Activity report of visit to InGRID
research infrastructures
Please limit the report to max. 3-5 pages, including tables and
figures and use the following structure as much as possible.
Name and last name
Tim Vetter
Project title
Influence factors on the immigration decision on the municipal level: A multilevel model
approach
Abstract (max 300-500 words)
The aim of the project is to identify variables that explain the volume of immigrant inflows into
municipalities. This shall be achieved through multilevel linear regression analysis. On the first level
of the model, the impact of individual and household variables on the individual’s migration
decision are analysed. On the second level of the model, variables containing aggregated data on the
municipal level are used to account for the influence of municipal macro structures on the
individual’s migration decision. Variables on the second level of the model thereby constitute an
initial moving probablilty that differs from municipality to municipality.
At CED, I decided to analise influence factors on the determinants of immigration of
unemployed persons into 42 Catalan municipalities. Catalunya is an interesting case as, following
the financial crisis (2009), unemployment was very compared to other European countries like
Germany. At the same time, unemployment rates varied significantly between municipalities (see
Table 1). If municipal unemployment rates have an impact on the individual descision to migrate
into a municipality, the inclusion of the municipal unemployment rate into a regression model is
more promising for understanding varying immigration numbers into municipalities than a model
that neglects these structural factors, e.g by relying solely on individual characterisitcs. Data relevant
for the first level of the model (individual characteristics of unemployed immigrants/stayers) as well
as data for the second level of the model (socio-economic characteristics of the Catalan
municipalities) can be extracted and, in the case of aggregated variables, calculated from IECM /
IPUMS database. Below, IECM / IPUMS data as well as some very preliminary test calculations are
presented.
Introduction and motivation of visit
On previous research (Vetter 2015) I analysed the impact of several macro-level variables on the
variation of immigrant inflows into Bavarian municipalities. The analysis would have benefitted
from an analysis that uses micro-level data, i.e., by taking characteristics of individual immigrants
into account. Gaining access to the relevant micro-data allows for such richer analysis.
Scientific objectives of visit
Not much empirical work explains migration patterns at the sub-national level, e.g. Buch et al.
(2013), Deas/Hincks (2014), and Sandell (2008). Also, migration is a “multifactorial and
multidimensional phenomenon“(Piché 2013: 157). It can be assumed that the macro structure (a
low unemployment rate of a municipality) has an impact on the individual’s migration decision
However, there are also many characteristics influencing the migration decision on the individual or
household level that should be taken into consideration, such as the age of the individual or family
ties. Hierarchial (or multilevel) modelling considers micro and macro variables systematically and
could help to gain new insights on the determinants of immigration into municipalities (Chi/Voss
2005; Matthews/Parker 2013).
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Reasons for choosing research infrastructure and datasets/surveys/...
There are several reasons why I chose to conduct my work at CED with IECM/IPUMS data.
Firstly, CED offers access to data of various countries and years, containing many variables. This
data is needed to conduct my work that relies heavily on microdata. Here, I had the possibility to
experiment with different variables, sample sizes, country selections. Furthermore, researchers at
CED have an expertise in migration and demography and are experienced in manipulating and
working with microdata. All this provided an excellent starting point. for my research activities.
Activities during your visit (research, training, events, ...)
Besides working on my project, I also had the opportunity to visit a lecture given by Mariona
Lozano Riera on domestic division of labour. Furthermore, I could visit research seminars (e.g,
Presentation given by Carles Millàs i Castellví on immigration into the Catalan municipality Olesa
de Montserrat from a long-term point of view 1581-1930).
Method and set-up of research
For the multivariate regression approach, a logit regression model is run for the Level 1 data. The
dependent variable is the migration decision of the individual (did the person move to the
municipality within the last 12 months – Yes/No).
At Level 2, the intercept and each coefficient are used as dependent variables, which can be
explained by independent variables at that level. The intercept and the coefficients can vary
significantly across the different municipalities. The unique characteristics of each municipality
provide and determine a unique “initial” move probability for the Level 1 model. For an
introduction to hierarchial modelling with R, see Bates (2010) or Bates et al. (2015).
The dependent variable is taken from the IECM and IPUMS census microdata database (did the
person immigrate into a Catalan municipality within the last 12 months). The sample (Spain 2011)
contains 604,627 observations collected from respondents whose residence was in one of the 42
Catalan municipalities in 2011. I don’t differ between international immigration (immigration from
abroad into the Catalan municipality) or internal migration (immigration from within Spain into the
Catalan municipality), as the focus of the analysis is on the migration into the municipality. This
covers internal migration as well as international migration into the municipality.
