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MIFARE Survey
Migrants’ Welfare State Attitudes
Methodological Report
Hidde Bekhuis
Troels Fage Hedegaard
Verena Seibel
Daniel Degen
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MIFARE Survey
Migrants’ Welfare State Attitudes
Methodological Report
Hidde Bekhuis, Troels Fage Hedegaard, Verena Seibel & Daniel Degen
In cooperation with Marcel Lubbers, Claudia Diehl, Christian Albrekt, Theresa Kuhn Lancee,
& Jeanette Renema
Nijmegen, January 2018
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PROJECT PERSONAL
Principal Investigator
Prof. Dr. Marcel Lubbers
Radboud University Nijmegen
Netherlands
Co-Applicants Team Members
Denmark
Prof. Dr. Christian Albrekt Dr. Troels Fage Hedegaard
Aalborg University Aalborg University
Germany
Prof. Dr. Claudia Diehl Daniel Degen, Msc.
University of Konstanz University of Konstanz
Dr. Verena Siebel
University of Konstanz
Dr. Theresa Kuhn Lancee
University Of Amsterdam
Netherlands
Dr. Hidde Bekhuis
Radboud University Nijmegen
Jeanette Renema, Msc.
Radboud University Nijmegen
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TABLE OF CONTENTS
0. BEFORE USING THE DATA 5
1. INTRODUCTION: PURPOSE OF THE MIFARE STUDY 6
2. RESEARCH DESIGN 7
2.1 Research design and selection of countries and groups
2.2 Sampling strategy, sampling method and sampling rates
2.2.1 The Netherlands
2.2.2 Denmark
2.2.3 Germany
2.3 Survey methods
3. SURVEY INSTRUMENTS 12
3.1 Topics of the questionnaires
3.2 Challenges of translation and national adaption
3.3 Questionnaire for natives
3.4 Pre-test
4. FIELDWORK 19
4.1 Time frame of the fieldwork
4.2 Coordination of the fieldwork
4.3 Strategies to increase response rate
4.3.1 The Netherlands
4.3.2 Denmark
4.3.3 Germany
5. RESPONSE RATES AND SELECTIVITY 22
5.1 Overview of response rates across all countries
5.2 The Netherlands
5.3 Denmark
5.4 Germany
6. DATA PROCESSING 37
6.1 Data cleaning
6.2 Variables in the data set
6.3 Constructed variables
6.4 Open-ended questions
7. QUESTIONNAIRE 43
8. REFERENCES 77
A1. APPENDIX A: PILOT QUESTIONNAIRE (separate file)
A2. APPENDIX B: QUESTIONNAIRES (separate file)
A3. APPENDIX C: CONSTRUCTION OF ISCED VARIABLES (separate file)
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0. BEFORE USING THE DATA
The data can be used for free by all those who are employed at a university, an academic
research institute, or another non-profit organization. For others, permission must be obtained
before using the data. To obtain permission, contact the research team at
When using the data set, always include the following citation to the data set:
Bekhuis, H., Fage Hedegaard, T., Seibel, V., Degen, D. & Renema, J. (2018).
MIFARE Study – Migrants’ Welfare State Attitudes. Dataset. DANS (Data Archiving
and Network Services). KNAW.
When using information from this report, cite the report as:
Bekhuis, H., Fage Hedegaard, T., Seibel, V. & Degen, D. (2018). Design and
content of the MIFARE Study. Methodological Research report. Radboud University
Nijmegen, Netherlands.
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1. INTRODUCTION: PURPOSE OF THE MIFARE SURVEY
The MIFARE project (“Migrants’ Welfare State Attitudes”) is a comparative survey among
immigrants in Europe which focuses on welfare state attitudes. The MIFARE project is funded
by the NORFACE research programme Welfare State Futures. Coordinated by Marcel Lubbers
(Radboud University Nijmegen), the MIFARE project has been conducted by researchers at
Aalborg University, University of Amsterdam and University of Konstanz.
The MIFARE study is the first cross-national survey that focuses on immigrants’
attitudes towards the welfare state. In Europe, the field of research on welfare state attitudes has
paid little attention to the perspective of immigrants. Due to migrants’ socialization in different
welfare regimes, and their often disadvantaged socio-economic positions, the immigrant
perspective provides a unique opportunity to test the central theories in the field on the role of
self-interest (Andreß & Heien, 2001; Gelissen, 2002; Van Oorschot, 2006; Jaeger, 2006b;
Svallfors, 2012), group-loyalty (Esser, 2009; Maliepaard, Lubbers & Gijsberts, 2010) and of
socialization in different welfare regimes (Esping-Andersen, 1990; Jaeger, 2006a; Larsen,
2008; Jaeger, 2009; Van der Waal et al., 2013). The MIFARE study aims to study immigrants’
welfare state attitudes, and to explain differences across migrant groups, as well as differences
compared to the overall public opinion in the country of origin and the host country.
In order to study migrants’ welfare state attitudes, and to explain differences across
migrant groups new data are collected. The questions used in this new survey are partly based
on the ISSP 2006 questionnaire “Role of the government”, the ESS 2008 questionnaire
“Welfare state attitudes” and new questions which were piloted first. This document describes
the data collection, the representativeness, the questions and response of the MIFARE survey.
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2. RESEARCH DESIGN
The MIFARE survey was designed to focus on immigrants who migrated to the receiving
country at an age of 16 years or older, from different origin countries across three European
countries. Efforts were taken to harmonize the data collection across the three countries under
study. In this chapter the research design and the rationale for the selection of countries and
groups included in the study is described (section 2.1). The sampling strategy and the method
of drawing the sample are presented separately for each country (section 2.2). The survey mode
is addressed in more detail in the last section of this chapter (section 2.3).
2.1 Research design and selection of countries and groups
The MIFARE survey has been conducted in three countries: Denmark, Germany and the
Netherlands. All three countries have the opportunity to sample from population registers,
including immigrants. The opportunity to sample randomly from the registers enables us to test
for representativeness of the survey, to approach migrant groups that are smaller in number,
and guarantees comparable designs in the three countries. We proposed to sample immigrants
from the age of 18 and older, and a native control group (to be able to compare between migrants
and natives also for the questions specifically developed for the proposed survey). We chose 4
intra-EU origin countries and 6 extra-EU origin countries, including the most numerous migrant
populations: (first generation) immigrants from Poland, Romania, Spain and the UK for the
intra-EU origin countries. As for the extra-EU origin countries, we selected China (mainland
only, excluding Hong Kong), Japan, Turkey, the Philippines (not in Germany due to sampling
issues), Russia, and the US. China and Turkey are the only countries not included in either the
ISSP or ESS when the welfare-state attitudes rounds were conducted.
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2.2 Sampling strategy and sampling method
The sampling method chosen in the three countries depended on the national data sources
available to identify immigrants. Although data from local or central registry offices could be
used in all countries, there are differences in the possibility to select on migration age, which
affects the sampling strategy. Since tailored sampling strategies were used in each country, the
sampling procedure is described in separate sections for the Netherlands (section 2.2.1),
Denmark (section 2.2.2) and Germany (section 2.2.3).
2.2.1 The Netherlands
In the Netherlands, migrants who stay for longer than 4 months are required to register at the
municipality. Statistics Netherlands (CBS) sampled the immigrants from these municipality
based registrations. The aim was to have 300 questionnaires filled out per immigrant group and
natives. Based on other surveys in the Netherlands, Americans, Brits, Dutch natives, Japanese
and Spaniards were expected to have a response rate of 33.3%. While Chinese, Philippines,
Polish, Romanian, Russian and Turks were expected to have a lower response of 27%. These
expectations resulted in a sample of 900 immigrants from the first mentioned groups, and a
sample of 1100 from the last country of origin groups listed.
The sample for the survey is a stratified sample, one stratum for the native Dutch and
one stratum for each of the ten migrant groups. From each stratum a simple random sample
without replacement was drawn. Since people between 18 and 75 years were selected it was
possible that immigrants from the Soviet Union were selected. Statistics Netherlands had also
information on the area in which migrants from the former Soviet Union had lived. Only
immigrants from the Soviet Union who lived in what is now Russia are selected.
From the sample that was drawn, Statistics Netherlands successively requested the
names and addresses from the National Identity Data (RvIG). When it turned out that a selected
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person at an address had already been approached for a regular CBS survey in the last year,
then this person was removed from the sample. If a person lived at an address of an institution
then the person was also removed from the sample. The sample was drawn on October 23th
2015, 2 weeks before the first invitations were sent.
Table 2.1 provides an overview of the group sizes in the Dutch population and in target
population size in the MIFARE sample.
