Review of Sta,s,cal Issues in Measuring and Profiling Homelessness in Europe

Transcription

1 Review of Sta,s,cal Issues in Measuring and Profiling Homelessness in Europe InGRID Summer School Reaching out to hard-to-survey groups among the poor Leuven, 29 May 2016 Prof. Dr. Volker Busch-Geertsema, GISS Bremen, Germany and Coordinator of European Observatory on Homelessness The European Observatory on Homelessness and European studies on measurement of homelessness ETHOS, the European Typology of Homelessness and Housing Exclusion and ETHOS Light Why measuring homelessness? Approaches to defining and measuring homelessness in different European member states Profiles of homeless people Covering homelessness in the PopulaNon Census A retrospecnve module in EU SILC? Data collecnon in pracnce: Examples from Denmark, Germany and Spain Conclusions 1

2 Organised by FEANTSA (the European FederaNon of NaNonal OrganisaNons working with the Homeless) and financed by the European Commission Core group of six researchers from DK, DE, IE, PT, HU and the UK coordinated by the speaker Main tasks each year: } European Research Conference } European Journal of Homelessness (2 issues) } ComparaNve research (2014: Extent and Profile of Homelessness in European Member States. A StaNsNcal Update) All publicanons available for free online: Brousse (2004) The ProducNon of Data on Homelessness and Housing DeprivaNon in the European Union. Survey and Proposal (Brussels. European Commission) h`p://ec.europa.eu/eurostat/documents/ / /ks-cc en-toc.pdf/ 17dd4711-8f84-4fe0-8b83-415ac5197bcc?version=1.0 Edgar, Harrison, Watson and Busch-Geertsema (2007) Measurement of Homelessness at European Union Level (Dundee/Brussels: European Commission) ec.europa.eu/employment_social/social.../study_homelessness_en.pdf : MPHASIS (Mutual Progress on Homelessness Through Advancing and Strengthening InformaNon Systems) h`p:// Edgar (2009) European Review of StaNsNcs on Homelessness (Brussels: European Observatory on Homelessness/FEANTSA) h`p:// Busch-Geertsema, Benjaminsen, Filipovič Hrast and Pleace (2014) Extent and Profile of Homelessness in European Member States. A StaNsNcal Update (Brussels: European Observatory on Homelessness/FEANTSA) h`p:// 2

3 5 Developed by Bill Edgar and Henk Mert (2005) in close cooperanon with FEANTSA s data collecnon working group DisNnguishes four broad categories: } Rooflessness } Houselessness } Inadequate Housing } Insecure Housing AccommodaNon based but based conceptually on the exclusion from three different spheres which consntute a home: } Physical domain: decent dwelling (or space) adequate to meet the needs of the household } Social domain: being able to maintain privacy and enjoy social relanons } Legal domain: having exclusive possession, security of occupanon and legal Ntle 3

4 The Domains of Homelessness and Housing Exclusion Exclusion from the physical domain 5 Exclusion from the legal domain Exclusion from the social domain Seven Theore,cal Categories of Homelessness Conceptual Category Homelessness Opera,onal Category Physical Domain Legal Domain Social Domain 1 Rooflessness No dwelling (roof) No legal Ntle to a space No private and safe for exclusive possession personal space for social relanons 2 Houselessness Has a place to live, fit for habitanon 3 Insecure and Inadequate housing Has a place to live (not secure and unfit for habitanon) No legal Ntle to a space No private and safe for exclusive possession personal space for social relanons No security of tenure Has space for social relanons Housing exclusion 4 Inadequate housing and social isola,on within a legally occupied dwelling 5 Inadequate housing (secure tenure) Inadequate dwelling (unfit for habitanon) Inadequate dwelling (dwelling unfit for habitanon) Has legal Ntle and/or security of tenure Has legal Ntle and/or security of tenure No private and safe personal space for social relanons Has space for social relanons 6 Insecure housing (adequate housing) Has a place to live No security of tenure Has space for social relanons 7 Social isola,on within Has a place to live Has legal Ntle and/or No private and safe a secure and adequate context security of tenure personal space for social relanons Source: Edgar et al

