The topic that I have chosen is economic policies that enable imm.docx

The topic that I have chosen is “economic policies that enable
immigrants to thrive”. Immigrants play an important role in the
history of the world. Many of the current nations were formed
by the successive waves of immigrants moving in and out of the
country. Immigration has helped to form the foundation of
nations, win wars, and drive counties to great prosperity.
Immigration is currently a hot button issue, wars and falling
regimes have led many to leave their countries to seek a better
life in western countries. The treatment of these immigrants has
been lukewarm at best. Countries should change their attitudes
regarding how they deal with immigrants. By instituting the
right policies, countries can leverage the skills and hard work of
immigrants to propel the country to great heights of prosperity
and cultural integration. I feel this topic is a pertinent one in
addressing the current immigration crisis.
A lot of ink is spewed everyday on the economic merits
or demerits of immigration. For the purposes of this topic, I
have chosen two scholarly articles to provide insight and
guidance. The first source is immigration economics by authors
George and Borjas. The book explores economic themes in
relation to economics. It examines the effect that immigrants
have on their host country from an economical perspective. The
book hopes to shape the discourse on immigration by diluting it
to its core economic underpinnings that will enable legislators
to clearly understand the issue and make better decisions
(Borjas, George, 2014). The second source I will use is
economic growth & immigration: bridging the demographic
divide, written by the immigration policy center. It explores
themes of immigration and the economic growth of a nation. As
such, it is very relevant to the topic I have chosen and will
provide valuable insight.
Scholarly articles should be used because they present

the opinion and finding of authors who are much educated on
the topic at hand. They allow successive students and authors on
the topic to build upon the findings and advance the topic
further. They also enlighten one and provide guidance in
writing about the topic.
References
Borjas, George (2014). “Immigration Economics.” Cambridge,
Harvard University Press.
“Economic Growth & Immigration: Bridging the Demographic
Divide” (2005) Immigration Policy Center, American
Immigration Law Foundation.
J Popul Econ (2004) 17:133–155
DOI 10.1007/s00148-003-0143-4
Settlement policies and the economic success
of immigrants
Per-Anders Edin1, Peter Fredriksson1, Olof Åslund2
1 Department of Economics, Uppsala University, and Institute
for Labour Market Policy
Evaluation (IFAU), Box 513, SE-751 20 Uppsala, Sweden (Fax:
þ46-18-4711478;
e-mail: {per-anders.edin; peter.fredriksson}@nek.uu.se)
2 Institute for Labour Market Policy Evaluation (IFAU), Box
513, SE-751 20 Uppsala, Sweden

(Fax: þ46-18-4717071; e-mail: [email protected])
Received: 18 January 2001/Accepted: 3 September 2002
Abstract. Many countries use settlement policies to direct the
inflow of im-
migrants away from immigrant dense areas. We evaluate a
reform of Swedish
immigration policy that featured the dispersion of refugee
immigrants, but
also a change in the approach to labor market integration. We
focus on how
immigrants fared because of the policy. The evaluation
indicates that immi-
grants experienced substantial long run losses. The bulk of the
effect stems
from a common component that affected immigrants regardless
of location.
We interpret the common component as being related to a shift
in policy
focus, from labor market assimilation to income support.
JEL classification: J15, J18, R23
Key words: Immigration, settlement policies, labor market
outcomes
1. Introduction
The past couple of decades have seen an acceleration of
immigration to
developed countries. Concomitantly, there has been a shift in
the ethnic
All correspondence to Per-Anders Edin. We thank two
anonymous referees, Magnus Löfström,

seminar participants at the Institute for Labour Market Policy
Evaluation (IFAU), Uppsala
University, Stockholm University, the Swedish Institute for
Social Research (SOFI), the Re-
search Institute of Industrial Economics (IUI), and the CEPR
conference on ‘‘Marginal Labour
Markets in Metropolitan Areas’’ for valuable comments and
Lisa Fredriksson for expert data
assistance. We are also grateful to Sven Hjelmskog, Roland
Jansson, Stig Kattilakoski, Christina
Lindblom, Anders Nilsson, Kristina Sterne, and Lena Axelsson
of the Immigration Board, and
Anna Gralberg of the Ministry of Culture, who generously
found time to answer our questions.
This research has been partly financed through a grant from the
Swedish Council for Work Life
Research (RALF). Responsible editor: Christoph M. Schmidt.
composition of the immigrant inflow and an associated decline
in the labor
market performance of recently arrived immigrants compared to
natives in
these countries; see, e.g., Borjas (1999). These developments
have put immi-
gration policies high on the political agenda.
One kind of immigration policy imposes restrictions on where
newly
arrived immigrants can settle. We call such policies settlement
policies. Many
countries practice (or have practiced) settlement policies;
examples include
the UK, Germany, and Sweden.1 Under the new UK Asylum and
Immigra-

tion Bill, refugee immigrants are placed outside London and
Southeast Eng-
land – the two regions where most previous immigrants reside.
Germany
imposes severe restrictions on where refugee immigrants can
settle: unless
having found a paid job, people must stay in a part of the
country assigned
by the government. In Denmark as well as the Netherlands,
authorities try to
disperse immigrants by obliging all municipalities to provide
dwellings for a
certain number of refugees (Dutch Refugee Council 1999). In
addition, local
dispersal policies have been used within European metropolitan
areas (Mus-
terd et al. 1997). Sweden is another example, where a new
system for refugee
immigrant reception was introduced in the mid 1980s. One
aspect of the sys-
tem was that asylum seekers were placed in regions outside the
metropolitan
areas to a greater extent. Thus, settlement policies are
commonly employed,
and a vital ingredient of the policies is the attempt to reduce
immigrant con-
centration in big city areas.
Broadly, two arguments have been put forward to rationalize the
place-
ment of new immigrants outside immigrant dense regions. The
first argument
is based on the idea that living in immigrant enclaves stalls
immigrant
assimilation by decreasing the rate of host country skill
accumulation. The

second argument is based on the perception that immigrants
impose a fiscal
and social burden on the host regions. Placing immigrants
outside the major
cities thus distributes the perceived cost more equally across the
host country.
In this paper we examine the versatility of the first type of
argument.
In particular we ask the question: How did the Swedish
settlement policy
affect the economic outcomes of the immigrants subjected to the
policy? The
Swedish experience may be instructive for countries pursuing,
or thinking of
implementing, similar kinds of policies.
Immigrant enclaves are not necessarily harmful for immigrants
living
there. The enclave may present the immigrant with more
opportunities for
gainful ‘‘trade’’ in the goods and labor markets; e.g., Lazear
(1999). Also,
ethnic neighborhoods may constitute an environment where the
immigrant is
exposed to less discrimination. Moreover, an ethnic ‘‘network’’
may improve
labor market outcomes by providing information on job
opportunities. Of
course, network effects may also operate in the opposite
direction by, e.g.,
disseminating information on welfare use; see Bertrand et al.
(2000). One can
also tell stories based on human capital externalities. According
to one ver-
sion, the quality of the enclave (the average stock of human

capital) deter-
mines whether segregation is good or bad; e.g., Borjas (1998).
In sum, the
effect of living in an immigrant dense region is a priori
ambiguous.
Many empirical studies find a negative association between the
economic
outcomes of ethnic minorities and neighborhood characteristics
such as seg-
regation, immigrant density or ethnic concentration; see Kain
(1992) and
Ihlanfeldt and Sjoquist (1998) for surveys. Whether these
estimates should be
treated as causal effects is another issue, since the majority of
these studies
134 P.-A. Edin et al.
take local characteristics as exogenous when they are arguably
endogenous.
In related work we have examined the severity of this problem;
see Edin et al.
(2003).2 There we used the settlement policy as an exogenous
source of
variation arguing that initial placements were independent of
unobserved
individual characteristics. We found a substantial downward
bias in estimates
that do not account for sorting; in fact, estimates that were free
of sorting
bias suggested that an increase in ethnic concentration improved
the labor
market outcomes for low skilled refugee immigrants. Thus, from

an empirical
point of view, the effects of redirecting the inflow of
immigrants away from
enclaves are far from clear-cut.
There was more to the policy shift than trying to increase the
dispersion of
immigrants in Sweden. Prior to 1985, the integration of refugee
immigrants
had a direct connection to labor market policy since handling
refugee issues
was the responsibility of the Labor Market Board. This link was
broken in
1985, when the Immigration Board became responsible for
refugees. One of
the implemented changes was that immigrants were, by default,
placed on
welfare for an introductory period of about 18 months.3 Thus, it
seems that
the new policy effectively shifted the focus from labor market
integration to
income support. Given that there were two facets of the policy
shift, we try to
decompose the effects of the shift into its component parts: one
associated
with redirecting the inflow of immigrants from immigrant dense
regions; and
another which we interpret as being associated with the shift in
the approach
to integration policy.
Our results can briefly be summarized as follows. The overall
effect of the
policy was that immigrants suffered substantial long-run
earnings losses. We
estimate an earnings loss of about 25% for those subjected to

the policy. In
addition, idleness increased by one third and welfare receipt by
almost 50%.
The decomposition analysis suggests that the bulk of this loss is
due to the
shift in the approach to integration policies. With respect to the
settlement
policy per se, we find that had individuals stayed on in the
assigned munici-
palities their labor market prospects would have been decidedly
worse.
However, individuals moved out of regions with bad
employment prospects,
so that the initial effects were undone.
The remainder of the paper is outlined as follows. Section 2
gives a
description of the institutional setting. In Sect. 3, we present the
evaluation
framework. Section 4 turns to the empirical analyses. We use
longitudinal
micro data derived from the database LINDA (see Edin and
Fredriksson
2000). In Sect. 5 we evaluate the policy in terms of three
outcomes: earnings,
idleness, and welfare dependence. Section 6 concludes.
2. Background
Immigration to Sweden for labor market reasons virtually
ceased in the mid
1970s. Since then immigration from non-Nordic countries is
mainly related
to asylum reasons. Refugees applying for asylum in Sweden are
granted a
residence permit in case of a favorable decision. Upon receiving

