In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

This indicator presents available data at national level on the number of people reported by public authorities as homeless. Data are drawn from the OECD Questionnaire on Affordable and Social Housing (QuASH 2021, QuASH 2019, QuASH 2016) and other available sources. Overall, homelessness data are available for 36 countries (Table HC 3.1.1 in Annex I). Further discussion of homelessness can be found in the 2020 OECD Policy Brief, “Better data and policies to fight homelessness in the OECD”, available online (and in French). Discussion of national strategies to combat homelessness can be found in indicator HC3.2 National Strategies for combating homelessness. Comparing homeless estimates across countries is difficult, as countries do not define or count the homeless population in the same way. There is no internationally agreed definition of homelessness. Therefore, this indicator presents a collection of available statistics on homelessness in OECD, EU and key partner countries in line with definitions used in national surveys (comparability issues on the data are discussed below). Even within countries, different definitions of homelessness may co-exist. In this indicator, we refer only to the statistical definition used for data collection purposes. Detail on who is included in the number of homeless in each country, i.e. the definition used for statistical purposes, is presented in Table HC 3.1.2 at the end of this indicator. To facilitate comparison of the content of homeless statistics across countries, it is also indicated whether the definition includes the categories outlined in Box HC3.1, based on “ETHOS Light” (FEANTSA, 2018). Homelessness data from 2020, which are available for a handful of countries and cover at least part of the COVID-19 pandemic, add an additional layer of complexity to cross-country comparison. The homeless population estimate in this case depends heavily on the point in time at which the count took place in the year, the method to estimate the homeless (through a point-in-time count or administrative data, as discussed below), the existence, extent and duration of emergency supports introduced in different countries to provide shelter to the homeless and/or to prevent vulnerable households from becoming homeless (such as eviction bans). Where they are available, homeless data for 2020 are thus compared to data from the previous year in order to facilitate comparison with other countries.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

BackgroundAddressing Citizen’s perspectives on homelessness is crucial for the design of effective and durable policy responses, and available research in Europe is not yet substantive. We aim to explore citizens’ opinions about homelessness and to explain the differences in attitudes within the general population of eight European countries: France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, and Sweden.MethodsA nationally representative telephone survey of European citizens was conducted in 2017. Three domains were investigated: Knowledge, Attitudes, and Practices about homelessness. Based on a multiple correspondence analysis (MCA), a generalized linear model for clustered and weighted samples was used to probe the associations between groups with opposing attitudes.ResultsResponse rates ranged from 30.4% to 33.5% (N = 5,295). Most respondents (57%) had poor knowledge about homelessness. Respondents who thought the government spent too much on homelessness, people who are homeless should be responsible for housing, people remain homeless by choice, or homelessness keeps capabilities/empowerment intact (regarding meals, family contact, and access to work) clustered together (negative attitudes, 30%). Respondents who were willing to pay taxes, welcomed a shelter, or acknowledged people who are homeless may lack some capabilities (i.e. agreed on discrimination in hiring) made another cluster (positive attitudes, 58%). Respondents living in semi-urban or urban areas (ORs 1.33 and 1.34) and those engaged in practices to support people who are homeless (ORs > 1.4; p<0.005) were more likely to report positive attitudes, whereas those from France and Poland (p<0.001) were less likely to report positive attitudes.ConclusionThe majority of European citizens hold positive attitudes towards people who are homeless, however there remain significant differences between and within countries. Although it is clear that there is strong support for increased government action and more effective solutions for Europe’s growing homelessness crisis, there also remain public opinion barriers rooted in enduring negative perceptions.

Financial overview and grant giving statistics of World Aid for Homeless Children Incorported

The number of people left homeless due to wildfires in 2023 amounted to **, a considerable decrease when compared to the figures of 2022 and 2021, when ***** and ***** people lost their homes due to such disasters.

Additional file 5. A condensed version of the data repository from all 77 documents.

Transitional Housing Services Market size was valued at USD 100 Billion in 2023 and is projected to reach USD 342.6 Billion by 2031, growing at a CAGR of 15.2% during the forecast period 2024-2031.