5 ETHOS European Typology on Homelessness and Housing Exclusion Conceptual Category Opera,onal Category Living Situa,on ROOFLESS 1 People living rough 1.1 Public space or external space 2 People staying in a night shelter 2.1 Night shelter HOUSELESS 3 People in accommodanon for the homeless 3.1 Homeless hostel 3.2 Temporary accommodanon 3.3 TransiNonal supported accommodanon 4 People in women s Shelter 4.1 Women s shelter accommodanon 5 People in accommodanon for 5.1 Temporary accommodanon / recepnon centres INSECURE INADEQUATE Source: Edgar (2009), p. 73 immigrants 6 People due to be released from insntunons 7 People receiving longer-term support (due to homelessness) 8 People living in insecure accommodanon 5.2 Migrant workers accommodanon 6.1 Penal insntunons 6.2 Medical insntunons 6.3 Children s insntunons / homes ResidenNal care for older homeless people Supported accommodanon for formerly homeless persons Temporarily with family/friends No legal (sub)tenancy Illegal occupanon of land 9 People living under threat of evicnon 9.1 Legal orders enforced (rented) 9.2 Re-possession orders (owned) 10 People living under threat of violence 10.1 Police recorded incidents 11 People living in temporary/non-conven Nonal structures 11.2 Mobile homes Non-convenNonal building Temporary structure People living in unfit housing 12.1 Occupied dwelling unfit for habitanon 13 People living in extreme overcrowding 13.1 Highest nanonal norm of overcrowding Developed in context of study on Measurement of Homelessness at European Union Level (2007) funded by European Commission Explicit aim to be companble with RecommendaNons for Census 2010/11 on PopulaNon and Housing Shorter version with explicit focus on what is feasible to be measured Insecure and inadequate housing is leo out, but some categories are included as they are seen as consntunng homelessness in a number of EU countries (people about to leave insntunons with nowhere to go, non-convennonal housing and sharing temporarily with friends or relanves because of lack of housing) 5

6 ETHOS Light Harmonised Defini,on of Homelessness for Measurement of Homelessness at European Union Level Study Opera,onal Category Living Situa,on Defini,on 1 People living rough 1 Public space / external space Living in the streets or public spaces without a shelter that can be defined as living quarters 2 People in emergency accommodanon 3 People living in accommodanon for the homeless 4 People living in insntunons 7 5 People living in non-convennonal dwellings due to lack of housing 6 Homeless people living temporarily in convennonal housing with family and friends (due to lack of housing) 2 Overnight Shelters People with no place of usual residence who move frequently between various types of accommodanon 3 Homeless Hostels 4 Temporary AccommodaNon 5 TransiNonal Supported AccommodaNon 6 Women s shelter or refuge accommodanon Health care insntunons 8 Penal insntunons 9 Mobile homes 10 Non-convenNonal building 11 Temporary structure 12 ConvenNonal housing, but not the person s usual place of residence * The period of one year was chosen to allow consistency with UNECE/EUROSTAT Census recommendanons Source: Edgar et al., 2007, p. 66 Where the period of stay is less than one year * Stay longer than needed due to lack of housing No housing available prior to release Where the accommodanon is used due to a lack of housing and is not the person s usual place of residence Where the accommodanon is used due to a lack of housing and is not the person s usual place of residence ETHOS Light Relevance in Different Member States (2014 Study covering CZ, DK, FL, FR, DE, HU, IE, IT, NL, PL, PT, SI, ES, SE, and UK) Opera,onal Category Living Situa,on Use and Data at Na,onal Level 1 People living rough 1 Public space / external space Accepted as homeless in all countries, but counted separately only in few 2 People in emergency accommodanon 2 Overnight Shelters Accepted everywhere and data available (somenmes mixed with OC 3) 3 People living in accommodanon for the homeless 4 People living in insntunons 7 5 People living in non-convennonal dwellings due to lack of housing 6 Homeless people living temporarily in convennonal housing with family and friends (due to lack of housing) 3 Homeless Hostels 4 Temporary AccommodaNon 5 TransiNonal Supported AccommodaNon 6 Women s shelter or refuge accommodanon Health care insntunons 8 Penal insntunons 9 Mobile homes 10 Non-convenNonal building 11 Temporary structure 12 ConvenNonal housing, but not the person s usual place of residence 3), 4) and 5) defined as homeless in most countries, data available in most countries (but note service paradox ) 6) is most controversial, excluded in 6 of 15 countries Difficult to measure and ooen not included, data available in 4 of 15 countries Included in defininon of homelessness in most countries (in some even as part of OC 1 ). Data available only for 4 of 15 countries Defined as homeless in a number of countries while excluded from defininon in others (almost 50/50), data available only for 5 of 15 countries Source: Busch-Geertsema et al. (2014), pp. 22 ff. and pp. 50 ff 6

