Presentation given by Michela Braga, University of Milan, Italy a FEANTSA Research Conference on "Homelessness and Poverty", Paris, France, 2009

1. HOMELESSNESS and LABOR
FORCE PARTICIPATION.
Evidence from an Original Data
Collection in Milan
Michela Braga
University of Milan
Homelessness and Poverty in Europe
Paris, September 18th, 2009

2. MAIN OBJECTIVES
 Quantitative and qualitative data collection:
 First Census of homeless in Milan
=> count and localization
 Data collection to understand not only the number of homeless and the
concentration, but also to capture characteristics
=> questionnaire
 Are homeless people different from the general population? If yes in
which dimension?
 Are homeless people rational according to economic theory?
=> case study: labor market behavior

3. MOTIVATION
 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

4. METHODOLOGY: data collection
Point in time survey using the S - Night approach (Shelter and Street Night): January 14th
2008
 All individuals that in the reference night sleep in
 places not meant for human habitation = street homeless;
TARGET  emergency shelters = sheltered homeless;
 disused areas/shacks/slums
 65 small census blocks
 Reduce risk of double count (3/4 hours for each block)
COUNT  Simultaneous full census of the whole city
 Localization and detection of observable characteristics
 Costs: monetary, human, time vs Benefits: accuracy, limit under estimates
 Sampling procedure:
INTERVIEW  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)
 Trade off between accuracy of the data collection and loss of observations

5. THE HOMELESS POPULATION
 408 individuals, 34.5% interviewed
STREET  12% refusal rate
 21% not found
 17% sleeping
SHELTER
 1152 individuals, 80% of the sampled
interviewed
 2% refusal rate
 7% not found
DISUSED
 2300 adults, 66% of the sample interviewed
AREAS
 33% not found
Total adult population: 3863
Final Sample: 941 homeless

6. Legend: [ blue] =Localization of unsheltered homeless, each dot=1 homeless
[ pink ] =Localization of shelters, each dot =10 homeless
[ grey] =Localization of slums, each dot =10 homeless

7. DATA: socio – demographic characteristics
 Differently from the general population, the homeless are mainly men (72% vs.
48%) and immigrata (68% vs 5.8%)
… but there is a significant variation by sex and nationality in the three sub
samples
% Females % Italians
Street 10 56
Shelters 16 40
Disused areas 49 11
 Geographical origin in line with general population
 First generation immigrants => starting period of their migration project
 High incidence of divorce (20%) and loss of strong family ties ( sons, parents)

8. DATA: age
 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
 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 younger in disused areas (30.7) as in general population (30.9 years)

9. DATA: education
Disused General
All sample Italian Foreign Street Shelter
areas population
None 14.45 8.88 17.11 10.71 6.84 25.5 6.8
Elementary school 21.68 29.28 18.05 18.45 17.45 28.37 26.4
Middle school 33.16 39.47 30.14 34.52 34.43 30.95 31.7
High school 25.19 19.41 27.94 30.36 32.78 13.47 27.2
University 5.53 2.96 6.75 5.95 8.49 1.72 7.9
 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

10. DATA: labor market behavior
 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
 Unemployed people are actively looking for a job
 Reservation wage 827 €
Population non excluded from the labor market but less stable

11. DATA: income
 Low take up rate to social assistance programs and welfare state
 Weekly average income 151 €. Higher in disused areas (164€) than on
street and in shelters (140 and 145)
=> not lower than the poverty line treshold in Italy (246.5€ for
a two person household) but not sufficient to afford everyday
expenditures in Milan

12. ARE HOMELESS PEOPLE
RATIONAL AGENTS?
Homeless people are thought to be no rational agents (from an economic
point of view) as a result of their housing condition, drug/alcohol use,
physic and psychic disorders
 Determine which variables affect homeless people's labor market behavior
 Test if they are in line with the underlying theoretical framework of utility
maximization and labor-leisure choice

13. EMPIRICAL ANALYSIS
yi= β0+ β1 Xi +μi
yi = binary variable defining individual labour market status (in
vs out labour force), employment status
(employed/unemployed), source of income (legal/illegal)
Xi = exogenous explanatory variables
μi = error term