Global Transitional Housing Services Market Drivers

The market drivers for the Transitional Housing Services Market can be influenced by various factors. These may include:

Increasing Homelessness Rates: The rising rates of homelessness globally are a significant market driver for transitional housing services. Factors such as economic instability, lack of affordable housing, and social issues contribute to this increasing trend. Many cities report surges in homelessness, prompting governments and NGOs to seek robust solutions. Transitional housing serves as an intermediary step, offering individuals and families temporary support while they work towards permanent housing solutions.

The Homeless Survey (EPSH) reports on the situation of homeless people aged 18 and over who live in the municipalities of the Basque Country and who go to centres that offer accommodation or catering services; this group, the object of social intervention policies, is the center of the statistical information of this operation.

In 2024/25, 13,231 people who were seen to be sleeping rough in London compared with 11,993 in the previous reporting year, and the most reported during this time period. The number of people reported to be sleeping rough has steadily increased throughout this time period, with the dip in 2020/21, and 2022/23, likely related to the COVID-19 pandemic. Demographics of London's homeless As of the most recent reporting year, over 2,000 of London's rough sleepers were in the borough of Westminster, the most of any London borough. In terms of gender, the majority of rough sleepers are male, with more than 10,000 men seen to be sleeping rough, compared with 2,149 women, and 18 non-binary people. The most common age group was among those aged between 36 and 45 years old, at more than 3,900, compared with 1,411 25 and under, 3,580 aged between 26 and 34, 2,860 aged 45 and 55, and around 1,578 over 55s. Homelessness in the U.S. Homelessness is also an important social issue in several other countries. In the United States, for example, there were estimated to be approximately 653,104 people experiencing homelessness in 2023. This was a noticeable increase on the previous year, and the highest number between 2007 and 2023. When looking at U.S. states, New York had the highest homelessness rate, at 52 individuals per 10,000 population, followed by Vermont at 51.

Seroprevalence of Toxoplasma gondii has been extensively studied in a variety of different human populations. However, no study has focused on homeless populations. Accordingly, the present study aimed to assess the seroprevalence of anti-T. gondii antibodies and the risk factors associated in homeless persons from homeless shelter of São Paulo city, southeastern Brazil. In addition, anti-HIV antibodies and associated risk of T. gondii and HIV coinfection have been evaluated. Anti-T. gondii antibodies were detected by indirect fluorescent antibody test. In addition, anti-HIV levels were tested by chemiluminescence enzyme immunoassay, with positive samples confirmed by rapid immunoblot assay. Overall, IgG anti-T. gondii seropositivity was found in 43/120 (35.8%) homeless persons, with endpoint titers varying from 16 to 1,024. The only two pregnant women tested were negative for IgM by chemiluminescence enzyme immunoassay, with normal parturition and clinically healthy newborns in both cases. There were no statistical differences in the risk factors for anti-T. gondii serology (p > 0.05). Anti-HIV seropositivity was found in 2/120 (1.7%) homeless persons, confirmed as HIV-1. One HIV seropositive individual was also sero-reactive to IgG anti-T. gondii, and both were negative to IgM anti-T. gondii. This is the first study that reports the serosurvey of T. gondii in homeless persons worldwide. Despite the limited sample size available in the present study, our findings have shown that the prevalence of anti-T. gondii antibodies in homeless persons herein was lower than the general population, probably due to homeless diet habit of eating mainly processed food intake. No statistical differences were found regarding risk factors for anti-T. gondii exposure in homeless persons. Future studies should be conducted to fully establish risk factors for anti-T. gondii exposure in homeless persons.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: Yes - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Dwelling is any inhabited physical place, constructed or adapted for housing people. - Households: Household is a group of people, related or otherwise, who occupy the dwelling. - Group quarters: Collective houshold is a group of people who share the dwelling in a non-familial system, for reasons of work, health, discipline, religion, punishment, etc.