Table 2.1: Natives and immigrants in the Dutch population and target population in
the Dutch MIFARE sample
18-75 year olds in Dutch
population on 1-1-2015
18 - 75 years old in
MIFARE sample
Native Dutch 9,616,462 900 China 42,891 1100 Japan 4,711 900 Philippines 11,026 1100 Poland 96,380 1100 Romania 15,046 1100 Russia1 56,438 1100 Spain 19,780 900 Turkey 183,915 1100 UK 39,808 900 US 18,804 900
1 The sample of migrants from Russia is drawn from the population with a Russian origin; not all may have been
born in Russia. Additional information about these migrants is used to select only those who were born in what
is now called Russia.
2.2.2 Denmark
All immigrants who have stayed in Denmark for more than three months, and have a
permanent housing, can apply for the status of living in Denmark in the Civil Registration
System (Det Centrale Personregister or CPR-register in Danish). This is unless they have a
residence permit, which is necessary for migrants from some countries, in which case status
of living can be applied for from the first day. The Danish respondents were sampled, using
the Civil Registration System among Danes, and ten migrant groups. For the Danes the
sample was drawn randomly from all who are born in Denmark, both parents are Danish
citizens, at least 18 years old and are living in Denmark. For the migrant groups the sample
was drawn randomly based on the following criteria:
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• The respondent must be born abroad, in one of the ten selected countries
• The parents of the respondent cannot be Danish citizens at the time they migrated to
Denmark, that is, when the respondent obtained living status.
• The respondent must be at least 16 years old when they migrated to Denmark
• The respondent must have lived in Denmark (living status) for at least 12 months.
The aim was to have 300 filled out questionnaires per migrant group and natives. Based on an
expectation of higher non-response in some groups 900 Danes, Americans, Brits, Japanese
and Spaniards where sampled, while 1000 Chinese, Philippines, Polish, Romanian, Russian
and Turks where sampled (Font & Méndez, 2013). Only migrants from the Soviet Union who
lived in what is now Russia are selected. Similar to the sample from the Netherlands this is
also a stratified sample drawn separately among the natives, in this Danes, and the ten migrant
groups. The sample was drawn in October of 2015 and therefore less than a month before
fieldwork begun. Table 2 provides an overview of the group sizes in the Danish population
and in the targeted MIFARE sample. For the Danish sample the name and address was
provided.
Table 2.2 shows that the sample sizes and the total population in some cases are not
very different, e.g. among the Japanese immigrants. Furthermore, the total populations
reported below are “too large”, in the sense that list drawn from Statistics Denmark online
database cannot include all the selection criteria listed above. The result of this is that we
sampled almost all Japanese who fit the criteria, and large parts of the Spanish and Russians
living in Denmark.
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Table 2.2: Natives and migrants in the Danish population and in the
targeted Danish MIFARE sample
Population Sample
Native Danes 3,963,422 900
China 9,521 1,000
Japan 1,373 900
Philippines 9,690 1,000
Poland 31,561 1,000
Romania 17,532 1,000
Russia 5,047 1,000
Spain 4,783 1,000
Turkey 31,537 1,000
UK 12,543 900
US 7,243 900 Note: Based on FOLK2 from Statistics Denmark online database, for 1 of January 2015. Definition:
Migrants are born abroad. No parents are Danish citizens born in Denmark. If there is no information
on any of the parents, and the person is born abroad, the person is counted as a migrant. Note that is
not fully identical with the sampling criteria described above.
2.2.3 Germany
In Germany, migrants are required to register after 2 months of stay at their municipalities.
Registration data are not available on national level but have to be acquired from each
municipality separately. No information is provided for date of migration which led to over-
sampling in order to sample a sufficient number of migrants who migrated after the age of 18.
To get a sample that is most likely to represent the whole population of the respective migrant
(and native) group, we decided to cluster communities according to their size. Since we do not
want respondents from bigger cities exclusively, we also added smaller communities. We
decided to divide all German communities into four clusters (500k). In a second step we used data from the Mikrozensus 2011, which was the newest
dataset when it comes to individual characteristics for all German communities.
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To make our sample representative, we decided to sample only in those communities of a
certain size, where at least 10% of a migrant group is living (e.g. Chinese only from
communities with at least 100000 inhabitants).
Table 2.3: Distribution of migrant groups in Germany, by size of municipality Community Size
Poland Romania Spain Turkey UK Russia USA Japan China Germany
500k 26.7% 17.7% 100.0% 31.0% 100.0% 18.8% 100.0% 100.0% 82.2% 18.6%
TOTAL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Source: Microzensus 2015
Table 2.3 shows how many immigrants of each group should be sampled in the respective
community size. We decided to sample those groups proportionally that the sample represents
whether a group is rather living in big communities or not. Hence, around 27,5% of our Polish
sample should be drawn from municipalities who count less than 50 000 inhabitants.
For each group, we want to have 3 communities per group. We decided to choose those
communities where most people of each group are living. In cases, where we were not able to
obtain data we chose the community which was next on the list. After obtaining the data from
the communities, we sampled the respondents proportionally to their group size in the
respective city. Also, we limited the sample to communities who are in West Germany and
excluded communities which are part of the former German Democratic Republic.
Furthermore, we had to consider that respondents end up in the sample that have not migrated
before the age of 18 or even second generation immigrants. Therefore, we oversampled for
each community with the respective factor of having such respondents in the sample. In a
final step to decide the group size, we oversampled certain groups due to previous research
indicating a lower response rate such as migrants from Turkey or Poland.
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Table 2.4: Natives and migrants in the German population and in the targeted
German MIFARE sample
18-75 year olds in German
population 2015 (in 1000)
18 - 75 years old in
MIFARE sample
Native German 81,700 900 China 87 1020 Japan 20 1602 Poland 800 1560 Romania 340 1133 Russia1 514 1620 Spain 68 1279 Turkey 642 2051 UK 55 1114 US 55 1316
1 The sample of migrants from Russia is drawn from the population with a Russian origin; not all may have been
born in Russia. Additional information about these migrants is used to select only those who were born in what
is now called Russia.
2.3 Survey mode
Postal surveys were conducted in all countries. Respondents were invited by mail to participate
in the survey. The invitation letter was bilingual; in the language of the country of residence
and in the language of the country of origin of the respondent. Respondents could participate
by filling out a written questionnaire which was sent with the invitation. Or respondents could
participate via Computer Assisted Web Interviews (CAWI ); the interviewee completed the
questionnaire supplied to them via a website link with an unique login code.
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3. SURVEY INSTRUMENTS
In order to collect comparable data across the four countries the team developed a harmonized
survey instrument. Apart from a few questions that were asked exclusively in selected countries
or for specific ethnic groups, identical questions were asked in the Netherlands, Denmark and
Germany. Many of them were adopted from established survey instruments, such as the
International Social Survey Program, or the European Social Survey. This facilitates
comparisons with other studies.
Since it was anticipated that migrants were not all able to fill out a questionnaire in the
receiving country language, the questionnaire was translated into immigrants’ native languages.
It was assumed that this would reduce the non-response and would increase the accuracy of
answers. This procedure included processes of translation and re-translation for testing the
correct meanings of the questions in different languages.
In the next sections, the topics in the questionnaire are discussed (section 3.1), the
variation in the questionnaire between the Netherlands, Denmark and Germany (section 3.2).
Section 3.3 describes how the questionnaire for native differs. Finally, section 3.4 outlined the
pre-test which was done in Germany.
3.1 Topics of the questionnaire
The questionnaire covered a broad range of topics:
A. Demography and migration biography
B. Government responsibilities
C. Household and Health
D. Language and Contacts
E. Religion
F. Media use and Political attitudes
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G. Knowledge and opinions about welfare state use
H. Experiences with residence country
I. Education and employment
J. Household assets
In the first module, the respondents were asked about demographic characteristics and questions
about their migration biography. The second module asked respondents’ opinion about the role
of the government in the receiving country. The third module covered respondents’ household
situation in the receiving country, respondents’ health, the health of their household members
and relatives and the experience and satisfaction with health care services in the receiving
country. The fourth module addressed language proficiency and social contacts with natives,
other immigrants and people from the country of origin. In the fifth module, the respondents
were asked about their religious affiliations, beliefs and practices. The sixth module covered
respondents’ media usage, both from the country of origin and receiving country. As well as
respondents’ political attitudes, including party preference, opinion about taxes, the right
migrants should have to vote and the role the EU should have. In the seventh module
respondents were asked about their actual knowledge about the welfare state rights migrants
from their country of origin have, as well as the rights of EU and non-EU migrants have. In
addition to the actual knowledge also the opinion about these rights were asked. In the eighth
module topics about discrimination experiences and the role migrants have in the receiving
country were covered. The ninth module looked into migrants’, and their possible partner’s,
education and employment history. Finally, the tenth module examined migrants’ earnings, and
the use of welfare state benefits in their household. On average, the fill out a survey lasted about
25 minutes.
