At the Margins. Homelessness and Social Exclusion

in an International Perspective

Michela Braga Bocconi University and fRBD

Rome, June 27, 2014

The report in a nutshell • The 2008 crisis worsened socio-economic conditions for the

majority of the population • Longer unemployment spells translate in lower income and

higher difficulties in paying rents or servicing mortgages – ↑ number of households having problems in accessing and maintaining

suitable accommodation – Higher demand for affordable housing and social allowances in many

countries • ↓ public resources for welfare systems budget constraints • ↑ extreme poverty and the lack of adequate housing • A progressive deterioration process can lead to homelessness

Which factors can predict homelessness?

The structure of the report

International perspective 1) USA: lessons from 35 year of experience

2) Australia: evidence based on new data collections

1) census 2) longitudinal data

3) EU: evidence based on 1) new aggregate data 2) original Italian micro data on homeless people in Milan

(2008 and 2013), Turin (2010) and Rome (2014)

Motivation

Homelessness is one of the most extreme forms of poverty in developed countries

…but

• Little coverage in media

• Little attention from politicians

• Little (or no) representation

• Few research in economics

Invisible and stereotyped

google results repec/ideas

papers 2013 - 2014

Homelessness 5.150.000 60Social exclusion 1.100.000 30.383Poverty 47.900.000 7.238

Our contribution

• To understand – the determinants of homelessness – the variation in the homelessness rate across countries => role of

macroeconomic factors, institutions, culture

• To identify differences and similarities across countries: EU, USA, Australia, Italy

• Presenting new data of the reference population

Actual problems

• In most countries homeless people are not part of official statistics

• Collecting data on homeless people is a difficult task

– Problems in defining the reference population – Problems in identifying the population – Problems in surveying a highly mobile population

The phenomenon

• Complex and multidimensional phenomenon • Process, not a status, conducting to social exclusion • Underlying causes: structural, institutional, relational and

personal factors – Different causes ask for different interventions

• Variable length – temporary, recurrent or persistent phenomenon – in and out frequent movements

• Significant related costs: direct and indirect as well as social

and private

The reference population • Different countries adopt different definitions • Broad and not technical definition based on the lack of a regular and

adequate residence – people living on the streets, sleeping in shelters and transitional

housing or in other places not suitable for human habitation • European Typology of Homelessness and housing exclusion (ETHOS)

=> framework covering all living situations that can be assimilated to homelessness

a) rooflessness (people sleeping rough and living without a shelter of any kind) b) houselessness (people living with a place to sleep in temporary institutions or shelter) c) people living in insecure housing (people at risk of severe exclusion because of insecure tenancies, eviction, domestic violence) d) people living in inadequate housing (people in caravans on illegal campsites, in unfit housing, in extreme overcrowding)

The reference population Physical dimension Legal dimension Social dimension

Homelessness

Rooflessness no dwelling no legal title for exclusive possession

no private and safe personal space for social relations

Houselessness place fit for sleep no legal title for exclusive possession

no private and safe personal space for social relations

Housing exclusion

Insecure and inadequate housing

place to live (not secure and unfit for habitation)

no secure tenure space for personal relations

Inadequate housing and social isolation within a legally occupied dwelling

inadequate dwelling (unfit for habitation)

legal and secure tenure No private and safe personal space for social relations

Inadequate housing (secure tenure)

inadequate dwelling (unfit for habitation)

legal and secure tenure space for personal relations

Insecure housing (adequate housing)

place to live no secure tenure space for personal relations

Social isolation within a secure and adequate context

place to live legal and secure tenure No private and safe personal space for social relations

Counting homeless people

Different methods are used 1) Census: Headcount of rough sleepers and people in temporary or emergency shelters

– Count • conducted simultaneously in a whole metropolitan area (S-Night

approach with point-in-time survey) • lasted for more days

• Unintentional biases downward (people are missed or not

found/identified) or upward (people are counted twice or false positive are included)

2) Sampling methods: public places method, service users, stratified sampling

• Biases since the real size is unknown

THE EU EVIDENCE: MACRO APPROACH

A new dataset on homeless people in EU

• Focus on homelessness strictu sensu: persons who reside in (i) external places not meant for human habitation (rough sleepers or unsheltered homeless) (ii) emergency shelters, temporary accommodations and transitional supported accommodation (sheltered homeless)

• Data source: administrative data, local agencies, statistical

offices, NGO

• Extensive effort to reconcile the estimates provided by different data sources in order to ensure comparability