For my test calculations, I decided to exclude persons employed and inactive persons from the
sample. I decided to focus on influence factors on the immigration decision of unemployed persons
aged 20-64 to exclude family migration to a certain extent. 66,341 respondents aged 20-64 were
unemployed, of whom 1,740 persons (2.6% on all unemployed persons) had immigrated into a
Catalan municipality within the last 12 months.
On the first (individual) level of the model, the following variables were extracted from IECM /
IPUMS database: age, nationality, and marriage status (married/not married) with or without
children. These variables are included in the regression model as individual cost-benefit calculations
influence the migration decision. Other variables I also worked with were variables concerning
skills levels, sex, or data on the Spanish province level. For a summary of factors determining the
immigration decision, see e.g. Massey et al. (1993) or Bodvarsson/Van den Berg (2013).
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For unemployed persons, I assume that one influence on immigration into a municpality on the
second (municipal) level of the model is the municipal unemployment rate. This is because high
unemployment rates discourage immigrants. Also, the share of persons living in their own
houses/flats as a share on the municipal population is added to include a housing market variable.
It can be assumed that unemployed immigrants are less likely to buy own property and therefore
look for rental housing. If the share of persons living in their own houses is comparatively low, a
comparatively high share of housing space is available to rent. Municipalities with a comparatively
low share of persons living in their own houses on the total population might therefore be more
attractive for unemployed immigrants.
All indicators are taken or calculated from IECM and IPUMS census microdata database. I
restricted my analysis to integrated variables contained in the IECM and IPUMS census microdata
database. Therefore, it should be possible to conduct the same calculations for non-Spanish (or
non-Catalan) municipalities or samples from other years at a later time.
Project achievements during visit (and possible difficulties encountered)
Archievements:
Generally, besides gaining experience in working with micro data and hieracial modelling, it was
very fruitful to exchange views with researchers and Ph.D. students at CED. I gained insight into
Spanish and Catalan migration patterns, which in part, did not match my prior expectations. These
experiences will help to set up a more sophisticated research set-up.
It is often difficult to obtain data on the sub-national level. Especially on the municipal level,
working with micro data proved to be very useful, as the data can be adaptated freely.
Difficulties:
In the German case, the latest data contained in the IECM/ IPUMS database is from 1987. It was
therefore not possible to conduct my research on the geographical level of NUTS3 for Germany
(which corresponds to the German municipal level). It would have been interesting to see if the
findings of my previous research would have been confirmed when working with microdata.
However, it was sufficient for the purpose of my visit to use Spanish census data from 2011, as the
focus of my work was manly to to gain experience in working with micro data and to work with the
mixed model regression methodology.
One main advantage of analysis of immigration of unemployed persons is that all mentioned
variables (which are however not exhaustive for analysing these migration streams) can be taken or
aggregated from IECM and IPUMS datasets. Analysis of other groups than unemployed would
demand a more diverse dataset including more variables extracted from different sources. For
instance, analysis of immigration decisions of employed persons should also include variables like
municipal wage rates that are not contained in the Spanish sample that was extracted from the
IECM/IPUMS database. Also, more detailed housing market variables would be useful to analise
initial migration probability into a municipality (e.g. level of rental obligations on the municipal
level). It is therefore necessary to include variables that need to be extracted from other sources. It
is however difficult to combinine data from different sources due to varying geographical
classifications on the sub-national level, depending on the source of the data. E.g., in contrast to
Germany, NUTS 3 classification does not correspond to the municipal level in the Spanish case. It
was therefore not resonable to include data on the NUTS3-level that could have been extracted
online from Eurostat Database.
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Preliminary project results and conclusions
Municipal unemployment rates ranged from 16.95% (in San Cugat del Vallés) to 34.85% (in
Blanes) in 2011 (see Table 1). The share of persons living in their own homes ranged from 12.99%
(in Castelldefels) to 40.19% (in Mataró). The Gini index indicates that the assumption of varying
initial migration probabilities is valid, as it is not zero and does therefore not indicate equal
distribution. All Catalan municipalities were assigned to one of four unemployment groups (from
low unemployment to high unemployment) as well as to one of four groups indiciating the share of
persons living in their own homes.