Universe

All the population in the national territory at the moment the census is carried out. Homeless, passengers in transit (international flights), personnel on duty in hospitals, factories, institutions, and other places, employees of the National Institute of Statistics, embassies and consulates

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics, Ministry of Planning and Coordination, Republic of Bolivia

SAMPLE SIZE (person records): 642368.

SAMPLE DESIGN: Systematic sample of every tenth dwelling with a random start; drawn by IPUMS Homeless, passengers in transit (international flights), personnel on duty in hospitals, factories, institutions, and other places, employees of the National Institute of Statistics, embassies and consulates

Mode of data collection

Face-to-face [f2f]

Research instrument

A single booklet that consists of sections on geographic location, dwelling, and population (individual)

Journeys Home: A Longitudinal Study of Factors Affecting Housing Stability was a national survey of Australians who were either homeless or at high risk of becoming homeless. Data collection commenced in September 2011 with a further five waves of data collected approximately six months apart. Journeys Home was funded by the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), and run by the Melbourne Institute of Applied Economic and Social Research at The University of Melbourne. Roy Morgan Research (RMR) was sub-contracted to undertake the fieldwork. It was designed as a tool for enabling research that would improve understanding of the pathways into and out of homelessness in Australia and the consequences of homelessness for long-term outcomes. Three different data releases are available, depending on your research requirements and location. These releases are General, Restricted, and International. In the General and International releases some variables (such as location, industry and debt) are confidentialised. The International release also limits some income related variables.

BackgroundHomelessness represents a widespread social issue globally, yet the risk of neurodegenerative diseases (NDDs) associated with people experiencing homelessness (PEH) has not received sufficient attention. Therefore, this study aimed to explore the risk of NDDs among PEH and its variation across countries and regions through meta-analysis and systematic review.MethodsSearching from databases such as PubMed and Web of Science, relevant research articles on PEH and NDDs were identified. After multiple screening, eight articles were selected for meta-analysis. Statistical methods and models were used to evaluate the association between PEH and NDDs, stratified by disease type and country.ResultsWe found that PEH had a 51% higher risk of NDDs (OR = 1.51 (95% CI: 1.21, 1.89)) compared with those with stable housing. Specifically, PEH had a significantly higher risk of developing multiple sclerosis (OR = 4.64 (95% CI: 1.96, 10.98)). Alzheimer’s disease and related dementias (ADRD) (OR = 1.93 (95% CI: 1.34, 2.77)), dementia (OR = 1.69 (95% CI: 1.26, 2.27)), and cognitive impairment (OR = 1.07 (95% CI: 0.98, 1.16)) were all at higher risk. Furthermore, country and regional differences were observed, with countries such as Iran (OR = 4.64 (95% CI: 1.96, 10.98)), the Netherlands (OR = 2.14 (95% CI: 1.23, 3.73)), the United States (OR = 1.66 (95% CI: 1.25, 2.22)), and Canada (OR = 1.06 (95% CI: 1.01, 1.10)) showing a higher risk of NDDs among the PEH.ConclusionsThe study emphasizes the significant NDD risks among PEH, providing novel perspectives on this issue and shedding light on national disparities influenced by variations in healthcare systems and social environments. This will be beneficial for academia and government to prioritize the health of PEH with NDDs, aiming to mitigate disease incidence and economic burdens while preserving social stability and upholding basic human rights.

Global Temporary Shelters market size is expected to reach $52.74 billion by 2029 at 5.3%, government funds drive growth in temporary housing market

