Ethnic networks and immigrants self-selection:
Evidence from public housing
VERY PRELIMINARY AND INCOMPLETE
Morgane Laouénan∗
Gregory Verdugo†
April 20, 2015
Abstract
Using a policy change which allowed the access new immigrants with children to
public housing during the 1970s in France, we estimate whether the interaction between ethnic networks and public housing influence the location choice and selection
of immigrants across local labor markets. Testing both for the effect of the quantity or the quality of the network in public housing, we find that quality matters:
new immigrants with children are attracted to cities with a larger participation rate
of their group in public housing. These immigrants are also negatively selected:
they are less educated and more likely to be unemployed than other comparable
immigrants in the location living in public housing. Our results add to the growing
body of literature suggesting that welfare policies influence the selection and the
outcomes of immigrants.
JEL classification:
Keywords: Location choices, public housing, immigrants
∗
Université catholique de Louvain. [email protected]
Banque de France and IZA. Address: 31 rue Croix-des-petits-champs, 75049 Paris Cedex 01, France.
Email:[email protected]
†
1
1
Introduction
The question of immigration is the subject of continuing debates and disagreements among
economists. However, two ideas seem beyond dispute. First, the economic impact of immigration on the host country depends in a large part on where immigrants choose to
settle. Theoretically, it would be optimal for the host country that immigrants choose to
settle in the most promising labor markets (Borjas, 2001).
Second, that ethnic networks affect immigrants outcome and their location choice is a
well-documented fact. A large literature argues that social ties between immigrants from
the same national origin provide information on the availability of jobs or housing across
locations.1
A growing literature has also explored the role of ethnic networks in the participation to welfare programs of immigrants and minorities (Bertrand et al., 2000; Aslund and
Fredriksson, 2009). Understanding whether and how welfare policies influence immigrants
behavior is of practical policy importance for the design of welfare programs. Theoretically, ethnic networks might serve as important channels for information transmission on
welfare availability and reduce the stigma associated to welfare participation.
However, while there is compelling empirical evidence of the effect of ethnic networks
on a wide range of outcomes, estimating their interaction with welfare poses additional
difficulties and, as a result, these issues remain highly controversial. This difficulty arises
in part because welfare participation of a given immigrant community is unlikely to be
exogenous and might reflect the effect of differences in unobservable characteristics across
communities and individuals (Manski, 1993). As a result, estimates using ethnic participation rates to identify the effect of networks might be biased by the correlation of these
participation rates with unobservable characteristics of ethnic groups.
In this paper, we investigate the relationship between ethnic network, welfare and
immigrant selection across French urban areas. We offer a different approach for identifying the causal impact of ethnic networks by focusing on a welfare program for which
the participation rate of immigrants is very large: public housing. Our study builds on
the observation that public housing is a major issue for immigration policymakers, par1
See e.g. Bartel (1989).
2
ticularly in Europe.2 In Denmark and in the Netherland, more than 50% of immigrants
live in social housing (Whitehead and Scanlon, 2007, p. 27). In Amsterdam, more than
80% of Turkish and Moroccan immigrants lived in public housing in 1990 (Musterd and
Deurloo, 1997). In London, 40% of foreign-born residents are social tenants (Rutter and
Latorre, 2008). In France, public housing participation rates of non-European immigrants
have increased to reach 50% in the last decades and are currently three times as large
than for natives (Verdugo, 2011).
One question of particular interest is whether ethnic networks in public housing influence the probability to live in a given location and, importantly, the selection and
outcomes of immigrants across locations. This question is particularly important for two
reasons. First, ethnic networks in public housing might distort the location choice of immigrants, attracting them in cities with a larger network but relatively unfavorable labor
markets prospects. Second, these networks might attract negatively selected immigrants
in neighborhoods and cities characterized by relatively high poverty levels and, as a result,
such inflows might be difficult to absorb by the local labor market and communities.
Several features of immigration and public housing in France are central to our analysis. In particular, changes in immigrants accessibility in public housing provide a unique
historical setting to credibly identify the effect of ethnic networks on the location choice
and selection of immigrants. While new immigrants were discriminated in public housing
during the 1960s, their access was liberalized to immigrants with children in the middle
of the 1970s. We can exploit these variations to derive whether the location choice of
immigrants after the policy change respond to differences in participation rates in public
housing across ethnic groups and locations. In addition, as public housing was designed
mainly for families with children, this reform did not affect immigrants without children.
Therefore, we can test whether any differential changes in the location choice between the
two groups could be attributed to ethnic networks in public housing.
Using a difference-in-differences identification strategy circumvent many of the omitted variable biases that typically plague estimates of network effects. By differencing
between groups and over time, we get rid of any common unobservable factors attracting
2
In the US, the supply of public housing is declining and very low, with a share of only 3% of occupied
rental units in 2000 (Baum-Snow and Marion, 2009).
