BEING AN HOMELESS IN MILAN: A DESCRIPTIVE ANALYSIS Michela Braga (University of Milan) joint with Lucia Corno (Bocconi University) Workshop "Social Minima" fRDB - Milan, January 12 th, 2009

MOTIVATION Homelessness is a public policy issue in many developed countries …but the lack of reliable data on this population limits effective strategies to prevent and reduce the phenomenon and creates no incentive for academic research in economics

OUTLINE  Paper contribution  The existing surveys  Methodology Count Interviews  Results Count: number/localization Interviews: refuse - answer rate Descriptive statistics (disaggregated by street/shelter/slums) Demographics Labor/income Social links Help  How to ameliorete data collection  Current research

 Quantitative and qualitative data collection: First Census of homeless in Milan => count and localization Extensive data collection on different aspects => questionnaire  Are homeless people different from the general population? If yes, in which dimensions? PAPER CONTRIBUTION

WHY IS IT IMPORTANT?  Information on the number and characteristics of the homeless is necessary for program planning Quantitative and qualitative data are necessary to quantify economic resources to reduce homelessness and to prevent it with policies  Baseline survey for further studies => program evaluation  Cross countries analysis: gap between Italian and international research: In US, systematic data collection year by year starting from the early 80’s In Europe some attempts have been made …but in a non systematic way No data available in Italy

THE EXISTING SURVEYS  Only few countries provide official statistics on homelessness S – NIGHT APPROACH using PUBLIC PLACES METHOD:  U.S. Census Bureau large-scale effort in 1990 to count homeless people at shelters and selected street sites  In Australia homeless census started in 1996 and takes place every 5 years HOMELESS MANAGEMENT INFORMATION SYSTEM: U.S. Department of Housing and Urban Development (HUD) requires counts every two years on a national sample of 80 communities in different geographical areas during a given period using a service based enumeration CAPTURE RECAPTURE APPROACH: for street homeless who tend to not use shelters

METHODOLOGY  Point in time survey using the S - Night approach (Shelter and Street Night) full census of the whole city Costs: monetary, human, time Benefits: accuracy, limit under estimates  Extensive and representative survey in the following days Trade off between accuracy of the data collection and loss of observations COUNT INTERVIEW Procedure in two steps integrating different methodologies

THE COUNT January 14 th, 2008

POPULATION DEFINITION All individuals that in the given night reside in  places not meant for human habitation, such as cars, trucks, parks, doorways, sidewalks, stations, airports (unsheltered homeless);  emergency shelters (sheltered homeless);  people living in disused areas/shacks/slums.

THE COUNT  City divided in 66 sufficiently small census blocks Reduce risk of double count (3/4 hours for each block) Simultaneous full census of the whole city After 10 p.m.  Pre established itinerary to be followed with a complete list of all streets in the census block  Localization of 5 headquarters to distribute materials to volunteers (torches, hot tea, etc)  Informing the homeless for the next day interviews with a flyer  Collect information on the exact localization Necessary for the survey  Detection of some observable characteristics (sex, average age, place )  Collect the lists of names in each shelter  Detection of disused areas and cross-check of their dimensions with previous control

Example: Area N. 9

THE HOMELESS POPULATION  408 individuals  1152 individuals  2300 adults STREET SHELTER DISUSED AREAS Total adult population: 3863

Street homeless

Sheltered homeless

THE SURVEY January 15 th, 16 th, 19 th 2008

SAMPLING  Sampling procedure: Street: all population Shelter: Random sample proportional to the shelter dimension. Over – sampling for the small ones and under – sampling for the big ones Disused areas: Stratified random sample according  City administrative division (9 areas)  Official area classification (authorized, non authorized, shacks, abandoned buildings, disused areas, ride men);  Dimension: small (n≤30), medium (30<n<100) and big (n≥ 100)

THE SURVEY  Few interviewers (75) to minimize answer bias and to exploit the learning by doing effect;  Interviewers trained to produce accurate and complete questionnaire to approach the homeless to avoid risky situations  2 volunteers/assistants for each interviewer;  Voucher to avoid time consuming interviews “Ticket Service”;  Questionnaires in different languages (IT, EN, RUM);  Average length of each questionnaire: 30’

THE HOMELESS POPULATION  408 individuals: census % interviewed  12% refusal rate  15% not found  16.4% sleeping  21% not found  1152 individuals, sample % of the sampled interviewed  2% refusal rate  6.7% not found  7.3% no time  2300 adults, sample % of the sample interviewed  33.5% no time STREET SHELTER DISUSED AREAS Total adult population: 3860 Final Sample: 910 homeless