a residence
permit, refugee immigrants are, as a rule, treated as Swedish
citizens; the sole
exception from this rule is the right to vote in the national
elections. So, work
permits and residence permits go together and immigrants are
immediately
eligible for transfers such as social assistance (welfare).
Settlement policies 135
The remainder of this section describes the institutional setting
prior to
the reform and the major institutional changes that were
implemented in
1985 when the settlement policy was introduced.
2.1. Refugee reception before the reform4
Prior to 1985, the Swedish Labor Market Board handled refugee
issues. Pre-
sumably this was a remnant from the time when most
immigrants entered
Sweden for labor market reasons. Nonetheless, this meant that
there was a
natural focus on labor market integration and immigration
policy was linked
to labor market policy.
A majority of the refugee immigrants traveled directly to a
municipality
and applied for asylum there. The rest of the refugee immigrants
were so
called quota refugees who stayed in refugee centers before

moving to a
municipality.5 The Labor Market Board was responsible for
organizing
housing (if necessary) and assisting refugees in finding suitable
training or
employment. It seems that all refugee immigrants could
influence the
choice of initial residence; in any case, a vast majority arrived
in the regions
of Stockholm, Gothenburg or Malmo (Ministry of Labor 1981,
1983).
The transition from basic language training, via additional
courses in
Swedish, to work or training was relatively smooth. Immigrants
who had
traveled directly to a municipality waited 2–5 months after
receiving their
residence permit before moving on to work or training.
2.2. Refugee reception after the reform
The responsibility for handling refugee issues was transferred to
the Swedish
Immigration Board. Formally the change took place in 1985, but
there was a
trial period in the autumn of 1984.
After the reform, all refugee immigrants, apart from
reunification immi-
grants, were subjected to the settlement policy. In principle, an
asylum seeker
was placed in a refugee center, which were distributed all over
Sweden, while
waiting for a decision from the immigration authorities. After
receiving a

residence permit, the Immigration Board assigned immigrants to
a munici-
pality of residence; during this process there was no interaction
between
refugees and municipal officers. Municipal authorities, in turn,
assigned
immigrants to an apartment.
Reception in the municipalities was regulated in agreements
between the
Board and the municipality in question. At first the intention
was to sign
contracts with 60 (out of Sweden’s 284) municipalities that had
suitable
characteristics for reception, such as educational and labor
market oppor-
tunities. Due to the increasing number of asylum seekers in the
second half of
the 1980s, a larger number of municipalities became involved;
in 1989, 277
out of Sweden’s 284 municipalities participated. The factors
that were
supposed to govern placement were abandoned from the start.
Instead, the
availability of housing was the deciding factor.
Employment opportunities were scant in the majority of
municipalities
that became involved in asylum reception. To rationalize
placement in
regions with poor employment prospects, the Immigration Board
divided the

integration process into two periods: an introductory period
lasting for 18
months when the immigrant participated in Swedish courses and
lived off
welfare;6 after the introductory period, integration into the
labor market
commenced.
The move to a strict settlement policy was a reaction to the
concentra-
tion of immigrants to large cities that had taken place. The
immigrants were
to stay in the assigned municipalities during the entire
introductory period.
However, there were no restrictions against relocating if
individuals could
find a place on their own. The only real cost to the immigrant
consisted of a
wait for a new place in a language course. Receipt of welfare
was not condi-
tional on residing in the assigned municipality and the central
government
reimbursed the local governments for their welfare
expenditures. From 1985
through 1990, municipalities were reimbursed for their actual
expenditures,
so there was little incentive for local governments to pursue a
policy that
produced early assimilation into employment. This changed in
1991 when
municipalities were given a fixed amount per refugee.
Formally, the policy of assigning refugees to municipalities was
in place
from 1985 to 1994. In 1994, a new law was passed that gave

immigrants the
right to choose the initial place of residence provided that they
could find
an apartment on their own.7 However, the strictness of the
placement policy
gradually eroded during 1992–1994, when there was an
immigration peak
caused by the war in Bosnia-Herzegovina. The post-1991 period
is less attrac-
tive for our purposes, since it contained larger degrees of
freedom for the
individual immigrant to choose the initial place of residence.
The strictest application of the assignment policy was between
1987 and
1991. In 1988, a new law was passed which required
‘‘extraordinary reasons’’
for all others than family members to get the right to stay in a
municipality
instead of a refugee center while waiting for a residence
permit.8 In effect, it
seems that the law formalized a stricter practice, which had
been introduced
in 1987. During 1987–1991, the placement rate, i.e. the fraction
of refugee
immigrants assigned an initial municipality of residence by the
Immigration
Board, was close to 90%.
In a companion paper (see Edin et al. 2003) we argue the
settlement policy
provides an exogenous source of variation that identifies the
causal effect of
regional characteristics. The essence of the argument is that the
placement
rate was high (in particular during 1987–1991), the housing

market was
booming (making it difficult to find vacant housing in attractive
areas), and
there was no interaction between representatives of the
municipality and the
refugee in question. Therefore, it is realistic to treat the
municipal assignment
as exogenous with respect to the random components of the
outcomes of
interest, conditional on observed characteristics.9
The municipal placement policy is an obvious difference
between the pre-
and post-reform periods. Our reading of the facts is that the
reform also
shifted the policy focus away from labor market assimilation to
an increased
reliance on income support. The reform meant that direct link to
the employ-
ment offices providing job search assistance, subsidized
employment, or
training programs was broken. Partly as a result of this, the
integration into
the labor market became less smooth and contained longer
spells of
inactivity. While immigrants arriving prior to the reform got
some attach-
ment to the labor market fairly quickly, individuals arriving
after the reform
were granted (or subjected to) a long initial period of welfare
receipt.

To conclude, on our reading the reform brought two major
changes: the
placement policy where the government assigned the initial
place of resi-
dence, and a shift in focus away from labor market integration
in favor of
income support.
3. The evaluation framework
In order to set the stage for the empirical analysis, we devote
this section
to making clear what we can estimate and the kind of
restrictions that are
necessary to estimate the parameters of interest.10 The problem
is that
we think that location matters for outcomes, but due to sorting
we cannot
directly obtain consistent estimates of the impact of regional
characteristics.
In this section we therefore ask two questions. What does an
estimate of the
overall effect of the policy – one derived without conditioning
on regional
characteristics – actually measure? Which restrictions must be
satisfied for
this estimate to be meaningful?
We are also interested in decomposing the estimate of the
overall effect of
the policy. Our approach is to combine a difference-in-
differences estimate of
the overall effect with an estimate of the distribution effect. The
distribution
effect relates to the fact that immigrant arrival cohorts were
subjected to

different regional environments because of the placement
policy. The residual
in this decomposition is by construction the common effect of
the policy. The
distribution component can be estimated for the refugees
arriving during the
strict settlement policy since the initial placement of these
individuals pro-
vides exogenous variation that identifies the effects of regional
characteristics.
For purpose of concreteness, let us consider the determination
of earnings
for immigrants (indexed by m). For expositional reasons let us
introduce the
following assumptions. First, let the conditioning on exogenous
individual
characteristics be implicit. Second, assume that there are only
two regions;
associate a dummy variable d with one of them and let the
characteristics of
this region relative to the other be denoted zs. Third, suppose
that the
individual return to regional characteristics is constant and
equal to bm.
Consider a simple structural earnings equation for an immigrant
i in
arrival cohort s ¼ 0; p (s ¼ p for those who arrived during the
strict place-
ment policy)
ln ymis ¼ a
m
s þ b

mzsdis þ amis ð1Þ
where amis denotes unobservable ability relative to the mean
outcome ða
m
s Þ
in cohort s. The estimation of Eq. (1) is complicated by the fact
that
individuals choose where to reside. In Edin et al. (2003), we
considered the
sorting bias that may arise in this setting. In the simple
framework we are
examining here, the bias arises to the extent that there is a
covariance be-
tween ais and regional characteristics zs. To be concrete,
suppose for the
moment that zs reflects immigrant density; then, if high ability
individuals
choose to live outside immigrant enclaves, the OLS estimate of
bm is biased
downwards – and vice versa.
Assuming that there are no time effects, one can in principle use
Eq. (1)
to form a before-and-after estimator. However, given the time
period we are
considering this is an unattractive assumption. We measure the
outcomes in
1995–1997 for those subjected to the policy, and in 1989–1991
for pre-reform

immigrants. In the beginning of the 1990s the unemployment
rate in Sweden
skyrocketed from less than 2% in 1989 to over 9% in 1993.
To eliminate the time effect, suppose instead that there exists a
proper
comparison group (superindexed c). By analogy with Eq. (1),
the earnings
equation for the comparison group is given by
ln ycis ¼ a
c
s þ b
czsdis þ acis ð2Þ
If we had some exogenous information identifying the
coefficient on
regional characteristics for all groups, we could use Eqs. (1)
and (2) to form
a difference-in-differences estimator of the common effect of
the policy.
This estimator would be given by p ¼ðamp % a
m
0 Þ%ða
c
p %a
c
0Þ, if the change
in unobserved characteristics is the same in the two groups,
where ðacp % a
c