This round of Eurobarometer surveys diverged from the Standard Eurobarometer measures and queried respondents on the following major areas of focus: (1) poverty and social exclusion, (2) mobile phone use, (3) economic crisis, and (4) international trade. For the first major area of focus, poverty and social exclusion, the survey queried respondents about their own definition of poverty, the extent of poverty and homelessness in their area, and whether or not respondents believed poverty had increased in their area and elsewhere. Respondents were queried about what necessities people must be able to afford to meet a minimal acceptable living standard, who is most at risk for poverty, as well as the social, political, and personal causes of poverty and homelessness. Respondents were also asked about how poverty impacts peoples' chances of completing certain activities, such as getting a good education or finding a job. Respondents were then asked whether or not they trust the European Union (EU), their governments, charities, other citizens, and miscellaneous institutions to effectively respond to poverty. Respondents were also asked to whom they assign primary responsibility for reducing or preventing poverty, what policies their governments should focus on in the future in the effort to help people out of poverty, and whether or not respondents approved of their government's existing anti-poverty measures. Respondents were also queried about their perception of social tensions between groups, and about what they have done personally to help poor people. Additionally, respondents were queried about their own living conditions, satisfaction with life, ability to keep a job, efforts to fight poverty, finances, and their own risk of falling into poverty. For the second major area of focus, mobile phone use, the survey asked respondents about whether or not they owned a mobile phone, their mobile phone use in other EU countries, and the cost of cellular phone service in those countries. For the third major area of focus, economic crisis, the survey questioned respondents about the degree to which the crisis personally affected them, how the crisis affected the EU and its policy efforts, who should bear responsibility for the crisis, who should bear the burden of response to the crisis, and how the European Parliament and other bodies should respond to the crisis. For the fourth major area of focus, international trade, the survey queried respondents on whether they pay attention to the country of origin for products they purchase, how trade impacts respondents and their countries, what European Union trade policy should be going into the future, and the European Union's international economic standing. Demographic and other background information collected includes age, gender, nationality, marital status and parental relations, left-right political self-placement, occupation, age when stopped full-time education, household composition, ownership of a fixed or a mobile telephone and other durable goods, difficulties in paying bills, level in society, and Internet use. In addition, country-specific data includes type and size of locality, region of residence, and language of interview (select countries).

Abstract

The Household Living Conditions Survey has been carried out annually since 1999 by the State Statistics Service of Ukraine (formerly the State Statistics Committee of Ukraine). The survey is based on generally accepted international standards and depicts social and demographic situation in Ukraine.

From 2002, items of consumer money and aggregate expenditures have been developed in line with the International Classification of Individual Consumption of Goods and Services (COICOP-HBS), recommended by Eurostat.

The State Statistics Service of Ukraine has been implementing a new system of household sample survey organization and delivery from 2004. A unified interviewer network was established to run simultaneously three household surveys: Household Living Conditions Survey, households' economic activity survey and the survey of household farming in rural areas. A new national territorial probability sampling was introduced to deliver the three sampling surveys in 2004-2008.

Geographic coverage

National, except some settlements within the territories suffered from the Chernobyl disaster.

Analysis unit

• Households,
• Individuals.

A household is a totality of persons who jointly live in the same residential facilities of part of those, satisfy all their essential needs, jointly keep the house, pool and spend all their money or portion of it. These persons may be relatives by blood, relatives by law or both, or have no kinship relations. A household may consist of one person (Law of Ukraine "On Ukraine National Census of Population," Article 1). As only 0.50% households have members with no kinship relations (0.65% total households if bachelors are excluded), the contemporary concepts "household" and "family" are very close.

Universe

Whole country, all private households. The survey does not cover collective households, foreigners temporarily living in Ukraine as well as the homeless.

Kind of data

Sample survey data [ssd]

Sampling procedure

12,977 households representing all regions of Ukraine (including 8,975 in urban areas and 4,002 in rural areas) are selected for this survey. Grossing up sample survey results to all households of Ukraine is done by the statistic weighting method.

Building a territorial sample, researchers excluded settlements located in the excluded zone (Zone 1) and unconditional (forced) resettlement zone (Zone 2) within the territories suffered from the Chernobyl disaster.

Computing the number of population subject to surveying, from the number of resident population researchers excluded institutional population - army conscripts, persons in places of confinement, residents of boarding schools and nursing homes, - and marginal population (homeless, etc).