Socio – demographic characteristics  Different from the general population, the homeless are especially men (72% vs. 48%) and immigrants (68% vs 5.8%) … but the distribution over sex and nationality varies significantly among the three sub samples  The countries of origin are in line with those found in the general population: European (especially Romania), African (Tunisia, Morocco, Egypt), Asian …as expected, especially new immigrants have no house

Socio – demographic characteristics  Current civil status is significantly different from the one found in the general population HL: 32.4% married, 35.4% single, 4.1% widow, 18.9% divorced, 8% other GP.:50.4% married, 40,5% single, 7.7% widow, 1.5% divorced  High incidence of mortality in their kids and parents (especially for those in the street)  Family as insurance against adverse shocks

Age  Homelessness affects adults in the central part of their life (average age 39.9) => failures in individual life projects (lack/loss job, family relationships, divorces..) …but the total population is spread across all age groups  The homeless population is a little bit younger than general population (42.6) for the high incidence of immigrants. All categories are older than in the general population HL: Italian M=51.1 Foreign M=35 Italian F = 45.6 Foreign F=35.2 GP: Italian M=41.6 Foreign M=30.4 Italian F = 44.5 Foreign F=31.3  Average age is higher among street homeless (49) than among sheltered homeless (43). Population young in disused areas (30.7) as in general population (30.9 years)  Differently from the general population males are 4 year older than females

Education  Education distribution is in line with the one found in the general population  Higher proportion of people with no education  More educated people tend to stay in shelter  As in the general population, on average, immigrants are more educated than native born Native have 8.2 years of education Immigrants have 9.7 years of education …but the higher education level reflects their age structure

FIRST REASON FOR HOMELESSNESS  Unemployment is among the most cited causes of homelessness (consistent with SF data) together with familiar problems Italians: family relationships (35.1%), loss of job (21%), drug/alcohol dependency (9.2%), previous convictions (7.5%), eviction (5%), free choice (8%) Immigrants: immigration/language problems/documents(27%), loss of job (17.3%), family relationships (8.8%)  Failure in life project => crucial to design adequate policies for social inclusion

HOMELESSNESS AND PRISON  High rate of criminality with respect to the general population About 30% have been in prison at least once (39% of Italians and 23% of immigrants) 70% of whom spent a period in prison after becoming homeless and 30% of whom have been in prison before it

LABOR MARKET and HOMELESSNESS  45% of the population were working before loosing the house  Possible to find a job being on street

LABOR MARKET  Labor force participation is higher compared with the general population  The 29.3% was employed at the time of the survey. Among unemployed people the 17% worked during the previous month More than half of people are employed in the black market compared with the 12.1% in the general population Only 13% have permanent contract and a significant percentage (20%) has temporary contract while in the general population the percentages are 65% for permanent and 10% for temporary

 People are employed as low skilled workers, especially as factory workers (33%), domestic workers, nannies, cleaners (15.3%), bricklayers, carpenters, electricians, plumbers (9.4%), unskilled service workers (12.9%), cooks/waiters (5.9%)  Unemployed people look for a job through informal channels  Individual reservation wage is 827 euro/month LABOR MARKET

INCOME  Low rate of participation into government program => few individuals receive social assistance. How to reach the excluded?  On average weekly income is 151 €. It increases in disused areas (164€) with respect to street and shelter (140 and 145) …not below the relative poverty threshold 246.5€ for a two persons hh but insufficient to afford house expenditures

IN KIND HELP  People receive help especially from catholic associations, non profit organizations, advocacy groups  Some categories appear disadvantaged Is there any distortion in the existing distribution mechanism? Are some groups self selected? How to reach all needy people?

HOMELESS AND HELP  In-kind help is the main form of help  Family as the main source, followed by voluntary associations

SOCIAL NETWORKS  Can you tell me name and surname of your first 5 homeless friends?  49% do not have any friends  Higher percentage of links for those on the street

CRONICALITY vs. IN AND OUT  The 75% of the population never slept in a house after the first night on street The 52% of the street homeless, the 67% of the sheltered homeless and the 93% of the population of disused areas On average, the street homeless lost the house 4.3 years ago, the sheltered homeless 3.3 years ago and disused areas inhabitants 11 years ago  People doing in and out rent a house/room, stay with relatives and parents for short periods

CURRENT RESEARCH  Relationship between crime and social network by exploiting a dyadic data structure => evidence of fascinating peer effect in the realm of criminality  Determinants of the labor markets => variables affecting labor market behavior are in line with the underlying theoretical framework of utility maximization and labor-leisure choice

HOW TO AMELIORATE DATA COLLECTION  Collect data on a regular basis Capture seasonality Monitor trends Verify efficacy and efficiency of applied policies => costs/benefits analysis  Capture re – capture approach  Exploit available administrative data from all servicers  Multi disciplinary approach Economy Sociology Psychology

POLICY INTERVENTIONS To design adequate social inclusive policies it is important to go over the traditional iconography