Download Does immigration increase labour market flexibility?

Does immigration increase labour market flexibility?
Marianne Røed and Pål Schøne
Institute for social Research
Oslo, Norway
December 2010
Abstract
The question we investigate empirically in this paper is whether immigration makes the Norwegian
labour supply more responsive to regional differences in economic conditions. We examine three
stages in the mobility of immigrants: Firstly, the settlement pattern of newly arrived immigrants.
Secondly, the immigrants´ subsequent mobility between Norwegian regions and, thirdly, their eventual
exit from the regional labour markets to abroad. The mobility is defined according to which of the 19
Norwegian counties immigrants move into, between, or from when leaving the country. The groups
studied are refugees and labour immigrants who arrived in Norway during the period 1995 to 2004.
As comparison, the mobility pattern of a representative group of native Norwegians is analyzed.
In all three stages the geographical mobility of immigrants is sensitive to variations in regional
economic opportunities. Furthermore, we find that the mobility of immigrants is considerably more
responsive to regional economic differences than the mobility of natives. Taken together the analyses
in this paper clearly indicate that higher immigration makes the Norwegian labour supply more
responsive to regional differences in economic conditions.
Index terms: Immigrants, regional mobility, regional equilibrium
JEL codes: J61, R23, R13
Corresponding author: Pål Schøne
Institute for Social Research,
Pb 3233 Elisenberg, 0208 Oslo
Phone: + 4723086182
Email: [email protected]
1. Introduction
Parallel to the increase of immigration to many developed countries, there has been a growing
interest in analyzing economic consequences of immigration in receiving countries. A large
share of the economic studies has analyzed the effect on native wage and employment
opportunities (Friedberg et al. 1995; Borjas 2003; Card 2001, 2005; Ottaviano and Peri 2008).
In this paper we build on a smaller literature analyzing the effect of immigration on labour
market efficiency. In regionally divide national labour markets, efficiency requires that two
conditions are met: Marginal productivity of homogeneous workers should be equalised
across markets and no unemployment should prevail. The main idea in this literature is the
following: If the mobility of immigrants is more responsive to regional differences in labour
market opportunities they contribute more – than natives - to reduce inefficiency caused by a
suboptimal regional distribution of labour.
In that case the immigrants “greases the wheels
of the labour market” 1 and this efficiency gain should be accounted for when costs and gains
from higher immigration are summarized. Empirical evidence from the US in Blanchare and
Katz (1992) suggests that that labour mobility, more then any other adjustment mechanism, is
decisive in reducing regional disparities in economic conditions.
The question we investigate empirically in this paper is whether immigration makes
the Norwegian supply of labour more responsive to regional differences in economic
opportunities. The mobility of workers between regions, as well as mobility between
participation and non-participation in the regional labour forces, affects this responsiveness.
We examine three stages in the regional mobility of immigrants with regard to variations in
local economic opportunities: Firstly, the settlement patterns of newly arrived immigrants,
secondly, their subsequent mobility between Norwegian regions and, thirdly, their eventual
emigration out of Norway. By moving in and out of the country immigrants may affect the
economic responsiveness of labour forces participation. By moving within the country they
may affect the economic responsiveness in the distribution of labour between regions.
The seminal contribution to this particular part of the immigration-effect literature is
Borjas (2001), where the issue is analyzed both theoretically and empirically.
In the
theoretical part of this paper, he outlines the efficiency gains of inducing a more mobile group
of workers into a competitive labour market. Since US is the empirical case in Borjas (2001)
the competitive labour market seems to be the appropriate model. In Norway on the other
hand, wage bargaining is quite centralized. Thus, the competitive labour market model is not
1
The title of Borjas (2001) is : “Does immigration grease the wheels of the labour market?”.
an obvious point of departure for the analysis of this topic in the Norwegian case. Norwegian
wage setting may be described as a two-tire procedure: Taking the national employment
situation into account, representatives for the central unions and employer organizations first
agree on some national wage increments. Then negotiations between local parties add wage
drift depending on the tightness of the regional labour markets.
Jimeno and Bontolila (1998) analyze a simple equilibrium model for regionally
divided labour markets where wages setting take place at the centralized and at the local level.
The national and local unemployment rates have both a negative influence on regional wage
rates. It follows from this model that, given quit general assumptions, the level of national
unemployment is reduced when the mobility between labour force participation and nonparticipation becomes more responsive to variations in regional wage and unemployment. It
also follows that the regional convergence of both wage and unemployment rates speed up
when the mobility of labour between regions becomes more responsive to regional variations
in economic opportunities.
In the empirical analysis of this paper all the three stages in the geographical mobility
of immigrants are studied with regard to its’ responsiveness to regional differences in average
wage levels and unemployment rates. The groups studied are refugees and labour immigrants
who arrived in Norway during the period 1995 to 2004. In addition, the mobility pattern of a
representative group of native Norwegians is analyzed. The main findings are that, in all the
three stages, the geographical mobility of immigrants is sensitive to variations in the
indicators of regional economic opportunities. That is, they move towards regions with lower
unemployment rates and higher wages. Furthermore, the mobility of immigrants seems to be
considerably more responsive to such regional economic differences than the mobility of
natives.
Although international comparative statistics is hard to find in this area, there may be
reasons to believe that the regional mobility in Norway is relatively low. On the one hand, this
may partly be due to low regional variation in both wages and unemployment (OECD 2009).
When this is the cause, low mobility does not necessarily come at the expense of economic
efficiency. On the other hand, a low mobility in the Norwegian population may be the result
of factors which decreases the return to migration, given the wage and unemployment
differences that reflect productivity differences between regions. Some factors of this kind
which seems to be relatively pronounced in Norway are: Firstly, a generous unemployment
insurance implies that unemployed people may wait longer before moving to find a job in a
more prosperous region. Secondly, high labour force participation among women, which
implies that in many households more than one adult is attached to the regional labour market.
Thirdly, a high share of people is homeowners. Fourthly, the areal size of the country is large
relative to the number of individuals.
If the Norwegian population due to such factors are relatively immobile, the inflow of
high mobility persons into the labour force may be particularly efficiency enhancing.
The paper proceeds as follows: In the remaining parts of this introduction we take a
brief look at the arguments substantiating the hypothesis that the mobility of immigrants is
more responsive to regional differences in economic opportunities. Then, we give a
presentation of earlier research and what we consider to be the contributions of this paper.
The introduction ends with a short description of the Norwegian immigration policy and the
institutional setting. In section 2. the empirical method and the data is presented. In Section 3
we present the results and in Section 4 we conclude.
Why immigrants move more
The hypothesis that immigrants are more geographically mobile is substantiated by arguments
that they belong to a group with lower mobility costs, and higher preferences for income
gains, than the native population. Persons who have lived in a country for a long time have
roots in specific regions. Thus, fixed moving costs are high. In contrasts, newly arrived
immigrants already have induced the fixed costs related to uprooting. The cost of choosing
one region over another in the new country may be insignificant.
Income maximizing
immigrants will choose region which offer the best combination of employment probabilities
and wage rates. Labour migrants are a selected group of workers from the origin country with
relatively low aversion for moving and relatively high preferences for income gain. That is,
since they - in contrast to those staying behind - have chosen to bear the fixed cost of
uprooting.
Immigration to Norway may roughly be divided in two quite different kinds of flows,
the forced movers; refugees and their families, and the voluntary movers; mainly labour
migrants and their families. The arguments of a self selection on low moving costs and high
preferences for income obviously not apply to the first group. However, the arguments related
to lack of roots in specific regional communities is valid also for the refugees.
Chain migration is a well established empirical phenomenon which refers to that
migrants – as a tendency - choose destinations where there are large communities of fellow
citizens (Massey et al 1993, Hatton and Williamson 2005, Pedersen et al 2008). The central
mechanism behind this is the so called network effect; earlier immigrants are both a very
important source of information, a channel of entry and a source of support during the settling
process in a new country. The network effect may hamper the geographical mobility of
immigrants and its’ responsive to regional differences in economic conditions. That is, since
this mechanism make them move in the direction of fellow citizens in stead of labour market
opportunities.