14. RESULTS (I):
Labor market participation
 Variables affecting labour market participation in line with the
utility maximization and labour-leisure choice framework
 Traditional income effect
 Education ↑ probability to be active
 Gender gap
 Awareness and degree of information ↑ probability to be active

15. Labor force participation (1) (2) (3) (4)
Female -0.0737*** -0.0718*** -0.0738*** -0.0600***
[0.0156] [0.0166] [0.0168] [0.0177]
Age 0.0281*** 0.0285*** 0.0287*** 0.0275***
[0.0033] [0.0030] [0.0031] [0.0033]
Age (squared) -0.0004*** -0.0004*** -0.0004*** -0.0004***
[0.0000] [0.0000] [0.0000] [0.0000]
Primary Edu.Level 0.0846*** 0.0848*** 0.0861*** 0.0978***
[0.0173] [0.0195] [0.0195] [0.0213]
Middle Edu. Level 0.1414*** 0.1418*** 0.1439*** 0.1555***
[0.0058] [0.0170] [0.0174] [0.0208]
Secondary Edu. Level 0.0718** 0.0741 0.0782 0.0866
[0.0358] [0.0559] [0.0544] [0.0574]
Universitary Edu. Level -0.0303 -0.0285 -0.0251 -0.0233
[0.1173] [0.1494] [0.1506] [0.1530]
Received money from family -0.1214*** -0.1224*** -0.1232*** -0.1121***
[0.0315] [0.0279] [0.0275] [0.0234]
Non-financial help -0.1533*** -0.1575*** -0.1649*** -0.1624***
[0.0371] [0.0075] [0.0058] [0.0069]
Essential inkind help 0.3051*** 0.3124*** 0.3208*** 0.3181***
[0.0442] [0.0631] [0.0651] [0.0763]
Prison before -0.0822* -0.0557* -0.0215 -0.0173
[0.0468] [0.0329] [0.0377] [0.0438]
Shelter 0.0491*** 0.0450***
[0.0044] [0.0071]
Disused area 0.2037*** 0.1920***
[0.0491] [0.0418]
Romanian 0.0510*** 0.0506** 0.0461** 0.0448**
[0.0180] [0.0201] [0.0231] [0.0219]
Other Europe -0.0442* -0.0496** -0.0455** -0.0477***
[0.0261] [0.0213] [0.0188] [0.0136]
African 0.1351*** 0.1313*** 0.1333*** 0.1305***
[0.0056] [0.0051] [0.0070] [0.0084]
Asian/American and other 0.1511*** 0.1466*** 0.1465*** 0.1457***
[0.0153] [0.0164] [0.0163] [0.0165]
Non labor income -0.1762*** -0.1688*** -0.1710*** -0.1711***
[0.0076] [0.0050] [0.0038] [0.0053]
Sick in the past month -0.0532* -0.0548* -0.0523
[0.0297] [0.0291] [0.0352]
Wrong month -0.1069** -0.1075** -0.0997*
[0.0545] [0.0543] [0.0559]
Shelter 0.0431*** 0.0578***
[0.0075] [0.0097]
Authorized disused area 0.1898*** 0.2128***
[0.0401] [0.0417]
Non authorized disused area 0.1347*** 0.1536***
[0.0301] [0.0287]
Read new spaper 0.0776***
[0.0089]
Information 0.0094 0.0095

16. RESULTS (II): Employment
 Factors affecting the probabily to be employed are in line with
those of the general population
 Gender gap in favour of males
 More educated people have a relative advantage
 Traditional income effect
 Awareness and degree of information ↑ probability to be employed
 Previous convictions not correlated with the probability to be employed