Table 1
Overview of random effects / level 2 variables
Variable (level 2, 42
observations)
Municipal
Min
Median
Mean
Max
Gini index
unemployment
rate (% on labour force, age
16.95
25.12
25.19
34.85
0.08
12.99
19.83
20.17
40.19
0.16
15-64)
share of persons living in
their own homes (% on total
population)
Source: IECM/IPUMS, own calculations
Level 1 variables are shown in Figure 1. The figure shows that immigration is more likely if an
unemployed person is young, of foreign citizenship, and not married as well als childless.
Figure 1
Immigration of unemployed persons into Catalan municipalities by age, nationality and
marriage status
Source: IECM/IPUMS, own calculations
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Preliminary hierarchial regression results indicate that immigration into a Catalan municipality is
less likely for older unemployed than for younger unemployed persons. Unemployed who are not
married and have no children are more likely to immigrate into a Catalan municipality. Unemployed
with Spanish citizenship are also less likely to decide to immigrate into a Catalan municipality. In
model 2, model 1 is supplemented by the municipal unemployment rate as a second level 2 variable
(or second random effect). Surprisingly, model 1 shows a better fit than model 2 (lower AIC and
BIC values – see Table 2).
Table 2
Comparison of models
Df
AIC
BIC
logLik
deviance
model 1
5
14826.81
14872.32
-7.408.406
14816.81
model 2
6
14828.76
14883.37
-7.408.379
14816.76
Chisq
0.05217589
Chi Df
Pr(>Chisq)
1
0.8193194
Source: IECM/IPUMS, own calculations
There is still much methodological refinement to do (e.g. allowing for random slopes within
municipalities, definition of groups). Furthermore, more variables need to be included (e.g.
variables on education). Model 1 contains the above mentioned level 1 variables (fixed effects) age,
nationality, marriage status, and the share of persons living in their own homes as a level 2 variable
(random effect).
Outcomes and future studies
My visit at CED was very fruitful. The whole CED staff was very forthcoming and provided
support for all administrative and scientific questions. Initially, I planned to publish a working
paper or a scientific article. After my visit at CED, I also consider to focus on the topic more
extensively within the scope of a PHD thesis.
References
Bates, Douglas M. (2010): lme4: Mixed-effects modelling with R. Springer, Berlin. Online:
http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf.
Bates, Douglas; Maechler, Martin; Bolker, Ben; Walker, Steve (2015). Fitting Linear MixedEffects Models Using lme4. Journal of Statistical Software, 67(1), 1-48.
Bodvarsson, Örn B.; Van den Berg, Hendrik (2013). Springer, New York.
Buch, Tanja; Hamann, Silke; Niebuhr, Annekatrin; Rossen, Anja (2013): What Makes Cities
Attractive? The Determinants of Urban Labour Migration in Germany. Urban Studies 1-19, 2013.
Chi, Guangqing; Voss, Paul (2005): Migration Decistion-making: A Hierarchial Regression
Approach. The Journal of Regional Analysis & Policy 35 (2), p. 11-22.
Deas, Iain; Hincks, Stephen (2014): Migration, Mobility and the Role of European Cities and
Regions in Redistributing Population. In: European Planning Studies 22 (12), S. 2561-2583.
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Massey, Douglas S.; Arango, Joaquin; Graeme, Hugo; Kouaouci, Ali; Pellegrino, Adela; Taylor, J.
Edward (1993): Theories of International Migration: A Review and Appraisal. Population and
Development Review 19 (3), pp. 431-446.
Matthews, Stephen A.; Parker, Daniel M. (2013): Progress in Spatial Demography. In:
Demographic Research 28 (10), p. 271-312.
Piché, Victor (2013): Contemporary Migration Theories as Reflected in their Founding Texts. In:
Population 68 (1), S. 141-164.
Sandell, Rickard (2008): Immigration and cumulative causation: Explaining the ethnic and spatial
diffusion of Spain's immigrant population 1997-2007. Madrid Insitute for Advanced Studies Social
Science Department, working paper series 2008/12.
Vetter, Tim (2015): Rahmenbedingungen für Zuwanderung aus dem Ausland nach Deutschland:
Bestimmungsgründe kommunaler Unterschiede in Bayern (Framework conditions of immigration from
abroad to Germany: Determinants of municipal differences in Bavaria). Master Thesis, FernUni Hagen.
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