7 QuesNons about the Observatory? QuesNons about the defininons? What is the specific use of EU-definiNons like ETHOS or ETHOS light? Which groups are accepted in your own countries, which are more controversial? Which ones may be more easy to count and for which groups might it be parncularly difficult to count them? Can we define someone who is about to loose his/her (insntunonal) accommodanon as homeless before he/ she is released? Indicator of Social Exclusion Measure extent of urgent housing need of homeless people Measure trends over Nme Provide data on which groups are most effected Measure specific needs/problems Develop policies to alleviate and reduce/prevent homelessness for specific subgroups: informed policy making Measure effecnveness (and perhaps also unintended effects) of intervennons Cover complete populanon in populanon censuses (also those who are difficult to reach) What else? 7

8 Approaches to Defining and Measuring Homelessness in European Member States Prof. Dr. Volker Busch-Geertsema, GISS Bremen, Germany and Coordinator of the European Observatory on Homelessness (FEANTSA) 15 Use of administranve data (mostly service users stansncs) for nanonal numbers and/or profile of service users: examples } Annual shelter stansncs in Denmark and Slovenia } PATH (Pathway AccommodaNon and Support System) in Ireland records all service user data from statutory and non-statutory services for homeless people (rolled out nanonally in 2014) } Central online data base of all registered social services in Hungary (homeless clients may be analysed separately) } Central data collecnons of service users by different social services in Spain and Portugal (Assistencia Médica InternaNonal, Caritas etc.; homeless clients may be analysed separately) } Dutch NaNonal StaNsNcal Office uses a combinanon of data from nanonal populanon register (covering data on night shelter users), a data base on homeless benefit claimants and data from the nanonal alcohol and drug informanon services for nanonal esnmates of rooflessness } A core set of data of NGO providers of services for homeless people is analysed each year by the nanonal umbrella organisanon in Germany to produce stansncs on the profile of service users 8

9 Recurrent surveys (examples); mostly point in Nme (or week) - snapshots } CollecNon of individual data: } In Denmark and Sweden all potennal contact services (like homeless shelters, addicnon treatment centers, psychiatric facilines, municipal social centers, social drop-in cafes etc.) are asked to document informanon on homeless clients in individualized quesnonnaires within a certain week. Double counnng is excluded by unique idennfiers. Surveys are repeated bi-annually in Denmark and every six years in Sweden. } In France and Spain a sample of randomly selected users of free food services and emergency accommodanon is asked to complete a large quesnonnaire and results are then projected to all cines with more than 20,000 inhabitants. In both countries such surveys were conducted twice, last in 2012 and before in 2005 (Spain) / 2001 (France). } In Budapest (Hungary) every 3 rd of February each year a homelessness survey has been conducted since It is carried out by services for homeless people and also covers rough sleepers; parncipanon is voluntary for services and clients and a self-complenon quesnonnaire is used. Recurrent surveys (examples); mostly point in Nme (or week) - snapshots } CollecNon of aggregated data: } In Finland all municipalines have been asked to provide an elaborated esnmate of the number of homeless people every year (since 1987) as part of a general housing market survey. } In the German regional state North Rhine-Westphalia (17.8 million inhabitants) a survey is conducted 30 June each year among all municipalines and NGO services for homeless people. } In Poland a number of homelessness surveys were conducted by nanonal government between 2009 and 2013, but approaches differed each Nme Local surveys and one-off surveys, including street counts } Many regional and local surveys with different methodologies in Spain, same but less frequent in Portugal, several street counts (Barcelona, Brussels, Hamburg..) } Important first nanonal survey in Italy covering canteens and emergency shelters Very few data on prevalence / past experience of homelessness } Only recent source: Scovsh household survey and UK Poverty and Social Exclusion Survey 2012 } Some data from Survey of English Housing in 1990s and Census data in France from 2006 (repornng life-nme prevalence of 5 %) 9

