AUTNES Comparative Study of Electoral

Systems Post-Election Survey 2013 - Documentation

(Edition 2.0.0)

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AUTNES Comparative Study of Electoral Systems Post-Election Survey 2013

Sylvia Kritzinger, Kathrin Thomas, Christian Glantschnigg, Julian Aichholzer, Konstantin Glinitzer, David Johann, Markus Wagner, Eva Zeglovits.

(Edition 2.0.0, 2016)

[email protected]

http://www.autnes.at 2 Page Page

Contents

1. Introduction 4 1.1 How to cite these data 4 1.2 Changes in Edition 2.0.0 4 2. Conditions of Use 5 2.1 Restrictions 5 2.2 Confidentiality 5 2.3 Deposit Requirement 5 3. Study Description 6 3.1 Title 6 3.2 Principal Investigators 6 3.3 Funding / Acknowledgments 6 3.4 Fieldwork 6 3.5 File Name 7 3.6 Keywords 7 4. Study Design 8 4.1 Fieldwork 8 4.2 Sampling Procedure 8 4.3 Quality control 8 4.4 Response Rate 11 4.5 Post Stratification Weight 11 5. Questionnaire 14 5.1 Questionnaire Development and Quality Assessment 14 5.2 Language 15 5.3 Deviation Notes 15

6. Codebook 34

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1. Introduction

The Documentation of the Austrian National Election Study (AUTNES) Comparative Study of Electoral Systems Post Election Survey 2013 accompanies the data files and provides useful information about the data, coding and any related issues.

The Austrian National Parliamentary Elections were held on 29 September 2013.

1.1 How to cite these data

Data users are kindly asked to acknowledge the data and the accompanying release document. Please refer to the GESIS data catalogue (www.gesis.org) for a recommendation on how to cite the data and the documentation.

1.2 Changes in Edition 2.0.0

For an overview of changes between the first version of this data (Edition 1.0.0; 2014) and the current version 2.0.0, please look at the document “ZA5856_changes_in_v2-0-0.pdf” available on the GESIS website.

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2. Conditions of Use

2.1 Restrictions

The data are available for non-profit use without any restrictions.

2.2 Confidentiality

AUTNES, the Principal Investigators and the funding institutions are neither responsible for the use of the data or for interpretations or inferences based on their use, nor do they accept liability for indirect, consequential or incidental damages or losses arising from use of the data.

2.3 Deposit Requirement

In order to facilitate exchange within the scientific community and to provide the funding agencies with the essential information about the use of the archival resources, users of the AUTNES data are requested to notify the AUTNES team of all forms of publications based on the AUTNES data.

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3. Study Description

3.1 Title

The Comparative Study of Electoral Systems Post-Election Survey 2013.

3.2 Principal Investigators

Wolfgang C. Müller, University of (AUTNES Supply Side) Sylvia Kritzinger, (AUTNES Demand Side) Klaus Schönbach, University of Vienna (AUTNES Media Side)

3.3 Funding / Acknowledgments

The survey was carried out under the auspices of the Austrian National Election Study (AUTNES) and the National Research Network (NFN). It was sponsored by the Austrian Research Fund (FWF) (S10902-G11). FWF Austrian Science Fund.

3.4 Fieldwork

Jaksch & Partner Schillerstraße 8 4020 Linz Email: [email protected] http://www.jaksch-partner.at/

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3.5 File Name

The AUTNES provides a German and an English version of the data as well as two different formats. A SPSS readable data file (.sav), and a Stata format (.dta).

ZA5856_de_v2-0-0.sav ZA5856_en_v2-0-0.sav ZA5856_de_v2-0-0.dat ZA5856_en_v2-0-0.dat

3.6 Keywords

Distributional politics and social protection, mobilization, political knowledge

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4. Study Design

4.1 Fieldwork

Fieldwork was carried out by the field institute Jaksch & Partner between 1 October 2013 and 29 October 2013 using Computer Assisted Telephone Interviewing (CATI). In total, 1000 interviews were completed.