17. (1) (2) (3) (4) (5)
Female -0.1373*** -0.0715*** -0.0569*** -0.0562*** -0.0555**
[0.0418] [0.0249] [0.0181] [0.0177] [0.0241]
Age 0.0168*** 0,0022 -0,0013 -0,0014 -0,0012
[0.0065] [0.0094] [0.0079] [0.0078] [0.0074]
Age (squared) -0.0002** 0 0 0 0
[0.0001] [0.0001] [0.0001] [0.0001] [0.0001]
Primary Edu.Level -0.3215*** -0.1486*** -0.1625*** -0.1619*** -0.1647***
[0.0110] [0.0111] [0.0281] [0.0288] [0.0410]
Middle Edu. Level -0.3037*** -0.1636*** -0.1856*** -0.1851*** -0.1894***
[0.0242] [0.0266] [0.0060] [0.0055] [0.0054]
Secondary Edu. Level -0.2876*** -0.1396*** -0.1581*** -0.1591*** -0.1622***
[0.0221] [0.0274] [0.0183] [0.0185] [0.0176]
Universitary Edu. Level -0.2196*** -0,0896 -0,1032 -0,1026 -0,1053
[0.0618] [0.0983] [0.0789] [0.0786] [0.0731]
No family Relations -0.0551* -0,0218 -0,0253 -0,0274 -0,0276
[0.0291] [0.0464] [0.0368] [0.0357] [0.0389]
Faith 0,0034 -0,0115 0,0118 0,0121 0,0109
[0.0598] [0.0573] [0.0630] [0.0626] [0.0610]
Received money from family -0.1893*** -0.0499*** -0.0589** -0.0581** -0.0610***
[0.0226] [0.0183] [0.0236] [0.0236] [0.0220]
Received money from friends -0.1442*** -0.0378* -0.0330* -0.0344* -0,0325
[0.0250] [0.0201] [0.0191] [0.0205] [0.0208]
Financial help from close relatives -0,0011 0,0007 0,0008 0,0009 0,0009
[0.0012] [0.0016] [0.0017] [0.0017] [0.0017]
Non-financial help 0.1695*** 0,093 0,0741 0,0779 0,0776
[0.0444] [0.0728] [0.1315] [0.1294] [0.1236]
Essential inkind help -0.2380*** -0,1521 -0,1306 -0,1354 -0,134
[0.0669] [0.0972] [0.1563] [0.1557] [0.1536]
Additional inkind help -0,0115 -0,0142 -0,0069 -0,007 -0,0068
[0.0399] [0.0419] [0.0383] [0.0382] [0.0401]
Prison before -0,0512 0.0368** 0,0027 0,0034 0,0033
[0.0438] [0.0179] [0.0396] [0.0397] [0.0491]
Prison after -0,0499 -0,0195 -0,0102 -0,0111 -0,0096
[0.0483] [0.0382] [0.0441] [0.0436] [0.0434]
Shelter 0.0344*** -0,0033 -0,0113
[0.0097] [0.0102] [0.0110]
Disused area 0.1451*** 0.2029*** 0.1867***
[0.0552] [0.0500] [0.0418]
Romanian 0,0077 0,0211 0,0295 0,0336 0,0371
[0.0767] [0.0841] [0.0753] [0.0785] [0.0853]
Other Europe 0,0965 0,0555 0,078 0,0764 0,0779
[0.1184] [0.1219] [0.1129] [0.1129] [0.1109]
African -0.1774** -0.1450*** -0.1316*** -0.1344*** -0.1334***
[0.0852] [0.0361] [0.0309] [0.0305] [0.0296]
Asian/American and other 0.1233*** 0.1761*** 0.1798*** 0.1788*** 0.1799***
[0.0319] [0.0388] [0.0468] [0.0465] [0.0499]
Duration 0.0158*** 0.0142** 0.0153** 0.0145**
[0.0059] [0.0060] [0.0063] [0.0067]
In and out 0.1250*** 0.1210*** 0.1202*** 0.1200***
[0.0235] [0.0222] [0.0214] [0.0220]
Non labor income -0.5243*** -0.5259*** -0.5258*** -0.5251***
[0.0287] [0.0301] [0.0304] [0.0291]
Sick in the past month -0.0643** -0.0642** -0.0646**
[0.0289] [0.0291] [0.0297]
Wrong month -0.0738* -0.0717* -0,0744
[0.0384] [0.0399] [0.0500]
Wrong year -0,0005 -0,0018 0,0003