10 Use of administranve data (mostly service users stansncs) for nanonal numbers and/or profile of service users: } Problem of full coverage; ooen only covers a secnon using parncular services. May snll be useful to learn more about the profile of these users, but ooen doesn t help with enumeranon of complete number of homelessness. Specific case: NL, using capture-recapture to determine number of roofless people Recurrent surveys: } Best way of enumeranng homelessness and gevng trend data } Full coverage vs sampling } Involves quite a lot of repornng agencies } Can be expensive, but helps evaluanng policy measures, showing parncularly vulnerable goups etc etc. } Few countries with annual surveys (Finland since 1987; North Rhine- Westphalia, Germany, since 1960s), Denmark bi-annual, in some other countries interval rather large (France/Spain) Recurrent surveys; collecnon of individual data vs aggregated data } Individual data much be`er basis for research and in-depth analysis, but also involves much higher operanng expenses Local surveys } May be very helpful at local level; ooen much easier to organise than regional and nanonal data; BUT: common mistake to extrapolate nanonal trend from local survey results. Profile data versus total number of homeless people } It is usually easier to get informanon on profiles of a certain sample of homeless people than enumerate a total number, BUT sample may not be representanve (example of medical studies, ooen focussing on street homeless or shelter users). } Data of services for homeless people may tell a lot about profile of these service users, but may not help with enumeranng total size of homelessness. Annual prevalence data versus point in Nme/week prevalence } Point in Nme most used enumeranon method (prevents double counnng, may lead to overesnmanon of long-term homelessness and serious underesnmanon of number of people affected by homelessness during a longer period). } SNll common problem of combining both types of data 10

11 Trend data versus exact numeranon } Total number usually main aim } It is much easier to detect trends (given that same type of measurement is used and same groups are covered) than enumerate the total number of homeless people. Double (mulnple counnng) and undercounnng } Use of unique idennfiers (data protecnon issues) } Use of decoys in US street counts (fake homeless people wainng to be counted at Census night) Progress made in many countries towards some type of nanonal measurement, but snll large variety of approaches Data are snll not comparable across Europe (using different approaches, including different groups and periods, no nanonal data available in several countries) SNll few data on the category for which consensus is greatest (rough sleepers) Increase in all countries covered, except Finland } France increase esnmated as up to 50%, between 2001 and } Germany 21% and Denmark 16% (between 2009 and 2013) } Netherlands 17% (between 2010 and 2012) } Sweden 29% (for selected categories), UK (stat. homelessness and rough sleepers) 22 11

12 Why is it so difficult to reach an EU-wide number of the size of homelessness? Do you know important methods which haven t been mennoned? Which groups of homeless people are the most difficult to count? Which methods serve best for which purpose? Further quesnons? Profile of Homeless People Variables and trends Prof. Dr. Volker Busch-Geertsema, GISS Bremen, Germany and Coordinator of the European Observatory on Homelessness (FEANTSA) 24 12

13 Street counts / Census data etc. ooen scarce profile informanon Some registranon systems have very rich data. E.g. all sooware systems of service providers for homeless people in Germany have an agreed set of client data which allows for aggregated analysis on nanonal level once a year (basic demographics but also data on income, employment, health status etc. at Nme of entry and of leave) Sample of nanonal surveys like those in Spain and France are using long quesnonnaires with many quesnons. Problem: costly exercise, only repeated in long intervals, translanon in different languages needed etc. 25 MPHASIS Core Variables: } Age, } Sex, } NaNonality (Country of cinzenship) } Country of birth } Household structure/ living situanon } Previous accommodanon, night before entering service and current accommodanon situanon (at date of counnng) } DuraNon of (current) homelessness } Reason(s) for last period of homelessness as defined by the homeless person For recent profiles of homeless people in Europe see } Busch-Geertsema, Benjaminsen, Filipovič Hrast and Pleace (2014) Extent and Profile of Homelessness in European Member States. A StaNsNcal Update (Brussels: European Observatory on Homelessness/FEANTSA) h`p://

14 Gender varianons, men predominanng, but homelessness of women more ooen hidden Sharp increase of youth homelessness in a number of countries RelaNvely high share of older homeless people in Poland and Hungary but not in other EU countries (reflecnng differences in methods) Increasing share of migrants VariaNon in parncipanon in labour market 27 Covering Homelessness in the 2011 Census A disappoin,ng exercise Prof. Dr. Volker Busch-Geertsema, GISS Bremen, Germany and Coordinator of the European Observatory on Homelessness (FEANTSA) 28 14