3
immigrants in a given location and potentially correlated with the variables capturing the
effects of ethnic networks on the location choice. In particular, such methodology allows
us to control for time varying city fixed effects which affects in a similar way immigrants
with and without children from the same ethic group.
In practice, we estimate a series of location choice models that include both the direct
effect of ethnic networks and the effect of networks in public housing. We approximate
ethnic networks in public housing with two variables: the participation rate of the ethnic
group in public housing, which should capture the quality of the network, and the share
of the ethnic group among total public housing inhabitants in the city, which should captures the quantity of contacts from the network associated with living in public housing.
The estimates reveal that new immigrants with children react strongly to the share
of the group living in public housing. To benchmark the magnitude of this result, the
likelihood that an immigrant living as a couple with children choose the average city is
increased by about 1 percentage points (on a base rate of 3 percent) if one increase by
one standard deviation the participation rate in public housing of the group (about 20%),
holding constant a wide range of controls. The magnitude of this coefficient is similar to
the estimated effect of traditional ethnic networks captured by the share of immigrants
among total city inhabitants. We present a variety of checks to probe the robustness of
our results.
Then, in an effort shed light on the mechanisms linking public housing networks to
the location choice, we go on to investigate whether the selection of immigrants across
locations is related to the characteristics of the ethnic networks. We find that immigrants
communities characterized by larger ethnic networks are more likely to be unemployed
than similar immigrants in public housing from other ethnic groups. Overall, these results
suggest that immigrants attracted by ethnic networks do not make up a random sample
of the immigrant population and are negatively selected.
Taken together, our findings add new and important insights for the evaluation of
welfare programs and immigration policies. First, we contribute to the literature on the
impact of welfare policies on immigrants. While most recent work on the relationship between ethnic networks and welfare has focused on the US, studies on European countries
4
are more rare.3 Welfare programs are quantitatively much more important in Europe
than in the US and, as a result, have potentially a larger impact on immigrants which is
interesting to explore.
In particular, our results have also important implications for the design of welfare
programs targeted to improve the housing conditions of low income households (Olsen,
2003). Given their large budgetary impact, the amount of research on public housing
in Europe is relatively small.4 To the best of our knowledge, this is the first article to
consider ethnic networks in public housing. The fact that networks are an important
determinant of the probability to live in public housing suggests these policies might have
important disadvantages with respect to more individualized programs. In particular,
specific public housing projects run the risk of becoming magnets for particular ethnic
groups.
This paper also contributes to the literature on immigrant selection (Borjas, 1987,
1999). We provide within-country evidence that the interaction between ethnic networks
and welfare benefits can change the selection of immigrants across locations.
The remainder of this paper is organized as follows. Section 1 discusses the theoretical
framework. Section 2 describes the data and provides a brief overview of public housing
and immigration in France. Section 3 lays out our econometric framework and identifying
assumptions. Section 4 presents our main estimation results. Section 6 investigates the
relation between immigrant selection and ethnic networks in public housing. We present
some concluding remarks in the final section.
3
For the US, see e.g. Borjas (1999) and Kaushal (2005). For Europe, important exceptions include
Bratsberg et al. (2010) and De Giorgi and Pellizzari (2009).
4
Important exceptions for the French case are Le Blanc and Laferrère (2001) and Algan et al. (2011).
Our research also builds on Verdugo (2013) which studies the consequences of differences in public housing
supply across cities on the location choice of immigrants. We concentrate here on the effect of ethnic
networks and, in contrast to Verdugo (2013), we do not study the consequences of the dispersion of the
public housing stock. We also study how ethnic networks interacts with the selection of immigrants which
is not explored in Verdugo (2013).
5
2
How ethnic networks in public housing might influence the location choice and selection of immigrants?
Ethnic networks might influence the location choice through many channels, both directly,
because they might increase the number of speaker of the same language (Lazear, 1999),
or indirectly through their effects on other outcomes such as employment or housing. As
argued by Bertrand et al. (2000), ethnic networks might also provide information about
welfare opportunities. In the case of public housing, ethnic networks could influence the
location choice through two channels: first, informational through its quality, by giving
information on public housing availability or its characteristics in the location; second,
through its quantity, by changing the benefits related with living in public housing since
public housing projects with more members from the same ethnic group provide a larger
pool of available contacts. The location decision will also depends on idiosyncratic factors
such as the family size (Mincer, 1978). Housing costs might be larger for families than for
unencumbered persons, and, in addition, they will differ across locations if only families
are eligible to public housing.