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3.2 Challenges of translation and national adaption
The master questionnaire was constructed in English. Native-speakers, hired via the translation
agency “VVH translations” translated the English version into the different immigrant
languages. To control the quality of the translations, re-translations were performed by the
translation agency. During the process of constructing, translating, and pretesting the
questionnaire, further national and group specific adaptations were made. A major challenge
was related to the different welfare state arrangement in Denmark, Germany and in the
Netherlands. Some welfare state arrangements were present in one of the countries only, for
example, only the Netherlands has the 30% tax rule for immigrants. While other arrangements
have a, slightly, different meaning in the three countries. For example, what in the Netherlands
is known as social assistance, is something different in Germany. The Dutch social assistance
is called “Hartz 4” in Germany. So, there are differences in the number of welfare state
arrangements between the survey countries. And in some cases, the names of the arrangements
look different but content-wise relate to the same form of arrangement.
3.3 Questionnaire for natives
Besides respondents from ten different countries, also natives from Denmark, the Netherlands,
and Germany were included in the survey. Questions regarding migration history, perceived
discrimination and identification were straightforwardly not asked at natives. In general, the
wording of the questions for natives was adjusted such that they could not derive from the
questionnaire that it was a study on migrants.
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3.4 Pre-test
To test whether the questions were understood by a larger group, to check the mailing
coordination from one country, the Netherlands, and to make a very limited prognosis about
the influence of an unconditional incentive a pre-test was done in the German city Freiburg
among native and American immigrants in August and September 2015.
The aim of the national pre-test in Germany was not only to obtain information about
potential pitfalls of the questionnaire, but also to get more information about how the sampling
procedure works for Germany. Since in Germany, researchers had to contact every municipality
we got useful information about the procedure of how to get in touch with the municipalities
and how to contact them most efficiently. We decided to field two versions of the questionnaire,
one for the native Germans and one for American immigrants. Furthermore, we obtained
information about the different measures we wanted to apply to the full sample, like variance
and item non-response. Additionally, information about the response rate, not deliverable mails
and other logistic problems was obtained. In what follows, we discuss the sampling, fieldwork,
and the response (section 3.4.1), as well as the actual questionnaire fielded (section 3.4.2).
3.4.1 Pre-test: Sampling, Fieldwork, and Responses
We decided to field the questionnaire in a large city that would not be in our actual sample,
since the immigrant group would be likely to receive the pre-test version as well as the actual
version of the questionnaire. The city of Freiburg (around 225,000 inhabitants) was one city
that matched the requirements. Additionally, the close distance to Konstanz would allow us to
also travel to the city if something would go completely wrong, which was not necessary in the
end. After the contact with the municipality we received a dataset containing the address data
to launch the pretest. We obtained the random address data of 100 native Germans and 200
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American immigrants. As in the main study, the questionnaires were printed by the Dutch
company I&O Research. They were also responsible for the fielding by sending the mail to the
potential respondents. The procedure was carried out according to Dillman (2000), by sending
a postcard as a first reminder one week after the respondents had received the invitation. A
second reminder containing the questionnaire and a letter was sent two weeks after the first
reminder. Every participant was offered a 10€ voucher of their choice (Amazon, Media Market,
Müller).
We started to send the invitation letter containing the questionnaire and the invitation
code for the online version on August, 10th 2015, followed by the first reminder on August 31st
2015. The second reminder was then sent on September 14th 2015. We were not able to track
the time of return, since the post office in Konstanz just delivers the mail once a week. Also for
those who participated using the online version, information about response date and response
time was not obtained.
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Table 3.1: Response rate pre-test
Group Invited Letters not
delivered
Respondents Naïve Response
Rate
Response Rate
Native German 100 2 (2%) 48 48% 49.0%
American 200 85 (42.5%) 54 27% 47.8%
Total 300 87 (29%) 102 34% 47.9%
Looking at the returns and responses we must state that for the Americans 29% of the letters
could not be delivered. For the Germans, we did not have those problems (2%). We assume that
besides immigrants being a more mobile group (return migration) we additionally could have
the problem that some of the potential respondents are exchange students, who have returned
to America. This is likely since Freiburg is known as a university town. Hence, a large share of
an uncommon immigrant group for Germany as the Americans might be consisting of students.
Since the new semester starts in October it is likely that American students left the country in
the time between we received the address data and the time the questionnaire was fielded.
Nevertheless, the response rate indicated a high response rate. The actual response rate for
native Germans and for Americans is close to 50%. The high response rate might be a result of
the topic we are asking; it is higher than most other surveys conducted in Germany.
3.4.2 Questionnaire
The aim of the pre-test was also to get more insights on problems that arose within the
questionnaire. In conclusion, we adjusted the order, added, rephrased, and omitted questions
according to the results. The questionnaire contained the following modules:
A. Demography and migration biography
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B. Government responsibilities
C. Household and Health
D. Language and Contacts
E. Religion
F. Media use and Political attitudes
G. Knowledge and opinions about welfare state use
H. Experiences with residence country
I. Education and employment
J. Household assets
The questionnaire can be found in Appendix A.
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4. FIELDWORK
Despite frequent team meetings and central coordination, the data collection could not be done
in exactly the same way in all countries. Partly due to different sampling strategies (see section
2.2), and due to different strategies to increase the response rate. In this chapter, the cross-
country time frame of the data collection is summarized (section 4.1) followed by details on the
coordination of fieldwork (section 4.2). Country specific approaches to increase the response
rate are discussed in section 4.3. The final section briefly outlined the strategies to check if the
questionnaire was filled out by the invited respondent (section 4.4).
4.1 Time frame of the fieldwork
Although the initial planning was to have the same fieldwork period in Denmark, Germany
and the Netherlands, due to an extended sampling period in Germany, the German fieldwork
was later than the Dutch and Danish period. Table 4.1 provides an overview of the fieldwork
period in the Netherlands, Denmark, and Germany.
Tabel 4.1: overview of fieldwork period
Delivery invintation Delivery first reminder Delivery second reminder End data collection
Netherlands December 1 2015 December 6 2015 December 19 2015 January 25 2016
Denmark December 1 2015 December 6 2015 December 19 2015 January 25 2016
Germany December 8 2015 December 13 2015 February 5 2016 April 2 2016
With the first mailing, all respondents received an invitation letter to participate in our
research. This letter also included the website reference and unique login code to participate
online. In addition to this login code, respondents received two questionnaires; one
questionnaire in Danish, Dutch or German and one questionnaire in respondents’ mother
tongue.
The first reminder was delivered five days after the invitation. The first reminder was a
(fancy) postcard that contains a reminder to participate, together with the website address and
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login code to participate online (straightforwardly, natives only received the questionnaire in
Danish, Dutch, or German). Since there was limited time between the invitation and the first
reminder there was a likelihood of cross posting (respondents who already participate still
received a reminder), which was noted in the text.
The second reminder contained a reminder letter and again the two questionnaires. Since
the likelihood that migrants visited their families in that period the decision was made to
postpone the second reminder after the Christmas holidays. Due to some difficulties with the
German mail the second reminder was delivered in Germany on February 5th. In the Netherlands
and Denmark, the data collection was closed four weeks after the second reminder. In Germany,
this period was extended to seven weeks due to a lower response and the weekly, instead of
daily in the Netherlands and Denmark, mail delivery at the university.
4.2 Coordination of the fieldwork
Because of the budget, the data fieldwork was done as much as possible by the universities
itself. For pragmatic reasons the coordination was done by the Radboud Univeristy. Although
coordination with the fieldwork agency was done by the Radboud University, the Danish and
German university provided the input to the fieldwork company directly about the respondents
who needed a reminder and who not.
The online questionnaire ran on a server at the Aalborg University. The three countries
were each responsible for creating a national file of the online response.
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4.3 Strategies to increase response rate
To increase the response rate both in the Netherlands and Denmark a conditional incentive was
used. In Germany, an experiment with none, a conditional, an unconditional and the
combination of conditional and unconditional incentives was implemented.
4.3.1 The Netherlands
To increase the response rate in the Netherlands, a conditional incentive was used.
Respondents who participated could choose to receive a 10-euro gift voucher from three
(online) shops: Blokker, Bol.com, and Hema.
4.3.2 Denmark
To increase the response rates in Denmark a conditional incentive of a movie ticket valued at
75 DKR (10 euro) was used. The incentive was sent out digitally after the collection of the
survey was completed.
4.3.3 Germany
For the incentive experiment, respondents were grouped into either receiving an unconditional
incentive only, an unconditional and conditional, a conditional incentive only or no incentive
at all. The unconditional incentive was a small handy-cleaner with the Konstanz University
Logo imprinted. For the conditional incentive, respondents who participated could choose
between a 10€ voucher from Amazon, Media Markt, or Müller.