0Þ
identifies the pure time effect.
For reasons outlined above we do not want to condition on
region of
residence. The question then is: What does the difference-in-
differences esti-
mate without conditioning on d measure? To answer this
question, average
the structural earnings equations separately within group and
time period and
calculate the difference in difference using these averages.
Assuming that any
change in unobserved ability over time is equal in both groups,
this yields
d ¼ D ln ym % D ln yc ¼ððamp %a
m
0 Þ%ða
c
p % a
c
0ÞÞ|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflf
flfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
1
þbmzp½Ddm %
Ddc'|fflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl
fflfflfflfflffl}
2
þðbm %

bcÞzpDdc|fflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl
fflfflffl}
3
þbcðzp % z0Þðdm0 % d
c
0Þ|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflffl
fflfflfflfflfflfflfflfflfflffl}
4
þðbm % bcÞðzp %
z0Þdm0|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflff
lfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
5
ð3Þ
where D is the difference operator, and dl reflects the
distribution across
regions in group l ¼ m;c. Notice that we can estimate the
coefficient on
½Ddm % Ddc' by applying instrumental variables to the
immigrant cohort
subjected to the settlement policy.
Equation (3) is written on a form that separates the components
of inter-
est from the ones we want to eliminate by imposing restrictions
and choosing
a suitable comparison group. It is clear that the first and the
second compo-
nent are key elements of the policy change. As we have argued,
there was a
common component in the policy shift; the first component
measures this

effect. Also, there was a regional dimension to the policy since
immigrants
were dispersed across different regions – this distribution effect
is measured by
the second component in Eq. (3). We refer to the sum of the
common and
distribution effect as the overall effect of the policy. The
remaining compo-
nents should not be attributed to the policy.11
In the following proposition, we detail the conditions that the
comparison
group must satisfy in order for the ‘‘raw’’ difference-in-
differences estimator
ðdÞ to give a sensible estimate of the overall effect of the
policy.
Proposition. A proper comparison group should have the
following charac-
teristics relative to the refugee immigrants: (i) the pure time
effect should
be the same; (ii) the changes in unobserved characteristics (if
any) should be
equal; (iii) the return to regional characteristics should be the
same
ðbm ¼ bcÞ; and (iv) the distribution across regions prior to
treatment should
be equal ðdm0 ¼ d
c
0Þ. Under conditions (i)–(iv) the difference-in-differences

estimator of the overall effect is given by12
d ¼ððamp % a
m
0 Þ%ða
c
p % a
c
0ÞÞþ b
mzp½Ddm % Ddc' ¼ p þ c½Ddm % Ddc' ð4Þ
The first condition suggests that we should look for comparison
groups
along the observable skill dimension. The fourth condition
arises because we
do not want to condition on region of residence. It may
disqualify many
groups as comparisons simply because we know that residence
patterns
among, e.g., natives and immigrants are very different.
Empirically, the
fourth condition is likely to be of some importance since it
eliminates the
influence of the fourth component in (3). We know for a fact
that, e.g.,
the dispersion of regional unemployment increases (i.e. zp % z0
changes)
along with the overall rate of unemployment. So the time effects
that render
the before-and-after estimator implausible will also reduce the
plausibility of
estimates derived using a comparison group that does not satisfy

the fourth
criterion. When we return to the selection of comparison groups
in the next
section, we discuss whether these four conditions are likely to
be fulfilled.
Given that the conditions in the proposition are satisfied, we
can mean-
ingfully decompose the overall effect of the policy into a
common effect, p, and
a dispersion effect c½Ddm % Ddc'. The decomposition is made
possible by the
fact that the settlement policy gives exogenous variation in
regions of residence
that can be used to identify g. We can thus apply an
instrumental variables
approach to the cohort that was subjected to the policy to
estimate the
coefficient on the regional dummy. It is important to realize that
these esti-
mates are free from sorting bias if one believes in the quasi-
experimental
nature of the data. In the next section, Eq. (4) and the criteria
for identifying
it, will guide our choice of empirical strategy and comparison
group.
4. An empirical prelude
The section begins by describing the data, sample selections,
and the charac-
teristics of the treated and ‘‘control group’’. Then we move on
to discuss the
choice of comparison group.
4.1. Data and sample selection

The empirical analysis is based on the LINDA database. LINDA
contains
two panels: one of around 20% of the foreign-born population,
and another
of approximately 3% of the total Swedish population. The data
are cross-
sectionally representative. Data are available from 1960 and
onwards, and
are based on a combination of income tax registers, population
censuses and
other sources; for more details, see Edin and Fredriksson
(2000).
We cannot identify refugee immigrants directly from our data.
Instead we
identify them by country of origin.13 As a general rule we
include immigrants
from countries that were not members of the OECD as of 1985
and countries
outside Western Europe. The only exception from this rule is
Turkey,
which is included since it was the origin of a substantial inflow
of refugee
immigrants during the period. We exclude persons belonging to
a household
with an adult already residing in Sweden, since these
individuals were likely
to have immigrated as family members and, consequently, were
not
‘‘treated’’. We also apply an age restriction and base our

analysis on in-
dividuals aged 18–55 at the time of entry into Sweden. Lastly,
we focus on
the immigration waves during 1987–1989, when placement rates
were of the
order of 90%.
4.2. The characteristics of the treated and the control group
In order to evaluate the reform it is necessary to construct a
counterfactual.
For this purpose, we use individuals who are identified as
refugee immigrants
(according to the above criteria) during the years 1981–1983.
Since we want to use the 1981/83 cohort as an approximation of
the
counterfactual for the 1987/89 cohort it is vital that the cohorts
are similar in
terms of observed and unobserved characteristics.
In panel a) of Table 1, we compare the cohorts in terms of a set
of
standard individual characteristics: age, schooling, gender, and
ethnicity.
Table 1. Descriptive statistics by immigrant cohort
Immigrant cohort
1981/83 1987/89
Panel a)
Age 37.55
(8.46)

38.12
(8.29)
Years of schooling 10.83
(2.95)
11.16
(2.99)
Female 0.479 0.445
Region of origin
Eastern Europe 0.373 0.180
Africa 0.092 0.116
Middle East 0.233 0.457
Asia 0.142 0.083
South America 0.160 0.164
Panel b)
Big city area (at time of arrival, t) 0.62 0.41
Big city area (8 years later, tþ8) 0.69 0.59
Immigrant dense area (t) 0.61 0.38
Immigrant dense area (tþ 8) 0.64 0.55
High unemployment area (t) 0.12 0.18
High unemployment area (tþ8) 0.12 0.13
# individuals 2,679 9,883
Notes: Years of schooling are imputed from highest degree
attained. Individuals with missing
information on education were given the same number of years
of schooling as those with less
than 9 years of schooling. The first time we observe education
is in 1990. Therefore, education
is measured 9 years post immigration for those arriving in 1981
and 8 years after immigration
for later arrival cohorts. Panel b) presents means of indicator
variables. Big city area refers to the

Stockholm, Gothenburg or Malmo metropolitan areas.
Immigrant dense areas are municipalities
where the immigrant share is at least twice as large as the
immigrant share of the population.
High unemployment areas refer to municipalities where
unemployment is in the highest quartile
of the municipal unemployment distribution. Area
characteristics are defined with respect to a
specific point in time (1986) for both cohorts.
There are no important differences in terms of age and gender.
Also, the
cohorts have very similar amounts of schooling: the 1987/89
cohort has 0.3
additional schooling years on average. The difference between
the two
cohorts in terms of ethnicity may be a greater source of
concern. The chief
discrepancy between the two cohorts is that the 1981/83 cohort
has more of
the mass among immigrants from Eastern Europe, while the
later cohort has
the greatest fraction of immigrants originating in the Middle
East.
For the regression analysis, the difference in terms of ethnicity
is not a
problem as we can condition on the country of origin. However,
the
calculation of the distribution component requires the
counterfactual loca-
tion distribution. Therefore, we generate the counterfactual

distribution by
reweighing observations in the 1981/83 cohort such that the
distribution over
region of origin conforms to the 1987/89 cohort. Whenever we
talk about
location differences across cohorts in the sequel, we refer to the
differences
between the 1987/89 cohort and the weighted 1981/83 cohort.
In panel b) of Table 1 we illustrate some of the consequences of
in-
troducing municipal placement. It reports a set of characteristics
of the mu-
nicipality of residence at the time of arrival and eight years
later for the two
cohorts. At the time of arrival, those subjected to the placement
policy have a
substantially higher probability of residing outside the
metropolitan areas,
are more likely to live in high unemployment regions, and are
substantially
less likely to live in immigrant dense areas. So, at least
initially, the policy
objective of reducing immigrant concentration in big city areas
was fulfilled.
This policy objective may have come at a significant cost,
however, as
the probability of being exposed to a high unemployment
environment also
increased.
If we look at the cohorts eight years later it is evident that the
difference
between the two cohorts in terms of residence characteristics
has decreased
markedly. In particular, it seems that the 1987/89 cohort

escaped high
unemployment areas to a greater extent; the difference between
the two co-
horts has almost disappeared after eight years in the host
country. It is also
noteworthy that the two distributions have not converged. The
probability of
residing in metropolitan areas and immigrant dense areas is still
around 10
percentage points lower for the 1987/89 cohort relative to
1981/83 cohort.
The last issue we want to discuss with the aid of Table 1
concerns the
implications of the size of the inflow for the location
distribution of refugee
immigrants. The last row shows that the inflow during 1987/89
was 3–4
times larger than the inflow during the earlier period. Is the
municipal distri-
bution of the 81/83 cohort an appropriate counterfactual
location distri-
bution? It is possible that the increased inflow of immigrants
would have
changed the distribution also in absence of the reform.
However, this prob-
lem is what the difference-in-differences between the
distributions in Eq. (4) is
meant to handle. The distribution of the 81/83 cohort plus the
change within
an appropriate comparison group is arguably the best
approximation we can
get of what the distribution would have been if the program had
not been
implemented.