4.2 Sampling Procedure

The sampling method employed was stratified random sampling. The stratification characteristics were the nine Austrian provinces (“Bundesländer”). Within the Austrian provinces an unlimited random sample was drawn and respondents were sampled proportional to the population size. Phone numbers were randomly selected using a dual sampling frame (Random Digit Dialling: 89.1% and Randomised Last Digits: 10.9%). Within each household, the last-birthday method was used to randomly select the respondent. The last birthday method is a quick and easy method of selecting respondents within a sampled household in random-digit dialling surveys. It asks for the eligible person within the sampling unit who had the most recent birthday.

The sample is representative of the eligible Austrian voters. These are citizens aged 16 and older. Please see also Section 4.5.

4.3 Quality control

In order to ensure the quality of the data, several quality checks were conducted throughout the data gathering process: 1) Quality control of the questionnaire 2) interviewer training, 3) quality control during the field period, 4) post-field work

interview verification, and 5) data cleaning. 8

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(1) Quality control of the questionnaire was conducted by trained members of staff at Jaksch & Parter with regard to the completeness, plausibility, and formal correctness of the questionnaire.

(2) Prior to the interviewing process, a training session for the interviewers was held at the field institute under the supervision of senior researchers at Jaksch & Partner as well as a member of the AUTNES team. The training included:

 Intensive interviewer briefing by the project leader at Jaksch & Partner  Presentation and practice of the questionnaire  Group as well as individual exercises  Observation and control by the senior researchers

The senior investigators at Jaksch & Partner as well as an AUTNES team member were available to answer questions about any component of the survey throughout the training session.

(3) During the field period, respondents were contacted up to five times until a non-sample was declared. In addition, several techniques were employed to persuade those respondents who indicated at first contact that they were undecided whether or not they wanted to participate. For instance, the household was passed on to a more experienced interviewer and they re- contacted the household up to three times in order to persuade them to participate in the survey. Individual interviews and interviewer feedback were recorded for quality control in the call centre. Jaksch & Partner also established a daily quality control process of the field work by the project leader.

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(4) After the interview, some respondents were re-contacted by Jaksch & Partner in order to verify the data and assess their quality. In total, 10% of the interviews were evaluated and respondents asked about their personal interview situation.

(5) Team members at Jaksch & Partner also did the data cleaning, which included checking the completeness, plausibility and consistency of the data. If applicable, missing or incorrect information were re-collected or corrected. The data cleaning was conducted manually as well as computer assisted. In addition, members of the AUTNES team double checked for duplicates, removed typographical errors as well as validated and corrected values against a known list of entries, whenever possible. The latter predominantly applied to contextual data that could be inferred from the postal code.

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4.4 Response Rate

The response rate for the CSES post-election survey is 52.1%. Please see Table 1 for a detailed account.

Table 1: Response Rate Total % of Total % of Number of the overall the net Cases sample sample No response after 5 contact attempts/non-contact 4238 28.2% No contact/incorrect number 6721 44.8% No private household 1765 11.8% Housing units with no eligible respondents 356 2.4%

Completed interviews 1000 52.1% Interrupted interviews 0 0.0% Target respondent identified, but refused 739 38.5% Target respondent identified, but unable to participate 78 4.1% Target respondent identified, but language problems 84 4.4% Other reasons for non-response 19 1.0% Total 15000 100.0%

4.5 Post Stratification Weight

The dataset includes a post-stratification weight (gew). The weight variables were computed on the basis of the following socio-demographic characteristics:

 Gender  Age  Education  Household size

 Provinces (‘Bundesländer’)

 Employment status 11 Page Page

The weight’s values range from a minimum of 0.51 to a maximum value of 3.57. The target distributions are based on StatCube Micro Census data 2012 (Statistics Austria, StatCube, last accessed in September 2013). StatCube data refers to all Austrian citizens aged ≥ 15, a total of 6325600 residents. Table 2 displays the actual estimates of the total Austrian target population as well as the weighted values in the sample:

