MIFARE Survey Migrants Welfare State Attitudes Methodological … · 2018. 10. 24. · 2 MIFARE Survey Migrants’ Welfare State Attitudes Methodological Report Hidde Bekhuis, Troels

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


Jeanette Renema, Msc.


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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


4.3.2 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

[email protected].

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).


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.


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

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.

28

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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.

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.

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

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

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

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

35

Table 5.11: Register based unemployment in percentage of time over the last year.


Denmark 2,12 Denmark 1,75




GB 2,02 GB 2,81


USA 2,89 USA 1,49





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

36

Table 5.13: Income in DKR in 2015






GB 303783 GB 323121


USA 269296 USA 291010





Note: based on LOENMV_13. 1 Euro ≈7.45 DKR

Table 5.14: Total received DKR from any type of public pension in 2015






GB 13739 GB 13257


USA 8632 USA 6801





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

37

Table 5.15: Total DKR received in social benefits from in 2015






GB 32384 GB 35292


USA 23962 USA 23118





Note: Based on OFF_OVERFORSEL_13. 1 Euro ≈7.45 DKR.

Table 5.16: Year immigrated to Denmark





GB 1997,2 GB 1997,8


USA 2002,8 USA 2001,7





http://www.dst.dk/da/Statistik/dokumentation/Times/personindkomst/off-overforsel-13

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

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

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

44

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







hh_1_age age family member 1







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

45

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

46

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

47

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

48

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.

49

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.


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

50

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


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


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


51

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.


Various groups of people live in the Philippines such as, CO specific examples, and many others.


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


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


1 Romanians

2 Roma

3 Hungarians

4 Other

98 Too many answers

99 No answer

Only asked if CO is Romania.

52

belongru Group to belong to RU

Various groups of people live in Russia such as, CO specific examples, and many others. Which of


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


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


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.


1 English

2 Scots

3 Welsh

4 Indians

5 Other

98 Too many answers

99 No answer

Only asked if CO is UK.

53

belongus Group to belong to US

Various groups of people live in the US such as, CO specific examples, and many others. Which of


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





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





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





98 Too many answers

99 No answer

54

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





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


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

55




family member?

1 partner

2 child


4 brother / sister

5 other

98 Too many answers

99 No answer




family member?

1 partner

2 child


4 brother / sister

5 other

98 Too many answers

99 No answer




family member?

1 partner

2 child


4 brother / sister

5 other

98 Too many answers

99 No answer




family member?

1 partner

2 child


4 brother / sister

5 other

98 Too many answers

99 No answer




family member?

1 partner

2 child


4 brother / sister

5 other

98 Too many answers

99 No answer

56




family member?

1 partner

2 child


4 brother / sister

5 other

98 Too many answers

99 No answer



RC regarding what relation you have with them and how old they are? What is his/her age?

Open answer




Open answer




Open answer


Can you provide in

Documents

MIFARE Survey Migrants Welfare State Attitudes Methodological … · 2018. 10. 24. · 2 MIFARE Survey Migrants’ Welfare State Attitudes Methodological Report Hidde Bekhuis, Troels