ISSN 2042-2695
CEP Discussion Paper No 1333
February 2015
The Impact of Immigration on the Local Labor Market
Outcomes of Blue Collar Workers: Panel Data Evidence
Javier Ortega
Gregory Verdugo
Abstract
Using a large administrative French panel data set for 1976-2007, we examine how low- educated
immigration affects the wages, employment, occupations and locations of blue-collar native workers.
The natives in the sample are initially in occupations heterogeneous in the presence of immigrants,
which might reflect a different degree of competition with low-educated immigrants. We first show
that larger immigration inflows into locations are accompanied by larger outflows of negatively
selected natives from these locations. At the same time, larger immigrant inflows into occupations
come with larger outflows of positively selected natives towards occupations with less routine tasks.
While we find no negative impact on employment, there is substantial evidence that immigration
lowers the median annual wages of natives. The estimated negative effects are also much larger in
cross-section than in estimates controlling for composition effect, which is consistent with the idea
that endogenous changes in occupation and location attenuate the impact of immigration on natives’
wages. We also find much larger wage decreases for workers initially in non-tradable sectors and
more particularly in the construction sector, which are much less likely to upgrade their occupation or
change location in response to immigration inflows.
K
eywords: Immigration, wages, employment
JEL codes: J15, J31
This paper was produced as part of the Centre’s Labour Markets Programme. The Centre for
Economic Performance is financed by the Economic and Social Research Council.
We thank the INSEE for having made the data available. The Census data used in this paper are
available upon request for researchers from the CMH. The authors accessed the DADS data via the
Centre d'Accès Sécurisé Distant (CASD), dedicated to the use of authorized researchers, following the
approval of the Comité français du secret statistique. The views expressed here do not necessarily
reflect those of any of the organizations with which the authors are affiliated. We thank Denis
Fougère, Kyle Mangum, Manon Dos Santos, Ahmed Tritah and Muriel Roger and seminar
participants at AMSE, Norface, SOLE, IZA-SOLE, CEP (LSE), and ESSLE-CEPR for very useful
suggestions.
Javier Ortega City University London and Centre for Economic Performance, London School
of Economics. Gregory Verdugo, Banque de France and IZA.
P
ublished by
Centre for Economic Performance
London School of Economics and Political Science
Houghton Street
London WC2A 2AE
A
ll rights reserved. No part of this publication may be reproduced, stored in a retrieval system or
transmitted in any form or by any means without the prior permission in writing of the publisher nor
be issued to the public or circulated in any form other than that in which it is published.
R
equests for permission to reproduce any article or part of the Working Paper should be sent to the
editor at the above address.
J
. Ortego and G. Verdugo, submitted 2014.
2
Introduction
A substantial part of the public concern about immigration in developed countries is a concern
about the impact that immigrants might have on the labor market outcomes of natives.
However, while much work has been done, credibly identifying the impact of immigration
still poses a significant empirical challenge.
A recent strand of the literature uses fixed and observable individual characteristics such
as education and experience to delineate the groups of natives and immigrants in competition
(see e.g. Borjas (2003)), or Aydemir and Borjas (2007)).
However, recent research has
criticized the hypothesis of perfect substitution within education/experience groups. Indeed, if
immigrants and natives with similar education and experience levels are not directly in
competition, changes in immigrant supply may have little impact on native wages.
4
As a
result, most of the impact of immigration could be concentrated on narrow groups of natives
who compete for jobs similar to the ones that immigrants tend to occupy.
To identify how much the impact of immigration varies across workers, a simple
empirical strategy would be to focus on natives who, before the immigration inflow, occupied
jobs where immigrants tend to be over-represented and are thus more likely to offer similar
skills in the labor market. However, such an approach faces important empirical challenges.
Indeed, natives initially occupying those jobs might endogenously respond to immigrant
inflows by changing their occupation or location. As a result, cross-sectional changes within
groups will be affected by compositional changes in the characteristics of workers.
5
Because
4
See Ottaviano and Peri (2012) and Manacorda et al. (2012) for evidence that immigrants and natives might be imperfect substitutes within
education/experience cells in respectively the US and the UK. Peri and Sparber (2009) show that low-skill natives in local labor markets
receiving more immigrant inflows tend to specialize in occupations requiring more abstract tasks in response to immigration. Dustmann et al.
(2013) show that recent immigrants start working in occupations offering a much lower wage than natives with similar observable
characteristics.
5
Using US decennial data, Card (2001) and Cortes (2008) have found no evidence of native outflows in response to immigrant inflows while
Borjas (2006), on the other hand, reports strong displacement effect. More recently, using US annual aggregate data, Wozniak and Murray
(2012) find that immigrant’ inflows are correlated with declines in outflows of low skill natives in the shorter run of one year. Recent
European studies found stronger evidence of displacement: using Italian data, Mocetti and Porello (2010) find evidence of displacement of
low skilled natives following immigrant inflows. For the UK, Hatton and Tani (2005) find consistently negative displacement effects.
3
job changers might not be selected randomly from the sending population, it is challenging to
identify how immigration affects the outcome of these workers if the composition of workers
across occupations changes significantly.
In this paper, we revisit the effect of low-educated immigration on the local labor
market opportunities of natives by exploiting detailed information on individual labor market
trajectories from a very large administrative panel data of the French labor force over a period
of 30 years. This large panel data provides for about 4% of French private sector employees
exhaustive information on the wages, occupations, the number of days of work, and the
geographical location at the municipality level of each job held during the period 1976-2007.
We also use very large (25%) sample extracts from the Census to estimate changes in low-
educated immigrant inflows across locations and to construct our instrument for immigrant
inflows. We combine these two datasets to investigate how the labor market outcomes of
blue-collar natives respond to inflows of low-educated immigrants.
Using panel data in this setting is attractive for several reasons. A first key advantage is
that detailed information on individual labor market trajectories is available. This implies we
can follow groups of workers narrowly defined by their initial occupation and test for
potential heterogeneity. In addition, given we would expect natives initially in jobs with many
low-educated immigrants to be more affected by immigration, the econometric model is
estimated separately for natives initially occupying blue-collar jobs across industries
heterogeneous in their initial share of immigrants.
A second advantage of using panel data is that we are able to control for unobserved
heterogeneity of workers. With longitudinal data, we can isolate the causal effect of
immigration on wages from any compositional change. In addition, we can directly
4
investigate how immigration affects the selection patterns of natives across occupations or
locations.
In the first part of the paper, we present some new facts on the effect of immigrant
inflows on the selection of natives across locations and occupations. We find compelling
evidence in alternative datasets of a moderate positive correlation between low-educated
immigrant inflows and outflows of blue collar natives from the location. Quantitatively,
baseline 2SLS estimates suggest that a 10 p.p. increase in the immigration rate (defined as the
ratio between the number of low-educated immigrants and blue collar natives in the
commuting zone) increases the outflow rate of blue collar natives by 3.2 p.p. An important
result is that outflow rates vary dramatically across occupations. In particular, our IV
estimates indicate a much larger displacement effect for workers initially in the most
immigrant-intensive industries such as non-tradable industries and particularly the
construction sector.
Second, consistent with evidence from Peri and Sparber (2009) for the U.S. or Ortega
and Verdugo (2014) for France, we find that natives are more likely to change occupation
following immigrant inflows. In particular, our results suggest they tend to move to
occupations of better quality which require less routine tasks. However, once again, the
estimated effect varies importantly across occupational groups. In particular, we do not find
any evidence of occupational upgrading for low-skill workers in the construction sector or in
the non-tradable sector. This last result suggests that a substantial share of workers,
particularly those with the lowest skill levels are not able to protect themselves from
competition with immigrants by moving to other occupations.
Third, there is also strong evidence that, within groups, workers changing location or
occupation are not a random sample of the sending population. Specifically, workers moving
to occupations with less routine tasks tend to be positively selected, in the sense that they
5
initially have higher wages conditional on their location and initial occupation. In contrast,
workers changing location tend to be negatively selected.
In the second part of the paper, we examine the impact of immigration on the wages and
employment of natives. We use variations from a balanced sample to isolate the impact of
changes in composition from the impact of immigration on wages. We find no evidence of a
negative impact of immigration on the average number of days worked or the employment
rate. In contrast, we find immigration to be negatively correlated with median annual wages,
the effect being the largest for workers in the non-tradable sector, particularly those in the
construction sector. For this group, our estimates suggest that an increase in 10 p.p. in the
immigration ratio at the local level generates a decrease of 3.6 log points in the median annual
wage. We also show that the equivalent cross-sectional estimates –i.e. the estimates with the
same data when their longitudinal dimension is not exploited- systematically overestimate the
impact of immigration on wages.
Overall, these results suggest that the impact of immigration is heterogeneous both
across and within occupational groups. The selective reallocation of a substantial share of
natives to different occupations and locations attenuates the final effects on wages, as argued
by Peri and Sparber (2009), but the extent of this reallocation varies widely across groups of
workers. In particular, there is no evidence of occupational upgrading for low skilled workers
in the construction sector. Finally, there is also strong evidence that within groups workers
moving to better occupations tend to be initially positively selected which implies that the
negative effect on wages is concentrated on workers with the lowest wages within groups.
