing a vital role in the overall market landscape. Software solutions form the backbone of this market, with facilities increasingly adopting comprehensive platforms that integrate various functionalities. These software solutions are designed to s

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim – extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction. In the process leading to its first Poverty Reduction Strategy (that is the National Strategy for Socioeconomic Development, now renamed the National Strategy for Development and Integration), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis the information it needs to inform policy-making. Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country. They provide an indispensable tool to assist policy-makers in monitoring and targeting social programs. In its first phase (2001-2006), this monitoring system included the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys. The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a subsample of LSMS households (in 2003, and 2004), drawing heavily on the 2001 census information. A poverty profile based on 2002 data showed that some 25 percent of the population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by poor access to basic infrastructure (regular supply of electricity, clean water), education and health services, housing, etc. The 2005 LSMS was in the field between May and early July, with an additional visit to agricultural households in October, 2005. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank.

Abstract

The JSLC gleans household and individual data from a subset of the population covered by the Labour force Survey. Information is collected on consumption, health, education, nutrition, housing, demographic characteristics, and the Food Stamp Programme. The main purpose of the survey is to provide the government with information for policy development and planning. Although modeled of the World Bank's Living Standards Measurement Study (LSMS) household surveys, it has a narrower focus and greater emphasis on policy impact. In 2001, the complete the survey and a module on youth 17-29 years was fielded.

Geographic coverage

National coverage

Analysis unit

• Individuals;
• Household.

Universe

All non-institution dwellings All persons living in non-institutional dwellings in Jamaica

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample dwellings for the JSLC is a random sub-set of the sample of approximately one-third of the preceding Labour Force survey (LFS) to facilitate linkage of the data from both surveys. The sampling design is a two-stage stratified random sampling with the first stage being the selection of areas (enumeration districts (ED)) and the second stage a selection of dwellings. For the selection of EDsall in the country are grouped into Sampling Regions (SR) of strata of approximate equal size, in terms of the number odf dwellings. Two EDs are selected from each sampling region with probability proportionate to size. In each ED, a list of dwellings is prepared and this becomes the frame for the selection of the Master Sample of dwellings for the labour force.

Mode of data collection

Face-to-face [f2f]

Research instrument

A household questionnaire was administered in each household to the household head or a representative. Household information such as housing and household expenditure are collected. Information on household members includes: Sex, age, marital/union status, etc. Education for all persons 3 years and over Health for all persons Child health for children less than five years

Cleaning operations

Before data entry, all the questionnaires are edited and coded. All questionnaires partially completed, or not properly filled out, were removed from data entry operations if they could not be corrected. The questionnaires were then screened by the supervising Statisticians, before being submitted for data entry.

Response rate

Refusals 10.7% Vacant/closed dwellings 23.9% Total non-response - 34.6% These rates were higher than usual and were attributed the fact that the sample used in this survey was the same one used in the previous 3 years. Thus the survey experienced respondent fatigue manifested in the refusals and more households being demolished or merged.

Sampling error estimates

These are done and recorded in the report

Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together separately from the 2003 datasets.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was, as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further three years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS) – and more popularly known as Living in BiH (LiBiH). Birks Sinclair & Associates Ltd. in cooperation with the Independent Bureau for Humanitarian Issues (IBHI) were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for three years following the LSMS, in the autumns of 2002 and 2003 and the winter of 2004. The LSMS constitutes Wave 1 of the panel survey so there are four years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel - Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003 - Wave 4 Fourth interview with sub-sample respondents in Winter 2004

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observations on the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty and movements in an out of poverty are experienced by different types of households and individuals over the four year period. Most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within BiH at a time of social reform and rapid change.

In order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiH MTDS, EPPU commissioned this modified fourth round of the LiBiH Panel Survey.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 4 sample comprised of 2882 households interviewed at Wave 3 (1309 in the RS and 1573 in FBiH). As at previous waves, sample households could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions assume the same standard definitions used at Wave 3. While the sample membership, status and eligibility for interview are as follows: i) All members of households interviewed at Wave 3 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview, i.e. younger than 15 years. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design provides that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in other cases an individual member may move away from their previous wave household and form a new "split-off" household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefits of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are:

i. Movers out of the country altogether i.e. outside BiH This category of mover is clear. Sample members moving to another country outside BiH will be out-of-scope for that year of the survey and ineligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. Personal details of "movers" are passed between the statistical institutes and an interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 4 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko
Are followed for interview. When coding, Brcko is treated as the entity from which the household moved.

Feed-forward

Details of the address at which respondents were found in the previous wave, together with a listing of household members found in each household at the last wave were fed-forward as the starting point for Wave 4 fieldwork. This "feed-forward" data also includes key variables required for correctly identifying individual sample members and includes the following: - For each household: Household ID (IDD); Full address details and phone number - For each Original Sample Member: Name; Person number (ID); unique personal identifier (LID); Sex; Date of birth

The sample details are held in an Access database and in order to ensure the confidentiality of respondents, personal details, names and addresses are held separately from the survey data collected during fieldwork. The IDD, LID and ID are the key linking variables between the two databases i.e. the name and address database and the survey database.