Will changes in unobserved heterogeneity bias the evaluation?
It is difficult of course to assess whether unobserved
heterogeneity will bias
our policy evaluation. There are two issues here: first,
independently of the
reform there may be a decline in cohort quality over time;
second, because of
the reform there may be selection of refugees with unobserved
characteristics
that are not conducive to labor market success.
With respect to the first question, Borjas (2000b) argues that
national
origin is the crucial factor behind changes in cohort quality in
the US. Since
our data include information on country of origin we can
control for the
major influence of changes in cohort quality over time.
With respect to the second question, one might worry that the
reform
changed the selection of immigrants in terms of their
disposition to work
since it had an emphasis on income support and implied less
pressure to enter
the labor market quickly. Let us present three facts suggesting
that this is not
a major issue.
First, pre-reform refugees also had the possibility to live off

welfare. Once
granted a residence permit, they were eligible for social
assistance in the same
way as other residents. If there is a group of asylum seekers
who base their
destination choices on welfare opportunities, they probably
found Sweden
equally appealing before the reform.
Second, the share of asylum seekers going to Sweden did not
change
during the 1980s. The decade saw an overall increase in refugee
migration,
but as Fig. 1 shows the relative number of people seeking
asylum in Sweden
remained about the same during the entire decade.
Third, observed skills did not decrease. It is plausible that the
highly
educated are less inclined to live off welfare (see, e.g., Hansen
and Lofstrom
1999); if selection was a problem we would expect the
education level of ref-
ugees to fall after the reform. According to Table 1, however,
average years of
schooling was more or less equal in the two cohorts; this result
also holds
when we condition on country of origin.
Based on the facts presented in this section we conclude that
unobserved
heterogeneity is not likely to be a major problem.
0
0. 04

0. 08
0. 12
0. 16
0. 2
81 82 83 84 85 86 87 88 89
The World
Western Europe
Fig. 1. Asylum seekers in Sweden as a fraction of asylum
seekers in other regions, 1981%1989.
Notes: Asylum seekers according to UNHCR (2000). Sweden is
not included in ‘‘Western
Europe’’
4.3. The choice of comparison group
In Sect. 3 we identified properties that a comparison group must
have to
make a meaningful analysis possible. We have considered three
groups: low-
skilled natives; OECD immigrants during 1981–1983 and 1987–
1989; and
non-OECD immigrants during 1975–1980.14 The suitability of
these groups
depends on how well they conform to the conditions described

in the propo-
sition of Sect. 3. Since we have discussed condition (ii) already,
we focus on
the remaining conditions here.
Let us start with condition (i): a common time effect. This
property is vital
to credibly estimate the overall effect of the policy. Finding a
group with
identical time effects as refugee immigrants is of course a
delicate matter, but
it makes sense to think that the susceptibility to macro events is
related to the
amount of host country skills. The logic for considering the
above compar-
ison groups is based on this consideration. For instance,
immigrants from the
OECD countries arriving in the same time periods as the treated
and control
groups is the proper comparison group if the susceptibility to
the cycle pri-
marily is related to the time in the host country. If immigrants
from certain
regions fare worse than others during economic recessions, on
the other
hand, then the 1975/80 cohorts of non-OECD immigrants should
be com-
parable with the samples of refugee immigrants.15
The fourth condition of the proposition stipulates that the
distributions
across regions prior to the reform should be equal. This
condition potentially
disqualifies natives, as immigrants (in particular low-skilled
immigrants) are
much more prone to live in metropolitan areas than natives. On

the basis of
this consideration, the preferred comparison group consists of
immigrants
from non-OECD countries.16
It is a priori plausible that the 1975/80 cohorts of non-OECD
immigrants
best meet the third condition, i.e., that there is equality of the
return to local
characteristics. In Edin et al. (2003), we find that members of
high-skilled
ethnic groups gained by being surrounded by a greater number
of individuals
from their own country. It is quite likely that this effect is
different for natives
and perhaps also for OECD immigrants.
There are additional reasons to prefer prior immigrants arriving
from
countries outside the OECD. One may worry that the size of the
inflow itself
affects outcomes. This supply effect is only a problem if recent
arrivals are
affected differently than the rest of the refugee immigrant
population; other-
wise, the 1975/80 comparison group should experience the same
change.17
Finally, relative to OECD immigrants, there is also the
advantage of having a
larger number of observations.
In sum, then, we primarily base our results on using immigrants
from
refugee countries arriving 1975–1980 as our comparison group.
In some cases

we present results using the OECD group. For reasons given
above low-
skilled natives is not a proper comparison group.
5. Policy evaluation
The purpose of this section is to evaluate the reform in terms of
the economic
outcomes of the participants. First, we investigate the overall
effect of the
policy, then we estimate the distribution effect of the policy
and, finally, we
decompose the overall effect into a common effect and a
(geographical) dis-
persion effect.
5.1. The overall effect of the policy
In this section, we set out to estimate the total effect of the
reform on the
economic outcomes of the participants. We use a difference-in-
differences
approach and relate the 87/89 and 81/83 immigrant cohorts to
the com-
parison groups discussed in Sect. 4.2. We measure outcomes
eight years
after immigration. The reason for choosing outcomes such a
long time after
immigration is that we are interested in the long-run impact on
partic-
ipants. Also, the policy did not aim at initial labor market

success, since it
prescribed language training and other introduction activities
rather than
immediate entry on the labor market. Note, though, that we have
also
looked at outcomes four and six years after arrival. The
estimates are very
similar.18
The re-direction of the inflow away from big-city regions was
an impor-
tant part of the policy change. Partly, this was motivated by a
belief that
regions matter for outcomes. However, conditioning on region
of residence
will potentially result in estimates suffering from sorting bias.
Under the
conditions outlined in the proposition we can still estimate a
sensible overall
program effect.
We focus on three outcome measures: (the log of ) annual
earnings, idle-
ness (defined as neither having positive earnings nor being
enrolled in edu-
cation), and welfare receipt (defined as being a member of a
welfare receiving
household). The difference-in-differences estimates of the total
effects of the
reform are derived from the following prototype specification:
outcomeik ¼ ak þ /00Xik þ /
0
1Xikmik þ g0mik þ g1pik þ dmikpik þ eik ð5Þ
where outcomeik is the outcome in tþ 8 for individual i from

source country
k. ak is a country of origin fixed effect, X a vector of individual
characteristics
(gender, age, age squared, marital status, and level of
education), m a dummy
variable for belonging to either the treated or the control group,
and p a
dummy for the reform period. In this setup, d is the parameter
of primary
interest; it is the difference-in-differences estimator of the
overall effect of the
new policy. To simplify the decomposition of the overall effect,
we estimate
linear probability models for the binary outcomes.
Table 2 shows the results for the three outcomes, using two
different
comparison groups. Starting with earnings, we find that the
effects are sub-
stantial; according to the estimates, program participants with
earnings eight
years after arrival suffered an earnings loss of 25–29% due to
the policy.
Furthermore, the policy increased the likelihood of being idle.
The esti-
mates imply that the probability of idleness is about 6–8
percentage points
higher for those subjected to the policy; in relative terms this
translates to an
increase of about a third.
For welfare receipt, the estimates indicate that program
participants were
9–11 percentage points more likely than previous immigrants to
receive social

assistance eight years after arrival; in relative terms this
amounts to an increase
in the probability of close to 50%.
Supported by our discussion in Sect. 4.2, we believe that the
two
comparison groups used in Table 2 capture the relevant time
effects in a
reasonable way. Of course, we could always eliminate the
program effect by
comparing with a very restricted group, with especially bad
outcomes. To
provide a sense for the stability of the estimates, we have also
excluded the
quartile predicted to have the best outcomes in the two
comparison groups;
the results from this exercise are very similar to those in Table
2.19
There is no doubt that the outcomes of people subjected to the
policy
were worse than those of previous immigrants in absolute terms.
The more
difficult task is to separate the program effect from a time
effect that would
have changed the outcomes also in absence of the policy
change. We have
presented evidence that regardless of the comparison group, the
program had
a deteriorating effect on all outcomes. Furthermore, this holds
in instances
when we exclude high-skilled individuals (in the observed

sense) from the
comparison groups. Therefore, our conclusion is that the policy
change
adversely affected the economic outcomes of refugee
immigrants.
5.2. Effects of the placement
We now turn to investigating the effects that placement had on
economic
outcomes: Were people located where there were good or bad
prospects? In
terms of Eq. (4), this analysis considers the distribution
component, i.e., the
effect of the change in distribution over municipalities on
outcomes.
The residential location after some time in the host country is
not
exogenous. Therefore, we estimate versions of the following
regression for
the 1987/89 cohort:
outcomeijk ¼ ak þ /0Xik þ
X
j
cjdijk þ eijk ð6Þ
Table 2. Difference-in-differences estimates of the overall
effect of the policy
Outcome
Log(earnings) Pr(idle) Pr(welfare)