Table 2: Expected Socio-Demographic Characteristics and Weighted Values Expected Values after Socio-Demographics values weighting Gender male 48.6% 48.4% female 51.4% 51.6% Age 15 to 17 years old 4.3% 3.8% 18 to 21 years old 5.7% 5.2% 22 to 24 years old 4.3% 4.1% 25 to 34 years old 13.8% 14.2% 35 to 44 years old 17.2% 17.7% 45 to 54 years old 19.1% 19.2% 55 to 64 years old 14.1% 14.7% 65+ years old 21.6% 21.0% Education no school/primary school/lower secondary level 24.5% 23.9% vocational training/vocational school 36.6% 36.3% higher vocational school (BMS) 13.8% 13.4% secondary school leaving certificate (=Matura) 14.4% 15.0% university-related institution/tertiary education 10.7% 11.3% Household size single person household 19.2% 19.8% 2 person household 29.5% 30.2% 3 person household 20.5% 19.9% 4+ person household 30.8% 30.1% Provinces Burgenland 3.6% 3.4% (‘Bundesländer’) Carinthia 7.0% 7.2% Lower Austria 19.9% 19.4% Upper Austria 17.2% 17.7% Salzburg 6.2% 6.8% Styria 15.3% 14.7%

Tyrol 8.3% 8.4 %

Vorarlberg 4.2% 4.8% 12 Vienna 18.2% 17.5%

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Employment status employed 53.3% 54.0% military or civilian service 0.4% 0.0% maternity leave 1.5% 1.8% unemployed 3.0% 2.9% retired 27.2% 27.9% permanently incapable of employment/disabled 1.4% 0.8% housewife/househusband 5.6% 5.4% pupils/students 7.1% 6.8% other 0.5% 0.4% Gender x education female x no school/primary school/lower secondary level 63.4% 63.1% female x vocational training/vocational school 37.2% 38.2% female x higher vocational school (BMS) 68.5% 67.9% female x matura 51.4% 51.6% female x tertiary education 51.0% 51.2% male x no school/primary school/lower secondary level 36.6% 36.9% male x vocational training/vocational school 62.8% 61.8% male x higher vocational school (BMS) 31.5% 32.1% male x matura 48.6% 48.4% male x tertiary education 49.0% 48.8% Gender x age female x 15 to 17 years old 48.6% 46.7% female x 18 to 21 years old 48.6% 48.3% female x 22 to 24 years old 48.6% 49.4% female x 25 to 34 years old 40.0% 48.3% female x 35 to 44 years old 49.7% 49.9% female x 45 to 54 years old 49.7% 51.3% female x 55 to 64 years old 51.8% 51.9% female x 65+ years old 57.6% 57.3% male x 15 to 17 years old 51.4% 53.3% male x 18 to 21 years old 51.4% 51.7% male x 22 to 24 years old 51.4% 50.6% male x 25 to 34 years old 51.0% 51.7% male x 35 to 44 years old 50.3% 50.1% male x 45 to 54 years old 50.3% 48.7% male x 55 to 64 years old 48.2% 48.1% male x 65+ years old 42.4% 42.7% (Source: Micro Census data 2012, Statistics Austria, StatCube, last accesseed September 2013)

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5. Questionnaire

5.1 Questionnaire Development and Quality Assessment

The CSES questionnaire was run as an independent survey module and most of the items on the original CSES questionnaire were adopted without any changes. A few questions deviated from the original question wording provided by the CSES. The reasons for this are related to translation issues as well as to knowledge gained from pre-testing. With regard to the former, some questions did not easily translate into German, which is why a very similar question wording was chosen that nevertheless deviates from the original question wording provided by the CSES. With regard to the latter, while no exclusive pre-tests were run for the CSES module, the AUTNES research team pre-tested a larger number of survey questions within the framework of other surveys that were run by the AUTNES. Any amendments were reviewed and evaluated thoroughly by the members of the AUTNES research team through cognitive and quantitative pre-testing. The AUTNES team opted for the question wording that showed to be more precise and to capture the concept intended to be measured better than the original question provided. Please note that these cases are an exception and that the AUTNES tried to implement the CSES module 1:1 whenever possible in order to ensure the quality of the CSES data and to guarantee the comparability of the data with other countries. Some questions were dropped from the original CSES questionnaire as they were not applicable to the Austrian context. Please see Section 5.3 for a more detailed account of the amendments undertaken by the AUTNES.