Mode of data collection

Face-to-face [f2f]

Research instrument

Approximately 70% of the questionnaire was based on the Wave 3 questionnaire, carrying forward core measures in order to measure change over time. However in order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiHDS the Wave 4 questionnaire additionally contained the Wave 1 Consumption module and a few other LSMS items to allow direct comparability with the Wave 1 data.

Cleaning operations

Dat entry

As at previous waves, CSPro was the chosen data entry software. The CSPro program consists of two main features intended to reduce the number of keying errors and to reduce the editing required following data entry:
- Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes).

The Wave 4 data entry program had similar checks to the Wave 3 program - and DE staff were instructed to clear all anomalies with SIG fieldwork members. The program was tested prior to the commencement of data entry. Twelve data entry staff were employed in each Field Office, as all had worked on previous waves training was not undertaken.

Editing

Instructions for editing were provided in the Supervisors Instructions. At Wave 4 supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The SIG Fieldwork Managers examined every Control Form.

Response rate

The level of cases that were unable to be traced is extremely low as are the whole household refusal or non-contact rates. In total, 9128 individuals (including children) were enumerated within the sample households at Wave 4, 5019 individuals in the FBiH and 4109 in the RS. Within in the 2875 eligible households, 7603 individuals aged 15 or over were eligible for interview with 7116 (93.6%) being successfully interviewed. Within co-operating households (where there was at least one interview) the interview rate was

Abstract

China Living Standards Survey (LSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

Regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The China LSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in North-eastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930's. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930's. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus, the intended sample size was 780 households, 130 from each county. Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list. In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY All responses obtained from the household interviews were recorded in the household questionnaires. These were then entered into the computer, in the field, using data entry programs written in BASIC. The data produced by the data entry program were in the form of household files, i.e. one data file for all of the data in one household/community questionnaire. Thus, for the household there were about 880 data files. These data files were processed at the University of Toronto and the World Bank to produce datasets in statistical software formats, each of which contained information for all households for a subset of variables. The subset of variables chosen corresponded to data entry screens, so these files are hereafter referred to as "screen files". For the household survey component 66 data files were created. Members of the survey team checked and corrected data by checking the questionnaires for original recorded information. We would like to emphasize that correction here refers to checking questionnaires, in case of errors in skip patterns, incorrect values, or outlying values, and changing values if and only if data in the computer were different from those in the questionnaires. The personnel in charge of data preparation were given specific instructions not to change data even if values in the questionnaires were clearly incorrect. We have no reason to believe that these instructions were not followed, and every reason to believe that the data resulting from these checks and corrections are accurate and of the highest quality possible.

(b) DATA EDITING The screen files were then brought to World Bank headquarters in Washington, D.C. and uploaded to a mainframe computer, where they were converted to "standard" LSMS formats by merging datasets to produce separate datasets for each section with variable names corresponding to the questionnaires. In some cases, this has meant a single dataset for a section, while in others it has meant retaining "screen" datasets with just the variable names changed. Linking Parts of the Household Survey Each household has a unique identification number which is contained in the variable HID. Values for this variable range from 10101 to 60520. The first number is the code for the six counties in which data were collected, the second and third digits are for the villages within each county. Finally, the last two digits of HID contain the household number within the village. Data for households from different parts of the survey can be merged by using the HID variable which appears in each dataset of the household survey. To link information for an individual use should be made of both the household identification number, HID, and the person identification number, PID. A child in the household can be linked to the parents, if the parents are household members, through the parents' id codes in Section 01B. For parents who are not in the household, information is collected on the parent's schooling, main occupation and whether he/she is currently alive. Household members can be linked with their non-resident children through the parents' id codes in Section 01C. Linking the Household to the Community Data The community data have a somewhat different set of identifying variables than the household data. Each community dataset has four identifying variables: province (code 7 for Hebei and code 8 for Liaoning); county (six two digit codes, of which the first digit represents province and the second digit represents the three counties in each province); township (3 digit code, first digit is county, second digit is county and third digit is township); and village (4 digit code, first digit is county, second digit is county, third digit is township, and third fourth digit is village). Constructed Data Set Researchers at the World Bank and the University of Toronto have created a data set with information on annual household expenditures, region codes, etc. This constructed data set is made available for general use with the understanding that the description below is the only documentation that will be provided. Any manipulation of the data requires assumptions to be made and, as much as possible, those assumptions are explained below. Except where noted, the data set has been created using only the original (raw) data sets. A researcher could construct a somewhat different data set by incorporating different assumptions. Aggregate Expenditure, TOTEXP. The dataset TOTEXP contains variables for total household annual expenditures (for the year 1994) and variables for the different components of total household expenditures: food expenditures, non-food expenditures, use value of consumer durables, etc. These, along with the algorithm used to calculate household expenditures are detailed in Appendix D. The dataset also contains the variable HID, which can be used to match this dataset to the household level data set. Note that all of the expenditure variables are totals for the household. That is, they are not in per capita terms. Researchers will have to divide these variables by household size to get per capita numbers. The household size variable is included in the data set.