15 Census authorines did not make use of recommendanons developed by EU project on Measuring Homelessness } Came too late in the process but would also have required specific effort Main focus of Census authorines: to cover the whole populanon (including homeless people) but not to document extent of homelessness Producing data on homelessness was not made obligatory in Census regulanons Considerable effort was made to cover homeless people in a number of countries (for some it was the first Nme) In other countries no a`empt was made at all In some countries homeless people were covered by special surveys, but results were then lumped together with other groups of people living in insntunons DefiniNon of The homeless with no place of usual residence : 1. Primary homelessness (or rooflessness): this category includes persons living in the streets without a shelter that would fall within the scope of living quarters. 2. Secondary homelessness: this category may include persons with no place of usual residence who move frequently between various types of accommodamons (including dwellings, shelters, insmtumons for the homeless or other living quarters). This category includes persons living in private dwellings but repormng no usual address on their census form. See United NaNons Economic Commission for Europe (ed.; 2006) Conference of European StaNsNcians RecommendaNons for the 2010 Censuses of PopulaNon and Housing, New York and Geneva: United NaNons, p

16 Source: h`ps://ec.europa.eu/censushub2 A Retrospec,ve Module in EU SILC A possible small step forward? Prof. Dr. Volker Busch-Geertsema, GISS Bremen, Germany and Coordinator of the European Observatory on Homelessness (FEANTSA) 32 16

17 IniNaNve by EUROSTAT to test a retrospecnve quesnon on homelessness as part of revised EU SILC modules EU SILC (European Union StaNsNcs on Income and Living CondiNons) one of the very few European-wide household surveys with same quesnons in all countries. Widely used for comparanve poverty analysis Does by defininon not cover currently homeless people, but covers people who have experienced homelessness in the past. PotenNal for internanonal comparisons Problems: } Small nanonal sample sizes of EU SILC, but some country examples show substannal share of populanon with experience of homelessness over Nme } Unknown probability of formerly homeless people to be included in household surveys later on } As understanding of homelessness differs across EU, accommodanon situanons have to be defined What could be the potennal of a retrospecnve quesnon on homelessness in EU SILC? What kind of informanon would it provide and what could we NOT learn from it? What would be the right prevalence period? Other problems than those already mennoned? Further quesnons? 17

18 Data Collec,on in Prac,ce Examples from Denmark, Germany and Spain Prof. Dr. Volker Busch-Geertsema, GISS Bremen, Germany and Coordinator of the European Observatory on Homelessness (FEANTSA) 35 First count in week 6, February Copying methodology from similar Swedish and Norwegian counts. Since then counts in 2009, 2011, 2013 and 2015, always in week 6. Service based count, but including a wide range of local welfare services: Homeless shelters, street outreach teams, addicnon treatment centres, psychiatric hospitals, municipal social centres, jobcentres, social drop-in cafés etc. Each unit fills out a two-page quesnonnaire for each homeless person they are in contact with or know about. The quesnonnaire can be filled out by staff only or through interview with the homeless person. Double counts are controlled through individual informanon personal numbers, birthdays, ininals or other informanon 36 18

19 InformaNon collected in the count } Homelessness situanon: sleeping rough, shelter, staying with family or friends etc. } Basic demographics: gender, age, children, nanonality } Income source } Health: mental illness, substance abuse problems, physical illness } Reasons for homelessness (e.g. evicnons, financial problems, substance abuse problems etc.) } Services/IntervenNons received e.g. addicnon treatment, psychiatric treatment, social support, assigned for housing } Personal idennfiers 37 Results: Overall trend Homelessness situa,on Week 6, 2009 Week 6, 2011 Week 6, 2013 Street/rough sleeper Emergency night shelter Homeless shelter Hotel Family/friends Short term transinonal InsNtuNonal release, prison InsNtuNonal release, hospital Other Total

20 Profile of support needs % in 2013 count, all age groups Men Women Mental illness Substance abuse problems Alcohol Hash Hard drugs Medicine Mental illness and/or substance abuse Mental illness and substance abuse (dual diagnose) Strengths Broad defininon not only rough sleepers and shelter users but also hidden homelessness couch surfers are included More comprehensive overview of the extent of homelessness than a onenight street count or a count solely based on homelessness services Detailed trend and profile data over Nme changes in composinon, reasons etc. Monitor results of policies and input for policy development Weaknesses No extensive, systemanc street count Always a risk of people not being counted and a risk of double counts despite control procedures The control for double counts is very Nme consuming and must be done partly manually Extensive workload for local social services in the count week 40 20