The litterateur on immigrant regional mobility
Empirical studies of the regional mobility of immigrants within receiving countries are rather
scant. Bartel (1989) is an early work that analyses the geographical distribution of immigrants
across the US. This analysis indicates that their choice of location within the US is only
weakly sensitive to regional wage and unemployment differences. According to this study the
foreign –born first of all tend to locate in areas with large ethnic groups of similar origins.
Internal migration within the US occurs more often among immigrants than natives. Jaeger
(2000), examines how immigrant location choice in the US varies with admission category.
He finds that, even though the settlement patterns of all immigrants groups
are more
responsive to local economic conditions than the one of natives, there is a large variation
across admission categories in this regard. The labour immigrants are much more likely to
locate in areas with low unemployment and high wages than the other categories. He also find
a clear network effect; the concentration of individuals from an immigrant’s country of birth
is an important determinant of location choice.
Borjas (2001: 2) poses the question if immigration;
“.......greases the wheels of the
labour market by injecting into the economy a group of persons who are very responsive to
regional differences in economic opportunities.” He compares the mobility patterns of US
natives and immigrants. Using census data 1950-1990 he analyses the link between interstate
wage differences for particular skill groups and the geographical sorting of natives and
immigrants. The results show that new immigrants are more likely to live in states that offer
the highest wage for the skills they hold. In the same article Borjas analyses if immigration
speed up the convergence of wage across regional labor markets in the US. By linking the
degree of immigration to educational groups and the evolution of regional wage differences
within the same groups he finds that this is clearly the case.
Schündeln (2007) analyse the between-state mobility of natives and immigrants in
Germany 1996-2003. His results reveal that immigrants are more likely to move within
Germany than natives. Furthermore, he finds that the mobility of immigrants is significantly
more responsiveness to differences in labour market opportunities. Amuedo-Dorantes and de
la Rica (2005) use aggregated data from the Spanish Labor Force Survey from 1999 to 2004.
Their results suggest that immigrants to a greater extent than natives choose to reside in
regions where their employment probabilities are higher.
Åslund (2005) presents evidence on initial and subsequent locations of refugees to
Sweden during the 1980s. His findings indicate that refugees, and labour immigrants from
OECD countries tend to move to towards regions where the average earnings - and
employment rates - are higher. The mobility of both immigrant groups tend to be more
sensitive with regard to regional differences in unemployment than the mobility of natives.
Åslund (2005) also identifies a network effect since the refugees move towards regions where
the concentration of people from their own country is high.
We contribute to this literature in several ways: Firstly, we investigates all three stage
in the immigrants regional mobility; from their first settlement, to their subsequent mobility
between regions and, finely, their, eventual emigration.
Secondly, we distinguish between
groups of immigrants who have very different motives for leaving their home country, and
who also migrate within very different institutional settings; refugees and labour immigrants.
Thirdly, we exploit rich individual register panel data which enable us to follow individuals
between geographical regions, employers, and labour market staes.
Finally, we present
evidence from a country with a centralized wage setting system, which in an international
context has relatively small regional differences in labour market conditions.
Immigration to Norway and the institutional setting
The immigrants’ share of the Norwegian population has increased considerably during the last
decades; from approximately 2 per cent of the population in 1980 to approximately 10 per
cent in 2009. In the same period the composition of the immigrant inflow has changed, from
a majority being constituted by Nordic and western labour immigrants to a domination of
refugees and family immigrants from geographically and culturally more distant parts of the
world. In the ten year period we study; 1995 – 2004, the growth of the Norwegian immigrant
population was due to an increased inflow of the last type.
However, from 2006 labour
immigrants from Eastern and Central European countries constitute the majority in the yearly
migration inflows.
With regard to the immigration of refugees and labour migrants the institutional
context is very different. Refugees and their families are granted a residence permit if their
application for asylum is approved. They may apply after entering the country on their own,
or UN may apply for them (Quota refugees). When the residence permit has been granted the
new immigrants should be settled in a municipality within six months. This is accomplished
in cooperation between authorities at the central and local level. The municipalities are
financially supported by the state to provide the refugees with housing, health care,
integration programs and other kinds of public services, during their first years in the country.
In our context, the main point is that the refugees often have only a minor influence on their
first settlement. However, regarding their subsequent mobility they decide for themselves.
Particularly during their first years in Norway refugees are – as a tendency – confined to low
income jobs and a high rate of working age people are either unemployed or out of the labour
force. They are either low educated or they have problem getting their education approved off
in the Norwegian labour market.
Since the fifties Norway has a common labour market with the other Nordic countries.
These are neighbouring countries which are very similar with regard to economic
development, as well social and cultural conditions. In three, out of five, countries nearly the
same language is spoken. In 1994 Norway joined the common labour market within the
European Economic Area (EEA), which also includes the Nordic countries. Thus, in the
period we study citizens from that region may freely apply for work in Norway. During the
last decades the wage level in Norway has been higher then in neighbouring country Sweden.
Particularly during upturns in the Norwegian economy, Swedes have moved to Norway to
find work. In the period we study, labour immigrants from outside the Nordic region, and
specifically those who enters from outside the EEA, are most often highly qualified workers
who are recruited by Norwegian employers.
2. The empirical approach
2.1 The mobility model
The settlement decision
To analyse the initial location of labour immigrants and the settlement pattern of natives we
estimate a specifications of McFadden’s conditional logit model (McFadden 1974). We
assume that individual i’s perceived utility of living in receiving region r, in year t, is given
by:
(1)
U irt = C r + αX rt + ε irt
Xrt is a vector of region specific and time varying characteristics of value to the individual. Cr
is a vector of regional dummies capturing time invariant region specific attributes that
influence the location decision. ε irt is the individual error component. A person who
maximise utility will choose to locate in region r if U ir > U ij for all j∈ {1,..., J} different from
r, where J is the total number of possible destination regions. When this is the case it may be
reasonable to assume that ε irt is drawn from a i.i.d. extreme value distribution, which gives us
the conditional logit model. If region r is chosen then y irt = 1. 2 The probability that individual
i chooses to settle in region r: Pirt = Pr(yirt = 1) , can then be expressed as:
(2) Pirt =
exp(Cr + αX rt )
J
∑ exp(C
j
j
+ αX jt )
Given the main questions raised in this paper, the focus of interest is on the explanatory
variables which measure regional differences in marginal productivity and employment
probability. To isolate the effect of such labour market indicators we also include time
varying variables to control for the network effects, level of living expenses, and the size of
the labour market. The regional dummy variables control for the multitude of time invariant
factors in destination areas that may attract immigrants. The marginal effect of a change in the
continuous X-variables is given by:
(3)
∂Pirt
= Pirt [1 − Pirt ]α
∂X rt
Mobility between regions
To model the subsequent mobility between regions we estimate another specification of
McFadden’s conditional logit model. In this case it is the choice between moving from the
region where one is settled in year t-1 to one of the J-1 other regions in year t, or staying in
the same region, which is analysed. We assume that the change in perceived utility for
individual i, from choosing to reside in region r in the next period, if he or she is currently
living in region s, may be expressed as:
(4) Uisrt = Λisrt + εisrt ,
d
d
= X rt − Xst
Λ isrt = αX srt
+ C r + ( βr − βs )Z it + mM sr + dDsr , Xsrt
2
Each immigrant chooses one of the nineteen Norwegian counties as their first residence after arrival. Nineteen
observations are created for every immigrant. The dependent variable in the estimation of the conditional logit
model is equal to 1 for the county which is chosen, and zero for all the counties which is not chosen.
where Xrt and Xst signify time varying characteristics of the regions that the individual
consider to move to and from, respectively. Cr is a vector of dummy variables for the
receiving regions and Cs=0. Zit represents individual characteristics, which may affect the
choice of residence.3 Msr is a dummy variable, which is equal to 1 if a move is not necessary
to locate in region r. This variable accounts for fixed costs of moving, such as psychological
costs, as well as financial costs related to uprooting and establishing in a new environment.