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Table 4.2: Sample distribution by incentive group
Origin country No
incentives
Conditional
incentives
only
Unconditional
incentives
only
Conditional
and
unconditional
Total
Native Germans 180 240 240 240 900
China 204 272 272 272 1020
Japan 321 427 427 427 1602
Poland 312 416 416 416 1560
Romania 227 302 302 302 1133
Russia 324 432 432 432 1620
Spain 256 341 341 341 1279
Turkey 410 547 547 547 2051
UK 223 297 297 297 1114
US 263 351 351 351 1316
Total 2720 3625 3625 3625 13595
After the first and second reminder, the response rate of people who received either none or
only the conditional incentive was rather low (around 11.3%) compared to the groups which
were promised a voucher (around 15%) which led to the decision to offer these groups a
voucher as well in order to boost response rates after the second reminder.
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5. RESPONSE RATES AND SELECTIVITY
The response rate is defined as the proportion between the number of respondents who
participated by filling out the questionnaire online or by paper and pencil in relation to the
number of people approached (sample). In this section, a brief overview of the sample sizes and
response rates across all countries is given (section 5.1). Based on this, response rates for each
country are described in more detail (see section 5.2 for the Netherlands, section 5.3 for
Denmark, and section 5.4 for Germany).
5.1 Overview of response rates across all countries
Table 5.1 shows the overall response rate after cleaning for the Netherlands, Denmark, and
Germany. For the Netherlands and Denmark, the response after cleaning is almost equal in
both countries, where Denmark has a 1.65% higher response than the Netherlands. Germany
has with a response of 18% the lowest response rate In section 5.2., 5.3, and 5.4 the response
rates will be discussed in more detail.
Table 5.1: Sample size, response and response rates
Netherlands Denmark Germany
Sample size 11100 10500 13561
Response 3672 3647 2234
Response rate 33,1% 34,7% 18,2%
5.2 The Netherlands
The overall response rate of 33.1% in the Netherlands, which is relative high in comparison
with other surveys among immigrants in the Netherlands (Andriessen & Kappelhof, 2016;
Korte & Dagevos, 2011). Table 5.2 shows that there are large differences in response between
migrant groups though. The response among native Dutch is the highest with 47.4%. The
response from immigrants from Russia is with 43.18% almost as high as the response among
-
26
natives. The response from the Turks is the lowest, 19.3%, which resembles other surveys
among Turkish immigrants in the Netherlands (Andriessen & Kappelhof, 2016; Korte &
Dagevos, 2011).
The expectation was that Americans, Brits, Japanese and Spaniards would have a
response rate around 33.3%. While Chinese, Philippines, Polish, Romanian, Russian and Turks
were expected to have a response around 27%. Americans (27.89%) and Brits (29.89%) have a
somewhat lower response rate than expected. While the other groups, with exception of the
Turks, have a (much) higher response rate.
Table 5.2 also shows the difference between the response before cleaning and the
response after cleaning. The differences are almost equal between all groups, and are caused
because some people filled out the questionnaire twice (online and hard copy), or that a partner
of the respondent also filled out the questionnaire. In section 6.1 the data cleaning will be
discussed in more detail.
Finally, table 5.2 shows that in general more than seventy percent of the questionnaires
are filled out by paper and pencil and less than thirty percent online.
Table 5,2: Dutch response rate
Invited Before cleaningAfter cleaning
Native Dutch 900 442 427 47,44 69,09 30,91
China 1100 330 307 27,91 68,73 31,27
Japan 900 315 295 32,78 77,97 22,03
Philippines 1100 408 385 35,00 79,74 20,26
Poland 1100 377 353 32,09 79,04 20,96
Romania 1100 381 357 32,45 69,75 30,25
Russia 1100 518 475 43,18 74,32 25,68
Spain 900 367 341 37,89 63,64 36,36
Turkey 1100 237 212 19,27 80,19 19,81
UK 900 280 269 29,89 73,23 26,77
US 900 267 251 27,89 61,35 38,65
Total 11100 3922 3672 33,08 72,49 27,51
Response Response rate
after cleaning
% paper
pencil % online
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27
Table 5.3 shows the selectivity of the response in relation to sex and age. It appears
that significant more Chinese, Japanese and Polish women filled out the questionnaire than
expected on the male female ratio in the sample. Regarding age, native Dutch, Chinese,
Japanese, Brits and Americans who participate are significant older than native Dutch,
Chinese, Japanese, Brits and Americans in the sample.
Table 5.3: Dutch response rate, split by sex and age
Male:female
ratio
Mean
age Male Female
Male:female
ratio1
Mean
age1
Native Dutch 0,95 46,89 203 224 0,91 48,88
China 0,77 34,10 97 210 0,46 33,25
Japan 0,73 38,97 85 210 0,40 39,93
Philippines 0,18 38,69 47 338 0,14 39,09
Poland 0,84 35,12 128 225 0,57 35,82
Romania 0,65 33,99 115 242 0,47 34,73
Russia 0,34 38,89 104 371 0,28 38,79
Spain 0,79 33,38 142 199 0,71 33,51
Turkey 0,88 38,88 92 120 0,77 39,51
UK 1,62 40,77 147 122 1,20 43,47
US 0,83 37,37 110 141 0,78 39,931 figure in bold = significant difference between sample and data p < .05
Sample Response
Table 5.4 combines above descriptive results by showing the results from logistic
regression analyses predicting various reasons for participating. The results show to what extent
people with certain characteristics in the sample are more or less likely to participate than
others. Women are more likely to participate than men. The same applies for older people.
Native Dutch are more likely to participate than other ethnicities.
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28
Tab
le 5
.4: O
dd
s r
ati
os f
rom
lo
gis
tic r
eg
ressio
n p
red
icti
ng
reaso
ns o
f p
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on
(re
fere
nce c
ate
go
ry is p
art
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Sex
(male
= r
ef.
)1.4
68
**
*1.0
69
2.0
21
**
*2.4
05
**
*1.4
65
*1.7
63
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63
**
1.3
77
~1.1
71
1.1
86
1.6
35
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72
Ag
e1.0
11
**
*1.0
15
**
*0.9
87
*1.0
20
**
1.0
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1.0
13
1.0
10
0.9
97
1.0
03
1.0
09
1.0
26
**
*1.0
23
**
*
Eth
nic
ity
(D
utc
h =
ref.
)
Ch
ina
0.4
77
**
*
Jap
an
0.5
70
**
*
Ph
ilip
pin
es
0.5
70
**
*
Po
lan
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man
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Ru
ssia
0.8
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*
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ain
0.7
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rkey
0.2
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0.5
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0.4
63
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52
**
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28
**
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95
**
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31
**
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*0.2
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Ov
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Ph
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es
UK
US
Po
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om
an
iaR
ussia
Sp
ain
Tu
rkey
-
29
5.3 Denmark
As shown in Table 5.1 the overall response for Denmark is 34,7 per cent, which is very
similar to the results from the Netherlands, and must considered good considering that
migrants surveys often have lower response rates than surveys of the general population (Font
& Méndez, 2013). Table 5.5, below, shows the differences in response rate between the Danes
and migrant groups.
Table 5.5: Response rates by country of origin and method of answering
Invited
Response
before
cleaning After cleaning
Response
rate after
cleaning
% paper
pencil % online
Denmark 900 416 397 44,1 68,3 31,7
Poland 1000 312 293 29,3 74,0 26,0
Russia 1000 432 408 40,8 70,6 29,4
Romania 1000 284 277 27,7 59,9 40,1
Turkey 1000 228 216 21,6 81,1 18,9
Great Brittan 900 421 402 44,7 70,5 29,5
Philippines 900 296 280 31,1 72,6 27,4
Japan 900 393 379 42,1 72,0 28,0
China 1000 367 346 34,6 69,5 30,5
Spain 900 352 339 37,7 55,4 44,6
USA 900 319 310 34,4 60,2 39,8
Total/average 10.400 3820 3647 34,7 68,6 31,4
The goal was to get at least 300 respondents in each group, which we have not quite
succeeded in, since there are fewer from Turkey, Poland, Romania and the Philippines. These
are all groups with oversampling, but in retrospect this should have been higher in these
groups. For Russians and Chinese the oversampling was, however, not needed, as there were
over the 300 with good response rate. Generally, looking at response rates they were
relatively high, with over 40 per cent, for Russians, Danes, Brits, and Japanese. On the other
hand, they are relatively low for Turks and Romanians, which was as expected.
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30
Table 5.5 also shows the difference between the response before cleaning and the
response after cleaning. The differences are almost equal between all groups (between 9 and
24), and are caused because some people filled out the questionnaire twice (online and hard
copy). When duals existed they were compared, and if both where filled out, paper where
preferred over online. Finally, the table shows that paper questionnaires was generally
preferred by about two third of respondents, while about one third used the online option.