There is a growing but still relatively small literature on the impact of immigration on
natives’ labor market outcomes using panel data. Most of the existing papers (see in particular
De New and Zimmermann, 1994, Bratsberg and Raaum, 2012, and Bratsberg, Raaum, Røed,
and Schøne, 2014) do not exploit the geographical variation in the number of immigrants and
6
find the effect of immigration on wages to be heterogeneous across different groups of natives
depending on their degree of complementarity/substitutability with respect to immigrants.
Foged and Peri (2014) considers instead the geographical and cross industry variation in the
proportion of immigrants in Denmark, and shows that immigration has a positive wage impact
on the less skilled natives and encourages them to work in more complex occupations. With
respect to this literature, the main contribution of our paper is to identify the nature of the self-
selection of natives across cities and occupations following immigration and also to
understand the extent of the bias incurred when using cross sectional data instead of panel
data to assess the impact of immigration.
6
The remainder of the paper is organized as follows. The first section presents the data
and provides some descriptive evidence on immigration into France. The second section
discusses the empirical framework. The third section investigates the relationship between
native locations and occupations and immigrant inflows. The fourth section examines the
impact of immigration on employment and wages. The last section concludes.
I) Data and descriptive evidence
Data Sources
Our primary source of data for the analysis comes from the matched employer-employee
panel DADS (in French Déclaration Annuelle de Données Sociales) collected by the French
National Institute for Statistics (INSEE).
7
The sample contains earning histories for all
individuals born in even-numbered years in October. Annual DADS data are available from
1976 to 2007 except for 1981, 1983 and 1990 were the data were not collected.
6
See also Lull (2014) for an interesting evaluation of compositional changes in the native population following immigration using a structural
econometric approach.
7
See e.g. Abowd et al. (1999) or Combes et al. (2008) for recent examples of papers using this dataset.
7
Three features of this dataset make it well-suited for studying the impact of
immigration: first, DADS data are collected from compulsory fiscal declarations made
annually by all employers for each worker and are thus considered very reliable.
8
The annual
wage data is considered of very good quality: the reporting, made by the employer, is used to
compute the income tax of the worker. Employers have no incentives to misreport wages as
this is severely punished with fines. Second, DADS data being an administrative panel data
collected for fiscal purposes, involuntary attrition has been evaluated to be modest.
9
Most of
the attrition comes either from an exit from a sector covered by the DADS or a supply of zero
days of work in a given year. Third, the sampling size is very large: we have information on
wages for 350,000 individuals per year over the period, representing about 4% of the
population working in the private sector.
10
The data contains a unique record for each employee-establishment-year combination.
For each individual job spell of any length in a given firm, the DADS collects information on
earnings, whether the job was part or full-time, the number of days of work and the location at
the municipality level. One drawback is that information on the number of days of work and
the precise number of hours worked appears to be rather noisy.
11
A relatively large share of
workers is reported to have worked full-time full year but have wages well below the
minimum wage. This creates a limitation to evaluate daily wages or changes in number of
days worked.
8
Not all the sectors of the economy are covered each year and the degree of coverage increases over time. In particular, civil servants and
most large public sector firms are excluded until the 1990s. Using LFS data, we estimate that they represented approximately 8% of the labor
force during the 1980s.
9
Koubi and Roux (2004) document that most of the temporary attrition from the DADS panel corresponds in practice to inactivity or a work
outside of the DADS covered sector (such as self-employment, or work in the public sector until the 1990s). Attrition in the DADS panel has
also been shown to be much lower than in typical survey-based panels such as the European Community Household Panel (ECHP)( Royer
(2007) ).
10
The sampling size doubles in 2002 when individuals born in odd-numbered years in October are added to the sample.
11
Information on whether an employment spell was full or part time is available over the entire period but the number of hours worked is
only available after 1993 (see Aeberhardt et al. (2011) for a discussion). Following the current practice, we have chosen not to use it.
8
We aggregate each job spell to obtain the total annual income and number of days of
work within a year. We retain information on occupation and industry of the job held during
the largest number of days. Note that education is missing from the data. Another important
point is that there is no information on nationality in the DADS but the data indicate whether
an individual is born abroad. We define as natives, in this dataset only, individuals who are
born in France and exclude individuals who are born abroad from the native sample.
12
Because of this last limitation, we do not rely on DADS data to estimate changes in the
number of immigrants across local labor markets over time. The lack of information on the
country of origin makes it impossible to construct an instrumental variable for changes in the
immigration ratio using differences in settlement patterns across immigrant groups. Instead,
we rely on Census data to estimate the changes in the number of low-educated immigrants
across commuting zones. Censuses of the population took place in 1975, 1982, 1990, 1999
and 2007. An important advantage of this dataset is that we use 25% extracts (20% in 1975)
of the Census population to compute changes in the immigrant ratio across locations over
time. Such large sample size are essential for an analysis of the impact of immigration since it
renders the results immune from attenuation biases as identified in Aydemir and Borjas
(2011). As is conventional, an immigrant is defined as a foreign-born individual who is a non-
citizen or naturalized French citizen.
Local labor markets are defined using the 2010 definition of commuting zones (zones
d’emploi). Commuting zones are designed by the INSEE to approximate local labor markets
12
Many French-born citizens who should not be counted as immigrants were born in Algeria before independence in 1962: using the census,
their share among 18-65 years old natives is 2.2% in 1982 and 1% of in 2007. More generally, the share among natives of French-born
citizen who are born abroad is rather small and declining over time: 4.4% and 3.2% in respectively 1982 and 2007. Since we are not able to
distinguish them from immigrants, they are excluded from the DADS sample of natives.
9
using information on daily commuting patterns. They aggregate the 36 699 existing French
municipalities into 297 labor market regions.
13
Our empirical implementation uses variations in low-educated immigrant inflows across
commuting zones in France from 1975 to 2007. We estimate first-differenced models in
which we relate changes in native labor market outcomes obtained from the DADS data with
changes in the share of low-educated migrants obtained from the Census data using years in
which both census and DADS data are available.
14
Low-educated immigrants are defined as
immigrants with a level of education below high-school graduation. Since DADS data does
not contain information on individuals out of the labor force, we focus on prime-aged male
workers aged more than 25 and less than 54 who have relatively strong labor market
attachment and for whom non participation during a full year is less likely to be a major
issue.
15
This implies we concentrate on individuals aged 25 to 45 in census year t and 32 to 52
or 34 to 54 in census year t+1, where t is a census year and t+1 the year of the next census,
and that we consider changes in the number of immigrants within commuting zones over
periods of 7 to 9 years.
Immigration in France: Descriptive Evidence
According to the last census, in 2007, 5.2 million immigrants lived in France, which amounts
to 8.3% of the population. The share of immigrants in the population is thus lower than in the
U.S. and the U.K. (respectively 11.5% and 11.9%, see Dustmann et al., 2013, p. 11). However,
13
Commuting zones are also used with the DADS data by Combes et al. (2008) and Combes et al. (2012). They are defined in a consistent
way over time. We drop commuting zones from Corsica (less than 0.3% of the population), as a change in the département code in 1976
complicates their matching across datasets over time.
14
Given DADS data were not collected in 1975 and 1990, we match census data from the 1975 and 1990 census with respectively the DADS
data from 1976 and 1991.
15
We also apply these restrictions to avoid issues with changes in retirement age over time. Young workers are also eliminated to avoid
problems with potentially endogenous labor market participation in case immigration influences education decisions (see Hunt (2012) or
their employment probability (see Smith (2012).
10
from 1975 to 2007, France experienced an increase of 5 p.p. from 13% in 1975 to 18% in 2007
in the share of immigrants among the group of male workers
16
with a level of education below
high-school. The geographical origin of immigrants also changed during the period: the share
of European immigrants decreased from about 60% in 1975 to only 32% in 2007.
Table 1 reports the share of foreign born workers among blue collar workers in 1999 in
the tradable and non-tradable sectors for the country as a whole and for some large cities (see
Appendix for details on industries and occupation classifications used in the paper).
17
As shown
in the table, low-educated immigrants tend to be overrepresented in some sectors and regions,
particularly in the non-tradable sector. The share of foreign born workers is 4 p.p. and 10 p.p.
higher in respectively the non-tradable sector and the Construction sector relative to the tradable
sector. These figures suggest that competition for jobs with low-educated immigrants may be
strongest in the non-tradable and construction sector.
Immigrants are also unevenly distributed across regions: while only 3% of blue collar
workers are foreign born in Brittany in the non-tradable sector, the share of foreign born is 33%
in Paris. Similarly, the share of foreign born blue collar construction workers is 45% in Paris
compared to 5% in Brittany. However, Table 2 indicates that in both regions, the share of
foreign born workers expanded in the Construction sector in the last 30 years.
II) Theoretical Framework
Immigration may impact the labor market outcomes of natives through several channels which
are interrelated, including wages, location or occupations. Here we discuss these channels.
Assuming a CES production function with different occupation groups each of them
16
Unless otherwise indicated, figures in this section male workers aged 18-64 which are not students or in the military.
17
We rely on standard classification systems of industries. See appendix for details. Following Hanson and Slaughter (2002) and
, the group of tradable industries includes manufacturing, agriculture, mining, finance and real estate.