The parent population was stratified so that the sample could adequately represent basic specifics of the administrative and territorial division and ensure more homogeneous household populations. To achieve this objective, the parent population was divided into strata against the regions of Ukraine. In each stratum three smaller substrata were formed: urban settlements (city councils) having 100,000 or more inhabitants (big cities), urban settlements (city councils) having less than 100,000 inhabitants (small towns) and all districts (except city districts), i.e. administrative districts in rural areas. Sample size was distributed among strata and substrata in proportion to their non-institutional resident population.

Detailed information about selecting primary territorial units of sampling (PTUS) and households is available in the document "Household Living Conditions Survey Methodological Comments" (p. 4-7).

Mode of data collection

Face-to-face [f2f]

Research instrument

The HLCS uses the following survey tools:

1) Main interviews

Main interview questionnaires collect general data on households, such as household composition, housing facilities, availability and use of land plots, cattle and poultry, characteristics of household members: anthropometric data, education, employment status. Interviewing of households takes place at the survey commencement stage. In addition, while interviewing, the interviewer completes a household composition check card to trace any changes during the entire survey period.

2) Observation of household expenditures and incomes

For the observation, two tools are used: - Weekly diary of current expenditures. It is completed directly by a household twice a quarter. In the diary respondents (households) record all daily expenditures in details (e.g. for purchased foodstuffs - product description, its weight and value, and place of purchase). In addition, a household puts into the diary information on consumption of products produced in private subsidiary farming or received as a gift.

Households are evenly distributed among rotation groups, who complete diaries in different week days of every quarter. Assuming that the two weeks data are intrinsic for the entire quarter, the single time period of data processing (quarter) is formed by means of multiplying diary data by ratio 6.5 (number of weeks in a quarter divided on the number of weeks when diary records were made). Inclusion of foodstuffs for long-time consumption is done based on quarterly interview data.

• Quarterly questionnaire. It is used to interview households in the first month following the reporting quarter. Researchers collect data on large and irregular expenditures, in particular those relating to the purchase of foodstuffs for long-time consumption (e.g. sacks, etc.), and also data on household incomes. Since recalling all incomes and expenditures made in a quarter is uneasy, households make records during a quarter in a special quarterly expenditures log.

The major areas for quarterly observation are the following: - structure of consumer financial expenditures for goods and services; - structure of other expenditures (material aid to other households, expenditures for private subsidiary farming, purchase of real estate, construction and major repair of housing facilities and outbuildings, accumulating savings, etc); - importance of private subsidiary farming for household welfare level (receipt and use of products from private subsidiary farming for own consumption, financial income from sales of such products, etc.); - structure of income and other financial sources of a household. We separately study the income of every individual household member (remuneration of labor, pension, scholarship, welfare, etc.) and the income in form payments to a household as a whole (subsidies for children, aid of relatives and other persons, income from - sales of real estate and property, housing and utility subsidies, use of savings, etc.).

3) Single-time topical interviews

Single-time topical interviews questionnaires are used quarterly and cover the following topics: - household expenditures for construction and repair of housing facilities and outbuilding - availability of durable goods in a household - assessment by households members of own health and accessibility of selected medical services - self-assessment by a household of adequacy of its income - household's access to Internet

In recent years, demand for temporary housing has been one of the most significant issues for the industry. Because of rising rental costs, many have faced the risk of homelessness, with many experiencing financial hardship and persistent economic disadvantage. This has been exacerbated by the economic impacts of recent global events, including the COVID-19 pandemic, with the economic consequences resulting in more individuals and families under financial stress. The shortage of affordable housing and rising rent costs have placed significant pressure on homelessness, which has ramped up demand for crisis accommodation service providers. Industry revenue is expected to increase at an annualised 1.6% over the five years through 2025-26 to $1.8 billion. Greater housing stress has boosted demand for crisis housing in recent years. Family breakdowns are one of the main reasons for housing transiency and are a crucial driver of demand for crisis and refuge accommodation. Inflationary and cost-of-living pressures will sustain industry demand, limiting declines in revenue, which is expected to inch down 0.8% in 2025-26, mainly because of stabilisation in the housing market and gradual economic recovery, easing the need for immediate crisis intervention. The Budget 2025 includes considerable additional money for disability assistance and social housing, including $60 million per year for disability residential care and $128 million over four years for new social housing in Auckland, supporting industry profitability. Several factors, including a shortage of affordable housing, economic hardship, disabilities, mental health conditions and addictions to alcohol, drugs and gambling, influence homelessness. Though these issues suggest a continued demand for crisis and care accommodation services, industry growth is projected to be tempered by cost pressures in 2028-29. Also, while ongoing housing affordability issues and an aging population are anticipated to drive industry growth, funding constraints will limit the expansion rate. Overall, industry revenue is forecast to slightly plunge at an annualised 1.9% over the five years through 2030-31 to $1.6 billion.