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5.2 Language

The CSES survey was conducted in German. Members of the AUTNES team translated the original CSES questionnaire from English into German. A back- translation was conducted as well to pick up on any translation mistakes and deviations from the original CSES questionnaire as well as to assess the quality of the translation. Please see Section 5.3 for possible language-related amendments to the original questionnaire.

5.3 Deviation Notes

This section records which questions were amended, added to or dropped from the original CSES questionnaire. Additional quality checks are indicated by a star (*) when quantitative pre-testing took place and two stars (**) when cognitive pre-testing was conducted.

5.3.1 Additional Questions

The AUTNES Team added some additional questions of interest to the original questionnaire, which integrated nicely into the modules theme. Note that ‘don’t know’ and ‘refused’ answers are not recorded in the table below, but they are indicated by the values 88 (=don’t know) and 99 (=refused).

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Table 3: Additional questions Variable Label Question wording and coding Name q1* Austrian Are you an Austrian citizen? citizenship 1. Yes 2. No q9 Changes in the And if you now think about your region: How would you assess the state of economic economy in your region compared to other parts of Austria? Has the state of the situation in the economy in your region compared to the rest of Austria…? region (in [Region refers to the respondent’s own political district as well as bordering comparison to political districts – those districts, in which most people predominately stay.] Austria) 1. got better 2. stayed the same 3. got worse q10 Changes in the What would you say: Much better or somewhat better? economic 1. Much better situation in the 2.somewhat better region (in comparison to Austria): better q11 Changes in the What would you say: Much worse or somewhat worse? economic 1. Much better situation in the 2.somewhat better region (in comparison to Austria): worse q19 Current election Assuming you had voted in the elections, which party would you have most likely – Vote Choice if voted for? R had voted 1. SPÖ 2. ÖVP 3. FPÖ 4. FP Kärnten 5. BZÖ 6. Greens 7. KPÖ 8. NEOS 9. Team Stronach

10. Pirates

11. Other Party 16 12. Cast an invalid ballot

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Variable Label Question wording and coding Name q35 Contact in And which party contacted you personally in a direct conversation? person: Which 1. SPÖ party? 2. ÖVP 3. FPÖ 4. FP Kärnten 5. BZÖ 6. Greens 7. KPÖ 8. NEOS 9. Team Stronach 10. Pirates 11. Other Party 12. No party/nobody q37 Contact by And which party contacted you by phone? phone: Which 1. SPÖ party? 2. ÖVP 3. FPÖ 4. FP Kärnten 5. BZÖ 6. Greens 7. KPÖ 8. NEOS 9. Team Stronach 10. Pirates 11. Other Party 12. No party/nobody q39 Contact by text And which party contacted you by text message? message: Which 1. SPÖ party? 2. ÖVP 3. FPÖ 4. FP Kärnten 5. BZÖ 6. Greens 7. KPÖ 8. NEOS

9. Team Stronach

10. Pirates 17 11. Other Party

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Variable Label Question wording and coding Name q44 Campaign: TV Did you watch campaign advertisements on TV? ads 1. yes 2 no q45 Campaign: TV And of which party did you watch the campaign advertisements on TV? ads: Which 1. SPÖ party? 2. ÖVP 3. FPÖ 4. FP Kärnten 5. BZÖ 6. Greens 7. KPÖ 8. NEOS 9. Team Stronach 10. Pirates 11. Other Party 12. No party/nobody q46** Mobilization R. - During the election campaign, did you try to persuade any member of your close family family to vote for a particular political party? 1. yes 2. no 77. not applicable q47** Mobilization R. - And what about your close friends? friends 1. yes 2. no 77. not applicable q48** Mobilization R. – And work colleagues or peers at university or school? work/school 1. yes 2. no 77. not applicable q49** Mobilization R. - And neighbours? neighbours 1. yes 2. no 77. not applicable

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Variable Label Question wording and coding Name q63** Leader attributes Next, we will talk about the traits of some candidates in more detail. If I mention a - competence politician who you do not have an opinion about, please tell me. Let’s begin with competence, that is, whether a politician knows about factual issues. What do you think: Are the following politicians very, fairly, a little bit or not at all competent?