11
aggregating individuals heterogeneous in their labor productivity, the period t log wage for
individual
i
in local labor market l and in occupation group
k
can be written as (see Appendix):
log ( , ) log
k lt
it klt it i
klt
I
w k l B X
N
, (1)
where
is the elasticity of substitution across occupation groups,
i
is individual i’s
unobserved (and constant) productivity,
it
X
is a set of individual observable characteristics,
lt
N
is the number of natives in occupation group k and
lt
I
the number of low-educated
immigrants in location
l
. We discuss below how we empirically define these occupation
groups. A key point is that, as in Smith (2012) or Dustmann et al. (2013), we do not pre-
allocate low-educated immigrants to a particular group but estimate the response of various
native groups to a change in the share of low-educated migrants in the location. To introduce
heterogeneity in the impact of immigration across groups in the simplest way, we follow
Dustmann et al. (2013) and assume that a share
k
of low-educated immigrants have a skill
level corresponding to the occupation group
k
, and that they are perfect substitutes within
occupation groups.
Workers might endogenously adjust to immigration by changing location as argued by
Borjas et al. (1997) or occupation as argued by Peri and Sparber (2009) and Amuedo-
Dorantes and de la Rica (2011). If immigration changes the relative price of skills in the labor
market, some workers might move to another location or to another occupation in response to
immigrant inflows. To illustrate in a simple way how endogenous self-selection may
confound the impact of immigration in cross-section regression in this framework, assume
workers differ in their ability to move across locations as in Moretti (2011) or Beaudry et al.
(2010) or in their ability to move across occupations. As a result, individuals changing
location or occupation are not a random sample of the initial population of workers. Assume
next that native outflows are such that the efficiency units of labor supplied by natives change
12
by
klt k k lt
NI
ò
when the share of immigrant increases by
lt
I
in the location.
18
The
parameter
k
ò
is the share of native net outflows in group
k
with respect to a change in
immigrant labor supply. When
1
k
ò
, there is perfect displacement, while when
0
k
ò
there is
no response. This implies that changes in the average log wage
log
klt
w
of natives in
occupation group
k
, location
l
and period
t
can be expressed as:
1
log log
klt klt k lt klt klt klt
w B p X
(2)
where
,
1
klt i
i k l
klt
N
is the average productivity of workers and
,
1
klt it
i k l
klt
XX
N
is the
average individual observable characteristic. The term
lt
p
captures the change in the share
of low-educated migrants in the location while the term
1klt klt
captures changes in the
unobserved productivity of workers in occupation group
k
. There is positive selection
correlated with immigrant inflows if
1
,0
klt klt it
cov p
and negative selection
otherwise.
In this simple framework, the parameter
(1 )
k
kk
ò
is a function of the elasticity of
wages to the labor supply of immigrants, and of the elasticity of mobility of natives. If
mobility costs are sufficiently low for a large number of individuals, that is
1
k
ò
, native
outflows will equalize wages across locations and thus immigration does not have an impact
on the local wages of natives, but only at the national level, as in Borjas (2006). Instead, if
mobility costs are substantial, native internal mobility might not be sufficient to offset the
local effect of immigrant inflows on wages. To evaluate the importance of such channels, we
18
The previous equation is obviously a reduced form. Modeling the sorting of workers across locations is beyond the scope of this paper.
13
test for the impact of immigration on location and occupation and investigate the patterns of
selection of location and occupation-movers.
Econometric Model
To take the previous equations to the data, we need to make some additional assumptions. We
assume, as common in the literature, that changes in
log
klt
B
over time in a given location and
occupation can be decomposed by a full set of fixed effects. Then, equation (A) and (B) lead
to simple regression models of the form:
klt k lt k lt k klt kt kr klt
y p Z X
ò
(3)
where
klt
y
is the change in a given outcome between two periods for occupation group
k
in
location
l
,
kt
are time fixed effects and
kr
are region fixed effects. The vector
lt
Z
contains
several location and industry specific factors varying over time. Following the literature,
19
the
term
lt
p
in the empirical model is defined empirically as the change in the low-educated
immigrant ratio with respect to the initial number of blue collar workers in the location:
1
1
,
lt lt
lt lt
lt I I
lt
N
II
pe
. The use of a similar numerator across occupation groups facilitates the
interpretation of the results given the size of groups might vary widely. The term
1lt lt
II
e
is an
indicator function which is equal to one if the number of low-educated migrants is strictly
increasing in the location and is zero otherwise. We condition the immigration rate to be
positive to avoid our results to be affected by the rare locations and periods in which the
numbers of low-educated migrants decrease in the population.
The model is estimated by pooling multiple decades as stacked first differences.
Because the model is estimated in differences, it eliminates time-invariant wage differences
19
See e.g. Card (2001), Card and DiNardo (2000), or Mazzolari and Neumark (2012).
14
across occupation groups and locations that may be correlated with the share of immigrants.
The specification also includes changes in average individual-level demographic controls (
klt
X
) and changes in area level controls as well (
klt
Z
). The additional controls included in the
regressions are the changes in the share of white collar and blue collar workers, the share of
workers in construction, the overall share of workers in manufacturing industries and the
average age of workers. The model also includes regional fixed effects. As in
or Smith (2012), regressions are weighted by the number of observations used to
compute the dependent variable: this implies that we weight first-differenced equations by
1/2
1
(1/ 1/ )
klt klt
NN
where
lkt
N
is the number of observations used to compute the outcome
variable.
20
Native Groups’ Definition
Equation (2) makes clear that, absent a strategy for isolating variations in wages that are
independent of changes in the average unobserved
i
in the occupation, changes in wages
reflect both the impact of immigration on the supply of labor and on the unobserved average
productivity of workers. To get rid of the change in unobserved characteristics of workers, we
adopt a simple empirical strategy. The panel aspect of our dataset allows us to define the
‘treated’ occupation group of natives by their initial occupation and location.
Using the initial occupation to define occupation groups has several crucial
advantages. Natives initially sharing the same occupation are more likely to offer in the labor
market a more similar set of skills. Our rich dataset allows us to focus on narrow groups of
workers who are more likely to compete with low-educated migrants, namely those in
20
This formula comes from straightforward calculations of the variance of a first-difference variable measured with errors under the assumption
that the measurement error is proportional to the number of observations and is independent across years.
15
occupations with a larger share of immigrants. If the effect of immigration on wages differs
importantly across groups of natives, it might be important to allow for a different effect.
We also control directly for changes in unobserved heterogeneity through the use of a
balanced sample in which the composition of natives included in the sample is maintained
constant over time. A second interest of our approach is that our strategy controls directly for
changes in unobserved heterogeneity through the use of a balanced sample. Estimates of the
impact of immigration obtained from a balanced sample are by definition not driven by an
endogenous change in the composition of natives in the occupation group.
Occupation groups are defined here by using the interaction between being a blue
collar worker and working in a given industry in the initial period. We use four different
groups. We define a first group pooling all blue collar workers to estimate the average effect
of immigration on these workers. Two other groups are defined by distinguishing between
workers in the tradable and non-tradable industries. Finally, we define a fourth group isolating
workers from the construction sector from the non-tradable industries group. If the supply
effect of immigration differs across groups of natives, we should expect a larger effect on
workers initially in non-tradable industries, particularly in the construction sector. On the
other hand, if blue collar workers are all perfect substitutes in production, then we expect the
impact of immigration to be similar across groups.
Identification
As discussed previously, it is very unlikely that immigrants’ geographic settlement decisions
are exogenous to local labor market conditions. If immigrants settle disproportionately in
areas with better local labor market conditions, then ordinary least squares (OLS) estimates of
k
will be biased. One important concern is the possibility that pre-existing trends are
correlated with both changes in the immigrant ratio and changes in the variables of interest. If
this is the case, the estimates presented here may simply be a spurious correlation. To deal
16
with this issue, the model includes a control for regional specific trends
r
for 22 French
regions. Due to the inclusion of a vector of regional dummies
kr
, the coefficient of interest
k
is identified by within regional variation. The inclusion of regional dummies means that
any confounding factor would have to vary within region over time. Including these fixed
effects thus addresses some of the concerns raised by Borjas et al. (1997) when one does not
control for the various confounding factors affecting outcomes across locations.
As in Card (2001) and Cortes (2008), our identification strategy uses the initial
proportion of co-nationals in the commuting zone as an instrument for future immigrant
inflows. Specifically, the predicted number of low-skill immigrants in region
r
is given for
each census year
t
by
,1
1
,1
ˆ
*
cl t
lt ct ct ct
cc
ct
I
I I I
I
where
,1
,1
cl t
c
cl
t
I
I
is the proportion in the previous census date
1t
of country
c
immigrants,
including both low and high skill immigrants, living in region
l
, while
ct
I
is the total number
of immigrants from country
c
in France in year
t
. Given the large sample size of the census,
we distinguish groups of immigrants by using the maximum number of nationalities available,
namely the 54 different countries of birth which are always reported separately across
censuses. Following Hunt and Gauthier-Loiselle (2010), we explicitly determine
1ct
using
immigrants from all education and experience levels to have a greater role of geography and
ethnic networks. By doing so, our aim is to give less importance to economic factors that
might attract workers with low levels of education and experience specifically in a given
region. Because the endogenous variable is a percentage, we define our final instrument by
17
using the change in the number of predicted immigrants in the location divided by the initial
number of natives, to define our final instrument as:
1
1
ˆˆ
lt lt
lt lt
II
lt
II
L
e
The validity of the instruments used to predict changes in the immigrant ratio over
time is examined in Table 3. Observations correspond to changes between census years, i.e.,
1975-1981, 1981-1990, 1990-1999, and 1999-2007.