18. RESULTS (III): sources of income
 Rationality hypothesis seems to hold also for what concerns
individual income sources (legal/illegal)
 No gender gap nor nationality gap
 No age effect
 More educated people are less prone to act illegally to obtain income
 Traditional income effect
 Previous convictions not correlated with current illegal behaviour
 Drug use correlated with illegal behaviour

19. Illegal activities (1) (2) (3) (4)
Female -0,0099 -0.0117*** -0.0115*** -0.0127***
[0.0065] [0.0039] [0.0039] [0.0032]
Age 0.0029*** 0.0027*** 0.0027*** 0.0027***
[0.0006] [0.0004] [0.0004] [0.0005]
Age (squared) -0.0000*** -0.0000*** -0.0000*** -0.0000***
[0.0000] [0.0000] [0.0000] [0.0000]
Formal job -0.0348*** -0.0323*** -0.0321*** -0.0316***
[0.0022] [0.0033] [0.0033] [0.0034]
Primary Edu.Level 0.8592*** 0.8199*** 0.8193*** 0.7981***
[0.0252] [0.0322] [0.0313] [0.0384]
Middle Edu. Level 0.6479*** 0.6022*** 0.5994*** 0.5766***
[0.0808] [0.0878] [0.0867] [0.0934]
Secondary Edu. Level 0.7151*** 0.6546*** 0.6504*** 0.6297***
[0.0411] [0.0516] [0.0537] [0.0640]
Universitary Edu. Level 0.9252*** 0.9042*** 0.9042*** 0.8986***
[0.0638] [0.0744] [0.0763] [0.0857]
No family Relations -0.0095*** -0.0097*** -0.0099*** -0.0100***
[0.0031] [0.0001] [0.0002] [0.0003]
Received money from friends -0.0090*** -0.0054* -0.0055* -0.0045**
[0.0004] [0.0030] [0.0028] [0.0022]
Financial help from close relatives -0,0001 -0,0001 -0,0001 -0,0001
[0.0002] [0.0002] [0.0002] [0.0002]
Non-financial help -0,0029 0,0012 0,0021 0,0015
[0.0302] [0.0246] [0.0238] [0.0236]
Essential inkind help -0,0083 -0,0103 -0,011 -0,0105
[0.0329] [0.0309] [0.0314] [0.0294]
Prison before 0,0073 -0,0105 -0,0104 -0,0098
[0.0301] [0.0143] [0.0141] [0.0140]
Shelter -0.0138*** -0.0107***
[0.0015] [0.0018]
Disused area -0,0007 0,0076
[0.0070] [0.0104]
Romanian 0,0049 0,0077 0,009 0,0089
[0.0084] [0.0072] [0.0078] [0.0079]
Other Europe 0,0127 0,0114 0,0115 0,0117
[0.0144] [0.0105] [0.0105] [0.0105]
African 0,0063 0,0082 0,0072 0,0075
[0.0112] [0.0096] [0.0088] [0.0089]
Asian/American and other -0,0108 -0,0083 -0,0084 -0,0088
[0.0085] [0.0089] [0.0085] [0.0088]
Duration 0.0032*** 0.0031*** 0.0035*** 0.0030***
[0.0010] [0.0007] [0.0010] [0.0011]
Drug use 0.0170*** 0.0166*** 0.0165***
[0.0017] [0.0013] [0.0013]
Legal problems 0,0177 0,0178 0,0179
[0.0263] [0.0264] [0.0252]
Shelter -0.0104*** -0.0119***
[0.0023] [0.0030]
Authorized disused area 0,0038 0,0008
[0.0083] [0.0086]
Non authorized disused area 0,0136 0,0096

20. CONCLUSION
 Homeless population similar in many dimensions to the Italian general
population
 Variables affecting homeless people's labor market behavior are in line
with the underlying theoretical framework of utility maximization and
labor-leisure choice
 Rationality hypothesis satisfied
 Correlation vs. causality? Necessary to solve endogeneity problems
 In kind help = > variation in charity services within the city
=> journal articles on homelessness, news on
television
 Duration => weather conditions (average temperature, rainfall) from
the first arrive in street