Ethnic networks might also influence the sorting of immigrants by changing the selection of immigrants across locations. Our key hypothesis is that, while differences in
ability affect the return to economic opportunities across cities, they do not influence
housing costs which tend to be higher in high wage cities, or the benefits associated with
the ethnic network.5 If this assumption holds, high wage cities are only attractive to
immigrants with a minimum level of productivity which allows them to recoup the higher
housing costs or compensate differences related with ethnic networks. As a result, low
ability immigrants are more likely to be attracted by cities with a low living cost or with
5
We illustrate these ideas with a simple stylized framework. Newly arrived immigrants differ in their
ability to succeed the labor market and, at least initially, only receive a positive fraction αi of the average
wage. Assuming a linear and additively separable utility function, the utility of choosing a given city k
for immigrant i from ethnic group l is simply given by: Uilk = αi µk + plk − ck were µk is the average
wage in the city and ck denotes the housing costs and pkl the benefits associated with the ethnic network
that include both monetary and non-monetary components. Suppose now there are two locations: city H
and city L. Wages in city H are higher but so are housing costs. If differences in average wages are larger
than differences in housing costs, µH − µL > cH − cL , and differences in ethnic networks benefits are
)+(cH −cL )
such that immigrants above the threshold choose
small, there exist a threshold αi∗ = (pkL −pwkH
H −wL
to live in the high wage city while immigrants below choose the low wage city.
6
a larger ethnic network.6
For the receiving economy, the impact of immigrant inflows on the local labor market
will depend on the degree of labor mobility in response to immigrant inflows. If there
is a spatial equilibrium and differences in utility across locations are equalized for natives (Moretti, 2011; Roback, 1982), the initial location choice of immigrants will only
depends on local differences in benefits from ethnic networks, which by assumption do
not influence the location choice of natives. Labor markets will return to equilibrium as a
result of re-equilibrating outflows. If there exist a fixed costs of mobility across locations,
the adjustment may takes time.7 In this case, the optimal location choice of immigrants
would be the one which equalize spatial differences across locations which implies that
immigrant should choose locations with higher wages (Borjas, 2001).
However, if the location choice depends on welfare availability, immigration will directly have a negative fiscal impact on local communities. This negative impact will be
reinforced if, in addition, these immigrants are more likely to be unemployed because of
their lower skill levels or because they are segregated.
3
Data
We use a 25% extract of the 1968, 1975 and 1982 French census. The census includes
detailed information on labor force status, national origin and the location at the time
of the previous census. An important limitation is that it does not contain information
on wages or income, so we rely on information on labor force status or on occupations
to assess labor market outcomes. Another important limitation is that information on
public housing is not available in the 1968 census so we cannot estimate for this year any
variable related with public housing.
An immigrant is defined as a foreign-born individual who is a non-citizen or naturalized
French citizen. There is no arrival year variable so new immigrants are defined by those
6
The very homogeneous minimum wage of the French labor market reinforce such sorting if the lowest
wages are similar across locations.
7
The existing empirical evidence suggests local labor markets do not adjust instantaneously in response
to shocks (Blanchard and Katz, 1992). Evidence on outflows in response to immigrant inflows is also
relatively mixed (Card, 2001; Borjas, 2006).
7
reporting to have been living abroad at the time of the previous census.8 We focus on
male new immigrants since the location choice of females is overwhelmingly driven by
family migration in this period. Local labor markets are approximated using the 242
urban areas with more than 20 000 inhabitants. These urban areas aggregate contiguous
municipalities with constant boundaries so their geographical definition does not vary
over time.
Because of our large sample size, we define ethnic variables using the national origin
of immigrants, and not broad regional origins. We restrict our analysis to national groups
of immigrants from Europe and Maghreb with a significant number of observations both
in the 1960s and the 1970s: Algeria, Morocco, Tunisia, Spain, Italy and Portugal.9 In the
end, we focus on the location choice of ethnic groups arrived before the reform in 19621968 (observed in the and in 1968 census) and after the reform in 1975-1982 (observed in
the 1982 census). Additional information on the construction of the sample is provided
in the data Appendix.
4
Institutional Background : Public Housing Policies
and Immigration in France
Public housing in France is managed by local public housing authorities.10 Eligibility rules
are defined by the central government and are uniform over France. Eligible families can
apply to any public housing authority independently of their current location, given that
their income is under a threshold which is very high.11 For immigrants, one requirement
is to be legally living in France (as a French citizen or migrant with a valid residence
permit).12
Our empirical strategy exploits the fact that the accessibility of public housing to
immigrants changed between the 1960s and the mid-1970s. During the 1960s, strict rules
limited the eligibility of immigrants to receive public housing. Importantly, public housing
8
This is also the approach followed by the national statistical institute to estimate immigrant inflows
Immigrants from Asia or from South-Saharan Africa were quite rare before the 1980s. They represented only 0.4% and 0.3% of the population respectively in the 1960s against 1.8% and 1.6% in 2007.