Column (1) reports estimates from a simple bivariate model while column (2) includes
a full set a control variables. In both specifications, the coefficient is positive and strongly
significant. A comparison between columns (1) and (2) indicates that adding the control
variables lowers by a third the estimated parameter but also raises the precision of the
estimate. In column (3), we examine results from unweighted estimates: the coefficient
declines by a fourth but still remains statistically significant. Overall, the Fisher statistics of
the instrument indicate it is reasonably strong across the various specifications. With F-
statistics greater than 10 in most specifications, they easily pass the weak instrument test.
III) Immigrant Inflows and Natives’ Mobility Patterns
Before investigating the impact of immigration on wages and employment, we first provide
evidence on the relationship between immigrant inflows and natives’ location and
occupations. In contrast with the existing literature, the panel dimension of the data allows us
to focus on natives defined by their initial occupation and to investigate selection patterns. In
a first and a second subsection, we investigate the correlation between immigrant inflows and
changes in locations and occupation of natives. In a third subsection, we look at the selection
patterns of movers in an attempt to understand how selective change in location and
occupation affects the composition of natives labor force within locations and occupations.
Local labor market mobility
18
We begin by assessing the relationship between local immigrant inflows and native inflows
and outflows. A simple accounting identity relates the net annual change in native total
population
klt
N
of occupation group
k
in location
l
with the number of individuals who
moved into the location (
klt
I
) and the number of individuals who left the location (
klt
O
):
1 1 1klt klt klt
N I O
.
21
Following Card (2001), Card (2009) or Cortes (2008), we estimate
separately for each occupation group
k
the model of Eq. (3) in which the dependent variable is
either the inflow rate
1
/
k
lt klt
IN
or the outflow rate
1
/
k
lt klt
ON
.
Panel 1 in Table 4 shows the results for different groups of industries. Within each
panel, the first line provides OLS results while the second line reports 2SLS results. For all
groups of workers, with the exception of blue collar workers in the tradable sector, both IV
and OLS results indicate there is a positive correlation between immigrant inflows and native
outflows in the initial location. OLS results indicate that an increase of 10 p.p. of the
immigration rate into the location is associated with an increase in outflow rates of 0.7 to
0.9 p.p. depending on the group. On the other hand, IV estimates are up to four times larger
than OLS estimates. Interestingly, the estimated effects are much larger for blue collar
workers initially in immigrant intensive sectors such as those in the non-tradable sectors and
in the construction sector: we find that an increase of 10 p.p. in the immigration rate raises the
share of movers by 1.6 p.p. for blue collar workers and by 3.6 p.p. for workers in the
construction sector.
Turning now to the relationship between variations in the immigrant ratio and native
inflows, OLS results indicate for all occupation groups a strong positive correlation.
Parameter estimates are remarkably similar across groups of industries. The OLS estimates
21
Outflows are computed by using information on the occupation of the individual in the period t+1, and whether this individual has
changed location in t, independently of her occupation in period t. Inflows are computed by using the number of individuals in the
occupation in period t+1 who worked in a different location in period t independently of their initial occupation.
19
suggest that an increase in 10 p.p. of the immigration rate is correlated with an increase of
1.7 p.p. in the inflow rate of natives into these occupations. These parameter estimates imply
that the arrival of 100 immigrants into the location is correlated with the exit of 16 native blue
collar workers and the entry of about 15 native blue collar workers.
22
However, there is no
strong evidence for a causal effect on native inflows. While 2SLS estimates are positive and
not very different from OLS estimates, they are measured very imprecisely and are not
significantly different from zero.
The previous results have several limitations related to the characteristics of the DADS
data. The sample only includes individuals with a positive number of hours worked in the
private sector in both periods to compute inflows and outflows. Selective attrition to
nonparticipation or to a sector not covered by the DADS data could bias our results if
immigration is correlated with a large share of native workers dropping out of the labor
market or moving to the public sector. To address this concern, we assess the robustness of
the previous results by using alternative inflows and outflows rates computed with the French
census. An important advantage of the Census data is that it contains the entire population and
also includes retrospective information of the location at the municipality level at the time of
the previous census. Unlike in the DADS data, information on the initial occupation of native
workers is not available in the Census. Instead, we define groups by using information on
education and use information on the previous location to define inflows and outflows rates
across commuting zones for different education groups. We use four education groups: two
low skilled groups, primary or secondary education, and high-school and university
graduates.
23
To be able to make a comparison with the previous estimates, our dependent
22
When the model is estimated using blue collar workers, the native outflow rate and the immigrant inflow rate are both divided by the initial
number of blue collar workers in the location.
23
See the Appendix for details on the construction of these education groups.
20
variable is, as previously, the change in the share of low-educated migrants over the initial
number of native blue collar workers.
Consistent with the previous evidence, Census data estimates in panel 2 of Table 4
strongly indicate a positive correlation between both outflows and inflows for low skilled
workers. OLS estimates are slightly lower than those obtained with DADS data, indicating an
increase in 0.4 p.p and 1.1 p.p. for respectively inflows and outflows for an increase in the
immigrant ratio of 10 p.p. As previously, 2SLS estimates are only statistically significant for
outflows. The estimated IV coefficients are also much larger than the corresponding OLS
estimates, indicating an increase of about 2.4 p.p. and 2.7 p.p. of the share of movers for
respectively primary and secondary education workers for an increase of 10 p.p. in
immigration rate.
Taking the previous estimates together, three things are clear. First, immigrant inflows
appear to be positively correlated with both larger inflows and outflows of native blue collar
workers and of low skilled natives across employment areas. The evidence also suggests that
only native outflows seem to be causally related to immigrant inflows. Second, the fact that
there are two opposite inflows and outflows indicates that immigration is correlated with a
change in the composition of native blue collar workers in the location. Our results point to
the evidence of a much stronger displacement effect on native workers initially in jobs more
likely to be taken by low-educated immigrants. Third, the fact that immigrant and native
inflows are also positively correlated indicates that common positive economic shocks might
drive both native and immigrant location choice. This correlation between immigrant location
choice and local economic conditions should bias estimates of the impact of immigration on
labor market outcomes of natives.
21
Occupational Mobility
Next, we examine the impact of immigration on the occupations of natives. Following the
literature, we do not examine whether immigration impacts the probability to change
occupation but whether natives are more likely to move to better quality occupations in
locations with larger immigrant inflows. To capture changes in the skills supplied across
occupations over time, we focus on changes in the average routine to abstract intensity of
tasks performed in the occupations.
24
The task contents of an occupation provides an
approximation of the basic skills required to perform it ( Autor et al. (2003),Acemoglu and
Autor (2011),Goos and Manning (2007) ). To interpret the parameter estimates, our routine to
abstract intensity index variable is normalized to have an average of zero and a standard
deviation of one across the distribution of occupations. Note that, in the initial period, blue
collar workers can be in one of the 6 distinct occupations that we have in our classification.
25
Within the blue collar workers group, the occupation with the lowest routine to abstract skill
intensity is “laborer” with an index of 0.51 while “machine operators” have an index of 1.30.
In the final period, there is no restriction and workers initially in the blue collar worker group
may be in any kind of occupation.
Table 5 shows the results. Within each panel, the first column provides intent to treat
estimates using all workers initially in the occupation group, including those who have moved
to another location or occupation. In the second column, those moving from the location have
been excluded while the third column also excludes those who are not in the same occupation
group. Finally, the last column uses the variations from repeated cross-section in the occupation
group and location. Each subpanel refers to a different occupation group.
24
Abstract tasks are "complex problem solving" while routine tasks require repetitive strength and motion and non-complex cognitive skills
and thus do not require good language skills. Data on task intensity come from the abstract and routine task intensity indexes calculated by
Goos et al. (2010, Table 4 p.49) from the Occupational Information Network (ONET) database that we have matched manually with French
occupations classifications. See Appendix for details.
25
See Appendix for details.
22
In both samples, OLS results indicate very small, slightly positive coefficients, which
are most of the time statistically insignificant. On the other hand, 2SLS models indicate that
there is clear evidence that immigration is correlated with a decrease in average routine
intensity for native workers. Quantitatively, we find that an increase of 10 p.p. of the
immigrant ratio lowers the average routine to abstract intensity by 7.9 p.p. and 3.9 p.p. for
respectively workers initially in the tradable and non-tradable sector. In contrast, for
construction workers, there is no evidence of a correlation between immigration and the
change in routine intensity levels: the coefficient is much lower than for other groups and is
not statistically significant.
We also find very little difference between estimates including and excluding location-
movers in column 1 and 2. We also observe no effect of a much smaller effect when we
concentrate on those who stay in the same occupation group during both periods in Column 3.
Note that by definition these workers can only have moved to an occupation within the blue
collar worker group. The coefficient is also small and not statistically significant for most
occupation group with the cross-section sample. These results imply that most of the effect is
driven by workers who have moved to an occupation outside of their initial occupation group.