Comparison group
OECD
Estimate %0.345 0.079 0.089
Standard error (0.050) (0.013) (0.013)
R-squared 0.12 0.16 0.14
# individuals 13,187 18,279 18,279
Non-OECD immigrants 75/80
Estimate %0.285 0.062 0.108
Standard error (0.035) (0.009) (0.010)
R-squared 0.10 0.14 0.11
# individuals 42,587 54,448 54,448
Notes: The regressions also include a dummy for being in the
treated or the control group (m),
gender, age, age squared, level of education, country of origin,
marital status (dummies for (a) being
married, (b) havingat least one child under 16, and interactions
between gender and (a) and (b)), and
interactions between m and remaining individual characteristics.
The full set of estimates is available
in Table A2. For details on the different samples, see Table A1.
The comparison group is weighted
such that the sample composition over time corresponds to the
treated and control group.
where outcome denotes either log earnings, the probability of
being idle,
or welfare dependency, ak is a country of origin fixed effect, X
a vector of
individual characteristics (gender, age, age squared, marital
status, level of

education, and immigration year), and dij is a dummy variable
for residing in
municipality j. All variables included in Eq. (6) are measured
eight years after
immigration. To avoid bias due to sorting we instrument dij. We
instrument
the dummy for the individual’s current municipality with two
dummies: one
indicates that the individual was placed in the current
municipality, the
other that the individual was placed in the county block of the
current
municipality. The first stage equation is estimated separately for
each mu-
nicipality. To ensure that the municipality effects on outcomes,
gj, could be
identified, and reasonably precisely estimated, we required that
there should
be at least 5 refugee immigrants with positive earnings in our
sample that
reside in municipality j; if a municipality failed this
requirement it was
merged with a bordering municipality.20
We ask two questions in this part of the analysis. First, to what
extent
does the change in geographic distribution account for the
overall effects of
the policy? The answer to this question thus gets at the
distribution compo-
nent of Eq. (4). Second, what would the effect have been if
people had stayed
in their assigned municipalities?
We evaluate the placement effect in the following way. Let
dmjs be the

probability of residing in municipality j in time period s (0 if
pre-reform, p
if reform) for an individual belonging to the refugee immigrant
population
(m). Let dcjs be the analogous probability for the comparison
group (c). Our
measure to answer the above questions is then
X
j
ððdmjp % d
m
j0Þ%ðd
c
jp %d
c
j0ÞÞĉj ð7Þ
Thus, we use the estimates of cj in combination with the
differences in dis-
tributions of immigrants and the comparison group to calculate
weighted
averages of the location effects. We calculate two measures of
placement suc-
cess. First, we use the distributions corresponding to the time
point when the
treated and control groups have been eight years in Sweden; the
difference-in-
differences between the weighted averages is then the
distribution component
of equation (4). Second, we use the initial distributions, i.e.,
those that corre-

spond to the time of arrival for the treated and control groups.
We then esti-
mate what the effect would have been if people had remained in
their assigned
municipalities. Thus we examine whether program participants
were located
in better or worse regions relative to the choices of previous
immigrants.
Table 3 presents the estimates; the calculations use 1975/80
immigrants as
the comparison group (estimates on individual characteristics
are presented
in Table A3). The distribution components relevant for the
decomposition
analysis are given in the first row. It seems that the distribution
component
accounts for a limited part of the overall effect of the policy on
earnings; see
column (1). There is a decrease in earnings of almost 8 percent
associated
with the change in the distribution caused by the policy. For
idleness and
welfare it appears as if the distribution component had more of
an impact;
see columns (2) and (3). There is an increase in idleness of
around 3 percent-
age points as a result of the placement policy; analogously,
welfare receipt
increased by 4 percentage points.
Let us now turn to the second row and the comparison of the

initial location
effects in the immigrant cohorts. The estimates suggest that the
initial loca-
tion was associated with sizable earnings losses: had the
refugees stayed on in
the assigned regions their earnings would have been 42 percent
lower relative
to the control group.
People were initially placed in regions with bad prospects for
employ-
ment. The difference in idleness obtained when applying the
initial dis-
tributions is rather dramatic. The estimate suggests that had the
refugees
stayed on in the assigned municipality the probability of being
idle would
have been 19 percentage points higher compared to the situation
when they
could freely choose place of residence.
Column 3 shows that we get a similar result for welfare. The
probability
of being welfare dependent was 27 percentage points higher in
the 87/89
cohort than in the 81/83 cohort using the initial distribution.
For all three outcomes examined, we see that relative to the
1981/83
cohort individuals escaped regions with bad prospects by
moving out of
assigned municipalities. Most of the rather dramatic differences
observed
initially have disappeared after eight years in the host country.
Did people relocate to better or worse regions also in absolute

terms? To
answer this question, we must look at the change in the
distribution of each
cohort between the initial time of observation and eight years
later. To this
end, we calculate the difference in the weighted average
between the two time
points for each cohort. The calculations indicate that there is
very little
change among pre-reform immigrants, and that the bulk of the
decrease in
the difference between cohorts stems from program participants
moving to
better locations. Thus, there is no evidence that immigrants in
general move
to better regions in terms of labor market prospects, but that
this was a
phenomenon connected with the placement policy.
Why was the initial location bad for outcomes? In related work,
we esti-
mate the causal effects of a set of municipal characteristics on
the outcomes
of program participants; see Edin et al. (2000a). We find that
local
unemployment has a large negative effect. Compared to the
81/83 cohort, the
87/89 cohort was assigned to municipalities with higher
unemployment; see
Table 1. A significant part of the answer probably lies in the
fact employment
opportunities were scant in many of the assigned municipalities.
Table 3. How placement affected outcomes, standard errors in
parentheses

(1)
log(earnings)
(2)
Pr(idle)
(3)
Pr(welfare)
Distribution component of difference-in-differences
estimate. Distribution eight years after
immigration.
%0.077
(0.036)
0.030
(0.013)
0.039
(0.015)
Distribution component if people had stayed in the
assigned municipalities. Initial distribution.
%0.542
(0.277)
0.185
(0.093)
0.268
(0.104)
Notes: The calculations use the difference-in-differences of
distributions, with 75/80 immigrants

as the comparison group. Observations in 1981/83 cohort
weighted to conform to the region-of-
origin distribution in the 1987/89 cohort. Estimates for
individual variables presented in Table
A3. The estimates for idleness and welfare receipt were
obtained by applying the linear proba-
bility model.
Taken together, our findings suggest that the long run effects of
location
on earnings, employment, and welfare receipt were relatively
small. However,
this is because people moved out of bad regions, not because
placement cre-
ated a distribution that was equal to that chosen by the 81/83
immigrants in
terms of outcomes. On the contrary, had people stayed on in the
assigned
municipalities, their outcomes would have been substantially
worse.
5.3. Decomposing the overall effect
Since we have estimated the overall effect and the distribution
effect of the
policy, we can ‘‘back out’’ an estimate of the common effect of
the reform.
Table 4 presents the results of this exercise; the comparison
group is non-
OECD immigrants during 1975–1980.
The decomposition in Table 4 suggests that for all outcomes a

majority
of the loss is due to the common component. Therefore, we
conclude that the
bulk of the impact of the reform came from a common effect,
rather than
from the change in geographic distribution induced by the
placement policy.
Our interpretation of this result is that the policy shift increased
the initial
reliance on income support, which had detrimental effects on
people arriving
under the new policy.
6. Conclusion
The purpose of this paper has been to evaluate a Swedish reform
of the ref-
ugee immigrant reception system. The reform, which was
implemented in the
later half of the 1980s, had two facets. First, immigrants were
no longer free
to choose their initial place of residence; rather they were
assigned to less
immigrant dense regions. Second, the reform shifted the focus
away from
labor market assimilation. We have focused exclusively on how
refugee
immigrants fared because of the reform.
We use three outcome measures: log earnings, idleness, and
welfare receipt.
The overall effect of the reform was detrimental to all of these
outcomes. Our
analysis suggests that eight years after arrival earnings were
roughly 25%
lower because of the new policy; idleness increased by around

seven percent-
age points; and welfare receipt rose by about 10 points.
Because there were two facets of the reform we decompose the
overall
effect into a common component and a distribution component.
The distri-
bution component is associated with the shift in the regional
distribution of
immigrants across Sweden. The decomposition analysis
suggests that more
than half of the total effect can be attributed to the common
effect. A some-
what speculative reading of this is that the weaker link between
refugee
Table 4. Decomposition of the overall effect, 1975/80
immigrants comparison group
Total Common Distribution Common/total (%)
Log earnings %0.285 %0.208 %0.077 73.0
Idleness 0.062 0.032 0.030 51.6
Welfare 0.108 0.069 0.039 63.9
Notes: ‘‘Total’’ taken from Table 2 and ‘‘Distribution’’ from
the first row of Table 3. ‘‘Com-
mon’’ is the difference between ‘‘Total’’ and ‘‘Distribution’’.
reception and integration into the labor market, and increased
focus on
income support, had long-lasting negative effects on individual

outcomes.
The negative effects of the dispersion policy would, however,
have been
larger if people had stayed on in the assigned municipalities. If
they would
have stayed, the estimates suggest that the probability of being
idle, for
example, would have been almost 20 percentage points higher
because of the
placement policy. Since people moved out of the initial
locations, this loss
was largely undone, however.
In this Swedish case, the increased long-run dispersion of
refugee immi-
grants that was achieved with the policy came at the expense of
individual
outcomes in the labor market. An implication of our findings is
that with a
more careful choice of municipalities, and with a policy more
focused on the
labor market, this cost could at least to some extent have been
avoided. We
think this is an important lesson for future policies in Sweden
and elsewhere.
Appendix
Table A1. Descriptive statistics, means (standard deviations)
Variable Refugee
immigrants OECD immigrants
Non-OECD
immigrants 75/80