Dsr ,which is the geographical distance between the current location and alternative receiving
regions, captures the possibility that moving costs increase with the distance between
locations. ε isrt is the individual error component. The individual will choose location r if
U isrt > U isjt , for all j∈ {1,..., J} different from r. When the individual, in this manner,
maximize utility it may be reasonable to assume that ε isrt is drawn form an i.i.d. extreme
value distribution. If region r is chosen by individual i, living in s, then y isrt = 1.4
The
probability that person i, who is currently living in region s, Pisrt = Pr( y isrt = 1) , moves to
region r is then:
(5)
Pisrt =
exp(Λ isrt )
J
∑ exp(Λisjt )
j
And the marginal effect of differences in characteristics between receiving and sending
regions are:
(6)
∂Pisrt
d = Pisrt [1 − Pisrt ]α
∂X srt
This way of modelling the location choice is consistent with a notion that individuals make
the decision to move or not to move, and where to move, simultaneously.5
Emigration
3
Individual characteristic must be interacted with each of the 19 possible choices. Thos every individual
characteristic add 19 variable. Given that the numbers of observations we must limit the number of Z variables
to stay within the computational constraints.
4
Now every individual chooses each year between one of the nineteen Norwegian counties as their residence in
the next year, including the one they stay in this year. Thus, each year nineteen observations are created for
every one of them. The dependent variable in the estimation of the conditional logit is Pisrt = Pr( y isrt = 1) . yit is
equal to 1 for the county which is chosen by individual i in year t and zero for all the counties which is not
chosen.
5
This specification of the conditional logit model used to analysis the choice of relocation is more thoroughly
discussed in Davis et al. (2001) and in Keefe (2004). Ideally we would have included the possibility of
emigration from Norway in this choice set as well. However, since the relevant information about labour market
variables in receiving region of other countries are not available to us, we where not able to do that.
To model the emigration from Norwegian regions we estimate a multinomial logit model for
the choice between three alternatives (j=1,2,3): i) emigration out of Norway, ii) moving to
another Norwegian region, or iii) stay in region s. The utility individual i gets by choosing
alternative j is:
(7)
U ijt = Π isjt + ε ijt ,
Π isjt = C s + α j X st + β j Z it + τ jYt
Since we assume that individual i maximises utility and that εijt is drawn from the extreme
value distribution, the emigration probability and the marginal effects has the same structure
as in equation (5) and (6). In this context the Xst - variables refer to time varying labour
market characteristics of the region where individual i lives when making the mobility
decision. Cs captures region specific fixed effects, while Yt captures year specific effects.
This way of modeling the emigration decision is consistent with the notion that the
immigrant chooses emigration after finding this alternative superior to the alternative of
staying in the present region, as well as to the alternative of moving between regions within
Norway.
3.2 Data, sample and variables
Data
The data we use are extracted from public registers, collected and administered by Statistics
Norway. From this database we are able to construct panels for all persons living in Norway,
and follow their movements inside and outside the labour market.
That is, we have
information on periods in employment, unemployment, and out of labour force, as well as
mobility between employers, and geographical locations. We also have panel information on
demographic and educational events. We have panel information for the period 1995-2004.
From public registers we also extract relevant background information about the individuals;
gender, age, marital status, number of children, country of birth, reason for immigration
(refugee or labour immigrant), and years since migration.
Sample
The immigrant sample is confined to males that came to Norway in the period 1995-2004, and
that were between 20-55 years of age when they entered. We distinguish between immigrants
who by the authorities are registered as either: i) refugees, including those who are admitted
due to family reunion with refugees, or ii) labour immigrants. Immigrants who arrived for
other reasons, for instance family reunification or education are excluded from the analysis.
These two groups of immigrants are followed as long as they stay in Norway, in the period
1996-2005. Thus, the maximum length of stay we observe is ten years.
Table A1 in Appendix present the distribution of the major sending countries, for both
labour immigrants and refugees. Among refuges, the largest group comes from Iraq, followed
by Somalia and the former Yugoslavia. The largest group of labour immigrants by fare is
Swedish. Approximately 30 per cent of the registered labour migrants come from Sweden,
followed by 14 percent from United Kingdom and 12 percent from both Denmark and
Germany. Hence, in contrast to the refugee countries, the major labour immigrant countries
are characterised by being in short distance from Norway, both culturally and geographically.
To form a comparison group of natives we draw a representative sample of all native
men between 20-65 years of age each year.
Dependent variables
The mobility pattern of immigrants and natives is characterized by their: i) initial settlement,
ii) their subsequent mobility between regions, and iii) their eventual move out of a Norwegian
region to abroad. Administratively, Norway is divided into 19 counties which we define as the
regional labour markets. Thus, the three stages of geographical mobility are defined according
to which counties immigrants (and natives) move into, between, or from when leaving the
country. Initial settlement is identified by the registration of where the immigrants live at the
end of the year they arrived in Norway. Immigrants that move between counties during the
first year are excluded.6 Subsequent mobility between counties is identified by the county of
residence in the end of year t-1 and the county of residence in the end of year t. Individuals
who have changed county of residence, but are registered with the same employer, are not
registered as movers. Emigration from a Norwegian county is defined by the county of
residence in the end of year t-1 and the residence status in Norway in the end of year t. If the
individual is no longer registered as living in Norway at the end of year t, he is registered as
emigrated.
Explanatory variables
6
Sensitivity analyses shows that this applies to approximately 5 per cent of the arriving cohort, and that results
were not sensitive to them being included or not.
The initial and subsequent mobility patterns are explained by labour market related variables
which varies between counties and over time: As an indicator of the regional employment
probability we use the yearly unemployment rates, as registered at the county level by the
Norwegian Labour and Welfare Administration. Figure 1 presents the regional dispersion in
this variable; minimum, mean and maximum county level unemployment rates in the period
1995-2004. The difference between high and low unemployment counties is in the range of 34 percentage points.
Figure 1. Regional differences in unemployment, 1995-2004, maximum, mean, and minimum
levels of county averages
8
7
Percent unemployed
6
5
4
,
3
2
1
0
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
As an indicator of regional productivity we use the county averages of log hourly wage,
calculated among full time employed males by December each year. Figure 2 presents the
regional dispersion in this measure. The difference between high and low wage counties
moves around 0.4 log points.7
As an indicator for the regional level of living expenses we use the county averages of
housing expenses which are measured each year by Statistics Norway. Since such costs are
sensitive to regional business cycles and population pressure, this variable is positively
correlated with wage and negatively correlated with unemployment. As an indictor of the
size of the regional labour market we include the population size of the county. To control for
the network effect we use a measure of regional ethnic concentration: The fraction of the
counties population made up by people from the immigrant’s country of birth. In Table A2
7
In this litterateur education specific indicators for labour market opportunities have been used (Borjas 2003,
Schündeln 2007). Mainly because the share with unknown education is very high among relatively newly arrived
immigrants we have not been able to construct such indicators.
the yearly average values of the main county characteristics and their standard deviations are
presented.
Figure 2. Regional differences in log hourly wage, full time wage employees, 1995-2004
maximum, mean and minimum levels of county averages.
Finally, in the analysis of emigration out the Norwegian counties, we include a set of
individual characteristics which may affect mobility costs: Demographic characteristics: Age,
age squared, years since immigration, marital status, having children or not, having small
children (below 7 years), and having siblings living in the residential county or not.
Furthermore, we include an individual variable measuring the labour market status at the end
of the year preceding the (eventual) mobility: employed, unemployed, or out of the labour
force.
Table A3 in Appendix presents mean values of individual characteristics in the
samples of natives, refugees, and labour immigrants.
3. Results
3.1 The initial settlement of labour immigrants
In this section we analyze the first stage of immigrant mobility, i.e., the settlement decision of
newly arrived labour immigrants. As described in the introduction, the first settlement of
refugees is decided in cooperation between national and regional authorities. How the
outcome of this process relates to the development in regional labour markets is beyond the
scope of this analysis. Thus, in this section the main focus is on labour immigrants. However,
as a background for the analysis of subsequent mobility we also briefly describe the regional
allocation of the refugees’ first settlement.