Is there Positive or Negative selection in Change in Location or Occupation?
We now investigate how individuals changing occupation or location are selected with respect
to the sending population. To do so, we first estimate the residual wage dispersion within
occupation group and locations by regressing the individual log daily wages for each occupation
group in each year against commuting zone fixed effects and on a full set of age fixed effects.
By using a residual wage dispersion with respect to the sending population, we investigate
whether those who have moved or those who have left had lower or higher wages than those
who had stayed with respect to the wage distribution in the initial period. Then, following
23
Borjas (1999), we define positive selection for occupation group
k
initially in location
l
in
period
1t
as a situation in which:
(log | movers in t+1) (log |stayers in t+1)
iklt iklt
E rw E rw
where
iklt
rw
is the residual wage level in the original location in the initial period. If there is
positive (resp. negative) selection, emigrants are on average more (resp. less) productive than
non-migrants with respect to the distribution of wages in the initial location. To investigate
these selection patterns, we first run the following regressions at the individual level:
12
1
()
iklt k iklt k iklt lt k lt k lt k klt kt kr klt
Move rw rw p p Z X
ò
where the dependent variable
1iklt
Move
takes the value 1 if the individual has left the location
in period t+1. The coefficients of interest are
1
k
and
2
k
, respectively the coefficients of the
residual wage in the initial period and of an interaction term between the change in immigrant
ratio and the initial residual wage. The first coefficient indicates whether those who had moved
to another commuting zone had lower or larger relative wage with respect to the initial wage
distribution in the group in their initial location. The second coefficient tests for a potential
interaction between the selection term and inflows of immigrants in the location. Estimation is
based on 2SLS using the previously described instrument for
lt
p
and the interaction of this
instrument with the residual wage for the interaction term
iklt lt
rw p
.
Results are presented in Panel A of Table 6. Column 1 shows that movers in the
occupation group of blue collar workers are negatively selected. Parameter estimates indicate
that an increase of one standard deviation of the residual wage (about 0.32), decreases the
probability to change location by 3.7 p.p. (0.32 x 0.117). In Column 2, we introduce an
interaction term between the residual wage and the share of immigrant inflows. The term is
small and statistically insignificant. This suggests that there is no evidence that negative
24
selection varies in places receiving more or less immigrants. Other columns show similar
estimates for other occupation groups: for all groups, we also find a negative selection with
respect to the sending population and no effect of immigrant inflows on the selection patterns.
In Panel B, we estimate a similar model but in which the dependent variable is the change
in routine task intensity in the occupation between period t+1 and t for a given worker. In order
to guarantee that the variations in occupations come from individuals experiencing the
immigrant inflows and thus who have stayed in the location, we focus on location stayers in
this sample. Across occupation groups, we obtain a negative coefficient of the residual wage:
this implies that individuals with higher initial wages are more likely to experience a decrease
in their routine intensity during the period. Thus, individuals moving to occupations with more
abstract tasks and less routine tasks are positively selected. As previously, the interaction term
is not statistically significant which implies there is no strong evidence that the selection pattern
varies widely depending on immigrant inflows received by the location.
26
Overall, the results presented in this section confirm the intuition from previous work
that changes in locations and occupations are endogenously related to immigrant inflows.
Especially important is the fact that the impact differs across occupation groups, with no
evidence of an impact of immigration on occupations and larger displacement effects for
construction workers.
By studying the selection patterns of workers, we were also able to highlight that those
who changed occupation and location are not a random sample of the initial population: workers
who change location tend to be negatively selected and have lower initial wages with respect
to the sending population. This implies that the selective exit of workers with lowest initial
wage from the location will tend to increase wages in the location in the second period. In
26
We explored more flexible specifications to estimate the interaction term and also estimated the previous models using OLS. The results
were broadly similar and are available upon request.
25
contrast, workers who move to occupations with less routine tasks tend to have higher initial
wages and thus tend to be positively selected. This outflow of workers with higher initial wage
to other occupations will tend to decrease wages within the occupation group.
IV) The Effect of Immigration on Labor Market Outcomes
Next, we study the impact of immigration on the wages and employment of natives. We have
provided in the previous section evidence of selective mobility of natives across locations and
occupations in response to immigrant inflows. To assess how this endogenous mobility affects
the estimated wage impact of immigration, we estimate four alternative models which differ
with respect to their inclusion rule in the final period. First, we address concerns about sample
selection in our group of location stayers by carrying an intention-to-treat analysis. In this
specification, we include movers in the sample but assign them the immigrant inflow into
their initial location. Then, we estimate a second model in which individuals who have moved
from the location are excluded. This ensures that our identifying variation in this second
model arises from changes in immigrant inflows for stayers. A comparison between these two
estimates will indicate how much including stayers in the sample affects the results.
A third model is estimated by further restricting the sample to those who remained in
the same occupation group and location during both periods. Finally, we estimate a fourth
model using repeated cross-section of workers in the occupation group across locations.
Workers in the cross-section sample in the initial period are the same as in the balanced
sample, but they differ in the second period given some workers have left and new workers
have entered the occupation group in the location.
27
Differences across the estimated models arise through the sorting of individuals across
locations and occupations. A comparison of these estimates will thus indicate how much self-
27
To keep the results comparable, as in the balanced sample, we also focus on the observed change in outcomes for workers aged 25-45 in
the initial period and 35-55 in the end period.
26
selection into occupations and location affects the measured impact of immigration on wages.
If changes in occupation and location are not important at the aggregate level, we should find
little differences across models. Instead, if compositional changes are correlated with
immigrant inflows, the estimates should differ.
Effect on the number of days worked
We start by estimating the effect of immigration on the number of days worked. To do so, we
use as a dependent variable the relative change in the number of days worked
1kt kt
kt
DD
D
where
kt
D
is the average number of days worked by group k in period t.
28
The first column in each subpanel of Table 7 presents regression results using a balanced
sample of natives defined by their initial occupation. Overall, we find no evidence of a
correlation between the change in the average number of days worked and an increase in the
share of low-educated migrants. The coefficients of 2SLS models are sometimes negative but
relatively small in most specifications. A notable fact is that we find larger coefficients in the
construction sector: for this group, the estimates suggest that an increase of 10 p.p. of the
immigration rate would decrease by 2% of the average number of days worked. However, the
point estimates are very imprecise and are not statistically different from zero.
A potential risk for the validity of our results is attrition. One drawback of the previous
measure of number of days worked is that individuals supplying zero days of work are
excluded from the DADS sample. Our estimates might thus be affected by a selection bias if
some natives do not work during a year as a result of immigration. To address this limitation,
panel A in Table 8 uses data from a balanced sample of workers in which zero days of work
have been imputed to individuals who are not in the sample in period
1t
. These estimates
28
Results are identical if one uses changes in the log of the average number of days worked in the cell.
27
correct for the effect of non-participation in period t+1 under the strong assumption that all
individuals not observed in the sample have been out of the labor force during the year. Once
again, we do not find a negative impact of immigration in these specifications. If anything,
2SLS models report a positive correlation which is inconsistent with the hypothesis that
immigration might decrease the number of days worked of natives.
The previous estimates should be interpreted with caution given there are some doubts
about the quality of the variable on the number of days worked available in the DADS. For
this reason, Panel B in Table 8 reports estimates of the impact of changes in the proportion of
immigrants on the employment to population rate using Census data. As in the previous
section, we estimate separate models for various groups of natives defined by their education
level.
29
The general pattern is very similar: changes in immigrant rates do not appear to be
correlated with a decline in employment rate for prime-age male workers from different
education groups. Parameter estimates are always small, and most of the time statistically
insignificant.
30
Effect on wages
We next estimate the effect of immigration on wages. We focus on changes in the median
annual log wage for full time workers in a given occupation group. Using the median annual
wage has the advantage of providing estimates relatively insensitive to the presence of outliers
and, in addition, enables a series of robustness checks related to attrition that we present
below. We also explore other options below, however, with specifications that use median
daily wages and average daily wages.
29
Notice that we follow the same sample requirement, and use the change in employment rate of male aged 25-45 in period t and male aged
35-55 in period t+1.
30
We have also estimated similar model by using the initial location of a worker to calculate the employment rates, thus making an “intend to
treat” estimate. We also found no effects.
28
The relationship between changes in median annual log wages and immigrant inflows
is examined in Table 9. The OLS coefficients for most groups are rather small, positive and
non- significant in most specifications. In contrast, endogeneity-corrected parameter estimates
significantly differ across occupations. Point estimates are negative, relatively large and
statistically significant. Results with the balanced sample including location-movers indicate
that an increase of 10 p.p. in the share of low-educated immigrants lowers the median log annual
wage by -1.3% for workers initially blue collar in the non-tradable sector. For workers in the
tradable sector, the coefficient is also negative and has the same magnitude but is not
statistically significant. The impact of immigration also appears to be quite heterogeneous
across occupation groups: the results show larger effects for workers in the non-tradable sector
and particularly those in the construction sector. In the construction sector, the estimates
indicate that a 10 p.p. increase predicts a decrease of the median wage by 3.6 p.p. Qualitatively,
these estimates are in line with Bratsberg and Raaum (2012) who report that an increase in 10
p.p. in the immigration ratio is accompanied by a 6 p.p. fall in the wages of construction
workers.