81/83 87/89 81/83 87/89 81/83 87/89
Log(earnings) 11.48
(1.09)
10.96
(1.51)
11.63
(1.08)
11.52
(1.28)
11.59
(1.02)
11.40
(1.31)
Idleness 0.127 0.292 0.154 0.231 0.120 0.234
Welfare receipt 0.215 0.321 0.112 0.114 0.143 0.116
Female 0.479 0.445 0.533 0.448 0.486 0.487
Age 37.549
(8.456)
38.124
(8.289)
37.393
(8.674)
38.372
(8.914)

39.542
(7.961)
42.872
(8.398)
Married 0.605 0.592 0.521 0.463 0.665 0.609
Kid 0.500 0.514 0.434 0.402 0.575 0.491
Education
<9 years or missing 0.258 0.214 0.251 0.186 0.273 0.201
9–10 years 0.108 0.185 0.123 0.187 0.123 0.132
High school(2 years 0.288 0.168 0.239 0.217 0.285 0.317
High school > 2 years 0.135 0.185 0.126 0.144 0.111 0.121
University < 3 years 0.102 0.134 0.115 0.124 0.095 0.112
University)3 years 0.109 0.114 0.146 0.141 0.112 0.116
Ethnicity
Nordic – – 0.622 0.644 – –
Western Europe – – 0.312 0.262 – –
Eastern Europe 0.373 0.180 – – 0.351 0.338
Africa 0.092 0.116 – – 0.091 0.087
Middle East 0.233 0.457 – – 0.277 0.287
Asia 0.142 0.083 0.008 0.007 0.120 0.126
North America – – 0.051 0.070 – –
South America 0.160 0.164 – – 0.161 0.163
Oceania – – 0.007 0.017 – –
Notes: Idleness ¼ 1 if the individual neither had positive
earnings nor was enrolled in education,
0 otherwise. Welfare receipt ¼ 1 if the individual was a member
of a welfare receiving household,
0 otherwise. Kid ¼ 1 if there was a kid(15 years of age present
in the household, 0 otherwise.

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.0
9
2
(0
.0
0
4
)
0
.0
0
0
(0
.0
0
9
)

%
0
.0
1
8
(0
.0
0
3
)
m
*
p
%
0
.3
4
5
(0
.0
5
0
)
%
0
.2
8
5
(0
.0

3
5
)
0
.0
7
9
(0
.0
1
3
)
0
.0
6
2
(0
.0
0
9
)
0
.0
8
9
(0
.0
1
3
)

0
.1
0
8
(0
.0
1
0
)
F
em
a
le
%
0
.1
2
2
(0
.0
6
1
)
%
0
.0
6
5

(0
.0
3
0
)
0
.0
2
0
(0
.0
1
7
)
%
0
.0
0
0
(0
.0
0
9
)
%
0
.0
3
0
(0

.0
1
4
)
0
.0
0
5
(0
.0
0
7
)
m
*
(F
em
a
le
)
0
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2
1
(0
.0
8
2
)

0
.0
6
4
(0
.0
6
2
)
%
0
.0
2
7
(0
.0
2
3
)
%
0
.0
0
7
(0
.0
1
7
)
0

.0
9
2
(0
.0
2
0
)
0
.0
5
7
(0
.0
1
6
)
A
g
e
0
.0
3
5
(0
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2
0
)

0
.0
6
7
(0
.0
0
9
)
%
0
.0
1
8
(0
.0
0
6
)
%
0
.0
2
7
(0
.0
0
2
)
0

.0
1
0
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.0
0
4
)
%
0
.0
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4
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m
*
(A
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e)
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.0
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5
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.0

2
6
)
%
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7
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.0
1
9
)
%
0
.0
0
1
(0
.0
0
7
)
0
.0
0
8
(0
.0
0

4
)
%
0
.0
2
2
(0
.0
0
6
)
%
0
.0
0
8
(0
.0
0
4
)
A
g
e
sq
u
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re
d
(*

1
0
%
2
)
%
0
.0
4
1
(0
.0
2
4
)
%
0
.0
7
2
(0
.0
1
1
)
0
.0
2
8
(0

.0
0
7
)
0
.0
4
1
(0
.0
0
3
)
%
0
.0
1
1
(0
.0
0
5
)
0
.0
0
4
(0
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0

2
)
m
*
(A
g
e
sq
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re
d
(*
1
0
%
2
))
%
0
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1
1
(0
.0
3
1
)
0
.0
2

0
(0
.0
2
3
)
0
.0
0
6
(0
.0
0
8
)
%
0
.0
0
7
(0
.0
0
5
)
0
.0
3
2

(0
.0
0
7
)
0
.0
1
8
(0
.0
0
5
)
M
a
rr
ie
d
0
.2
6
9
(0
.0
7
0
)
0
.2

5
2
(0
.0
3
1
)
%
0
.0
5
5
(0
.0
2
2
)
%
0
.0
8
3
(0
.0
1
0
)
%
0
.0

7
4
(0
.0
1
5
)
%
0
.0
5
5
(0
.0
0
7
)
m
*
(M
a
rr
ie
d
)
%
0
.0
3
9

(0
.0
9
0
)
%
0
.0
2
2
(0
.0
6
4
)
%
0
.0
2
9
(0
.0
2
6
)
%
0
.0
0
1

(0
.0
1
7
)
0
.0
4
0
(0
.0
2
1
)
0
.0
2
1
(0
.0
1
6
)
K
id
0
.0
4
6

(0
.0
0
9
)
%
0
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0
2
(0
.0
3
0
)
%
0
.0
9
2
(0
.0
2
2
)
0
.0
9
9

(0
.0
7
1
)
0
.0
3
9
(0
.0
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7
)
0
.0
6
0
(0
.0
1
7
)
m
*
(K
id
)
%

0
.1
1
6
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.0
9
0
)
0
.0
8
4
(0
.0
6
2
)
0
.0
5
3
(0
.0
2
6
)
0
.0
0

7
(0
.0
1
6
)
0
.0
4
8
(0
.0
2
2
)
0
.0
6
9
(0
.0
1
6
)
M
a
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ie
d
*

fe
m
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le
%
0
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6
9
(0
.0
8
8
)
%
0
.0
9
7
(0
.0
3
9
)
0
.0
1
9
(0
.0

2
6
)
0
.0
3
4
(0
.0
1
2
)
%
0
.0
0
2
(0
.0
2
0
)
%
0
.0
5
6
(0
.0
0

9
)
m
*
(M
a
rr
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d
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fe
m
a
le
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0
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4
6
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4
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%
0
.0
2
6
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.0
8
2
)
0
.0
1
9
(0
.0
3
2
)
0
.0
0
4
(0
.0
2
1
)
%
0
.0
9
0
(0
.0
2

7
)
%
0
.0
3
6
(0
.0
2
1
)
K
id
*
fe
m
a
le
%
0
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6
2
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.0
9
0
)
%

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.0
6
4
(0
.0
3
6
)
%
0
.0
0
6
(0
.0
2
6
)
%
0
.0
1
3
(0
.0
1
1
)
0

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1
6
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.0
2
1
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0
.0
1
0
(0
.0
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8
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T
a
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A
2
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5

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0
m
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m
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8
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.0
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%
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0
4
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.0
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%
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8
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.0
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E
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<
9
y
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,
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–
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y
ea
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%
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5
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7
2
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3
4
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.0
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0
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0
7
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.0
2
0
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%
0
.0
9
4
(0
.0
0
9
)
0
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3
4
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.0
1
7
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%
0
.0
1
4
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.0
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7
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m
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0

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3
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7
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9
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sc
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8
9
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0
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1

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.0
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6
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%
0
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6
4
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.0
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9
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.0
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8
8
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.0
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4
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6
5
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6
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9
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0
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6
9
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%

0
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6
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6
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1
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4
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1
2
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6
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%
0
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1
3
(0
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2
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%
0
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0
7
(0
.0
1
5
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U
n
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ty
<
3
y
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0
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3
9
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0
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2
3
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.0
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0
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1
3
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.0
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0
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7
6
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0
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9
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5
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m
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4
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6
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4
5
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9
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8
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9
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1
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9
6
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7
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0
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0
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C
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#

in
d
iv
id
u
a
ls
1
3
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8
7
4
2
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8
7
1
8
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7
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5
4
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4
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1
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N
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s:
P
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fo
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th
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p
is
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u
m
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fo
r
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p
o
st
-r
ef
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rm
p
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.
S
ee
T
a
b
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A
1
fo
r
d
es
cr

ip
ti
v
e
st
a
ti
st
ic
s.
Endnotes
1 Until the last few years, Israeli authorities located immigrants
outside the major cities. Recently
the policy has changed from one of deliberate placement to one
of encouragement to settle in
development towns; see Hiltermann (1991) for details on the
Israel policy toward Russian
immigrants. US immigration authorities distribute refugees
through private organizations
that arrange housing; the dispersion of immigrants across the
US is not an explicit objective,
however (Borjas 2000a). Belgium is another country where
restrictions are imposed on the
residence of new immigrants.
2 Borjas (1995), Cutler and Glaeser (1997), and Katz et al.
(2001) are other examples of
attempts to handle the sorting bias.
3 Immigrants arriving prior to the reform were eligible for

welfare after receiving their residence
permits. The difference here is that practically all immigrants
were placed on welfare for an
initial period that in many cases appears to have been
considerably longer than 18 months.
Table A3. Estimates of the coefficients on individual variables
in Eq. (6)
(1)
log(earnings)
(2)
Pr(idle)
(3)
Pr(welfare)
Female %0.000
(0.073)
0.006
(0.016)
0.081
(0.017)
Age 0.062
(0.022)
%0.017
(0.005)
%0.015
(0.005)