• Time and geographical coverage: – 23 countries and 30 cities – 2000-2013 (exception of Finland data goes back to the 90s)

The EU evidence

• Homeless share in our sample varies, roughly, between 0.002% and 1% at both country and city level

• On average decreasing trend up to the Great Recession where a clear cut off point and a trend reversal appears – Denmark: ↑4.91% from 2009 to 2011 – Finland: ↑5.24% from 2007 to 2008, ↑1.89 from 2008 to 2009 – Germany: ↑ 5.21% from 2008 to 2009 – Hungary: ↑ 13.68% from 2008 to 2009 – Luxembrg: ↑ 26.02% from 2008 to 2009 – Netherland: ↑32.43 from 2009 to 2010

• Similar evidence at city level – Vienna: ↑ 54.21 from 2008 to 2009 – Brussels : ↑ 8.51 from 2008 to 2010 – Helsinki: ↑ 9.22 from 2008 to 2009 – Dublin: ↑ 16.67 from 2009 to 2010 – Milan: ↑ 71.59 from 2008 to 2013

Some recent figures

City Country Year homeless share (%)

% street homeless

Rome Italy 2014 0.124 0.476Salzburg Austria 2013 0.73 14.564Helsinki Finland 2013 0.689Budapest Hungary 2013 0.244 25.03Dublin Ireland 2013 0.027Vilnius Lithuania 2013 0.206Barcelona Spain 2013 0.18 29.835London United Kingdom 2013 0.007Oslo Norway 2012 0.224Stockholm Sweden 2012 0.332 3.803Vienna Austria 2011 0.194Lisbon Portugal 2011 0.437Goteborg Sweden 2011 0.635 1.514Malmo Sweden 2011 0.786 1.218Brussels Belgium 2010 0.178 16.924Prague Czech Republic 2010 0.314Bilbao Spain 2010 0.231 25.123Madrid Spain 2010 0.018Seville Spain 2010 0.036Zaragoza Spain 2010 0.033 71.493Marseille France 2009 0.338Paris France 2009 0.617 23.21Amsterdam Netherlands 2009 0.011Innsbruck Austria 2006 0.534 19.579

The geographical variability over time

reported total (share of population), first year available(.0019753,.0104831](.0009644,.0019753](.0003111,.0009644][.0000165,.0003111]No data

reported total (share of population), last year available(.0019753,.1](.0009644,.0019753](.0003111,.0009644][0,.0003111]No data

Yearly percentage change in incidence

Rough sleepers

The crisis: country level evidence

The crisis: city level evidence

The new homeless

• International evidence suggests that among the homeless we find – Traditional homeless: middle – aged single men – New homeless:

• Women for family breakdowns • Young people and families with children for income losses due to unemployment • Individuals hit by the collapse of the bubbles in the housing market ….individual not considered at risk of homelessness before the crisis (exogenous unexpected shock) • Immigrants (third country nationals, EU citizens and long-term residents as well

as return migrants)

A focus on Italy: Rome

• Most recent data based on the survey conducted in March 2014 by the fRDB and Roma Capitale provide some clear evidence of the profile of the homeless nowadays

CityDate

(temperature)Street Shelters* Total

Share over the population

(%)

Roma 19/3/2014 1587 1689 3276 0.00%average temperature

13°, min 6° 48% 52%

Milano 03/11/2013 531 2106 2637 0.21%

average temperature 8°, min 4° 20% 80%

Milano 14/01/2008 408 1152 1560 0.12%average temperature

6.9°, min 3° 26% 74%

Torino 2010 288 477 765 0.08%average temperature

-2°, min -5° 38% 62%Nota: * sono stati censiti 24 dormitori convenzionati (1479 posti) e 9 non convenzionati (210 posti). Mancano idati di 10 dormitori non convenzionati (non hanno comunicato tutte le informazioni sugli ospiti e i posti disponibili)

Some differences and similarities

• More females over time and compared to the general population (more or less 50 – 50)

• Average age equal to the general population (44)

Street Shelters Street SheltersMen 87% 78% 91% 86%

Women 13% 22% 9% 14%

Roma (2014) Milano (2013)

Total European Not European

Less than 35 29% 16% 60%

More than 35 71% 84% 40%

Street Total Italian ImmigrantsLess than 35 35% 7% 53%More than 35 65% 93% 47%Less than 25 15% 4% 22%25-34 18% 2% 28%35-44 16% 9% 19%44-60 30% 40% 24%More than 60 21% 44% 7%

Shelters

Some differences and similarities

• Good stock of human capital

• Active in the labour market but long term unemployment (5 years) => human capital depreciation