A comparison between Columns 2 and 3 within each panel indicates the extent to
which location and occupation stayers are a selected sample. The estimates present a
contrasting pattern: the estimates in Column 2 where location-movers have been excluded are
quite close for blue collar workers or lower for construction workers but they are slightly
larger for workers in the non-tradable sector. Overall, differences between models including
or excluding location-movers are relatively small for most groups.
In Column 3, where the sample excludes those who are not in the same occupation
group in the second period, the coefficients are unambiguously larger for most groups. The
estimates indicate that an increase of 10 p.p. of the immigration rate predicts a decrease of
29
median wage of 1.3 p.p. for blue collar, 1.7 p.p. for the non-tradable sector and 4.8 p.p. for the
construction sector.
Finally, results in Column 4 are based on the cross-sectional variation of wages within
occupations. The impact of immigration is substantially larger in cross-section estimates
except for the construction sector. With respect to estimates using the balanced sample, the
estimated coefficient is multiplied by two for blue collar workers and non-tradable sector
workers.
There are two main lessons from the previous results. First, the findings described
above point to a strong heterogeneity in the estimated effect of immigration across workers
depending on their initial occupation. Unsurprisingly, the estimates are much larger for
workers initially in the non-tradable sector and in the construction sector.
Second, we find a much larger effect of immigration on those who stay in the skill
group during both periods which implies that occupational mobility mitigates the impact of
immigration, particularly for workers who are initially in the construction sector. This
suggests that immigration affects to a larger extent the average wages in given occupations
than the wages of workers initially in those occupations. Instead, estimates including or
excluding location-movers are basically equivalent, which shows that geographical mobility
does not seem to play an important role in mitigating the impact of immigration.
Robustness
Table 10 examines the sensitivity of the results to the specification of the baseline model. We
examine in Panel 1 the robustness of estimates using the balanced panel while Panel 2 reports
cross-section estimates. We concentrate on the impact of immigration on wages and we focus
on blue collar workers and those in the non-tradable and in the construction sector. We focus
on 2SLS estimates to save space (OLS results are available upon request).
30
We first examine the results for the balanced panel in Panel 1. One issue with our
instruments might be that the lagged distribution of immigrants is correlated with persistent
trends in economic dynamism across locations. As a result, the exclusion restriction of our
estimates might not be perfectly valid. A simple way to test this hypothesis is to estimate
whether the estimates change when we exclude different sets of control variables.
31
If the
estimates change significantly, this would indicate that the immigrant inflows predicted by
our instrument are strongly correlated with other factors influencing wages across locations.
Rows 1, 2 and 3 examine the sensitivity of the previous estimates to the inclusion of an
increasingly detailed set of control variables. A comparison between regression results in row
1 which do not includes controls (except for time dummies) and row 2 suggests that results
are barely affected by the inclusion of additional covariates. There is also very little change to
the coefficients from adding regional trends (row 3). Overall, these patterns are not consistent
with the hypothesis that our instrument might be correlated with unobserved determinants of
wage changes across locations.
An additional test for the validity of our instrumental variable strategy is provided by
using more distant lags to compute the instrument. To do so, we construct an alternative
instrument using the distribution of immigrant communities corresponding to two censuses
before.
32
By increasing the distance between the initial distribution of immigrants across
locations used to compute the shift share and the change in the immigrant ratio predicted by
the shift share, we are more likely to purge the instrument for potential persistent correlations
with unobserved local trends. Row 4 provides results using this alternative instrument. As
previously, we obtain negative coefficients which are statistically significant. However, these
31
Another good reason to exclude the set of location specific control variables is that these controls might be endogenous. This would be the
case for example if variables such as the share of workers in the construction sector or in the manufacturing sector are significantly affected
by immigrant inflows.
32
Attempts to use very distant lags such as predicting changes from 1975 to 2007 by using only the 1968 distribution failed because they are
too weakly correlated with changes which occurred during the 1990s and the 2000s. This is due to the fact that more than half low-educated
migrants who arrived after 1980 come from Asia and South-Africa and these groups were quasi-absent from France before 1975.
31
coefficients are also smaller by a third for blue-collar workers and workers in the non-tradable
sector.
The analysis so far has been based on changes in median annual log wages. We now
explore the sensitivity of the results to the definition of the dependent variable. Row 5 uses
the log of the median daily wages in the cell instead as a dependent variable. The estimated
effects are somewhat lower, particularly for workers in the construction sector. Row 6 shows
results using average daily wages. The estimated coefficient is strikingly similar than those
obtained with median daily wages for blue collar workers and is larger for workers in the non-
tradable sector. In contrast, the estimated impact on construction workers diminishes widely
and becomes insignificant. We suspect this last result reflects the fact that a relatively larger
share of construction workers has several employers in a given year. This implies that there
might be much more measurement errors in the number of days worked for this group when
daily wages are used.
In Rows 7 and 8, we investigate the extent to which the results might be driven by
large locations such as Paris, Marseilles or Lyons, which attract a disproportionately large
share of immigrants. Row 7 presents estimates where the 30 largest commuting zones have
been excluded from the sample while row 8 reports unweighted regressions. Results are
broadly similar in these two models.
The final specification check in Row 9 is oriented towards addressing a number of
concerns related to attrition. Using median wages in the cell enables us to investigate the
sensitivity of the results to missing wage information by using simple imputation techniques
as in Neal and Johnson (1996) or Olivetti and Petrongolo (2008). Specifically, we investigate
how our results depend on including individuals with a missing wage observation in period
1t
in the sample under the assumption that all missing individuals are out of the labor force
and are thus earning a log wage of zero. Results of this exercise in Row 9 indicate our
32
estimates are reasonably robust. We also obtain a similar ranking across occupations, point
estimates being slightly larger for workers from non-tradable industries and lower for the blue
collar group.
33
Panel B reports the same robustness tests performed using the cross-section sample.
We also find the baseline results to be reasonably robust across most specifications but the
precision of the estimates diminishes in some specifications. Importantly, the estimates are
not statistically significant when the alternative instrument using lagged settlement patterns of
immigrants in used. Another noteworthy pattern is that unweighted specifications provide
much larger parameter estimates.
V) Discussion
In this paper, we have revisited the impact of immigration on the labor market outcomes of
natives. Unlike most of the previous literature, our rich dataset has provided us with a unique
opportunity to investigate heterogeneity in the impact of immigration while controlling for
composition effects. Specifically, we have tested whether the impact of low-educated
immigration differed across natives using homogenous groups defined by their initial
occupation. We have also controlled for changes in the composition of the labor force at the
local level by focusing on estimates using variations over time from a balanced sample.
First, our findings show that immigrant inflows are moderately correlated with both
native outflows and inflows, and with a reallocation of natives to occupations with less routine
tasks. Our results also point to the evidence that the correlation between immigrant inflows
mobility across locations and occupations varies strongly depending on the industry of origin.
Moreover, we find that location and occupation-movers are a selected subgroup of the sending
33
We have also evaluated the risk of attrition from a selective shift of some workers to a sector uncovered by the DADS panel such as the
public sector which is only partially covered by the DADS before 1990. We found basically no correlations between changes in the share of
government employees and a change in the share of migrants. These results are available upon request.
33
population. While location-movers tend to be negatively selected from the sending population,
those moving to occupations with less routine tasks tend to be positively selected. This implies
that the selection patterns related to changes in location and occupation strongly differ.
Empirically, we do not find strong evidence that the selection of natives across locations
affects importantly the estimates of the impact of immigration. On the other hand, the
endogenous selection of natives towards occupations requiring different skills decreases
significantly the impact of immigration on wages. We obtain a much larger impact of immigrant
inflows on wages on the selected subgroups of natives who do not change location and stay in
the same occupation group in both periods.
Importantly, our results point to a strong heterogeneity across occupation groups in the
impact of immigration. We find that the wages of blue collar workers initially in the tradable
sector are the less affected by immigrant inflows while the effect of immigration on changes in
occupations are found stronger for this group. In contrast, for the group of construction workers,
we find a much larger impact of immigration on wages but no correlation between immigrant
inflows and change in occupations for this group. This last result suggests that part of the
difference in the impact of immigration on wages across occupation groups might reflect the
fact that workers in some occupation groups are more able than others to protect themselves
from immigrant inflows by shifting occupation.
Our results have important implications for the analysis of the impact of immigration.
First, the results suggest that the wage impact of immigration varies across occupation groups
and individuals both because labor markets are segmented and also because natives between
and within groups react differently to immigrant inflows. Second, the fact that immigrant
inflows are correlated with native mobility across location and occupation groups complicates
the estimates of the impact of immigration. Native mobility responses imply that immigration
affects indirectly other locations and occupation groups.
34
There are however several limitations to the previous results. First, because we wanted
to minimize the risk that our results might be biased by non-participation which is not well
captured in our data, we have focused on prime aged male workers. According to recent work
from Smith (2012), immigrants might also be more in competition with young workers less
than 25 that were not included in our analysis. Another limitation is that we did not include
women in our analysis given that the treatment of labor market participation creates an
additional complexity for this group. Finally, the share of immigrants and natives in the service
sector is also rapidly growing while the share of blue collar workers is in constant decline. An
evaluation of the impact of immigration on this increasingly important segment of the labor
market would be of substantial interest for future work.