Age squared (*10%2) %0.001
(0.000)
0.000
(0.000)
0.000
(0.000)
Married 0.220
(0.070)
%0.075
(0.016)
%0.038
(0.017)
Kid 0.002
(0.068)
%0.045
(0.016)
0.118
(0.017)
Married* female %0.084
(0.091)
0.025
(0.021)
%0.096
(0.022)

Kid* female %0.332
(0.092)
%0.025
(0.022)
%0.010
(0.022)
Education (missing and <9 years, reference)
9–10 years 0.054
(0.065)
%0.110
(0.014)
%0.037
(0.014)
High school(2 years 0.206
(0.066)
%0.182
(0.015)
%0.109
(0.015)
High school > 2 years 0.201
(0.067)
%0.156
(0.015)
%0.098
(0.015)

University < 3 years 0.135
(0.072)
%0.254
(0.016)
%0.116
(0.016)
University)3 years 0.496
(0.075)
%0.244
(0.017)
%0.188
(0.018)
Immigration year (1987, reference)
1988 %0.049
(0.048)
0.037
(0.011)
0.013
(0.012)
1989 %0.017
(0.048)
0.062
(0.011)
0.082

(0.012)
Country of origin dummies Yes Yes Yes
R-squared 0.11 0.14 0.14
# individuals 6,418 9,883 9,883
# municipalities 168 168 168
Notes: Instrumental variables estimates. Heteroscedasticity
consistent standard errors in paren-
theses. See Table A1 for descriptive statistics.
4 The main sources for the material presented in Sects. 2.1 and
2.2 are The Committee on
Immigration Policy (1996), The Immigration Board (1997), and
interviews of placement
officers and other officials of the Immigration Board. We
conducted the interviews in order to
get information about how asylum reception worked in practice.
5 The absolute number of quota refugees was roughly constant
during the 1980s; thus, its share
of the total refugee inflow decreased when immigration rose. In
the fiscal year 1982/83, quota
refugees made up 25% of the inflow (Ministry of Labor 1983);
in 1987 their share of the in-
flow had declined to 10%.
6 The length of the introduction period appears to have varied
across municipalities and years;
in many cases it was considerably longer.
7 From then on more than 50% of the immigrants have used this

opportunity. The Immigra-
tion Board has placed the remainder of the immigrants.
8 This was a tightening of regulations in the following sense.
Prior to the change, refugees could
stay in a municipality of their own choice while waiting for a
residence permit and, in general,
the chance of being assigned the municipality of residence was
greater than being assigned
another municipality.
9 We provide more details substantiating this argument in Edin
et al. (2003).
10 See, e.g., Heckman et al. (1999) for an overview of different
approaches to the evaluation problem.
11 The third component relates to the differences in the returns
to regional characteristics; the
fourth can be labeled the initial distribution component; and the
fifth has to do with the change in
regional characteristics. One can also argue that the evaluation
of the policy success should
include the change in regional characteristics (the fifth
component). To our minds, however, the
effect of the policy on the characteristics of the region is likely
to have been of minor importance.
12 Notice that we have already imposed conditions (i) and (ii)
when deriving Eq. (3). Conditions
(iii) and (iv) are necessary to get from Eq. (3) to Eq. (4).
13 This implies that we cannot separate between quota refugees
and refugees applying for asylum
when arriving in Sweden. Therefore, we evaluate these two
groups together. In connection to
this notice that: (i) quota refugees constituted a small and

declining share of the total inflow;
(ii) the policy reform presumably changed the reception of
quota refugees as well, although
arguably to a lesser extent; and (iii) if anything we probably
underestimate the impact of the
reform since we cannot separate between the two groups.
14 See Edin et al. (2000b) for a more detailed discussion.
15 Since this group has been in Sweden for a minimum of nine
years before we measure outcomes,
we do not expect assimilation between 89–91 and 95–97 (when
we measure outcomes) to bias the
estimated time effects. Results in Edin et al. (2000c) show that
most of the economic assimilation
of immigrants occurs within a few years after arrival. According
to their results, 79% of the
earningsgrowthobservedinthe firstten yearsamongnon-
OECDimmigrantsoccursbetweenthe
first and fourth year; less than 2% of the growth comes between
the seventh and the tenth year.
For a discussion of immigrant assimilation out of welfare, see
Hansen and Lofstrom (1999).
16 In the beginning of the 1980s the probability of residing in
the Stockholm metropolitan area is
47% for recent arrivals of refugee immigrants; it is 46% for
refugees arriving during the sec-
ond half of the 1970s, 42% for OECD immigrants, and only 15%
for low-skilled natives. To
some extent we could correct for these discrepancies by
reweighing the data such that the
native comparison group to conform to the geographic
distribution of refugee immigrants.
17 We have also investigated whether the size of the inflow

affects outcomes more formally by
dropping individuals arriving in 1989 from the treated. The total
inflow in 1989 was 77 (54)%
larger than in 1987 (1988). The difference-in-differences
estimates of the total effects that we
are about to present are slightly smaller with this restriction.
18 The estimates are available on request.
19 The point estimates of the overall effects increase by about
0.02 for earnings. The estimates
for idleness and welfare change by less than 0.01. The estimates
are available upon request.
20 Excluding municipalities resulting from splits during the
relevant time period, the maximum
number of municipality effects we could have estimated would
have been 278. The minimum
limit of at least 5 resident immigrants reduces the number of
estimable municipality effects to
167. In principle, we also require that 1 assigned individual
should remain in the municipality
in question; otherwise the assigned municipality will have no
predictive power in the first stage
regression. The weight given to municipalities with few
observations is relatively low; 11% of
the sample lived in a municipality with less than 20
observations.
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Finnish Economic Papers – Volume 24 – Number 1 – Spring
2011
1
ECONOMIC IMPACTS OF IMMIGRATION: A SURVEY*
SARI PEKKALA KERR
Wellesley College,
e-mail: [email protected]
and
WILLIAM R. KERR
Harvard University and NBER,
e-mail: [email protected]
This paper surveys recent empirical studies on the economic
impacts of immigra-

tion. The survey first examines the magnitude of immigration as
an economic phe-
nomenon in various host countries. The second part deals with
the assimilation
of immigrant workers into host-country labor markets and
concomitant effects for
natives. The paper then turns to immigration's impact for the
public finances of host
countries. The final section considers emerging topics in the
study of immigration.
The survey particularly emphasizes the recent experiences of
Northern Europe and
Scandinavia and relevant lessons from traditional destination
countries like the US.
(JEL: H53, J23, J31, J61, J68)
1. Introduction
International migration is a mighty force glob-
ally. Over 175m people, accounting for 3% of
world's population, live permanently outside
their countries of birth (UN 2002). At the start
of the new millennium, European migration pat-
terns are very different than those from even 50
years ago. Europeans emigrated heavily in the
late 19th and early 20th centuries, but today the
reception and assimilation of immigrants is a
significant economic and social phenomenon in
many previous emigration countries. Altogether
27m foreign nationals lived in European Union
(EU15) countries in 2007, accounting for about
7% of the population. Figure 1 shows that most
of the recent population growth in Europe re-
sults from migration.

This paper surveys the economic impacts of
immigration for host countries. Empirical evi-
dence is drawn from the older and extensive lit-
erature regarding traditional destination coun-
tries like the US and Canada. However, this re-
* Comments are appreciated. The authors are grateful to
David Autor, Jennifer Hunt, Robert E.B. Lucas, Mark Part-
ridge, the editor and anonymous referees for helpful com-
ments. This research was funded in Finland by the Ministry
of Labour, Ministry of the Interior, Ministry of Education,
Ministry of Social Affairs and Health, the Association of Fin-
nish Local and Regional Authorities, and The Finnish Natio-
nal Fund for Research and Development (Sitra). Part of the
research was conducted while Sari Kerr was on sabbatical
graciously funded by the Yrjö Jahnsson foundation. Additio-
nal support through Harvard Business School Research is
gratefully acknowledged. An earlier version of this survey
was circulated as VATT Working Paper 362 by Sari Pekkala.
2
Finnish Economic Papers 1/2011 – Sari Pekkala Kerr and
William R. Kerr
view also emphasizes the recent experiences of
Northern Europe and Scandinavia; a central goal
is to highlight studies and lessons that have par-
ticular application within this region. Migrant
flows to some European countries are now of
similar magnitude to flows to the US, and it is
helpful to identify relevant lessons from the ex-
periences of the US and other traditional desti-
nation countries. Looking forward, the hetero-

geneity in recent European experiences and
policy environments provides an excellent labo-
ratory for identifying immigration's effects in a
new setting.
Section 2 begins by describing recent Euro-
pean immigration patterns. Section 3 considers
immigrant assimilation in the labor markets of
host countries, while Section 4 surveys evidence
on possible displacement effects for natives.
Section 5 evaluates how immigration impacts
the public finances of host countries. This is of
particular policy importance for Europe given its
ageing populations and fiscal imbalances. Sec-
tion 6 identifies new areas of study regarding
immigration that move beyond these traditional
topics; examples include the effects of immigra-
tion on housing markets, prices, and innovation.
The final section concludes.1
2. European migration patterns
Immigration is now a prominent feature in the
economic, social, and political landscape of
many European countries. In 2007, over 27m
people living in EU15 countries were foreign
nationals. This figure partially represented mi-
gration within the EU region, which accounted
for approximately a third of total migration. The
larger share was citizens of countries outside of
the EU25 area, which comprised two-thirds of
migrants and 5% of the EU15 population. The
aggregate size of this foreign national popula-
tion was larger than the US' comparable stock.