10
This section builds in part on Verdugo (2013).
11
In 2007, more 70% of household were eligible (see XXX).
12
There is no information on whether an immigrant is legally in France in the Census.
9
8
agencies required immigrants to first maintain residency for 10 years before applying
(Schor, 1996, p.214), which rules out an effect of public housing on new immigrants who
arrived during the 1960s. As a result, the participation rate of immigrants in public
housing was much lower than that of natives: Pinçon (1976) reports that in 1968, only
5.5% of foreign workers in the urban area of Paris lived in public housing compared to
15.3% of natives.13
These discriminatory policies, combined with large immigrant inflows during the 1960s,
resulted in many immigrants living in slums near French cities. That policy changed
drastically throughout the 1970s in response to increasing pressure from public opinion
to eliminate the slums. In 1974, the newly elected government decided to end immigrant
discrimination in regard to access to public housing (Schor, 1996).
The eligibility of immigrants in public housing led to a large increase in the share of
immigrants in public housing in recent decades, as shown in Table 1. From 1982 to 2007,
while the share of natives in public housing was stable, Table 1 indicates that the share of
non-European immigrants increased widely. Figures in the Table also shows that public
housing is particular important for non-European immigrants.
[Table 1 about here.]
In Table 2, we highlight that participation rates in public housing varies widely across
locations for natives but also for immigrants. As a part of these differences might be
due to differences in household characteristics across locations, in columns X and Z, we
report differences in participation rates with respect to natives which were adjusted using
observable household characteristics.14
While about ZZ% of these differences is explained, a large share of these differences
between group and cities remain unexplained.
[Table 2 about here.]
13
As stated previously, the census did not collect information on public housing participation during
the 1960s. Unfortunately, there is no alternative source to estimate the evolution of the participation
rate of immigrants in those years either at the national or local level.
14
To do this, we use a simple linear model which control for age and education interacted with family
size. See appendix for details.
9
5
Empirical Strategy
Our first objective is to estimate whether ethnic networks in public housing influence the
location choice of immigrants across local labor markets. The policy change we exploit
arise from the change in the accessibility of immigrants to public housing after 1975. By
exploiting changes in the location choice of immigrants over time, we are able to get rid
of the effects of city unobservable which are constant over time and correlated with the
location choice of immigrants.
An important fact is that immigrants without children are not eligible for public
housing. This allows us to use a difference-in-differences empirical strategy which control
for many common omitted variable biases. In particular, we account for all differential
changes in unobserved city characteristics that are correlated with ethnic networks.15 We
also use a variety of comparison group approaches.
We focus on the location choice of immigrants arrived before the reform between
1962-1968 and just after in the 1975-1982 period. We exclude from the sample the period
1968-1975 which was a transition period.16 To establish the influence of ethnic networks
in public housing, we estimate the following difference-in-differences model:
ln
pTklt
pC
klt
= α1 Xkl,t−1 + α2 Zl,t−1 + γ1 sgkl75 1[t>1974] + γ2 pgkl75 1[t>1974]
+γ3 sgkl75 1[t≤1974] + γ4 pgkl75 1[t≤1974] + Γl + eklt
(1)
were pTklt and pC
klt are respectively the share of male new immigrants from the ‘treated’ and
‘control’ group from ethnic group k, observed in France living in the city l in census year
t. This dependent variable capture the differences in probability of the group to locate in
a given city.
The variable 1[t>1974] is the treatment indicator function equal to one if the arrival
year is superior to 1974 and zero otherwise. Xklt−1 is a vector of controls varying at the
city by ethnic group level including the share of the group in the city population, their
average number of years of education and the share of members who are married. The
15
Such comparison group approach is also used by Gelbach (2004) and McKinnish (2007).
In our baseline specification, we do not include data from years after 1982, as the location choice of
those years might not be comparable.
16
10
vector of controls Zlt−1 varies at the location level and contains the unemployment rates,
regional differences in wages and housing costs, and share of labor force employed in the
manufacturing sector. As a proxy for the socio-demographic characteristics of the urban
area, we include the share of immigrants in the population, the share of university graduates and the logarithm of the total population.17 All variables are introduced lagged and
thus are measured before the arrival of the immigrant group in France.
The vector Γl is a location fixed effect which absorb any systematic differences between treated and control group to choose a given location. In some specification, we also
experiment with a location by time fixed effect.