Appendix
Data appendix
Occupations: DADS data contains information on occupation for CSP with 27 categories
before 1983 and 36 categories afterward. The category “Blue collar workers” aggregates 6
distinct sub-occupations over the period. We merge these occupations with tasks intensity
indexes by Goos et al. (2010, Table 4 p.49) based on the Occupational Information Network
(ONET) database.
Crosswalk tables for industry classifications: We use the industry classification which
remained unchanged for the longest period of time in the data. The NAP (Nomenclatures
d'Activités et de Produits 1973) is used in the 1975, 1982 and 1990 censuses and in the DADS
until 1993. We have created crosswalk tables with other industry classifications to match
them with the NAP at the four digit level. The NAF (Nomenclature d'Activité Française) is
used in the 1999 Census and in the DADS from 1993 to 2002. For the match between NAP
and NAF, we have used the 1994 LFS (Enquête emploi) in which both codes are also given to
establish a match at the four digit levels. Similarly, when several possibilities existed, we have
35
kept the most frequent correspondence. In both cases, the match has been completed manually
to include exhaustively all codes in the correspondence table at the four digit level.
Education The education variable reported in the Census indicates the diploma received by
the individual. We use the variable DIP in the 1968, 1975 and 1982 censuses, DIPL1 in the
1990 Census and DIPL in the 1999 Census. We classify individuals in four groups: Primary
education, Secondary education, High School and College. Primary education level includes
individuals which declare to have no diploma and people having the primary school
certificates. Secondary education level includes individuals which report to have a diploma of
a level equivalent to the Diplôme National du Brevet (BEPC) and includes individuals holding
a CAP or a BEP. High school education includes individuals who have a diploma equivalent
to the Baccalaureate. This group also includes general, professional or technical Baccalaureate
graduates. College level includes all individuals with a diploma of a level superior to the
Baccalaureate.
Theoretical Appendix
The model is a straightforward adaptation of Combes et al. (2008) and Combes et al. (2012)
which was initially used to investigate the sorting of workers with different skill levels across
local labor markets. We also follow Card (2001) and make the assumption that local labor
markets are stratified along “skill” group lines. For the moment, we abstract from labor
supply decisions and assume that each worker provides one unit of labor. This implies that
local labor supply is only determined by workers’ location decisions. The profit of the
representative firm in location
l
, and year
t
is given by:
lt lt lt it it lt lt
i lt
wp y l r z
36
In this expression,
lt
p
is the local price of output
lt
y
. For any worker
i
,
it
w
is the wage rate
and
it
l
is the total labor supply from workers of this type. Other factors of production are
represented by
lt
z
and
lt
r
is their price. The production function of the firm is:
1bb
lt lt lt lt
y A L z
where
01b
,
lt
A
is the total factor productivity in the area. We assume
lt
L
is a CES
aggregate of the labor inputs of workers from different occupation groups. As in Combes
et al. (2008), to introduce heterogeneity within occupation groups, we assume individuals in a
given occupation group are perfect substitute but supply different efficiency units of labor.
This implies that:
/1
/ 1 ( 1)/
( 1)/
lt klt it it
ikkklt
L L s l
where
determines the elasticity of substitution between labor types. The level of skills
it
s
augments the effective units of labor supplied by a given individual, thereby making workers
more productive. At competitive equilibrium, profit maximization implies:
1/
( , )
it lt it klt
w k l B s L
where
1
1
(1 ) 1/( 1)
(1 )
b
b
b
b
lt lt lt lt lt
B b b p A r L
. To take the model to the data, we assume that for
workers of type
i
:
log
it it i
sX
, where
it
X
is a vector of time-varying observed
characteristics of the worker and
i
is a worker fixed effect unobserved productivity.
Firms can employ native labor
klt
N
or immigrant labor
lt
I
, we assume that immigrant
and native labor of the same type are perfect substitute. Labor supply in an occupation group
can thus be decomposed by the efficiency units supplied by natives and low-educated
37
immigrants. Finally, using the following approximation,
log log
lt
klt klt k
klt
I
LN
N
, it is
straightforward to obtain Eq. (1).
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41
Table 1 : Share of Foreign born Workers among blue collar workers
across Selected Industries and Regions in France in 1999
Industry
Share of foreign born workers
Share total
Employment
France
Paris
Lyons
Brittany
Non-Tradable
14.5
62.0
16.2
3.4
62.0
Tradable
10.6
31.1
15.7
1.9
31.1
Construction
20.2
16.4
23.9
5.0
16.4
Source: Panel DADS. All figures refer to blue collar workers only. Paris and Lyons regions
refer respectively to the region “Ile de France” and “Rhone-Alpes”.
Table 2: Share of Foreign Born among Construction Workers
in the Paris and Brittany regions, 1976-2007
1976
1982
1990
1999
2007
Paris
37.2
37.1
35.6
45.7
41.7
Brittany
3.8
4.2
4
5.0
6.9
Source: Panel DADS. All figures refer to blue collar workers.
Table 3: First Stage Results
Dependent variable : Change in Low-Educated
Immigrant Ratio
lt
p
(1)
(2)
(3)
Predicted change
0.166***
0.121***
0.091***
(0.051)
(0.028)
(0.027)
F-stat
10.54
17.74
10.97
R-squared
0.13
0.33
0.29
Additional Controls
No
Yes
Yes
Weight
Yes
Yes
No
Note: All regressions use 1188 observations and include a full set of regions and time fixed
effects. Additional controls included in the regressions when indicated. Standard errors are
clustered at the commuting zone level. Regressions are weighted by
1/2
1
(1/ 1/ )
klt klt
NN
except when indicated otherwise. A (*) denotes statistical significance at the 10% level, a (**)
denotes at the 5% level, a (***) at the 1% level.
42
Table 4: Impact of Low-Educated Immigration on
Native inflows and outflows at the Commuting Zone Level
1. DADS Data
Sample: Male workers 25-45 in t, 35-55 in
1t
,
initially working in industry.
All
Blue Collar
Tradable
Non-
tradable
Construction
A. Dependent variable: Outflows between
t/t+1
OLS
lt
p
0.070***
0.046
0.089***
0.090***
(0.024)
(0.028)
(0.030)
(0.030)
2SLS
lt
p
0.160*
0.005
0.248**
0.358**
(0.091)
(0.118)
(0.122)
(0.154)
B. Dependent variable: Inflows between t/t+1
OLS
lt
p
0.160***
0.156***
0.164***
0.173***
(0.031)
(0.039)
(0.036)
(0.050)
2SLS
lt
p
0.147
0.165
0.144
0.158
(0.104)
(0.114)
(0.138)
(0.169)
2. Census Data
Primary
Education
Secondary
Education
High-
School
University
A. Dependent variable: Outflows between t/t+1
OLS
lt
p
0.044***
0.043**
-0.029
-0.027
(0.014)
(0.019)
(0.021)
(0.029)
2SLS
lt
p
0.238**
0.274**
0.238
0.125
(0.096)
(0.131)
(0.178)
(0.202)
B. Dependent variable: Inflows between t/t+1
OLS
lt
p
0.110***
0.137**
0.104
0.104
(0.041)
(0.057)
(0.074)
(0.109)
2SLS
lt
p
0.222
0.258
0.456
0.560
(0.176)
(0.230)
(0.357)
(0.514)
Note: All regressions use 1188 observations and include a full set of regions and time fixed
effects. Additional controls included in the regressions. Standard errors are clustered at the
43
commuting zone level. Regressions are weighted by
1/2
1
(1/ 1/ )
klt klt
NN
. A (*) denotes
statistical significance at the 10% level, a (**) denotes at the 5% level, a (***) at the 1% level.
44
Table 5: Impact of Immigration on Occupation Characteristics
A. Dependent variable : change in average routine task t/t+1
Sample: Male 25-45 in t+1, 35-55 in t
Blue Collar
Tradable
Balanced
sample
Location
Stayer
Location &
Occupation
Stayer
Cross-
Section
Balanced
sample
Location
Stayer
Location &
Occupation
Stayer
Cross-
Section
OLS
lt
p
0.03
0.023
0.017
0.010
0.003
0.000
0.028**
0.025**
(0.039
(0.050)
(0.012)
(0.013)
(0.054)
(0.069)
(0.014)
(0.011)
2SLS
lt
p
-0.623***
-0.653***
-0.147**
-0.049
-0.791***
-0.703**
-0.137*
-0.199**
(0.184)
(0.199)
(0.072)
(0.079)
(0.270)
(0.245)
(0.075)
(0.090)
Non-tradable
Construction
OLS
lt
p
0.052
0.053
-0.001
0.005
0.028
0.008
-0.020*
-0.016
(0.040)
(0.054)
(0.014)
(0.013)
(0.039)
(0.053)
(0.012)
(0.012)
2SLS
lt
p
-0.388**
-0.434**
-0.041
0.051
-0.181
-0.370
-0.02
-0.011
(0.168)
(0.214)
(0.080)
(0.092)
(0.197)
(0.293)
(0.082)
(0.063)
Note: All regressions use 1188 observations and include a full set of regions and time fixed
effects. Additional controls are included in the regressions. Standard errors are clustered at the
commuting zone level. Regressions are weighted by
1/2
1
(1/ 1/ )
klt klt
NN
. A (*) denotes
statistical significance at the 10% level, a (**) denotes at the 5% level, a (***) at the 1% level.