Item 1: Werner Faymann Item 2: Item 3: Heinz Christian Strache Item 4: Josef Bucher Item 5: Eva Glawischnig Item 5: Frank Stronach Item 7: Matthias Strolz

1. very competent 2. somewhat competent 3. a little competent 4. not at all competent q64** Leader attributes Now about honesty, that is, whether or not a politician is an honest person. What - honesty do you think: are the following politicians very, fairly, a little bit or not at all honest?

Item 1: Werner Faymann Item 2: Michael Spindelegger Item 3: Heinz Christian Strache Item 4: Josef Bucher Item 5: Eva Glawischnig Item 5: Frank Stronach Item 7: Matthias Strolz

1. very honest 2. somewhat honest 3. a little bit honest 4. not at all honest

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Variable Label Question wording and coding Name q65** Leader attributes Now about the candidates’ assertiveness, that is, whether a politician can - assertiveness translate their ideas into policy. What do you think, how assertive are the following politicians - are they very, fairly, a little bit, or not at all assertive?

Item 1: Werner Faymann Item 2: Michael Spindelegger Item 3: Heinz Christian Strache Item 4: Josef Bucher Item 5: Eva Glawischnig Item 5: Frank Stronach Item 7: Matthias Strolz

1. very assertive 2. somewhat assertive 3. a little bit assertive 4. not at all assertive q66** Leader attributes And now to the subject of charisma, that is, the impression politicians make on - charisma the public. What do you think, are the following politicians very, fairly, a little bit or not at all charismatic?

Item 1: Werner Faymann Item 2: Michael Spindelegger Item 3: Heinz Christian Strache Item 4: Josef Bucher Item 5: Eva Glawischnig Item 5: Frank Stronach Item 7: Matthias Strolz

1. very charismatic 2. fairly charisma 3. a little bit charismatic 4. not at all charismatic

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Variable Label Question wording and coding Name Q67** ICT A I will now read out 4 activities that some people have already done, but others have not. Please tell me, HOW MANY of these activities you have done over the past 3 years, don’t tell me which ones, but only how many: - Wrote an editorial letter - Was an active member in a club/ society - Changed the main place of residence - Donated money

[RECORD NUMBER 0 to 4] Q68** ICT B I will now read out 4 activities that some people have already done, but others have not. Please tell me, HOW MANY of these activities you have done over the past 3 years, don’t tell me which ones, but only how many: - Wrote an editorial letter - Was an active member in a club/ society - Voted in the national parliamentary election in September 2013 - Changed the main place of residence - Donated money

[RECORD NUMBER 0 to 5] q75 Ownership of Do you or a member of your household own an apartment or house that is rented apartment rented by somebody else? by someone else 1. yes 2. no q82 Filter for current Are you currently employed or seeking work? employment 1. yes status 2. no

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Variable Label Question wording and coding Name q88* Current What best describes your situation? employment 1. retired status – not in 2. in education: school labour force 3. in education: higher education 4. in education: other 5. maternity leave or others 6. housewife/househusband 7. military or civil service 8. unfit to work 9. unemployed or looking for work 10. other q89* If R was ever Were you previously employed; no matter if that was full-time or part-time? employed, no 1. fulltime or part time matter if fulltime 2. neither nor or part time q90* Socio economic And what was your last occupation? status in last job 1. white collar 2. worker 3. public sector / official 4. public sector / contract worker 5. self-employed (without employees) 6. self-employed (with employees) 7. independent contractor 8. farmer q91 Main occupation And what job did you have? in last job q92 Industrial sector Did you work in agriculture or forestry, in an industrial enterprise, or in the service in last job and public administration sector? 1. agriculture and forestry 2. industrial enterprise 3. service and public administration 4. other q93 Filter for Is your partner currently employed or seeking work? partner’s current 1. yes employment 2. no

status

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Variable Label Question wording and coding Name q99 Current What best describes their situation? employment 1. retired status – 2. in education: school spouse/partner 3. in education: higher education not in labour 4. in education: other force 5. maternity leave or others 6. housewife/househusband 7. military or civil service 8. unfit to work 9. unemployed or looking for work 10. other q100 If spouse/ Were they previously employed; no matter if that was full-time or part-time? partner were 1. fulltime or part time employed 2. neither nor previously, no matter if fulltime or part time q101 Socio economic And what was their last occupation? status 1. white collar spouse/partner 2. worker in last job 3. public sector / official 4. public sector / contract worker 5. self-employed (without employees) 6. self-employed (with employees) 7. independent contractor 8. farmer q102 Main occupation And what was the last job they had? spouse/partner in last job q103 Previous Did they work in agriculture or forestry, in an industrial enterprise, or in the service Industrial sector and public administration sector? spouse/partner 1. agriculture and forestry in last job 2. industrial enterprise 3. service and public administration 4. other