The settlement patterns
In Figure 3 we present the distribution of newly arrived refugees and labour migrants across
the 19 counties, together with the corresponding distribution of the Norwegian population.
Figure 3. Regional distribution of initial settlement of labour immigrants and refugees and
the settlement pattern of all natives. 1995-2004.
The settlement pattern of newly arrived refugees resembles that of the Norwegian population.
The exceptions in this regard are Akershus and Nordland where the refugees, compared to
the native population, are clearly under and over represented, respectively. Measured in
average wage, in the period we look at, Akershus is the wealthiest county in Norway,
Nordland is one of the three poorest. Labour migrants are more unevenly distributed than the
refugees, with two distinct spikes. Firstly, a very large share having the capital Oslo as their
first destination, almost one out of three labour migrants choose this county. Secondly, labour
migrants are also heavily concentrated in Rogaland, the “Oil-capital” of Norway, with a large
demand for workers in oil related industries.
19
A Herfindahl index: H i = ∑θij2 , is calculated for each group. Where θij is the share of
j =1
group i that lives in county j, with 0 ≤ H i ≤ 1 . The lower is Hi the lower is the level of
geographical concentration. The labour immigrants get an index value of 0.14, compared to
0.06 for both refugees and natives.
Responsiveness to regional labour market conditions
In Figure 4 the shares of all new labour immigrants who settle in a county each year, from
1995 to 2004, are plotted against relative wage and relative unemployment in the same county
and year. Due to the significant higher level of immigration to Oslo and Rogaland, the time
series are shown separately for these regions. The pattern of correlation indicates a negative
relationship between unemployment and immigration and a positive relationship between
wage growth and immigration.
In Table 3.1 we present the results from the analysis of the labour immigrants choice
of first settlement estimated by the conditional logit model. In Model 1 the probability for the
choice of one particular location is estimated with respect to the yearly county averages of
hourly (male) wage and unemployment.
Only county dummies are included as control
variables. The estimated value of the wage effect is positive, while the estimated value of the
unemployment effect is negative. We evaluate the size of the coefficients by the average
settlement probability, 0.052, and the mean values of the regional labour market variables
from 1995 – 2004. These values are presented in the last row of Table A2 in Appendix.
According to the coefficient estimated in Model 1 and the expression of the marginal effect in
Figure 4. Settlement pattern of newly arrived male immigrants in Norwegian counties in
relation to the regional wage distribution and the regional distribution of unemployment,
1995-2004 *
Share of immigrants to county
0,35
y = 0,7439x - 0,4172
R2 = 0,0346
0,3
0,25
Rest of the counties
Oslo
Rogaland
0,2
y = 2,238x - 2,1803
R2 = 0,5235
0,15
,,
0,1
y = 1,0725x - 1,0211
R2 = 0,5026
0,05
0
0,94
0,96
0,98
1
1,02
1,04
1,06
log(wage county)/log(wage Norway)
Share of immigrants to county
0,35
y = -0,1087x + 0,4469
R2 = 0,3584
0,3
0,25
Rest of the counties
Oslo
Rogaland
0,2
y = -0,1057x + 0,2356
R2 = 0,2481
,
0,15
,,
0,1
y = -0,0386x + 0,0712
R2 = 0,1634
0,05
0
0
0,5
1
1,5
2
2,5
Unemployment county/unemployment Norway
* The wage measure is: average hourly log (wage) in the county relative to average hourly log (wage) in
Norway, fulltime males 20-65 , measured in the last moth of each year .
The unemployment measure is: yearly rate of unemployment in the county divided by yearly rate of
unemployment in Norway.
equation (3), a one percent increase in the average wage raises the settlement probability by
0.49 percent. The average standard deviation in the county specific wage, from 1995 to
2004, constitutes nine percent of its mean. Thus, a one standard deviation increase in the wage
level from this mean value raises the settlement probability by 4.4 percent. Given the same
assumptions, a one percent point increase in the county specific unemployment rate decreases
the settlement probability by 13 percent. The average standard deviation in the unemployment
rate is 0.8 percent points. A one standard deviation raise in the county specific unemployment
rate, accordingly, decreases the settlement probability by 10.4 percent. These predicted
changes are presented in Table 3.2, together with the equivalent predictions following from
the estimated coefficients of the other models presented in Table 3.1.
In Model 2 we include the yearly county averages of log housing expenses and log
population size as explanatory variables. This reduces the estimated value of the
unemployment effect somewhat. However, it stays clearly negative. The value of the wage
effect is almost unaffected. The coefficient on housing expenses is positive. This suggests that
the coefficient on housing expenses probably picks up a business cycle effect rather than the
development in level of living expenses. The coefficient on population size is negative, which
indicate that the settlement of newly arrived labour immigrants do not follow the general
population trend in this period. In Model 4 we present the results from estimating the
probability model with the same independent variables as in Model 3, but without the capital
Oslo as a destination region. This sensitivity test reduces the number of observations by more
then one third. The negative unemployment effect is reinforced quite considerably, while the
wage effect is nearly not affected.
In fifth and sixed columns of Table 3.1 we present results from estimating Model 1
and Model 2 on a sample of natives. In the last two columns the same procedure is used to
analyze the probability that natives who move between two succeeding years settle in one of
the counties. As can be seen there are few significant relationships between the explanatory
variables and the probability of living – or settling - in one of the counties. The relationship
between average wage and the native settlement probability is negative, both in the case of the
population in total and in the case of movers. It is significantly negative when estimated
without controlling for housing expenses and population size. In Table 3.2 the native
coefficients are interpreted as marginal effects in the same manner as for immigrants.
The results from estimating Model 1 and Model 2, on the samples of natives and
newly arrived labour immigrants, indicate that the settlement pattern of the last group is more
responsive to labour market opportunities. This finding substantiates the hypothesis that
labour immigrants by their first settlement “greases the wheels” of the Norwegian labour
market.
In Model 3 we control for network effect in the settlement pattern of immigrants by
adding ethnic concentration as one of the explanatory variables.
The result indicates that
network has a strong positive influence on the settlement probability. Using the expression of
the marginal effect, the estimated coefficient predicts that a one percent point increase in the
ethnic concentration raises the settlement probability by 107 percent.
From 1995–2004 the
average standard deviation in this variable was 0.22 percent points. Thus, an average standard
deviation growth in ethnic concentration raises the settlement probability by 23 percent.
8
Controlling for ethnic concentration, the estimated value of the wage effect increases quite
strongly, while the unemployment effect is unaffected. This indicates that the network effect,
to some extent, has hampered the “greasing effect” in the Norwegian labour market of the
labour immigrants’ first settlement.
Table 3.1. Settlement pattern, newly arrived labour immigrants and natives. Males, 20-65
years of age, 1995-2004. Estimated logit coefficients, standard deviations in parenthesis
Model 1
0.525***
(0.20)
-0.136***
(0.023)
Labor immigrants
Model 2
Model 3
0.482**
0.740***
(0.211)
(0.213)
-0.092*** -0.098***
(0.027)
(0.028)
***
0.333
0.031
(0.130)
(0.131)
-1.635*** -1.671***
(0.608)
(0.612)
1.134***
(0.261)
-53255
-52155
0.191
0.208
All natives
Model 1
Model 2
-0.123**
-0.17
(0.064)
(0.13)
0.010
0.00
(0.009)
(0.01)
0.013
(0.05)
1.06***
(0.29)
Model 4
***
0.783
Log wage
(0.229)
-0.185***
Unemployment
(0.037)
0.321**
Log housing expenses
(0.153)
-1.607***
Log population
(0.662)
1.777***
Ethnic concentration
(0.406)
-53260
-38233
-259751
-259741
Log Likelihood
0.190
0.125
0.04
0.04
Pseudo R2
424973
272196
1730330
N
Note: Level of significance: *** 1 percent, ** 5 percent, * 10 percent. In all models receiving
effects are accounted for by county dummies.