• Among the employed, higher incidence of irregular workers (underground economy) 55% compared to 11% in the general population

Education

Italians Immigrants TotalGeneral

populationprimary/never attended 31% 28% 29% 21%lower secondary school 40% 24% 29% 32%upper secondary 2-3 y (professional diplo 9% 19% 16% 6%upper secondary 4-5 y (high school diplom 16% 19% 18% 29%bachelor/master/doctoral 5% 10% 8% 12%

Employment rate

HomelessGeneral

populationTotal 10% 55%Males 9% 64%Females 18% 47%

Not-employed but actively looking for a

HomelessGeneral

populationTotal 68% 27%Males 69% 33%Females 60% 24%

Changes over time: explanatory factors

• We correlate the percentage change in the homeless share with the percentage change of some possible explanatory variables – socio-economic conditions: positive correlation with inequality and

singleness

– labour market conditions: negative correlation with investment in ALM

– welfare regime: negative correlation with welfare state generosity (social expenditure – both total and for unemployed people – over GDP and welfare state generosity index)

– political orientation: negative correlation with left wing parliament/cabinets

– housing market features: negative correlation with ownership rate

Cross country variation in levels: explanatory factors

• Controlling for country and time fixed effects, some predetermined institutional features are related to homelessness incidence at country/city level – Not statistically significant effect of traditional macroeconomic

variables (GDP per capita, Gini index, share of immigrants) – Social expenditure negatively correlated with homelessness

– Social housing investment negatively correlated with

homelessness

– No statistical correlation with housing prices

• Institutional features to fight social exclusion matter

Some estimates

• Taking as benchmark the available average homeless share in 2013 we can estimate the number of homeless people in EU27 in about 2 million of people

• Estimates of the monetary cost of a single person on the street/shelter range between $ 35,000 and $ 40,000

• Total cost ranges between $70 and $80 billions

• Indirect and non monetary costs should be added

THE EU EVIDENCE: MICRO APPROACH

Homelessness, family values and cultural transmission

Lucia Corno Queen Mary, University of London

Motivation The number of homeless people varies considerably across countries

Recent estimates show that the share of homeless is equal to:

2% in US 0.12% in Australia 0.18% in Europe

The share of homeless also varies within countries:

0.006% in UK in 2011 0.43% in Portugal in 2011 More than 6% in California, Florida and less than 1% in Oklahoma, Nevada, Colorado

What factors are responsible for this variation?

This chapter

We test whether “culture” across different countries may influence the variation in the fraction of homeless In particular, we investigate whether family values and important attitudes for parents influence homeless shares

Indeed family structure/values and cultural transmission vary around the world

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This chapter

We test whether “culture” across different countries may influence the variation in homeless rate In particular, we investigate whether family values and important attitudes for parents influence homeless rate

Indeed family structure/values and cultural transmission vary around the world We use variation in (i) the share of homeless and in (ii) family values/important attitudes for parents in different countries and cohorts to test any relationship between homelessness and culture

Previous literature

Family ties and economic outcomes Stronger family ties negatively correlated with labor market and political participation positively correlated with unemployment and lower wages (Alesina and Giuliano, 2007, 2009; Alesina et al. 2014)

Cultural transmission and economic outcomes Transmission from mothers to sons => growing up with a working

mother influenced men’s preferences for a working wife (Fernandez et al. 2004)

Social capital - set of beliefs and values that facilitate cooperation - is transmitted from parents to their children (Guiso et al. 2007, Tabellini 2008)

Outline

Data

Descriptive Statistics

Empirical Strategy

Results

Conclusions

Outline

Data

Descriptive Statistics

Empirical Strategy

Results

Conclusions

Data Three sources of data 1. Cross sectional surveys among homeless people: 4 rounds of data collection (Milan 2008, Turin 2010, Milan 2013,

Rome 2014) Elicit information on country and year of birth

2. World Value Survey (WVS) carried out six times (1981-84, 1990-1994, 1995-98, 1999-2004,

2005-2009, 2010-2014) in different countries 3. Administrative data from the 2001 Italian Census number of legal residents by country and year of birth in Lombardy,

Piedmont and Lazio

Data Three sources of data 1. Cross sectional surveys among homeless people: 4 rounds of data collection (Milan 2008, Turin 2010, Milan 2013,

Rome 2014) Elicit information on country and year of birth

2. World Value Survey (WVS) carried out six times (1981-84, 1990-1994, 1995-98, 1999-2004,

2005-2009, 2010-2014) in different countries 3. Administrative data from the 2001 Italian census number of legal residents by country and year of birth in Lombardy,

