View
183
Download
0
Category
Tags:
Preview:
DESCRIPTION
Presentation given by Michela Braga, University of Milan, Italy a FEANTSA Research Conference on "Homelessness and Poverty", Paris, France, 2009
Citation preview
HOMELESSNESS and LABOR FORCE PARTICIPATION. Evidence from an Original Data Collection in Milan
Homelessness and Poverty in EuropeParis, September 18th, 2009
Michela Braga
University of Milan
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
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
METHODOLOGY: data collection
All individuals that in the reference night sleep in places not meant for human habitation = street homeless; emergency shelters = sheltered homeless; disused areas/shacks/slums
65 small census blocks Reduce risk of double count (3/4 hours for each block) 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: 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
TARGET
COUNT
INTERVIEW
Point in time survey using the S - Night approach (Shelter and Street Night): January 14th 2008
THE HOMELESS POPULATION
408 individuals, 34.5% interviewed 12% refusal rate 21% not found 17% sleeping
1152 individuals, 80% of the sampled interviewed 2% refusal rate 7% not found
2300 adults, 66% of the sample interviewed 33% not found
STREET
SHELTER
DISUSED AREAS
Total adult population: 3863
Final Sample: 941 homeless
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
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)
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)
DATA: education
All sample Italian Foreign Street ShelterDisused
areasGeneral
populationNone 14.45 8.88 17.11 10.71 6.84 25.5 6.8Elementary school 21.68 29.28 18.05 18.45 17.45 28.37 26.4Middle school 33.16 39.47 30.14 34.52 34.43 30.95 31.7High school 25.19 19.41 27.94 30.36 32.78 13.47 27.2University 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
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
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
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
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 variablesμi = error term
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
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
[0.0100] [0.0102]
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
(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
[0.0275] [0.0279] [0.0224]Drug use 0.1069*** 0.1049*** 0.1067***
[0.0274] [0.0281] [0.0229]
Determinants of being employed
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
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
[0.0148] [0.0144]Observations 882 856 856 856Pseudo R-squared 0,1884 0,2003 0,2011 0,2039
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
Recommended