8
Native movers
Model 1
Model 2
-0.285**
-0.233
(0.134)
(0.174)
0.001
0.004
(0.018)
(0.019)
-0.018
(0.087)
-0.395
(0.541)
-62539
-62539
0.107
0.107
448400
county fixed
In this analysis the county characteristics are measured in the same year as the immigrants
settle. The exception in this regard is ethnic concentration, which is measured the year
before. Thus, in this analysis we may have a problem with endogenous explanatory variables.
However, the values of the coefficients become nearly the same when the county specific
labor market variables are lagged one year.
Table 3.2. Marginal effects. Predicted percent change in settlement probability following
from one average standard deviation change in labour market indicators and in the ethnic
concentration
All labour immigrants
All natives
Native movers
Model 1 Model 2 Model 3 Model 1 Model 2 Model 2 Model 2
4.41
4.10
6.33
-1.05
-2.39
-2.39
-1.97
Wage
-10.42
-6.99
-7.45
0.76
0.00
0.08
0.30
Unemployment
23.6
Ethnic concentration
Note: The marginal effects are calculated using the estimated coefficients in Table 3.1, the formulation in
equation (3), an average settlement probability of 0.052, and the average values, 1995 to 2004, of the county
specific explanatory variables presented in last row of Table A2.
3.2 Mobility between regions
In this section we analyze the second stage of immigrant mobility, i.e., the movements
between counties within Norway. This analysis include by labour immigrants, refugees and
the native comprising group. The focus is on differences between groups in their response to
regional variation in labour market opportunities.
Figure 5. Regional mobility between regions with time spent in Norway .With time spent in
Norway for immigrants, mean value over time for natives. Males, 20-65, in the labour force
0,25
Share moving
0,20
0,15
0,10
0,05
0,00
1
2
3
4
5
6
7
8
9
10
Years in Norway
Refugees
Labour migrants
Native mean
Propensity to move between regions
Figure 5 presents the mean regional mobility rates for refugees, labour immigrants and
natives. With regard to the two immigrant groups the rates are presented conditional on years
since immigration, while the native rate is simply the mean value in the period 1995-2004.
During all their first ten years in the country, the immigrants have a higher propensity to
move between counties compared to natives. The propensity is clearly highest among the
refugees, but in this group the mobility rate falls steeply within the first few years. More than
one out of five refugees moves during their first year after their arrival in Norway. This is
reduced to one out of ten between the next two years. For the labour immigrants, the
propensity to move does also decrease with years since immigration, but it starts on a lower
level and the fall is much less steep. This difference between refugees and labour immigrants
may partly be explained by their different propensity to leave Norway. As we analyse more
closely in the next section, labour immigrants have a much higher propensity to move out of
the country. After ten years in the country both refugees and labour immigrants approach the
mean mobility level of natives.
In Table A4 in Appendix we present the shares of movers within each group that
move from and to the nineteen counties in the whole period 1995-2004. With regard to all
three groups the highest share settles in Oslo and Akershus; the capital of Norway and its’
neighbour county. That is, they move towards the most populated regions in the south. A
higher share among the immigrant groups - and particularly among the refugees - move to
Oslo. However, the main difference between the groups relates to where they move from.
While net flows to Oslo and Akershus are close to zero for both labour immigrants and
natives, it is clearly positive for the refugees, i.e., in this group a much higher share moves to,
than from this southern region.
At the same time, Nordland is the county with a
disproportionally high share of out moving refugees. This pattern is clearly related to the two
immigrant groups’ first settlement.
Responsiveness to regional labour market conditions
Using McFadden’s conditional logit model we estimate the probability of living in one
particular Norwegian county in one year, conditional on living in another – or the same –
particular county the year before (described in 2.1). The result are reported in Table 3.3. First,
Model 1 to Model 3 are estimated on the pooled sample of labour immigrants and refugees
(column 1-3), then we estimate the models separately for the two groups (column 4-9).
Finally we estimate Model 1 and Model 2 for the sample of natives (column 10 and 11). Only
males in the labour force are included in the analyses. In all models we control for receiving
county fixed effects, if the individual was unemployed before the eventual move, a dummy
for the first five year period, the distance between sending and receiving counties, and a
dummy equal to 1 if a move is not necessary.
First, we have a look on the importance of moving costs. The effect of unobserved
moving costs on the probability of moving may be deduced from the coefficients of the nonmigration dummy and from the distance variables. Looking at the pooled group of
immigrants, lower fixed moving costs of immigrants, compared to natives, increases their
probability of moving between Norwegian counties by approximately 153 percent.9 However,
when we compare the pooled group of immigrants with natives, their aversion towards
distance (variable moving costs) seems to be quite similar. When we estimate the models on
refugees and labour immigrants separately, the coefficients indicate that the first group has
higher fixed and lower variable moving costs, than the latter group. At zero distance this gives
the refugees a 62 percent lower probability of moving from one county to another, while at
average distance between Norwegian counties (625 km) the refugees have a 173 percent
higher probability of moving than labour immigrants.
These differences between the
immigrant groups do probably reflect their initial settlement pattern, which was described in
Figure 3. Thus, the distance variable helps to control for initial settlement.10
We now turn our focus on the effect of differences in regional labour market
opportunities on the mobility between counties. As in the previous analysis of first settlement,
we evaluate the size of the estimated coefficients by the mean values of regional labour
market variables and the formulation of marginal effects (equation 6). These calculations are
presented in Table 3.4.11
In Model 1 in Table 3.3 the differences in average wage and unemployment rates
between potential receiving counties and the current residential county are included as
explanatory variables, in addition to the other explanatory variables described above.
Estimated on the pooled group of immigrants the wage effect is positive and significant. To
asses the magnitude of the wage effect, assume that the workers in county r and in county s
both have an average wage level equal to the mean over the ten year period. Then the wage
level in county r increases by one standard deviation, which constitutes a 9.9 percent
9
This number is calculated using the estimates of the non–migration dummy in Model 1 for all immigrants and
natives and the hypothetical odds ratio between moving and not moving from s to r;
Pirst / Pisst = e m , where
Z=0, Xd=0,D=0.
10
Due to computational difficulties we are not able to include sending county fixed effects.
11
For most pairs of counties the yearly probability of moving between them is very small for all groups. Thus, in
the calculations of marginal effects We assume (1-Pisrt)=1. All average values and standard deviations used in the
calculations of predicted values in Table yy refers to the ten year means of the yearly average values and
standard deviations of county characteristics. These are presented in the last row of table A2, Appendix.
increase.12 According to the estimated coefficient in Model 1 this raises the probability that an
immigrant will move from county s to county r by 6.6 percent. Estimated on the pooled group
of immigrants the unemployment effect is negative and significant. Interpreted as marginal
change; a one standard deviation increase in the average unemployment rate in county r
lowers the probability that an immigrant will move, from s to r, by 19.2 percent.13
When we estimate Model 1 separately for labour immigrants and refugees, the wage
coefficients turn out insignificant for both groups. The effects of unemployment differences
are still clearly negative in both groups. When we estimate Model 1, on the sample of
natives, we find no significant effects of
neither regional differences in wages, nor
unemployment, on the probability of moving.
In Model 2 we include county differences in log housing expenses and log population
size as explanatory variables. When estimated on the pooled group of immigrants, and on the
group of refugees, the coefficient of housing expenses is positive. Since wage and
unemployment effects are weaker in Model 2 than in Model 1, we interpret this in the same
manner as in the analysis of first settlement, i.e., that housing expenses picks up a business
cycle effect, in addition to the level of living expenses. Regarding the impact of regional
differences in wages, this effect is reduced and it is no longer significant for the pooled group
of immigrants. When estimated separately for refugees and labour immigrants, the wage
effect is still not significant. The unemployment effect is reduced somewhat but is still
significant for both the pooled group of immigrants, and for refugees and labour immigrants
separately. Model 2 estimated on the sample of natives reveals – as in Model 1 - no effect of
regional wage and unemployment differences on the probability of moving.