1 Interested readers should also consult classic surveys
of immigration like Greenwood and McDowell (1986), Bor-
jas (1994, 1995a, 1999c), Friedberg and Hunt (1995), Bau-
er and Zimmermann (1999), Card (2005), and Bodvarsson
and Van den Berg (2009). Some of these surveys provided
formal theoretical backgrounds on the economics of immi-
gration that are touched upon very lightly in this paper. Zim-
mermann (1995) described the history of EU migration.
Figure 1. Components of EU25’s population change
Source: Eurostat 2006.
Figure 1. Components of EU25’s population change
Source: Eurostat 2006.
3
William R. Kerr
Table 1 shows that the majority of these EU15
foreign nationals resided in large countries like
Germany, France, Spain, and the UK. Measured
in terms of population shares, foreign nationals
were of comparable importance for many of the
smaller economies in Northern Europe. The
mean population share of EU15 foreign nation-
als of 7% was similar to the US share. Smaller
and geographically-remote nations like Finland
or Portugal tended to have below average shares.

Table 1 mostly presents Eurostat statistics that
are based upon nationality status. Defining im-
migrants through country of birth yields a simi-
lar picture, although some differences emerge.
Table 1 continues by documenting differences in
the rate at which European countries grant citi-
zenship to migrants. Europe as a whole had sub-
stantially lower rates of citizenship acquisition
than the US. Northern European countries tend-
ed to have higher rates of citizenship attainment
relative to Southern Europe for migrants; this
was especially true for Sweden and the Nether-
lands. Immigration is an even larger force in
these countries than statistics using foreign na-
tionals indicate. Likewise, the US immigrant
population share in 2000 defined through coun-
try of birth was 11% versus 7% defined through
citizenship.
The directions of migrant flows are very
asymmetric. A significant share of early mi-
grants moved from Europe to the US, Canada,
and Australia. While migration into these coun-
tries remains very strong, the composition of
source countries changed substantially over the
last 30 years or so. Most migrants to the US, for
example, now come from Latin America and
Asia instead of Europe. This composition
change of migration flows is also observed in
Europe. Table 2 presents the major source coun-
tries of immigration by host country for 1997.
This table considers legal migrants only; illegal
migration would further increase the migrant
share coming from outside the OECD for most
host countries. Composition shifts were quite
dramatic across Northern European countries

with the largest immigrant population shares.
Sweden, for example, received most of its mi-
grants from other Nordic countries until the late
1970s, but a substantial portion of its recent im-
migration has been refugees. Germany has re-
ceived large inflows from Turkey, while Moroc-
can immigrants were the largest share for the
Netherlands.2
This broader pool of migrants has led to great-
er heterogeneity in immigrant traits. The US
case is best documented. Recent immigrants
from Latin America tended to be less educated
than earlier European migrations to the US.
Over 35% of high-school dropouts in the US
were foreign born in 2000 (calculation based on
the Current Population Survey), far greater than
the overall immigrant population share. On the
other hand, the US has recently received large
flows of highly-educated immigrants. Asian in-
flows have been particularly important in sci-
ence and engineering sectors and account for the
majority of the US' 1990s growth in these oc-
cupations.3
Heterogeneity in immigrant types is also an
important dimension of European flows. Table 1
documents differences in the 2001 foreign na-
tional share of workers with primary/secondary
or tertiary educations. As discussed below, im-
migrants have weaker labor force participation
rates than natives, which generally leads to low-
er worker shares compared to population shares.
This is particularly evident in countries accept-
ing more refugees and asylum seekers. Most

highly-educated immigrants originated from
other European countries or the OECD more
generally; only a third came from developing
countries. Despite these high-skilled inflows, the
majority of recent immigrants to Europe had a
lower level of education than natives. 4
2 Germany also experienced inflows in the 1990s of ethnic
Germans from the former Soviet Union that were substanti-
ally larger than the listed immigration flows (e.g., Brücker
and Jahn 2010). These inflows are not captured, however, by
nationality surveys as citizenship was automatically granted
to ethnically German migrants. While typically preferred,
surveys collecting country of birth are unfortunately not
consistently available.
3 For example, Cervantes and Guellec (2002), Freeman
(2006), Kerr (2007), and Kerr and Lincoln (2010).
4 Recent work has begun to more systematically compare
immigrants across countries. Antecol, Cobb-Clark and Tre-
jo (2003) and Aydemir and Borjas (2007) provided studies
of quality differences in immigrants across several non-Eu-
ropean countries. Europe is relatively under studied in this
respect, with Algan et al. (2010) an important exception.
4
William R. Kerr
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5
William R. Kerr
Looking forward, observers place alternating
fear and hope in the role of immigration for
Northern Europe. These fears often relate to the
perceived fiscal and social strains that would ac-
company the assimilation of a large number of
less-educated workers. Cultural and social cohe-
sion is also a central policy concern. These is-
sues have been strongly debated in the lead up
to and evaluation of the integration of European
labor markets following the accession of Eastern
European states beginning in 2004.5 These is-
sues and concerns are increasingly focused on
expanding immigration from developing econo-
mies and the assimilation of refugees.
Other observers, however, hope that future im-
migration can partially rectify fiscal imbalances
resulting from ageing populations. By 2015, Eu-

rope's natural rate of population growth will turn
negative with deaths outnumbering births. Net
migration to the region is expected to maintain
a positive overall growth rate for the region's
population until 2035, but thereafter projected
immigration is insufficient to maintain a positive
rate (Eurostat 2009). While many observers con-
clude that immigrants cannot fully rectify these
fiscal and ageing imbalances (e.g., Freeman
2006, Feldstein 2006), it is clear that immigra-
tion will grow in importance for Europe over the
next 50 years (Coleman 2008). This is particu-
larly true with respect to labor markets and pub-
lic finances, to which this survey turns next.
3. Immigrant assimilation in the labor
market
This section and the next consider the impact of
immigration on the labor market. The choice to
migrate is first analyzed, as different motiva-
tions can yield distinctly different economic
outcomes. Assimilation of immigrants into the
host-country labor market over time in terms of
wages and employment is then discussed. The
section closes with future research needs. The
next section surveys how immigrants affect the
labor market outcomes of natives.
3.1. Migration choices
People move across countries for many reasons.
Economic theory most prominently highlights the
international labor mobility that descends from
wage differences across countries. Likewise,

many students from developing economies mi-
grate to advanced countries, for either short or
long durations, to study in the schools and uni-
versities of advanced countries (e.g., Borjas
2009). Sadly, many migrations are also the result
of hardships or oppressions, as the growth of
refugees in Northern Europe attests. The nature
of the migration will impact education levels,
ages, and tenures of immigrants, and consequent-
ly their probable assimilation. When migrants
have the power to choose, the nature of the mi-
gration will also impact the host country selected.
Unfortunately, the causes and decision frame-
works of immigration are significantly less stud-
ied empirically than the economic impacts of
resulting flows. International questionnaires
provide some evidence of migrant rationales,
particularly surrounding the choice of destina-
tion country. Migrants frequently cite higher
income levels, better personal safety, short dis-
tance to home countries, and established immi-
grant networks as the main reasons for choosing
their new host countries. Econometric studies
tend to support these conclusions, and the im-
portance of income differentials is also evident
in comparisons of income or GDP levels be-
tween host and source countries.6
This demand for entry meets with supply re-
strictions set by nations through immigration
quotas. Ruhs (2008) reviewed the considerations
inherent in immigration policy with particular
application to the UK's framework. His work
noted that economics provides more powerful
lessons for the selection of migrants, due to fac-

5 For example, IOM (1991), Layard et al. (1992), Cole-
man (1993), OECD (1998), Bauer and Zimmermann (1999),
Fertig and Schmidt (2001), and Boeri and Brücker (2005).
Zaiceva and Zimmermann (2008) described the modest ini-
tial migration.
6 For example, Lucas (1975), Straubhaar (1986), Long,
Tucker and Urton (1988), Faini and Venturini (1993), Zim-
mermann (1995), Massey et al. (1998), IOM (1998), Bauer
and Zimmermann (1999), Hatton and Williamson (1998),
OECD (2000), Coppel, Dumont and Visco (2001), Munshi
(2003), Mandorff (2007), and Kerr (2008b).
6
William R. Kerr
H
os
t
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u
n
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y
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m

ig
ra
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os
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om
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en
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15
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er
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15

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or
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or
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5
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ra
zi
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it
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%
S
ou
rc
es
:
C
op
pe
l,
D
um
on
t
an

d
V
is
co
(
20
01
).
Im
m
ig
ra
nt
fl
ow
s
re
fe
r
to
1
99
8

ex
ce
pt
f
or
A
us
tr
al
ia
a
nd
D
en
m
ar
k
(1
99
9)
.
Im
m
ig

ra
nt
s
to
ck
s
re
fe
r
to
1
99
7
ex
ce
pt
f
or
A
us
tr
al
ia
,

C
an
ad
a,
a
nd
D
en
m
ar
k
(1
99
6)
.
Im
m
ig
ra
nt
p
op
ul
at

io
ns
r
ef
er
t
o
fo
re
ig
n-
bo
rn
p
op
ul
at
io
n
fo
r
A
us
tr

al
ia
, C
an
ad
a,
a
nd
t
he
U
S
.
T
ab
le
2
. P
ri
m
ar
y
so

ur
ce
c
ou
nt
ri
es
o
f
O
E
C
D
i
m
m
ig
ra
ti
on
, 1
99
7–
19

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