Though there is a little variation between the group this pattern is quite persistent
5.3.1 In depth non-response analyses Denmark
Using register data, in Denmark it was possible to compare respondents to the full population
regarding to gender, age, household type, citizenship, labour force position, social economic
position, income, received benefits and migration history. The differences between respondents
and non-respondents are remarkably small. There are some differences between respondents
and non-respondents on labour market position and other socioeconomic factors none of them
are remarkably different, and often they vary between group, e.g. unemployment leads to higher
response rates for some groups and lower for other. Tables 5.6 to 5.18 compare the respondents
with the non-respondents in Denmark.
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31
Table 5.6: Gender. Presented as row percentages and total number of respondents.
Man Woman N Man Woman N
Respondents 43,2 56,8 3577 Non-respondents 42,7 57,3 7179
Denmark 48,8 51,2 396 Denmark 49,1 50,9 602
Poland 46,5 53,5 288 Poland 49,7 50,3 696
Romania 55,8 44,2 271 Romania 53,1 46,9 700
Spain 53,7 46,3 328 Spain 53,8 46,2 637
GB 73,5 26,5 397 GB 70,0 30,0 589
Turkey 45,3 54,7 216 Turkey 46,5 53,5 780
USA 57,2 42,8 297 USA 54,2 45,8 669
Japan 26,3 73,7 373 Japan 23,7 76,3 574
China 39,0 61,0 336 China 37,8 62,2 644
Philippines 8,1 91,9 271 Philippines 10,3 89,7 700
Russia 20,3 79,7 404 Russia 19,9 80,1 588
Note: Based on KOEN
Table 5.7: Age. Presented as mean scores by group.
Respondents 1972,0 Non-respondents 1972,3
Denmark 1966,2 Denmark 1963,3
Poland 1972,6 Poland 1974,3
Romania 1981,8 Romania 1981,8
Spain 1977,9 Spain 1977,8
GB 1965,0 GB 1965,5
Turkey 1966,3 Turkey 1967,8
USA 1972,2 USA 1970,3
Japan 1969,0 Japan 1969,8
China 1975,8 China 1976,3
Philippines 1975,3 Philippines 1974,9
Russia 1973,0 Russia 1971,8
Note: Based on aldernov. Age on the last work day of November.
http://www.dst.dk/da/Statistik/dokumentation/Times/fravaer/koenhttp://www.dst.dk/da/TilSalg/Forskningsservice/Dokumentation/hoejkvalitetsvariable/beskaeftigelsesoplysninger-der-vedroerer-ida-personer/aldernov
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32
Table 5.8: Household type. Presented as row percentages and total number of respondents.
Single man Single woman Married couple Other couple Other households
Respondents 6,7 11,0 40,9 11,5 29,9 3577
Denmark 11,4 17,7 44,7 13,4 12,9 396
Poland 4,9 11,8 35,1 12,2 36,1 288
Romania 4,4 7,0 25,1 14,4 49,1 271
Spain 7,0 6,4 24,1 18,3 44,2 328
GB 11,1 5,8 42,6 20,7 19,9 397
Turkey 6,0 13,0 50,9 3,2 26,9 216
USA 10,1 7,4 40,4 12,5 29,6 297
Japan 6,2 13,1 47,2 5,1 28,4 373
China 3,3 7,1 40,2 6,9 42,6 336
Philippines 1,5 11,1 44,3 5,9 37,3 271
Russia 5,0 17,8 51,5 10,2 15,6 404
Single man Single woman Married couple Other couple Other households
Non-respondents 6,5 9,7 40,5 10,2 33,3 7179
Denmark 12,0 17,9 42,2 15,5 12,5 602
Poland 7,0 9,5 29,7 14,8 38,9 696
Romania 4,4 4,9 25,3 12,7 52,7 700
Spain 7,9 4,7 24,3 16,8 46,3 637
GB 14,3 6,6 40,6 17,0 21,6 589
Turkey 5,0 10,6 50,5 3,9 30,0 780
USA 9,1 6,9 47,1 8,1 28,9 669
Japan 4,0 9,9 49,0 8,9 28,2 574
China 5,0 9,3 37,9 5,6 42,2 644
Philippines 0,6 8,9 46,1 3,9 40,6 700
Russia 3,2 18,4 53,7 6,6 18,0 588
Note: Based on hustype. A household is defined by the address. A household cover all persons in the
CPR/citizen register. Other households cover any household consisting of more than one family.
http://www.dst.dk/da/Statistik/dokumentation/Times/moduldata-for-befolkning-og-valg/hustype
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33
Table 5.9: Danish citizens, among migrant groups. Presented as row percentages and total number of
respondents.
No Yes N No Yes N
Respondents 88,4 11,6 3246 Non-respondents 88,7 11,3 6709
Denmark NA NA NA Denmark NA NA NA
Poland 88,1 12,0 293 Poland 91,1 8,9 706
Romania 92,8 7,2 277 Romania 94,0 6,0 721
Spain 95,0 5,0 339 Spain 96,2 3,8 661
GB 92,5 7,5 402 GB 94,0 6,0 598
Turkey 76,9 23,2 216 Turkey 79,3 20,7 783
USA 93,2 6,8 310 USA 94,9 5,1 689
Japan 90,0 10,0 379 Japan 92,2 7,9 586
China 88,7 11,3 345 China 85,5 14,5 655
Philippines 80,0 20,0 280 Philippines 79,9 20,1 720
Russia 83,0 17,0 405 Russia 82,0 18,0 590
Note: Recoded from STATSB. Citizenship for Danes is not included.
http://www.dst.dk/da/Statistik/dokumentation/Times/moduldata-for-befolkning-og-valg/statsb
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34
Table 5.10: Position in the labour force. Presented as row percentages and total number of respondents.
Not registered
While collar
Skilled worker
Unskilled worker
Other Worker N
Respondents 59,4 14,2 11,0 7,6 7,7 364
2
Denmark 52,3 20,2 15,4 3,8 8,3 396
Poland 43,0 7,5 22,5 18,1 8,9 293
Romania 57,4 5,8 7,2 15,2 14,4 277
Spain 65,5 19,8 6,5 3,5 4,7 339
GB 52,7 19,7 14,9 3,5 9,2 402
Turkey 65,7 6,9 8,3 13,0 6,0 216
USA 62,3 18,7 9,0 4,5 5,5 310
Japan 74,7 11,4 5,5 2,9 5,5 379
China 60,3 17,4 5,2 6,7 10,4 345
Philippines 60,7 1,8 14,6 17,1 5,7 280
Russia 59,8 18,0 11,6 4,2 6,4 405
Not registered
While collar
Skilled worker
Unskilled worker
Other Worker N
Non-Respondents 60,6 13,7 10,8 7,1 7,9
7313
Denmark 56,1 21,0 14,2 3,8 4,8 604
Poland 51,8 8,1 16,9 12,5 10,8 706
Romania 54,9 4,3 9,3 13,7 17,8 721
Spain 64,6 17,6 8,9 3,5 5,5 661
GB 51,8 23,9 12,5 4,2 7,5 598
Turkey 65,0 7,2 8,7 12,0 7,2 783
USA 62,7 23,2 7,1 1,2 5,8 689
Japan 75,9 11,8 7,7 1,0 3,6 586
China 57,7 16,0 7,9 5,5 12,8 655
Philippines 66,5 2,5 14,2 12,5 4,3 720
Russia 58,8 20,0 11,4 4,4 5,4 590
Note: Recoded from STILL
http://www.dst.dk/da/Statistik/dokumentation/Times/ida-databasen/ida-ansaettelser/still
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35
Table 5.11: Register based unemployment in percentage of time over the last year.