In Model 3 we add the difference in the population share from the immigrants’ origin
country between residing and potential receiving counties as explanatory variable. The
estimated value of the network effect is positive for both immigrant groups. However, the
effect seems to be twice as high among the refugees. Interpreted as marginal change; a one
standard deviation increase in the ethnic concentration of a potential receiving county raises
the probability that a labour immigrant will move there by 7.7 percent. The same change
increases the probability that a refugee will move by 17.3 percent.
14
The absolute values of
the wage and unemployment coefficients are somewhat higher in Model 2 than in Model 3.
13
14
We use the average standard deviation between counties, 1995-2004, presented in the last row of Table 2A.
Again we use the average standard deviation from 1995 to 2004 as reported in the last row of Table 2A
This may suggest that the network effect - to some extent – hamper the “greasing effect” of
the immigrants regional mobility within Norway. However, the difference is not significant.
In summary, the results in Table 3.3 suggest that the mobility of immigrants between
Norwegian counties is “greasing the wheels” in the labour market. That is, since their regional
mobility is more responsive, than the mobility of natives, to variations in labor market
opportunities.
Furthermore, the results suggest that it is differences in regional
unemployment that spurs mobility, not wages.
Table 3.3. The probability of moving from one county to another, between two succeeding years. Males 20-65, 1995-2004, in the labour force.
Estimated logit coefficients, standard deviations in parenthesis. To stay in the same county as last year is the reference outcome.
Non-Migration Dummy
Difference between other counties
and the current home county in:
Distance
Model 1
4.27***
(0.03)
-0.004***
(1.2-03)
1.56-06 ***
(5.2-08)
0.67***
(0.24)
-0.24***
(0.04 )
All immigrants
Model 2
Model 3
4.27***
4.25***
( 0.03)
(0.03 )
Labour immigrants
Model 1
Model 2
Model 3
4.11.***
4.11.***
4.10.***
(0.04 )
(0.04 )
(0.04 )
Model 1
4.61***
( 0.06)
Refugees
Model 2
4.61***
( 0.06)
Model 3
4.60***
(0.04 )
Natives
Model 1
Model 2
5.21***
5.21***
(0.05 )
(0.04 )
-0.004.***
(1.2-03)
1.56-06 ***
(5.3-08)
0.38
( 0.25)
-0.19***
(0.04 )
0.63***
(0.17 )
0.30***
(0.09)
-0.004***
-0.005***
-0.005***
-0.005***
-0.002***
-0.003***
-0.003***
-0.004***
-0.004***
(1.3-03)
(1.5-03)
(1.5-03)
(1.5-03)
(2.4-03)
(2.4-03)
(2.4-03)
( 1.7-03)
( 1.7-03)
Distance squared
1.57-06*** 1.9-06.*** 1.9-06.*** 1.9-06.***
9.4-07***
9.4-07***
9.4-07***
1.6-06***
1.6-06***
(5.3-08)
(6.2-08)
(6.2-08)
(6.2-08)
(1.1-07 )
(1.1-07 )
(1.1-07 )
(7.3-08 )
(7.3-08 )
Log wage
0.44*
0.39
0.19
0.26
0.71
0.59
0.70
-0.13
-0.31
(0.25 )
( 0.30)
(0.32)
(0.32)
(0.45 )
( 0.46)
(0.45 )
(0.44 )
(0.48 )
Unemployment
-0.22***
-0.16***
-0.15**
-0.17***
-0.23***
-0.16*
-0.21***
-0.08
-0.08
(0.04 )
( 0.05)
(0.04 )
(0.06 )
( 0.07)
(0.07 )
(0.09 )
(0.07 )
(0.08 )
Log housing expenses
0.54***
0.10
-0.02
0.72*
0.67*
-0.33
( 0.17)
(0.22 )
( 0.22)
( 0.27)
( 0.28)
(0.28)
Log population size
0.25***
0.49.***
0.44.***
0.40
0.16
0.53***
( 0.10)
(0.12)
(0.12)
(0.31)
(0.17)
(0.14)
0.35***
0.79***
Ethnic concentration
0.33***
( 0.03)
(0.04)
(0.12)
Log pseudo Likelihood
-26922
-26909
-26839
-17163
-17160
-17114
-9389
-9387
-9347
-13031
-13029
Pseudo R2
0.87
0.87
0.87
0.89
0.89
0.89
0.84
0.84
0.84
0.94
0.94
N
71317
71317
71317
51481
51481
51481
42095
42095
42095
70064
70064
Note: In all models we control five year period dummies; 1995-1999, 2000-2004, receiving county dummies and labour market status before moving eventually take place;
registered as unemployed or working.
Table 3.4. The probability of moving between to particular counties. Predicted marginal effects of one standard deviation change in the difference between counties in wage,
unemployment and ethnic concentration
All immigrants
Labour immigrants
Refugees
Natives
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
Model 1
Model 2
Wage
6.6
3.8
4.5
3.9
1.8
2.6
7.0
5.8
6.9
-1.28
-3.0
Unemployment
-19.2
- 16.0
- 17.6
-12.8
-12.0
-13.6
-18.4
-12.8
-16.8
-6.4
-6.4
Ethnic concentration
7.3
7.7
17.3
In Table A5 (Appendix) we present some sensitivity analysis. In this table all the
reported coefficients are estimated with the explanatory variables of Model 1 in Table 3.3
included. The exception is that we in all the models control for two–year period fixed effects
instead of the five-year period fixed effect used in Table 3.3.15 Comparing the coefficients of
the labor market variables in the first columns of Table A5 with the first column of Table 3.3
we find that this increases the estimate for the wage effect of all immigrants in the labor force,
while the unemployment effect is almost unaffected. Comparing column three in Table A5
with column four in Table 3.3 we find that the coefficients of Model 1, estimated for labor
immigrants, are not affected by the inclusion of the two-two year dummy. Comparing the
second last columns in the two tables we find that to substitute the five year period dummy
with the two year period dummies do increase the value of the wage coefficient for natives.
However, it is still far from being significant.
Furthermore, in the estimations reported in Table 3.3, only immigrants and natives
who are registered in the labour force are included. A larger share of immigrants than natives
are registered outside the labor force. Particularly with regard to relatively newly arrived
immigrants, this may not mean that they are not looking for work. One reason why people
register as unemployed is that this is a precondition for receiving unemployment benefits. To
gain the unemployment benefit one must have labour earnings above a threshold, which takes
time. Thus, many of the newly arrived immigrants have not yet gained this right and may
have weaker incentives than natives to register as unemployed by the authorities. In column
two and six of Table A5 we present results from estimating the model for the total groups of
immigrants and natives, respectively. For the immigrant group, including individuals from
outside the labour force reduces the absolute value of the estimated wage and unemployment
effect on mobility. Still, both are still clearly significant.
Finally, as presented in Table A4 in Appendix, a high share of the immigrants moves
to the capital Oslo. To test if moving to this destination is driving the results we estimate the
model, excluding immigrants moving to Oslo. The results are presented in column four of
Table A5 for immigrants in the labor force. The size of the wage coefficient is reduced and
less precise. However, the estimated unemployment effect is reinforced by the exclusion of
immigrants moving to Oslo.
15
The reason why this was done in the analyses presented in Table 3.3 is that, when including the two year
period dummies, it was not possible to gain convergence for the refugee group when the estimations where
performed separately for labour immigrants and refugees.
3.3 Emigration from Norwegian regions
In this section the immigrants’ emigration is examined and compared with the emigration of
natives.
The focus is again on the responsiveness to changes in regional labor market
opportunities and how this varies between immigrants and natives.
Propensity to emigrate
In Figure 6 we present the percentage of immigrants in the male sample who emigrate during
a year conditional on years since immigration. Not surprisingly the propensity to move out of
the country is much higher among the labor immigrants than among the refugees. The mean
yearly emigration rates were 4,6 percent and 18,5 percent among refugees and
labor
immigrants, respectively. In the sample of Norwegian males the corresponding percent was
only 0,2.
Analyses in Bratsberg et al (2005), studying return migration among immigrants in
Norway, confirm this pattern.
In the group of labor immigrants the propensity to emigrate clearly falls with years in
Norway. Among the refugees the propensity increases until the third year in Norway after
which it falls and approaches a very low level in a few years.