21 Most populous of 16 German regional states: 17.8 million inhabitants Long tradinon of counnng those homeless persons accommodated by municipalines under the police laws (duty to prevent rooflessness) Snapshot: 30 June each year Refined method from 2009 onwards, now also including all homeless persons accommodated by NGO services for homeless people (same date) and in contact with NGO advice services in month of June each year } Includes rough sleepers and couch surfers as far as these are in contact with services. Online survey conducted by regional stansncal insntute: Asks all municipalines and NGO services in the region for accumulated numbers Data covered: number of persons, household structure, age, sex, nanonality / migranon status, current accommodanon 41 h`ps:// } NRW } Gastzugang } Wohnungsnozallberichtersta`ung - Berichtersta`ung über Wohnungslose, die kommunal und ordnungsrechtlich untergebracht sind } Wohnungsnozallberichtersta`ung - Erhebung bei den freien Trägern der Wohnungslosenhilfe Results: h`p:// kurzanalysen/kurzanalyse-1_2015.pdf 42 21

22 Strengths Once implemented, relanvely easy to process (online) Trends can be analysed annually Broad defininon not only shelter users but also rough sleepers and hidden homelessness couch surfers are included Most relevant profile data available Monitor results of policies and input for policy development Weaknesses Basic requirements: Online-Computers everywhere. Can only be applied in regions with extensive service provision ParNcipaNon of services voluntary but most parncipate Rough sleepers and couch servers etc. are only included if they use advice services Only aggregated data available, opnons for analysis restricted Date (30 June each year) quesnonable with increase of registered homelessness in winter shelters 43 Madrid: Recuento Nocturno de personas sin hogar en la ciudad de Madrid Recurrent count in December each (second) year: 2006, 2008, 2009, 2010, 2012, Intensive use of volunteers (400 in 2014), street count + shelters h`p:// IgualdadDeOportunidades/SamurSocial/NuevoSamurSocial/ficheros/ DATOS%20VII%20RECUENTO.pdf } Pages 10 (main results, numbers), 11 (shelters), 12 (distribunon shelters/street), 13 (development street ), (distribunon in areas), 16 (sleeping or awake) 44 22

23 Barcelona, publicanon by XAPSLL (Xarxa d Atenció a les Personas Sense Llar -Red de Atención a Personas sin Hogar) Since 2008, 12 of March every year combinanon of shelter data and number of homeless people in the street known to SIS (Servicio de Inserción Social, outreach team) in the same month. Two street counts in March 2008 and November 2011 showed li`le devianon (12.8/15.4 percent more counted); no volunteers needed for addinonal street counts Items: Sex, age, main problem, geographical distribunon; for shelters also nanonality and income source h`p:// xarxa2_diagnosi2013_en.pdf 45 Conducted by INE (InsNtuto Nacional de EstaNstas) following closely methodology implemented in France (INSEE, INED) Conducted twice (2005 and 2012) Large quesnonnaire for interviews with sample of homeless adults over 18 who are frequennng welfare restaurants or staying in shelters of municipalines with more than inhabitants Costly exercise, extensive quesnonnaire h`p:// c=estadisnca_c&cid= &menu=ulndatos&idp=

24 ETHOS provides a good conceptual framework for defining homelessness and housing exclusion and has also been acknowledged as a useful typology in other parts of the (developed) world. ETHOS Light is somewhat easier to apply for measurement purposes but snll almost nowhere in Europe the full set of living situanons is accepted as homelessness and covered in a`empts to measure the extent of homelessness. There are ongoing debates which living situanon should be included or leo out. However, it provides us with a useful grid to prevent us comparing and combining situanons which are not comparable and helps us clarifying what we are talking or wrinng about. PracNces in EU member states of measuring homelessness snll differ to a large extent, but different types of approaches used by several member states may be idennfied. Simple methodological issues as the difference between point in Nme counts or coverage of a week versus annual prevalence, households versus individual persons etc. snll need special a`ennon, as different pracnces prevail. It is easier to collect valid informanon on the profile (at least of several subgroups) and on trends of homelessness than to determine the overall extent of homelessness. A retrospecnve quesnon in EU SILC might be a small step forward to achieve more comparanve data, despite a number of methodological problems. 24

25 Thank you for your a`ennon! QuesNons? Comments? CriNcism? SuggesNons? Contact Dr. Volker Busch-Geertsema Gesellschao für innovanve Sozialforschung und Sozialplanung e.v. (GISS, AssociaNon for InnovaNve Social Research and Social Planning) Kohlhökerstraße Bremen, Germany Fon: Fax: Mail: Internet: 25