Respondents 3,02 Non-respondents 3,19
Denmark 2,12 Denmark 1,75
Poland 5,00 Poland 4,82
Romania 1,97 Romania 2,68
Spain 2,32 Spain 2,45
GB 2,02 GB 2,81
Turkey 5,74 Turkey 6,64
USA 2,89 USA 1,49
Japan 1,85 Japan 2,47
China 3,72 China 2,49
Philippines 2,05 Philippines 2,14
Russia 4,26 Russia 4,15
Note: Calculated from on ARLEDGR
Table 5.12: Total DKR received in unemployment and social assistance in 2015
Respondents 19240 Non-respondents 18300
Denmark 15146 Denmark 9534
Poland 24440 Poland 20823
Romania 26328 Romania 17667
Spain 13114 Spain 10954
GB 13021 GB 16609
Turkey 50563 Turkey 39852
USA 8160 USA 9586
Japan 6005 Japan 4551
China 15644 China 17066
Philippines 14477 Philippines 11223
Russia 36431 Russia 40156
Note: Based on DAGPENGE_KONTANT_13. 1 Euro ≈7.45 DKR. This includes social assistance,
unemployment benefits, integration benefit (integrationsydelse), parental leave and sick leave.
http://www.dst.dk/da/Statistik/dokumentation/Times/ida-databasen/ida-personer/arledgrhttp://www.dst.dk/da/Statistik/dokumentation/Times/personindkomst/dagpenge-kontant-13
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36
Table 5.13: Income in DKR in 2015
Respondents 203017 Non-respondents 192337
Denmark 212680 Denmark 206181
Poland 199960 Poland 196238
Romania 169058 Romania 177056
Spain 238653 Spain 197812
GB 303783 GB 323121
Turkey 100333 Turkey 110451
USA 269296 USA 291010
Japan 170255 Japan 156551
China 176637 China 177709
Philippines 119500 Philippines 120490
Russia 204142 Russia 186317
Note: based on LOENMV_13. 1 Euro ≈7.45 DKR
Table 5.14: Total received DKR from any type of public pension in 2015
Respondents 12221 Non-respondents 12980
Denmark 35250 Denmark 45980
Poland 8070 Poland 9124
Romania 1713 Romania 2864
Spain 4539 Spain 4966
GB 13739 GB 13257
Turkey 32131 Turkey 29940
USA 8632 USA 6801
Japan 18786 Japan 15342
China 1982 China 3454
Philippines 6394 Philippines 7377
Russia 3536 Russia 4600
Note: Based on OFFPENS_EFTERLON_13. 1 Euro ≈7.45 DKR. Total public pension’s covers the
public pension, early retirement, and any supplements to these.
http://www.dst.dk/da/Statistik/dokumentation/Times/personindkomst/loenmv-13http://www.dst.dk/da/Statistik/dokumentation/Times/personindkomst/offpens-efterlon-13
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37
Table 5.15: Total DKR received in social benefits from in 2015
Respondents 42180 Non-respondents 41921
Denmark 62342 Denmark 67107
Poland 43967 Poland 41179
Romania 36853 Romania 30646
Spain 25816 Spain 24147
GB 32384 GB 35292
Turkey 98406 Turkey 84022
USA 23962 USA 23118
Japan 33775 Japan 29808
China 25400 China 29036
Philippines 31346 Philippines 30501
Russia 61787 Russia 63690
Note: Based on OFF_OVERFORSEL_13. 1 Euro ≈7.45 DKR.
Table 5.16: Year immigrated to Denmark
Respondents 2003,0 Non-respondents 2003,0
Poland 2004,2 Poland 2005,9
Romania 2009,4 Romania 2009,5
Spain 2006,6 Spain 2006,7
GB 1997,2 GB 1997,8
Turkey 1992,3 Turkey 1993,9
USA 2002,8 USA 2001,7
Japan 2000,9 Japan 2001,2
China 2005,5 China 2005,5
Philippines 2004,3 Philippines 2004,1
Russia 2004,9 Russia 2004,1
http://www.dst.dk/da/Statistik/dokumentation/Times/personindkomst/off-overforsel-13
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38
Table 5.17: Socioeconomic status in 2015. Presented as row percentages and total number of respondents.
Self-employed employed Unemployed Student Pensioner Other N
Respondents 3,71 50,11 9,8 6,4 10,52 19,47 3642
Denmark 3,84 56,16 8,22 28,77 3,01 365
Poland 1,37 68,73 10,65 6,87 12,37 100
Romania 3,2 70 11,6 1,6 13,6 250
Spain 3,48 55,75 8,71 4,53 27,53 287
GB 4,86 59,85 6,14 16,37 12,79 391
Turkey 7,44 31,16 29,3 24,19 7,91 215
USA 5,5 51,55 3,78 8,59 30,58 291
Japan 4,12 36,54 3,3 17,03 39,01 364
China 5,5 57,61 9,71 1,94 25,24 309
Philippines 2,92 43,8 6,93 5,84 40,51 274
Russia 2,15 54,57 22,31 4,3 16,67 372
Self-employed employed Unemployed Student Pensioner Other N
Non-respondents 3,8 50,09 9,34 6,33 11,25 19,19 7313
Denmark 3,44 52,8 4,52 38,16 1,08 553
Poland 3,4 69,28 9,6 7,83 9,9 677
Romania 0,62 74,07 7,92 2,8 14,6 644
Spain 2,26 52,96 5,75 5,23 33,8 574
GB 7,61 57,96 8,65 14,01 11,76 578
Turkey 6,85 38,5 23,13 23,39 8,14 774
USA 6,13 53,29 5,21 9,19 26,19 653
Japan 5,45 36,73 2 15,82 40 550
China 4,93 55,92 10,2 4,28 24,67 608
Philippines 1,16 44,27 5,81 6,97 41,8 689
Russia 2,55 53,27 24,18 5,09 14,91 550
Note: Recoded from PRE_SOCIO. Students excluded from the by country tabulation to comply with
rules on privacy. The pensioner’s category covers public pensions, early retirement and disability pension.
http://www.dst.dk/da/Statistik/dokumentation/Times/personindkomst/pre-socio
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39
Table 5.18: Percentage who is married. Presented as row percentages and total number of respondents.
Respondents 47,6 3642 Non-respondents 45,9 7313
Denmark 54,0 396 Denmark 54,5 604
Poland 44,7 293 Poland 52,0 706
Romania 56,7 277 Romania 56,9 721
Spain 69,0 339 Spain 68,8 661
GB 46,3 402 GB 48,7 598
Turkey 27,8 216 Turkey 24,8 783
USA 51,3 310 USA 42,8 689
Japan 43,5 379 Japan 40,6 586
China 41,5 345 China 37,9 655
Philippines 48,2 280 Philippines 44,9 720
Russia 36,8 405 Russia 34,4 590
Note: Based on recoding civst
http://www.dst.dk/da/Statistik/dokumentation/Times/cpr-oplysninger/civst
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40
5.4 Germany
The overall response rate in Germany was 18.15% which was lower than expected. The
response rate was highest among native Germans, migrants from China, Spain and the UK
with overall 28%. Response rates were particularly low for migrants from Poland (16.25%)
and Turkey (10.85%). Overall, respondents preferred pen and pencil over the online tool.
Table 5.19: German response rate
Group Invited Response
Before Response
After Valid
Observations Response
Rate Pencile
and Paper Online
Native Germans 900 250 233 866 26.91% 80.69% 19.31%
China 1,020 263 244 889 27.45% 70.49% 29.51%
Japan 1,602 364 316 1,465 21.57% 78.16% 21.84%
Poland 1,560 253 190 1,455 13.06% 87.89% 12.11%
Romania 1,133 200 150 1,021 14.69% 78.00% 22.00%
Russia 1,620 311 217 1,493 14.53% 77.88% 22.12%
Spain 1,279 328 298 1,117 26.68% 66.44% 33.56%
Turkey 2,051 224 138 1,907 7.24% 86.23% 13.77%
UK 1,114 297 237 963 24.61% 65.82% 34.18%
US 1,316 294 211 1,131 18.66% 58.77% 41.23%
Total 13,595 2,784 2,234 12,307 18.15% 74.17% 25.83%
Looking at the response rate by incentive group we see that we achieved the highest response
rate when people were promised a voucher in case they would participate in the study. The
unconditional incentive, however, had almost no effect on the response rate.
-
41
Table 5.19: Response rate by incentive group after first reminder
No Incentive Unconditional Conditional Conditional & Unconditional Total
Native German 13.8% 16.1% 19.9% 20.3% 17.8% China 14.7% 14.4% 18.5% 21.4% 17.4% Japan 10.8% 10.6% 15.8% 14.0% 12.9% Poland 6.1% 6.9% 7.4% 9.5% 7.6% Romania 9.0% 6.0% 12.4% 7.2% 8.6% Russia 7.0% 9.3% 9.1% 10.0% 9.0% Spain 11.2% 11.1% 16.4% 19.5% 14.8% Turkey 3.9% 2.9% 6.0% 3.4% 4.1% UK 14.5% 12.4% 12.7% 19.4% 14.7% US 6.6% 8.1% 13.7% 12.0% 10.3% Total 9.0% 9.0% 12.3% 12.5% 10.8%
Table 5.20 combines above descriptive results by showing the results from logistic regression
analyses predicting the likelihood of participation. We see that women are on average more
likely to participate then men. Also, the chances of participation increases the older people are.
With regards to ethnic differences we find mixed results; whereas migrants from China, Spain
and the UK were more likely to participate than natives, migrants from Poland, Romania,
Russia and Turkey were less likely to participate. No differences between migrants and natives
were found for Japan and the US.
Lastly, we observe that for all groups the unconditional incentive had no effect on the response
rate whereas the conditional incentive raised response rates at least for migrants from china and
the UK.