Poverty and social exclusion, social services, climate change, and the national economic situation and statistics.

Topics: 1. Poverty and social exclusion: own life satisfaction (scale); satisfaction with family life, health, job, and satisfaction with standard of living (scale); personal definition of poverty; incidence of poverty in the own country; estimated proportion of the poor in the total population; poor persons in the own residential area; estimated increase of poverty: in the residential area, in the own country, in the EU, and in the world; reasons for poverty in general; social and individual reasons for poverty; population group with the highest risk of poverty; things that are necessary to being able to afford to have a minimum acceptable standard of living (heating facility, adequate housing, a place to live with enough space and privacy, diversified meals, repairing or replacing a refrigerator or a washing machine, annual family holidays, medical care, dental care, access to banking services as well as to public transport, access to modern means of communication, to leisure and cultural activities, electricity, and running water); perceived deprivation through poverty in the own country regarding: access to decent housing, education, medical care, regular meals, bank services, modern means of communication, finding a job, starting up a business of one’s own, maintaining a network of friends and acquaintances; assessment of the financial situation of future generations and current generations compared to parent and grandparent generations; attitude towards poverty: necessity for the government to take action, too large income differences, national government should ensure the fair redistribution of wealth, higher taxes for the rich, economic growth reduces poverty automatically, poverty will always exist, income inequality is necessary for economic development; perceived tensions between population groups: rich and poor, management and workers, young and old, ethnic groups; general trust in people, in the national parliament, and the national government (scale); trust in institutions regarding poverty reduction: EU, national government, local authorities, NGOs, religious institutions, private companies, citizens; reasons for poverty in the own country: globalisation, low economic growth, pursuit of profit, global financial system, politics, immigration, inadequate national social protection system; primarily responsible body for poverty reduction; importance of the EU in the fight against poverty; prioritized policies of the national government to combat poverty; assessment of the effectiveness of public policies to reduce poverty; opinion on the amount of financial support for the poor; preference for governmental or private provision of jobs; attitude towards tuition fees; increase of taxes to support social spending; individual or governmental responsibility (welfare state) to ensure provision; attitude towards a minimum wage; optimism about the future; perceived own social exclusion; perceived difficulties to access to financial services: bank account, bank card, credit card, consumer loans, and mortgage; personal risk of over-indebtedness; attitude towards loans: interest free loans for the poor, stronger verification of borrowers by the credit institutions, easier access to start-up loans for the unemployed, free financial advice for the poor, possibility to open a basic bank account for everyone; affordable housing in the residential area; extent of homelessness in the residential area, and recent change; adequacy of the expenditures for the homeless by the national government, and the local authorities; assumed reasons for homelessness: unemployment, no affordable housing, destruction of the living space by a natural disaster, debt, illness, drug or alcohol addiction, family breakdown, loss of a close relative, mental health problems, lack of access to social services and support facilities, lack of identity papers, free choice of this life; probability to become homeless oneself; own support of homeless people: monetary donations to charities, volunteer work in a charity, help find access in emergency shelters and with job search, direct donations of clothes to homeless people, buying newspapers sold by homeless people, food donations; sufficient household income, or difficulties to make ends meet; ability to afford the heating costs, a week’s holiday once a year, and a meal with meat ever...