The differences between the two immigrant groups’ emigration probability profiles
may reflect different institutional terms that “regulate” their migration. The emigration
probability of labor immigrants falls continuously as YSM increase. This is probably a result
of a selection process. Labour migrants may to a large extent enter and leave Norway as they
want. It is reasonable to assume that those who leave each year are the relatively unsatisfied
ones. The stayers accordingly constitute a group who become increasingly more satisfied with
their choice of immigration country. The hump shaped profile of the refugees emigration
probability may reflect that they are a mixture of people seeking protection from conflicts in
their home countries of either relatively short or long durations. Those who are victims of the
first kind of crisis may not get used to the Norwegian way of life before they are able to go
home. Some are also only given a temporary residence permit. However, victims of more
long lasting conflicts may get accustomed to the way of life and the high level of living
standard in Norway. Thus, they may not want to leave when things get better in their earlier
home country.
Figure 6. Percent of immigrants moving abroad with time spent in Norway. Immigrant males
20-65 years of age.
30
25
20
15
10
5
0
1
2
3
4
5
6
7
8
9
10
Years in Norway
Labour migrants
Refugees
Responsiveness to regional labour market conditions
To investigate the emigration responsiveness to local labor market conditions we estimate the
multinomial logit model described in Chapter 2.
In this model the choice is between
emigration from Norway, moving to another county, or stay in the county where one currently
lives (the base category). Even though our main interest in this section is on emigration out of
Norway we think it is more theoretically sound to analyze the choice of emigration
simultaneously with the choice of moving inside Norway. In this analysis the labor market
variables are measured by their values in the sending county, i.e., in the counties where
individuals live in the year before they eventually move.
Table 3.5 presents the results. Model 1 and Model 2 are estimated separately for
refugees, labor immigrants, and natives. Model 3 is estimated for the first two groups only.
Since the coefficients of population size and housing expenses turned out insignificant, in all
groups we do not include them in any of the models.
In Model 1 we control for county fixed effects and year fixed effects only. For both
groups of immigrants, an increase in the average unemployment level in the county of
residence has a positive effect on the probability of emigration out of Norway. The
corresponding effect of an increase in the average wage level is strongly negative for both
groups. With regard to the probability of moving inside Norway, both the unemployment and
wage coefficients are insignificant for labor immigrants. Among the refugees there is a strong
positive relationship between higher unemployment in the county of residence, and the
probability of moving within Norway. However, the corresponding wage coefficient is not
significant. When estimating Model 1 on the sample of natives we find no significant
relationships between regional changes in wages and unemployment, and the probabilities of
moving from or within Norway.
In Model 2 we add controls for individual demographic characteristics and labour
market status. Compared to Model 1 this does not change the coefficients of the labour
market indicators in any significant way.
As in the previous analysis the predicted marginal effects are evaluated by the mean
probabilities within each group, and the mean values of regional labour market variables.
These calculations are presented in Table 3.6.16 We use the coefficients in Model 2 to
construct the probabilities. As can be seen the predicted marginal effects of changes in both
unemployment and wage on the probability of emigration from Norway are considerably
stronger among refugees than labour immigrants. Thus, while refugees have much lower
emigration probabilities, those who do move, are sensitive to changes in regional labour
market conditions.
In Model 3 we add controls for ethnic concentration, YSM, and the world region of
origin country. For the labor immigrants this does not alter the coefficients of the labour
market variables. The network effect has the expected negative influence on mobility, both
out of the country and out of the county within the country. However, for refugees the
negative wage coefficient related to emigration is reduced considerably compared to Model 2,
and it is no longer significant. This pattern may indicate that the strong negative relationship
between wage and emigration, detected in Model 2, is caused by a negative correlation
between exogenous emigration of refugees from certain origin countries and the wage growth
in the counties where they are overrepresented. By exogenous emigration in this context we
mean mobility out of Norway which is the result of events outside our simple model, fore
example sudden changes in the refugees’ home countries. The strong positive relationship
between regional unemployment and emigration for refugees is more robust. It is not
significantly affected by the inclusion of ethnic concentration, YSM, and world region of
16
All average values and standard deviations used in the calculations of predicted values in Table3.6 refer to the
ten year means of the yearly average values and standard deviations of county characteristics. These are
presented in the last row of Table A2, Appendix.
origin country. For the refugee group we also find a strong positive relationship between
unemployment in the county of residence and the probability of moving to another county
within Norway.
Table 3.5. The probability of migration out of and within Norway. Multinomial logit
coefficients, males, 20-65 years of age, not in education
Model 1
Out
Within
County average of:
Unemployment
Log Wage
0.09***
(0.03)
-0.99***
(0.28)
0.09
(0.06)
-0.42
(0.52)
Labour immigrants
Model 2
Out
within
0.11***
(0.03)
-0.80***
(0.29)
0.08
(0.06)
-0.40
(0.52)
Ethnic concentration
-Log likelihood
N
-59682
95590
-55469
95590
Model 1
Out
Within
County average of:
Unemployment
Wage
0.48***
(0.07)
-1.97***
(0.71)
0.16***
(0.05)
0.54
(0.41)
Refugees
Model 2
Out
within
0.51***
(0.07)
-2.18**
(0.71)
0.16***
(0.05)
0.54
(0.41)
Ethnic concentration
-Log likelihood
N
32649
69371
31246
69371
Model 1
Out
Within
County average of:
Unemployment
Wage
-Log likelihood
N
-0.13
(0.06
-0.08
(0.60
133352
1228269
-0.009
(0.02)
0.22
(0.22)
Out
Model 3
Within
0.12***
(0.03)
-0.78***
(0.30)
-11.6***
(2.05)
-52646
95590
Out
0.09
(0.06)
-0.25
(0.54)
-16.36***
(4.53)
Model 3
Within
0.43***
(0.07)
-0.81
(0.72)
69.9***
(7.7)
28791
69371
0.23***
(0.05)
0.57
(0.42)
-17.5*
(9.2)
Natives
Model 2
Out
Within
-0.15
(0.06)
-0.07
(0.61)
122599
1228269
-0.03
(0.02)
0.26
(0.22)
Note: The reference outcome is staying in the current county of residence. The estimated variable is the Log
odds of moving from Norway, or moving to another Norwegian county, and staying in the current county. In all
the models we include year dummies and county dummies. In Model 2 we in addition control for demographics:
age, age squared, children, children below seven years of age, siblings in the county of residence. And we
control for labour market status: working, unemployed or out of the labour force. In Model 3 we include year
since immigration, year since immigration squared, as well as origin region: Scandinavia, or rest of the world(
only labour immigrants), Asia, Africa, Eastern Europe, South America (only refugees).
Table 3.6. Marginal effects. Predicted percent change probability for migration out of or
within Norway following from one average standard deviation change in unemployment,
wage and ethnic concentration
Refugees
Wage
Unemployment
Ethnic concentration
Model 2
Out
Within
-18.83
4.32
39.17
11.39
Model 3
Out
Within
- 7.08
4.56
33.02
19.65
14.57
- 3.32
Labour immigrants
Model 2
Model 3
Out
Within
Out
Within
- 5.9
-3.42
-5.76
-2.16
7.22
6.14
7.87
6.91
-2.09
-3.42
Note: The marginal effects are calculated using the estimated coefficients in Table 3.5, the definition of
marginal effects in equation (6), an the average emigration and moving probabilities within groups 1995-2004,
and the average values, 1995 to 2004,of the county specific explanatory variables presented in last row of Table
A2
4. Concluding remarks
With the increase in immigration in many developed countries, there has been an increasing
interest related to analysing economic consequences of immigration in receiving countries. In
this paper we contribute to a rather scant literature focussing on immigrants’ contribution to
economic efficiency. The question we investigate empirically in this paper is whether
immigration makes the labor supply in the receiving country more responsive to regional
differences in economic opportunities.
The mobility of workers between regions, as well as the mobility between
participation and non-participation in the regional labour force affects this responsiveness. In
the empirical analyses mobility is defined according to which of the 19 Norwegian counties
immigrants (and natives) move into, between, or from when leaving the country.