-
Table 5.20: Odds ratios from logistic regression predicting reasons of participation
Overall Native German China Japan Poland Romania Russia Spain Turkey UK
Sex (male=ref.) 1.25*** 1.19 1.90*** 1.29 1.63* 1.29 1.37* 0.91 1.00 1.24
Age 1.02*** 0.99 1.02** 1.01 1.02* 1.02** 1.00 0.99 1.03*** 1.03***
Ethnicity (German=ref.)
China 1.66***
Japan 1.11
Poland 0.56***
Romania 0.62***
Russia 0.70***
Spain 1.60***
Turkey 0.30***
UK 1.36**
US 0.97
Incentive (None=ref.) Unconditional 1.00 1.04 0.96 1.17 0.72 1.07 1.14 0.68 0.79 1.38
Conditional 1.32*** 1.44 1.53* 1.14 1.14 1.06 1.45 1.02 0.84 2.12**
Unconditional & Conditional 1.26** 1.20 1.36 1.25 0.64 1.11 1.77** 0.49* 1.34 2.06**
-
6. DATA PROCESSING
6.1 Data cleaning
The survey design made it possible for respondents to fill out the questionnaire both online and
by paper and pencil. In addition, cross-posting, due to the limited time between de invitation
and reminders, could result in that respondents received the written questionnaire twice (with
the invitation and with the second reminder), which could have the effect that they handed in
the questionnaire twice. Double, or even triple responses are deleted, using the following
strategy. The least complete and, or latest returned questionnaire was deleted. In case of a fully
completed paper and pencil questionnaire and a fully completed online questionnaire, the online
questionnaire was deleted, assuming that the paper and pencil questionnaire is in general filled
out with more attention than the online questionnaire.
The survey design made it also possible that other than the invited person filled out the
questionnaire. During the data cleaning a check on sex, date of birth and ethnic background was
provided to ensure that the invited respondent is included in the dataset.
Finally, limited completed questionnaires, with less than half filled out, were also
deleted from the dataset. In the Netherlands the data cleaning resulted in deleting 250 entries
(6.37% of the initial response). And in Denmark 173 entries (4,52% of the initial response)
were deleted.
6.2 Variables in the data set
Table 6.1 gives an overview of the variables and variable labels in the MIFARE dataset.
Table 6.1: Overview of variable names and variable labels
Variable name Variable label
respondent_id respondent id
sex male or female
yborn year born
moveto Year moved to RC
livedrc Time lived in RC
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stayrc plan to stay RC
citizen citizenship
belongch Group to belong to CH
belongph Group to belong to PH
belongja Group to belong to JA (only paper for DK)
belongpo Group to belong to PO
belongro Group to belong to RO
belongru Group to belong to RU
belonges Group to belong to ES
belongtu Group to belong to TU
belonguk Group to belong to UK
belongus Group to belong to US
govres_a govres provide health care for the sick
govres_b govres provide living for the old
govres_c govres provide living for the unemployed
govres_d govres provide living for people unable to work
govres_e reduce income differences
helpto_a help to childcare
helpto_b help to elderly care
relatives relatives in household
househ_1 family member 1
househ_2 family member 2
househ_3 family member 3
househ_4 family member 4
househ_5 family member 5
househ_6 family member 6
househ_7 family member 7
hh_1_age age family member 1
hh_2_age age family member 2
hh_3_age age family member 3
hh_4_age age family member 4
hh_5_age age family member 5
hh_6_age age family member 6
hh_7_age age family member 7
health health
doctor visiting doctor
rec_care relatives receive care in RC
pro_care provide care to relatives in RC
serv_a child care services
serv_b elderly care services
sat_a satisfied child care in RC
sat_b satisfied elderly care in RC
sat_c satisfied health care in RC
lang_a speak
lang_b write
friends_a friends from OC
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friends_b friends from RC
friends_c friends from EU
friends_d friends from non-EU
contact_a contact with close family in OC
contact_b contact with relatives in OC
contact_c contact with friends in OC
ident_a belonging to OC
ident_b belonging to RC
ident_c belonging to migrants EU
ident_d belonging to migrants general
denom denomination
relig religiosity
media_a media use in OC
media_b media use in RC
media_c media use in other countries
state_a goverment spending
state_b goverment regulation
state_c traditional gender roles
state_d gay marriage
vote_DK vote DK
vote_NL
vote_DE
vote NL
vote DE
votem_a EU migrants right to vote
votem_b non-EU migrants right to vote
eu_uni opinion about EU unification
eu_dec health decision making
taxed_a taxes high incomes
taxed_b taxes middle incomes
taxed_c taxes low incomes
govsp_a government spending health
govsp_b government spending pensions
govsp_c government spending unemployment
govsp_d government spending child care
govsp_e government spending elderly care
govsp_f government spending social assistance
know_a knowledge health care
know_b knowledge pension
know_c knowledge unemployment benefits
know_d knowledge child care
know_e knowledge social assitance
knEU_a knowledge health care
knEU_b knowledge pension
knEU_c knowledge unemployment benefits
knEU_d knowledge child care
knEU_e knowledge social assitance
knNEU_a knowledge health care
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knNEU_b knowledge pension
knNEU_c knowledge unemployment benefits
knNEU_d knowledge child care
knNEU_e knowledge social assitance
opco_a opinion CO migrants health care
opco_b opinion CO migrants pension
opco_c opinion CO migrants unemployment benefits
opco_d opinion CO migrants child care
opco_e opinion CO migrants social assitance
opEU_a opinion health care
opeu_b opinion EU migrants pension
opeu_c opinion EU migrants unemployment benefits
opeu_d opinion EU migrants child care
opEU_e opinion social assitance
opNEU_a opinion health care
opneu_b opinion non-EU migrants pension
opneu_c opinion non-EU migrants unemployment benefits
opneu_d opinion non-EU migrants child care
opNEU_e opinion social assitance
cont_a contribute or benefit CO?
cont_b contribute or benefit West EU?
cont_c contribute or benefit East EU?
cont_d contribute or benefit Poor outside Europe?
cont_e contribute or benefit rich outide Europe?
exp_rc_a get ahead
exp_rc_b only trust a few
discri_a migrants of CO
discri_b migrants from East EU
discri_c migrants from poor countries outisde EU
Corrupt corruption in RC
Edu_other Education in ohther country
Edu_age Age completed education
workweek Regular work week
contract type of contract
organ what type of organiztion
pww regular work week, partner
as_DK_a household assets DK - state pension
as_DK_b household assets DK - disability benefit
as_DK_c household assets DK - unemployment benfit
as_DK_d household assets DK - child allowance
as_DK_e household assets DK - social assistance
as_DK_f household assets DK - housing benefit
as_NL_a household assets NL - state pension
as_NL_b household assets NL - disability benefit
as_NL_c household assets NL - unemployment benfit
as_NL_d household assets NL - child benefit
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as_NL_e household assets NL - social assistance
as_NL_f household assets NL - housing benefit
as_NL_g household assets NL - health benefit
as_NL_h
as_DE_a
household assets NL - 30% tax rule
household assets DE – state pension
as_DE_b household assets DE – occupational disability benefit
as_DE_c household assets DE - unemployment benfit
as_DE_d household assets DE – supplementary child allowance
as_DE_e household assets DE - social assistance
as_DE_f household assets DE - housing benefit
as_DE_g household assets DE – attendance allowance
source_i main source of family income
crt_inc main country for family income
income household income after tax in euro
happy how happy are you?
InsureNL which health insurance
CO country of origin
RC Receiving country
mode way of filling out questionnaire
isced_rc Education in RC
isced_co Education in CO
isced Educational level overall
6.3 Constructed variables
Some variables in the MIFARE dataset are constructed, such as age and ISCED score. In this
section the construction of these variables is discussed.
We added a unique respondent number respondent id to the data. Moreover, we added
the variable country of origin co to the dataset, which indicates the country of origin of the
respondent according to the population registers. Receiving country rc indicates whether a
respondent lives in Denmark, the Netherlands, or Germany. Mode indicates if the respondent
filled out the questionnaire online or offline. And if offline if he or she filled out the
questionnaire in the mother tongue or in Danish, Dutch, or German.
Finally, based on the highest completed education in both the country of origin and the
receiving country we created three ISCED variables. One ISCED score for the highest
educational level in the country of origin, isced_co. One ISCED score for the highest
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educational level in the receiving country isced_rc. And one ISCED score for the overall
highest educational level, isced. Appendix C shows how the ISCED variables are created.
6.4 Open-ended questions
Except of yborn (in which year are you born), edu_age (age of completed fulltime education)
and moveto (in which year did you moved to RC), the open-ended questions have not been
coded for the current release of the data. For privacy and practical reasons the text entries for
open-ended questions are not available in the public release data file. Please contact the research
team at [email protected] should you want to use the open-ended questions.