45
Table 6: Selection Patterns of Location and Occupation-Movers:
Individual Level Evidence
A. Dependent Variable: Moved to another Location
Blue Collar
Tradable
Non-Tradable
Construction
Residual Wage
-0.117
-0.119
-0.089
-0.081
-0.098
-0.105
-0.094
-0.116
(0.003)
(0.006)
(0.007)
(0.017)
(0.003)
(0.007)
(0.007)
(0.018)
RW x
lt
p
0.018
-0.114
0.101
0.287
(0.085)
(0.224)
(0.116)
(0.297)
lt
p
0.258
0.257
0.263
0.264
0.249
0.245
0.545
0.518
(0.091)
(0.094)
(0.115)
(0.118)
(0.095)
(0.098)
(0.116)
(0.121)
N
345 414
345 414
157 569
157 569
187 841
187 841
60 609
60 609
B. Dependent variable: Change in routine to abstract task intensity,
excludes location-movers
Residual Wage
-0.355
-0.361
-0.578
-0.585
-0.232
-0.227
-0.315
-0.345
(0.010)
(0.017)
(0.019)
(0.039)
(0.011)
(0.014)
(0.018)
(0.055)
RW x
lt
p
0.090
0.118
-0.077
0.436
(0.195)
(0.477)
(0.240)
(0.821)
lt
p
-0.502
-0.507
-0.369
-0.372
-0.377
-0.373
-0.455
-0.488
(0.182)
(0.187)
(0.153)
(0.154)
(0.173)
(0.180)
(0.367)
(0.351)
N
275 854
275 854
134 977
134 977
140 705
140 705
46 945
46 945
Note: All regressions include a full set of regions and time fixed effects. Additional controls
are included in the regressions. Standard errors are clustered at the regional level. All models
are estimated with 2SLS.
46
Table 7: Impact of immigration on Number of Days Worked
Dependent variable : change in average number of days worked t/t+1
Sample: Male 25-45 in t, 35-55 in t+1
Blue Collar
Tradable
Balanced
sample
Location
Stayer
Location &
Occupation
Stayer
Cross-
Section
Balanced
sample
Location
Stayer
Location &
Occupation
Stayer
Cross-
Section
OLS
lt
p
0.007
-0.003
-0.002
0.009
0.001
0.009
-0.000
0.019
(0.007)
(0.006)
(0.006)
(0.010)
(0.008)
(0.012)
(0.008)
(0.012)
2SLS
lt
p
0.013
-0.003
-0.015
-0.079
0.052
0.061
0.041
-0.047
(0.034)
(0.027)
(0.035)
(0.068)
(0.042)
(0.062)
(0.051)
(0.073)
Non-tradable
Construction
OLS
lt
p
0.011
-0.009
-0.010
-0.003
0.003
0.031
-0.013
0.000
(0.010)
(0.009)
(0.009)
(0.012)
(0.013)
(0.027)
(0.011)
(0.016)
2SLS
lt
p
0.011
-0.042
-0.024
-0.083
-0.148
-0.267
-0.232
-0.214
(0.010)
(0.042)
(0.055)
(0.085)
(0.105)
(0.178)
(0.211)
(0.151)
Note: All regressions use 1188 observations and include a full set of regions and time fixed
effects. Additional controls are also included in the regressions. Standard errors are clustered
at the commuting zone level. Regressions are weighted by
1/2
1
(1/ 1/ )
klt klt
NN
. A (*)
denotes statistical significance at the 10% level, a (**) denotes at the 5% level, a (***) at the
1% level.
47
Table 8: Additional Evidence on the Impact of Immigration on Number of Days Worked
Dependent variable: change in average number of days worked t/t+1
Sample definition: Male 25-45 in t, 35-55 in t+1
A. Balanced Sample : Zero imputed if missing in t+1
Blue Collar
Tradable
Non-Tradable
Construction
OLS
lt
p
-0.037
-0.035
-0.044
-0.027
(0.023)
(0.021)
(0.027)
(0.028)
2SLS
lt
p
0.328**
0.329
0.219*
0.280*
(0.148)
(0.196)
(0.124)
(0.165)
B. Census Data Evidence:
Dependent Variable: Change in Employment/Population Rate t/t+1
Primary
Education
Secondary
Education
High-School
University Graduates
OLS
lt
p
0.026**
0.009*
0.003
-0.001
(0.011)
(0.005)
(0.008)
(0.046)
2SLS
lt
p
-0.074
0.004
-0.031
-0.030
(0.095)
(0.047)
(0.046)
(0.046)
Note: All regressions use 1188 observations and include a full set of regions and time fixed
effects. Additional controls are also included in the regressions. Standard errors are clustered
at the commuting zone level. Regressions are weighted by
1/2
1
(1/ 1/ )
klt klt
NN
. A (*)
denotes statistical significance at the 10% level, a (**) denotes at the 5% level, a (***) at the
1% level.
48
Table 9: Impact of Immigration on Median Annual Wages
Dependent variable : change in median annual wage t/t+1
Sample: Male 25-45 in t+1, 35-55 in t
Blue Collar
Tradable
Balanced
sample
Location
Stayer
Location &
Occupation
Stayer
Cross-
Section
Balanced
sample
Location
Stayer
Location &
Occupation
Stayer
Cross-
Section
OLS
lt
p
0.015
0.016
0.030
0.042**
0.012
0.035
0.012
0.059**
(0.013)
(0.013)
(0.012)
(0.019)
(0.017)
(0.022)
(0.016)
(0.023)
2SLS
lt
p
-0.131**
-0.107*
-0.130*
-0.285*
-0.126
0.039
-0.185**
-0.235
(0.057)
(0.060)
(0.073)
(0.163)
(0.086)
(0.100)
(0.087)
(0.166)
Non-tradable
Construction
OLS
lt
p
0.008
0.016
0.019
0.007
0.022
0.031
0.015
0.027
(0.019)
(0.016)
(0.016)
(0.020)
(0.024)
(0.026)
(0.029)
(0.025)
2SLS
lt
p
-0.133**
-0.162*
-0.171*
-0.361**
-0.367**
-0.267*
-0.479**
-0.467**
(0.065)
(0.087)
(0.095
(0.166
(0.17
(0.154)
(0.240)
(0.199)
Note: All regressions use 1188 observations and include a full set of regions and time fixed
effects. Additional controls are included in the regressions. Standard errors are clustered at the
commuting zone level. Regressions are weighted by
1/2
1
(1/ 1/ )
klt klt
NN
where
klt
N
represents the size of the occupation group
k
in location
l
and year
t
. A (*) denotes
statistical significance at the 10% level, a (**) denotes at the 5% level, a (***) at the 1% level.
49
Table 10: Sensitivity of the Effects of Immigration
on Median wages to alternative specifications
A. Balanced Panel
Blue Collar
Non-tradable
Construction
N
1. No covariates
-0.178***
-0.177**
-0.354**
1188
(0.069)
(0.081)
(0.153)
2. Covariates
-0.177**
-0.174**
-0.331**
1188
(0.072)
(0.081)
(0.153)
3. Only Region FE
-0.139**
-0.154*
-0.416**
1188
(0.058)
(0.087)
(0.152)
4. Instrument lagged
-0.094**
-0.091*
-0.366**
1188
(0.042)
(0.054)
(0.106)
5. Dependent variable:
-0.170***
-0.112*
-0.206**
1188
Median daily wage
(0.063)
(0.060)
(0.130)
6. Dependent variable:
-0.170**
-0.223**
-0.077
1188
Average daily wage
(0.080)
(0.103)
(0.189)
7. Exclude largest cities
-0.178**
-0.141
-0.419*
1064
(0.090)
(0.127)
(0.255)
8. Without weights
-0.212**
-0.155
-0.399*
1188
(0.109)
(0.106)
(0.222)
9. Log wage of
-0.095*
-0.195**
-0.351**
1188
zero imputed if missing in t+1
(0.055)
(0.091)
(0.179)
B. Repeated Cross-section
1. No covariates
-0.221
-0.243*
-0.378*
1188
(0.133)
(0.146)
(0.201)
2. Covariates
-0.212
-0.246*
-0.388*
1188
(0.135)
(0.149)
(0.210)
3. Only Region FE
-0.283*
-0.374**
-0.467**
1188
(0.161)
(0.166)
(0.196)
4. Instrument lagged
-0.092
-0.108
-0.116
1188
(0.080)
(0.093)
(0.103)
5. Dependent variable:
-0.135*
-0.154*
-0.178
1188
Median daily wage
(0.079)
(0.080)
(0.144)
6. Dependent variable:
-0.121
-0.195
-0.295
1188
Average daily wage
(0.108)
(0.132)
(0.196)
7. Exclude largest cities
-0.426
-0.459*
-0.519*
1067
(0.276)
(0.264)
(0.307)
8. Without weights
-0.670*
-0.579**
-0.483**
1188
(0.397)
(0.282)
(0.221)
Note: See Table 9.
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