Key to the analysis is measuring the information on public housing actually available
to a given immigrant through its ethnic network. We follow much of the previous literature by using average group characteristics with respect to public housing as a proxy for
networks. Our first parameter of interest is γ1 which captures the effect of the share of
the group living in public housing in 1975, denoted sgkl75 , for immigrants with children
who arrived between 1975-1982, that is after the policy change. This last variable proxy
the knowledge and attitudes of immigrants with respect to public housing in the location.
The parameter γ2 captures the effect of the share of members from the ethnic groups
among total public housing inhabitants in 1975 denoted pgkl75 . This second variable is
our quantity measure and captures contact availability of new immigrants from the network in public housing.
Our model slightly differs from a standard difference-in-differences specification as we
do not have information on public housing in the censuses of the 1960s. As a result, we
cannot directly control for the effect of potential ethnic networks before the policy change.
We would have expected this effect to be statistically insignificant since new immigrants
in the 1960s were not allowed in public housing and were discriminated. As a partial
solution to this issue, the parameters γ3 and γ4 test for the effect of the ethic networks in
1975 on the location choice of immigrants arrived in the 1962-1968 period, that is before
17
There are three main advantages in estimating a linear model instead of a discrete choice conditional
logit models. First, such model allows us to include many ethnic group by year fixed effects which would
be more difficult to estimate in a non-linear framework. Second, using a linear model allows us to use
simple IV strategies to deal with endogenous variables. Third, a simple linear model simplify the inference
and allows to compute conservative standard errors. Specifications from a conditional logit model provide
qualitatively similar results.
11
the policy change. These estimates will provide a direct falsification test of our empirical
strategy. If we find that future ethnic networks have an effect on the location choice in
the 1960s, a period in which public housing was not accessible, this would suggest our
estimates are biased by reverse causality issues.
A final important point is that to deal with the fact that the size of the groups differ
widely, we standardize all variables such that they have an average of zero and a standard
deviation of one at the ethnic group by year level. This is equivalent to estimate the model
by using the dispersion within group of the variables. This an important issue since the
average share in the population or in public housing varies widely across groups.18 Standard errors are calculated using Huber-White robust standard error. Impact of removing
controls from estimates
6
Results: Ethnic networks in public housing and the
location choice of new immigrants
Results of estimates from the model of Eq. (1) are provided in Table 3.
[Table 3 about here.]
In column 1, we start by presenting results from a model in which the dependent variable is the location probability of couples with children. We find strong and statistically
significant effects for the quality of the network in public housing, defined as the share of
the group living in public housing, with a coefficient of about 0.2 measured precisely.
However, this result might reflect in a large part the effect of differences in ethnic
groups characteristics across locations. In the next two columns, we sequentially introduce controls for groups level characteristics and city level characteristics. In column 2,
we add controls for the average education, employment rate and age of the ethnic group
and find that the estimates are basically unchanged. In column 3, we add controls for city
characteristics. The coefficient decreases only marginally and remain strongly statistically
significant.
18
Small groups such as immigrants from Cameroon have an average share in the population of X%
while large groups such as immigrants from Algeria have Z%. By standardizing, we allow for a similar
effect of one standard deviation increase of the share of these groups.
12
These results contrast with those obtained with proxies for the quantity of the network in public housing. While the effect of the share of group among total public housing
inhabitants is positive and statistically significant in column 1 and 2, it is divided by three
when additional controls for city characteristics are included in the model. Overall, there
is little evidence of an effect of the share of the group in public housing.
In column 3, we estimate the model separately on singles. Since singles are not ineligible to public housing, these estimates provide a simple test of our identification strategy.
If we find an effect of ethnic networks in public housing on singles, this would suggests that
our proxies for network characteristics are correlated with several unobserved characteristics of ethnic groups or of cities. Reassuringly, we find small and statistically insignificant
effects of all variables related with public housing for singles.
In column 4, we report our baseline difference-in-differences estimate where the dependent variable is the relative location between couples and singles (using the log of
the odd ratio between these two groups as in Eq. (1)). With respect to specifications
estimated separately on couples with children, the coefficient of the share of the group
in PH increases by about 20%. In column 5, we include additional controls for city and
ethnic group characteristics. Once again, the results are basically unchanged. Finally, as
in Verdugo (2013), we also find a positive effect of the share of public housing inhabitants
in the city in columns 4 and 5.
Quantitatively, the estimated effect is relatively large. With estimates between 0.20
and 0.27, it is only slightly lower than the effect of the share of the ethnic group among
total inhabitants which are 0.29 and 0.27 for respectively couples and singles. As the average probability of choosing a city is about 3%, such coefficients implies that an increase
of one standard deviation of the share of the group in PH (20%), increases this initial
probability to 3.6 to 3.9%.19
An important result is that we find little evidence of an effect of future ethnic networks
in public housing on the location choice of those who arrived during the 1960s across all
specifications. Estimates of the effect of future ethnic networks are small and statistically
insignificant. While these tests are not definitive, they do provide some evidence that the
19
As discussed in the previous section, because the dependent variable are normalized, each coefficient
can be interpreted as the increase of the baseline probability for a one standard deviation increase of the
variable.