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7. QUESTIONNAIRE
In this section the questionnaire is given question by question. Variable categories are in
accordance with the categories in the MIFARE data set. Where RC is used, Denmark, the
Netherlands, or Germany should be read, depending on the country in which the questionnaire
was conducted. If CO is used, the country of origin of the respondent should be read. Moreover,
sometimes as condition is mentioned “asked to natives only”, or “not asked to natives”. In
Denmark natives means people who are born in Denmark; in the Netherlands natives means
people who are born in the Netherlands; and in Germany natives means people who are born in
Germany. In addition, open-ended questions are shown, although these are not in the MIFARE
data set.
Below an example of how the questions are described is given.
belongph Group to belong to PH
Various groups of people live in the Philippines such as, CO specific examples, and many others.
Which of these groups do you think you belong to?
1 Filipinos
2 Moros/Muslims
3 Bikols
4 Other
98 Too many answers
99 No answer
Only asked if CO is Philippines.
Variable name Variable label Question
Answer categories
Conditions
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respondent_id respondent id
constructed variable
sex male or female
Are you a man or a woman?
0 man
1 woman
yborn year born
In which year were you born?
Open answer (number)
moveto Year moved to RC
In which year did you first move to the RC to live her for more than 3 months?
Open answer.
Not asked to natives.
livedrc Time lived in RC
Ever since you forst moved to the RC to live here, would you say, you have lived …
1 most of the time in RC
2 partly in RC and partly in OC
3 partly RC and partly in other country additional open-ended question about other
country
4 most of the time in OC
5 most of the time in another country additional open-ended question about other
country
98 Too many answers
99 No answer
Not asked to natives.
stayrc plan to stay RC
How long do you plan to stay in RC?
-97 Don't know
1 1 year or less
2 More than 1 year, but less than 3 years
3 More than 3 years, but less than 5 years
4 More than 5 years, but less than 10 years
5 More than 10 years
98 Too many answers
99 No answer
Not asked to natives.
citizen citizenship
What is/are your country/countries of citizenship. Please choose the options that apply.
1 CO
2 RC
3 Other additional open-ended question about other citizenship
4 Both CO and RC
99 No answer
Not asked to natives.
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belongch Group to belong to CH
Various groups of people live in China such as, CO specific examples, and many others. Which of these
groups do you think you belong to?
1 Chinese
2 Hui
3 Zhuang
4 Other
99 No answer
Only asked if CO is China.
belongph Group to belong to PH
Various groups of people live in the Philippines such as, CO specific examples, and many others.
Which of these groups do you think you belong to?
1 Filipinos
2 Moros/Muslims
3 Bikols
4 Other
98 Too many answers
99 No answer
Only asked if CO is Philippines.
belongja Group to belong to JA (only paper for DK)
Various groups of people live in Japan such as, CO specific examples ,and many others. Which of these
groups do you think you belong to?
1 Japanese
2 Koreans
4 Other
98 Too many answers
99 No answer
Only asked if CO is Japan.
Only asked if modus is offline.
belongpo Group to belong to PO
Various groups of people live in Poland such as, CO specific examples, and many others. Which of
these groups do you think you belong to?
1 Polish
3 Germans
4 Other
98 Too many answers
99 No answer
Only asked if CO is Poland.
belongro Group to belong to RO
Various groups of people live in Romania such as, CO specific examples, and many others. Which of
these groups do you think you belong to?
1 Romanians
2 Roma
3 Hungarians
4 Other
98 Too many answers
99 No answer
Only asked if CO is Romania.
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belongru Group to belong to RU
Various groups of people live in Russia such as, CO specific examples, and many others. Which of
these groups do you think you belong to?
1 Russians
2 Ukrainians
3 Tatars
4 Other
98 Too many answers
99 No answer
Only asked if CO is Russia.
belonges Group to belong to ES
Various groups of people live in Spain such as, CO specific examples, and many others. Which of these
groups do you think you belong to?
1 Catilian Spanish
2 Canaries
3 Catalans
4 Galicians
5 Basques
6 Roma
7 Other
98 Too many answers
99 No answer
Only asked if CO is Spain.
belongtu Group to belong to TU
Various groups of people live in Turkey such as, CO specific examples, and many others. Which of
these groups do you think you belong to?
1 Turks
2 Kurds
3 Lazs
4 Cercezs
5 Other
98 Too many answers
99 No answer
Only asked if CO is Turkey.
belonguk Group to belong to UK
Various groups of people live in the United Kingdom such as, CO specific examples, and many others.
Which of these groups do you think you belong to?
1 English
2 Scots
3 Welsh
4 Indians
5 Other
98 Too many answers
99 No answer
Only asked if CO is UK.
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belongus Group to belong to US
Various groups of people live in the US such as, CO specific examples, and many others. Which of
these groups do you think you belong to?
1 white Americans
2 African Americans
3 Hispanics
4 Asian Americans
5 other
98 Too many answers
99 No answer
Only asked if CO is US.
govres_a govres provide health care for the sick
On the whole, do you think it should or should not be the government's responsibility to . . . provide
health care for the sick.
-98 Can't choose
1 Definitely should be
2 Probably should be
3 Probably should not be
4 Definitely should not be
98 Too many answers
99 No answer
govres_b govres provide living for the old
On the whole, do you think it should or should not be the government's responsibility to . . . provide a
decent standard of living for the old
-98 Can't choose
1 Definitely should be
2 Probably should be
3 Probably should not be
4 Definitely should not be
98 Too many answers
99 No answer
govres_c govres provide living for the unemployed
On the whole, do you think it should or should not be the government's responsibility to . . . provide a
descent standard of living for the unemployed
-98 Can't choose
1 Definitely should be
2 Probably should be
3 Probably should not be
4 Definitely should not be
98 Too many answers
99 No answer
govres_d govres provide living for people unable to work
On the whole, do you think it should or should not be the government's responsibility to . . .
provide a descent standard of living for people unable to work
-98 Can't choose
1 Definitely should be
2 Probably should be
3 Probably should not be
4 Definitely should not be
98 Too many answers
99 No answer
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govres_e reduce income differences
On the whole, do you think it should or should not be the government's responsibility to . . . Reduce
income differences between rich and poor
-98 Can't choose
1 Definitely should be
2 Probably should be
3 Probably should not be
4 Definitely should not be
98 Too many answers
99 No answer
helpto_a help to childcare
People also have different views on who should be primarily responsible for childcare for working
parents and care in everyday life for elderly people who cannot take care of themselves. Who do you
think should primarily help to . . . working parents who need child care?
-98 Can't choose
1 Family members or friends
2 People that live nearby (neighbours)
3 Government agencies
4 Non-profit organizations
5 Private providers that are paid for
98 Too many answers
99 No answer
helpto_b help to elderly care
People also have different views on who should be primarily responsible for childcare for working
parents and care in everyday life for elderly people who cannot take care of themselves. Who do you
think should primarily help to . . . elderly people who cannot take care of themselves?
-98 Can't choose
1 Family members or friends
2 People that live nearby (neighbours)
3 Government agencies
4 Non-profit organizations
5 Private providers that are paid for
98 Too many answers
99 No answer
relatives relatives in household
We are interested in your living situation here in RC. Are there family members (partners, children,
brothers, sisters, parents, of parents-in-law or other relatives) living with you household here in RC.
1 yes go to househ_1
2 no go to health
98 To many answers
99 No answer
househ_1 family member 1
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is your relation with this
family member?
1 partner
2 child
3 parent / parent-in-law
4 brother / sister
5 other
98 Too many answers
99 No answer
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househ_2 family member 2
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is your relation with this
family member?
1 partner
2 child
3 parent / parent-in-law
4 brother / sister
5 other
98 Too many answers
99 No answer
househ_3 family member 3
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is your relation with this
family member?
1 partner
2 child
3 parent / parent-in-law
4 brother / sister
5 other
98 Too many answers
99 No answer
househ_4 family member 4
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is your relation with this
family member?
1 partner
2 child
3 parent / parent-in-law
4 brother / sister
5 other
98 Too many answers
99 No answer
househ_5 family member 5
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is your relation with this
family member?
1 partner
2 child
3 parent / parent-in-law
4 brother / sister
5 other
98 Too many answers
99 No answer
househ_6 family member 6
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is your relation with this
family member?
1 partner
2 child
3 parent / parent-in-law
4 brother / sister
5 other
98 Too many answers
99 No answer
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househ_7 family member 7
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is your relation with this
family member?
1 partner
2 child
3 parent / parent-in-law
4 brother / sister
5 other
98 Too many answers
99 No answer
hh_1_age age family member 1
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is his/her age?
Open answer
hh_2_age age family member 2
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is his/her age?
Open answer
hh_3_age age family member 3
Can you provide information for up to 7 family members who live with you in your household here in
RC regarding what relation you have with them and how old they are? What is his/her age?
Open answer
hh_4_age age family member 4
Can you provide in