Piedmont and Lazio

Homeless survey

Individuals sleeping on: • The street (cars, parks, sidewalks, abandoned buildings) • Shelters

City divided in sufficiently small census blocks

• Reduce risk of double count • Simultaneous full census of the whole city (S-night

approach) • Localization and detection of observable characteristics

Sampling procedure:

• Street: all population • Shelter: Random sample proportional to the shelter

dimension

TARGET

COUNT

INTERVIEW

Data 3 sources of data 1. Cross sectional surveys among homeless people: 4 rounds of data collection (Milan 2008, Turin 2010, Milan 2013,

Rome 2014) Elicit information on country and year of birth

2. World Value Survey (WVS) carried out six times (1981-84, 1990-1994, 1995-98, 1999-2004,

2005-2009, 2010-2014) in different countries 3. Administrative data from the 2001 Italian number of legal residents by country and year of birth in Lombardy,

Piedmont and Lazio

World Value Survey 3 variables to construct the index of weak family ties: 1. how important is the family in one's life: from 1 (very

important) to 4 (not important at all)

2. agree with: (i) "Regardless of what the quality and faults of one's parents are, one must always love and respect them“ (value 1) or (ii) "One does not have the duty to respect and love parents who have not earned it“ (value 2)

3. agree with: (i) It is the parents' duty to do their best for

their children even at the expense of their own well-being, or (ii) "Parents have a life of their own and should not be asked to sacrifice their own well-being for the sake of their children“

WEAK FAMILY TIES

World Value Survey One question asking to list the qualities which children

can be encouraged to learn at home (up to 5)

Binary variables equal to one if respondents listed: "Hard work", "Obedience", "Independence", "Feeling of responsibility", "Imagination", "Tolerance towards others“

IMPORTANT ATTITUDES FOR PARENTS

For each wave, we normalized the variables on family ties and on important attitudes for parents between 0 and 1

We then average them by country and year of birth

Data 3 sources of data 1. Cross sectional surveys among homeless people: 4 rounds of data collection (Milan 2008, Turin 2010, Milan 2013,

Rome 2014) Elicit information on country and year of birth

2. World Value Survey (WVS) carried out six times (1981-84, 1990-1994, 1995-98, 1999-2004,

2005-2009, 2010-2014) in different countries 3. Administrative data from the 2001 Italian Census number of legal residents by country and year of birth in Lombardy,

Piedmont and Lazio N. of residents in the region as proxy for the residents in its main city

Census

= number of homeless by country and year of birth (from the homeless surveys) / the resident population by country and year of birth (from the census)

SHARE OF HOMELESS BY COUNTRY OF ORIGIN

AND COHORT

Outline

Data

Descriptive Statistics

Empirical Strategy

Results

Conclusions

Descriptive Statistics

Number of homeless people

Descriptive Statistics Fraction of homeless interviewed

Final sample

Merge of homeless data with WVS and census data Final sample of 1855 homeless from 46 countries born between 1926 and 1995 The countries with the higher number of observations are Italy, Morocco, Romania, Tunisia, Egypt and Bangladesh

Descriptive Statistics

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Descriptive Statistics

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Czech Rep

Bulgaria, Ukraina

Italy

Italy

Outline

Data

Descriptive Statistics

Empirical Strategy

Results

Conclusions

Empirical Strategy

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Hc,y=α+βFamilyValuesc,y+γXc,y+δc+ηy+εc,y

Hc,y is the share of homeless people from country of birth c, born in year y FamilyValuesc,y is an indicator for (i) the weakness of family ties (ii) or for important

attitudes for parents

Xc,y represent average socio-demographic reported in the WVS, such as education and income, by country and year of birth

δc country fixed effects in order to eliminate the effect of other country characteristics η y year of birth fixed effects to control for any shock at cohort level

εc,y is the error term

Outline

Data

Descriptive Statistics

Empirical Strategy

Results

Conclusions

Homelessness and Family Ties

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Homelessness and cultural transmission

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Outline

Data

Descriptive Statistics

Empirical Strategy

Results

Conclusions

Conclusions

Weak family ties increase the fraction of homeless people

The share of homeless is also positively correlated with attitudes to transmit to children such as imagination and tolerance and negatively correlated with attitudes such as hard work, obedience and responsibility

These findings should be taken into account when designing policies to prevent homelessness but also housing, incarceration and welfare policies, since the same intervention might have different effects in countries with different family ties and cultural traits