13
relationship between changes in public housing supply per capita and immigrant inflows
is not primarily driven by reverse causality.
Overall, our results point to strong evidence that the quality of the ethnic network
in public housing has a strong effect on the location choice of new immigrants living as
a couple with children. Importantly, our estimates do not change much when additional
controls for ethnic group or city characteristics are included in the model. This suggests
that our results are not primarily driven by the correlation of ethnic network variables
with unobserved group or city characteristics.
6.1
Robustness Checks
A first potential threat to our identification strategy is that singles from the same ethnic
group might be an invalid control group if ethnic networks are correlated with specific
amenities which attract couples with children but do not attract singles. We investigate
the plausibility of this hypothesis with a model including time by city fixed effects. By
including these fixed effects, we get rid of all common city specific factors which could
attract immigrants with children in a given location. Results of such model reported in
column 1 shows a slightly lower estimate, close to 0.19, but still statistically significant.
[Table 4 about here.]
In column 2, we test how much the results differ when using couples without children as
a control group instead of singles. Couples without children are potentially attracted by
the same unobserved factors than couples with children while they should be much less
attracted by public housing. On the other hand, this might be an imperfect comparison
group as they might also react in anticipation to public housing supply if they plan to have
children in the future. The results of such model is reported in column 2. Once again, we
obtain a positive parameter, which is lower and measured relatively imprecisely.
In column 3, we use an instrumental variable approach to estimate the model. As an
instrument for the share of the groups in public housing, we use the share of inhabitants
in the city living in public housing. This instrument is valid if the share of inhabitant
in public housing in the city do not have a separate effect on the location choice. This
exclusion restriction is questionable. In practice, we have included this variable in the
14
model in the previous regressions. The results indicate that the instrument is strong. The
coefficient we obtain is much larger.
As a final check, we run a series of placebo tests. To do this, we randomly assign a
different ethnic network than the ethnic network of the group and then we use the model
to estimate this impact. We repeat this procedure XXX times. Figure X represents the
distribution of these graphs.
Taken together, these robustness checks suggest that the basic results are not driven
by omitted variables. Although we cannot completely rule out this possibility, such shocks
would have to operate differentially across ethnic groups within cities. The precise pattern
observed in the data is difficult to reconcile with an explanation unrelated with the effect
of ethnic networks in public housing.
7
Ethnic networks in public housing and immigrants
selection
We have so far demonstrated that ethnic networks in public housing are an important
determinant of the location choice of immigrants. Quantitatively, the estimated effect is
substantial since it is similar to the effect of traditional measures of networks captured by
the share of the group in the population. In this section, we investigate the mechanisms
behind this effect.
We first ask how much new immigrants attracted by ethnic network in public housing
are a selected subgroup of the immigrant population. Our simple theoretical framework
suggest that, if the benefits of ethnic networks in public housing have a higher weight for
some individuals than wage differences across locations, immigrants attracted by ethnic
networks are going to be negatively selected. We test for the selection of immigrant using
the following model with individual data:
yiklt = α1 Xikl,t−1 + α2 Zl,t−1 + γ1 sgkl75 1[t>1974] + γ2 pgkl75 1[t>1974]
+γ3 sgkl75 1[t≤1974] + γ4 pgkl75 1[t≤1974] + Γl + eklt
15
(2)
where the dependent variable yiklt is an outcome for a new immigrant indexed by i from
ethnic group k observed in location l, such as employment or the median wage in the
occupation, or a characteristics of the immigrant such as the number of years of education.
The vector Xikl,t−1 is a vector of control including individual characteristics (xxx) and
ethnic group level characteristics (xxx) while Zl,t−1 contains the usual set of city level
control variables.
Results are provided in Table 5. In column 1, the dependent variable is the employment
probability. We find a negative correlation between the share of the ethnic group in public
housing and the employment probability of individuals from the group. Quantitatively, an
increase of one standard deviation of the share in public housing decrease the employment
probability by 3.6 p.p.
[Table 5 about here.]
In column 2, we add to the model city by year fixed effects which absorb all differences
across cities. The estimated parameter is smaller but still statistically significant, indicating a decrease in the employment probability of 1.8 p.p. for an increase of one standard
deviation of the share of the group in public housing.
In column 3, we investigate the correlation between This implies that and the level of
education by 0.3 years. We also find a negative coefficient.