We examine three stages in the regional mobility of immigrants: Firstly, the settlement
pattern of newly arrive immigrants. Secondly, their subsequent mobility between regions and,
thirdly, their eventual exit from the regional labour market to abroad. In all stages the
mobility is investigated with regard to its’ responsiveness to regional differences in average
wage levels and unemployment rates. In this regard, the mobility behaviour of immigrants are
compared to the behaviour of natives. The groups studied are refugees and labour immigrants
who arrived in Norway during the period 1995 to 2004. In addition, the mobility pattern of a
representative group of native Norwegians is analyzed. The data we use are extracted from
public registers, collected and administered by Statistics Norway. Frome this database we are
able to construct panels for all persons living in Norway, which enable us to follow them
between labour market states (employment, unemployment, and out of labour force),
between employers, and geographical locations. We also have information on demographic
and educational events.
The main finding is that, in all three stages the geographical mobility of immigrants is
sensitive to variations in the indicators of regional economic opportunities. With regard to
natives such a relationship is not reviled.
The first settlement of labor immigrants is positively affected by the average wage
level and negatively affected by the unemployment level, which prevail in the counties when
they enter the country. The regional distribution of the native labour force, as well as the
settlement pattern of native movers is not responsive to variations in these indicators of
regional labour market opportunities.
With regard to the subsequent mobility between regions both refugees and labour
immigrants move towards counties with lower unemployment and (when pooled) higher wage
levels. The regional mobility of natives is not responsiveness to differences in average wage
and unemployment levels between counties. The emigration of both labour immigrants and
refugees is sensitive to the labour market conditions in the counties where they live before
they move from Norway. For both groups the average level of unemployment has a positive
effect on the likelihood of emigration, while the average wage level has a negative effect. In
relative terms the emigration probability of refugees seems to be more strongly affected,
particularly by variations in the regional unemployment rate. Thus, even though refugees have
a much lower propensity to leave Norway, than labour immigrants, the emigration of thus
who go are more strongly affected by regional variations in economic opportunities.
Taken together the analyses in this paper clearly indicate that higher immigration
makes the Norwegian labour supply more responsive to regional differences in economic
conditions. In that sense immigrants; “grease the wheels” of the labour markets, both through
their initial settlement, their subsequent region-to-region mobility, and through their eventual
emigration out of Norway.
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Appendix
Table A1. Descriptive statistics. Major sending countries. Refugees and labour migrants.
Per cent
Refugees
Country of origin
Iraq
Somalia
Former Yugoslavia
Afghanistan
Iran
Labour migrants
Country of origin
Sweden
United Kingdom
Germany
Denmark
USA
Per cent
29
14
12
8
7
Per cent
30
14
12
12
4
Table A2 . Yearly mean values of county characteristics and their standard deviation
Hourly wage
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Mean all
years
Unemployment
Mean
88,6
88,9
98,1
103,7
108,8
111,2
118,2
123,2
130,1
137,2
Std dev
10,0
8,6
9,7
10,5
10,8
10,2
11,4
11,4
10,2
17,9
Mean
4,9
4,3
3,4
2,5
2,7
2,8
2,8
3,2
3,9
3,9
Std dev
0,9
1,0
0,8
0,7
0,7
0,8
0,8
0,8
0,8
0,7
Ethnic concentration
Labour immigrants
Mean
Std dev
0,28
0,18
0,28
0,14
0,31
0,17
0,33
0,19
0,36
0,24
0,36
0,23
0,35
0,28
0,34
0,26
0,34
0,24
0,39
0,29
110,8
11,07
3,44
0,8
0,334
0,222
Refugees
Mean
0,21
0,23
0,14
0,11
0,13
0,14
0,17
0,16
0,14
0,17
Std dev
0,07
0,15
0,10
0,11
0,11
0,08
0,15
0,10
0,09
0,10
0,16
0,106
Table A3. Mean values of individual characteristics in the period 1995-2004, native and
immigrant males 20-65 years of age
Years since arrival
Age
Married
Children
Children under 7
Siblings in county
Employed
Out of labour force
Unemployed
Refugees
4 or less
34
0,71
0,62
0,33
0,03
0,33
0,54
0,13
5 or more
40
0,75
0,80
0,45
0,06
0,47
0,40
0,13
Labour
4 or less
34
0,23
0,37
0,17
0,04
0,59
0,38
0,03
Natives
5 or more
38
0,32
0,52
0,37
0,17
0,70
0,26
0,04
42,15
0,51
0,78
0,23
0,55
0,70
0,27
0,03
Table A4. Mobility patterns of refugees, labour immigrants and natives. Males 20 - 65 years
of age. Percent moving to and from Norwegian counties, 1995-2004
Østfold
Akeshus
Oslo
Hedmark
Oppland
Buskerud
Vestfold
Telemerk
A-Agder
V-Agder
Rogaland
Hordaland
Sogn og Fjordane
Møre og Romsdal
S-Trøndelag
N-Trøndelag
Nordland
Troms
Finnmark
Refugees
Sending
2,1
4,2
5,5
6,0
7,5
3,9
2,3
3,0
2,9
3,0
4,1
6,7
4,6
7,1
5,5
5,0
15,1
5,2
6,2
Receiving
8,9
11,8
29,1
2,2
2,9
7,9
5,2
2,6
1,3
3,1
1,9
4,5
5,1
1,7
3,3
3,3
2,1
2,3
0,7
Labour
Sending
3,2
17,5
25,2
2,8
3,2
6,4
3,0
2,4
1,1
2,1
5,9
6,2
2,2
4,3
3,7
1,3
3,8
3,5
2,2
Receiving
4,3
20,8
23,4
2,5
2,5
6,1
3,9
2,3
1,4
2,3
3,0
7,3
5,7
1,4
3,2
4,0
1,0
3,3
1,8
Natives
Sending
4,0
14,3
19,1
3,4
3,8
5,4
3,9
2,7
2,2
2,7
4,7
5,9
2,4
4,1
4,9
2,6
5,7
4,6
3,4
Receiving
4,9
14,2
18,2
4,3
3,9
5,7
5,1
2,7
2,3
3,1
3,7
5,2
5,7
1,7
4,1
5,6
2,6
4,5
2,3
Table 5A. The probability of moving from one county to another, between two succeeding
years. Estimated logit coefficients, standard deviations in parenthesis. To stay in the same
Immigrants
Model 1
Model 1
Model 1
All in
All
Labor in
labour force
labour force
***
***
Non-Migration Dummy
4.26
4.29
4.11***
0.03
0.02
0.04
Difference between other counties and the current home county in:
Distance
-0.004***
-0.004***
-0.005***
1.3-04
8.9-05
1.6-04
Distance squared
1.6-06***
1.4-06***
1.9-06***
5.4-08
4.0-08
6.3-08
***
***
Log wage
0.85
0.52
0.41
0.28
0.19
0.35
Unemployment
-0.22***
-0.18***
-0.24***
0.05
0.03
0.07
Psedo R2
0.87
0.86
0.89
N
71317
127117
51481
Natives
Model 1
Model 1
All in labour In
force, ex Oslo labour force
5.02
5.19***
0.05
0.5
-0.003
1.4-04
1.0-06
5.8-08
0.54
0.33
-0.27
0.07
0.88
48197
-0.004***
1.9-04
1.6-06***
7.7-08
0.35
0.50
-0.11
0.08
0.94
69660
Model 1
All
5.18***
0.4
-0.004***
1.5-04
1.6-06***
6.6-08
0.35
0.41
-0.04
0.07
0.93
91070
county as last year is the reference outcome. Males 20-65, 1995-2004.
In all models we include two year period dummies and receiving county dummies. In the various Models we in
addition control for the following variables. Model 1: labour market status (unemployed, working), immigrant
type (labour, refugee). Model 2 : labour market status (unemployed, working, out of labour force), mmigrant
type. Model 3: Model 4: labour market status (unemployed, working). Model 5: labour market status
(unemployed, out of labour force, working). Model 6: labour market status (unemployed, working)