As previously, we find no effect of future variables on the characteristics of new immigrants who arrived during the 1960s. This reinforce the intuition.
Conclusion
The question of the assimilation of immigrants in their host society is of great practical
importance for public policy. Empirical work, however, has found it difficult to credibly
identify whether welfare policies interact with the location and selection of immigrants
across local labor markets in their host society. In this paper, we provide evidence based
on a policy change of public housing in France to shed some light in those issues.
A wide range of evidence indicate that ethnic networks in public housing influence
the location choice and selection of immigrants across locations. We find that immigrants
16
are attracted by ethnic networks in public housing. We find that these immigrants are
negatively selected.
Taken together, our results have important implications for immigration and for the
evaluation of the impact of public housing. Our ethnic network estimates point out that
ethnic networks in public housing can attract low skilled immigrants. Ethnic network may
be of particular importance in the ongoing debate about policies aimed at helping housing
for the poorest part of the population. This is particularly important when considering
the design of welfare programs.
17
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19
Appendix
A
Dataset
Description of the Census of population 1968-1990
B
Decomposition
We use a simple OB decomposition. The model is estimated using the 1982 sample
separately for each city and for natives and immigrants: PiG = Xi βlG + ui for individual
i from group G = I, N , either immigrants or natives, in location l with Xi age and
education. We note by PlG the participation rate in public housing of group G in the city
and we have by definition PlG = XlG β̂lG where XlG are the average characteristics of the
group and β̂ the OLS estimate. The decomposition is:
PlI − PlN = PlI − P̂lN (XlI ) + P̂lN (XlI ) − PlN
XlI β̂lI − β̂lN + XlI − XlN β̂lN
(3)
where P̂lN = X I βlN is the counterfactual participation rate of immigrant predicted using
the model estimated on natives. The first term is the difference predicted by observable
characteristics, the second term is interpreted as the residuals.
20
List of Tables
1
2
3
4
5
Temporal Trend in Social Housing . . . . . . . . . . . . . . . . . . . . . .
Participation rates in public housing across ethnic groups and cities . . .
The effects of ethnic networks in public housing on the location choice of
new immigrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Additional estimates of the effects of ethnic networks in public housing on
the location choice of new immigrants . . . . . . . . . . . . . . . . . . . .
Selection of Immigrants and Labor Market Outcomes . . . . . . . . . . .
21
. 22
. 23
. 24
. 25
. 26
Table 1: Temporal Trend in Social Housing
1982
1990
1999
2007
% of Natives in Social Housing
% of Immigrants
20,2
25,2
20,5
28,2
22,8
34,0
20,8
33,3
% Immigrant in Social Housing from
Europe
Maghreb
Afrique Sub
Asie
18,9
34,5
24,6
29,8
19,0
40,0
30,9
28,8
19,2
49,7
46,2
31,2
17,0
48,5
47,4
26,5
Spain
Portugal
Italy
22,1
27,1
13,6
21,9
26,6
13,4
21,4
24,4
13,7
18,8
20,41
12,64
Algeria
Morocco
Tunisia
34,9
38,5
27,1
43,0
43,7
31,2
52,5
50,7
40,4
51,55
47,95
40,61
Turkey
37,4
39,2
44,4
38,59
Notes: Social housing is defined as renting an empty flat (not
furnished) belonging to a social housing organisation ; Sample
restricted to individuals in large cities.
22
Table 2: Participation rates in public housing across ethnic groups and cities
Natives
City
Paris
Lyon
Marseille
Lille
Bordeaux
Toulouse
nice
Nantes
Toulon
Grenoble
Strasbourg
Rouen
Valenciennes
Cannes
Nancy
Saint-Etienne
Std deviation
21%
18
15
22
15
12
10
16
11
11
24
30
16
7
19
21
0,06
Natives
Paris
Lyon
Marseille
Lille
Bordeaux
Toulouse
nice
Nantes
Toulon
Grenoble
Strasbourg
Rouen
Valenciennes
Cannes
Nancy
Saint-Etienne
Std deviation
21
18
15
22
15
12
10
16
11
11
24
30
16
7
19
21
0,06
Difference wrt natives
A. Non-European Immigrants
Algeria
Morocco
Observed Unexplained Observed Unexplained
2%
-2%
0%
-4%
8
4
7
3
4
2
0
-2
12
9
26
22
17
14
19
15
18
15
20
17
-1
-3
3
1
27
24
41
37
3
2
6
3
16
14
12
10
9
1
25
15
11
9
20
15
18
20
0
0
-2
-3
2
0
3
2
29
24
18
14
11
6
0,08
0,09
0,12
0,11
Tunisia
Observed Unexplained
0%
-3%
2
-2
2
0
21
18
14
10
10
8
1
-1
36
34
8
4
17
14
12
5
18
14
55
52
1
-1
17
14
1
-5
0,15
0,15
B. European Immigrants
Italy
Spain
Observed Unexplained Observed Unexplained
-5
-7
0
-4
0
-3
12
8
-1
-1
3
2
-4
-5
9
7
-2
0
0
-1
-6
-5
7
6
-2
-3
2
1
8
9
-1
-2
9
7
0
-2
14
12
9
1
16
6
0
0
0
0
-3
-1
5
5
-2
-3
0
-3
-3
-4
4
1
-2
-4
9
8
0,03
0,02
0,05
0,05
Portugal
Observed Unexplained
0
-.0882507
4
-3
3
-2
14
8
1
-4
4
-1
-5
-9
30
23
-5
-9
12
8
9
-4
7
0
14
12
0
-3
7
0
2
-4
0,09
0,08
Notes: ...
23
Table 3: The effects of ethnic networks in public housing on the location choice of new
immigrants
Share group
in PH * (t > 74)
Couples with children (’C)
Singles (S)
(1)
(2)
(3)
(4)
0.203*** 0.217*** 0.180*** -0.062
(0.055)
(0.056)
(0.051)
(0.075)
Diff (C - S)
(5)
(6)
0.273*** 0.242***
(0.076)
(0.072)
Share group wrt PH
0.236*** 0.246*** 0,095
inhabitants * (t > 74) (0.092)
(0.087)
(0.066)
0,024
(0.077)
-0.104**
(0.051)
0,071
(0.047)
Share PH
/ city population
0,023
(0.053)
-0,001
(0.053)
-0.189**
(0.088)
0.134**
(0.068)
0.175**
(0.081)
Share group
/ city population
0.426***
(0.110)
0.375*** 0.288*** 0.272***
(0.103)
(0.079)
(0.089)
-0.022
(0.043)
0.016
(0.030)
Share group
in PH * (t < 74)
0.014
(0.033)
0.015
(0.031)
0.014
(0.030)
0.011
(0.046)
0.005
(0.043)
0.03
(0.043)
Share group wrt PH
0,121
inhabitants * (t < 74) (0.084)
0.082
(0.073)
0.054
(0.053)
0.045
(0.057)
-0.103*** 0.008
(0.037)
(0.037)
N
890
890
890
890
890
-0,014
(0.060)
890
City FE
Yes
Yes
Yes
Yes
Yes
Yes
Ethnic level Controls
No
Yes
Yes
Yes
No
Yes
City level Controls
No
No
Yes
Yes
No
Yes
Notes: ...
24
Table 4: Additional estimates of the effects of ethnic networks in public housing on the
location choice of new immigrants
Share group
in PH * (t > 74)
Diff
Diff Couples
Diff
(Couples - Singles) (with - without Child) (Couples - Singles)
(1)
(2)
(3)
0.194**
0,127
0.506***
(0.084)
(0.100)
(0.098)
Share group wrt PH
-0,071
inhabitants * (t > 74) (0.060)
0,02
(0.050)
-0.091*
(0.052)
Share group
/ city population
-0.032
(0.045)
0.051
(0.032)
-0.02
(0.044)
Share group
in PH * (t < 74)
-0,006
(0.042)
-0.029
(0.041)
0.024
(0.045)
Share group wrt PH
-0.131**
inhabitants * (t < 74) (0.053)
0.073
(0.050)
-0.111***
(0.039)
N
890
890
890
City FE
No
No
Yes
City x Year FE
Yes
Yes
No
Additional Controls
Yes
Yes
Yes
Method
OLS
OLS
IV
Notes: ...
25
Table 5: Selection of Immigrants and Labor Market Outcomes
Employment Prob
Years of Education
(1)
-0.036***
(0.008)
(2)
-0.018***
(0.006)
(3)
-0.335***
(0.091)
-0.032**
(0.013)
-0.027**
(0.012)
-0.412*
(0.243)
0,019
(0.014)
0.026*
(0.014)
-0,239
(0.242)
-0,142
(0.294)
0.006***
(0.002)
0.007***
(0.002)
-0.007
(0.026)
-0,022
(0.025)
Share in ph inhabitants x 68
-0.003
(0.015)
-0.004
(0.015)
-0.055
(0.200)
-0.213
(0.259)
N
21 073
21 073
21 073
21 073
Yes
Yes
Yes
Yes
Share ethnic group in PH
Share in ph inhabitants
Ethnic group city
Share ethnic group in PH x 68
Additional Controls
(4)
-0.239***
(0.065)
-0,288
(0.228)
City FE
Yes
No
Yes
No
City x Time FE
No
Yes
No
Yes
Notes: ...
26