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Variable Label Question wording and coding Name q109* Filter for Do you usually speak a language other than German at home? (if q69>1) language usually Do you usually speak a language other than German with your family? (if q69 spoken at home =1) 1. yes 2. no q115 How long lived in And how many years have you approximately lived in this residential area? community? q107* Filter for religious Do you belong to a religious group? denomination 1. yes 2. no q118 Type of phone Finally, we have a few technical questions. Did we reach you on your mobile connection phone or on your landline? 1. mobile phone 2. landline q119 If mobile phone And where did we reach you? in q118 1. at home 2. at work/at school/apprenticeship 3. out (alone) 4. out (with others) 5. other q120 If mobile phone Do you have a landline at home? in q118 1. yes 2. no q121 If mobile phone Do you use another mobile phone? in q118 1. yes 2. no q122 If landline in Do you also have one or more mobile phones? q118 1. yes 2. no q123 Interviewer String number q124* R.’s command of How would you evaluate the respondent’s command of German on a scale from German 0 to 10, where 0 means “very bad” and 10 means “very good”? (evaluated by Interviewer) q125 Telephone book Number from telephone book or RDD?

or RDD 1. telephone book 24 2. RDD

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5.3.2 Amended Questions Some of the original questions were amended due to translation issues or as a result of pre-testing. The question wording showed to be better suited was chosen instead of the original question wording. Thereby, it was ensured that the questions are comparable with the other country data in the CSES Module or easily recodeable into a matching format. Additional quality checks are indicated by a star (*) when quantitative pre-testing took place and two stars (**) when cognitive pre-testing was conducted. Note that ‘don’t know’ and ‘refused’ answers are not recorded in the table below, but they are indicated by the values 88 (=don’t know) and 99 (=refused).

Table 3: Amended Questions CSES AUTNES Label Differences Explanation Variable Variable Name Name Q5LH-a q16* ** Current LH Different question wording: The question format was election – In the federal election on 29 September pre-tested and it was cast a ballot 2013, a lot of people could not vote or chose shown that it better not to vote for good reasons. Which of the measured actual turnout following statements best describes you? and reduced social 1 desirability bias. It is also 1. I did not vote in the federal election on 29 easily recordable into the September 2013 original CSES variable 2. I thought about voting this time but didn’t and thus does not harm 3. I usually vote but didn’t this time the cross-sectional 4. I am sure I voted in the federal election on comparability of the 29 September 2013 CSES data. 5. [VOLUNTEERED] I voted by absentee ballot

25 1 Zeglovits, E. & S. Kritzinger (2013) New Attempts to Reduce Overreporting of Voter Turnout and Their Effects. International Journal of Public Opinion Research: doi: 10.1093/ijpor/edt010. Page

CSES AUTNES Label Differences Explanation Variable Variable Name Name Q6a Q20* ** Previous LH Different question wording: See above, the question election – This question is about the federal election of wording is better suited to cast a ballot September 2008. In this election, a lot of measuring actual turnout. people could not vote or chose not to vote for good reasons. This election is some time ago now. Which of the following statements describes you best? 1. I am sure I did not vote in the federal election in September 2008 2. I am not sure if I voted but I think it is more likely that I did not 3. I am not sure if I voted but I think it is more likely that I did 4. I am sure that I voted in the federal election in September 2008 Q17f q42 Mobilization: Different question wording: A short cut was used as Institutional Through the internet, in a social Network like a long battery of contact social Facebook or Twitter? questions on other ways network and 1. yes of mobilisation was asked web 2. no prior to this question. Q17g q43 Mobilization: Different question wording: Here a longer version Institutional And which party did contact you by mail, was used to emphasise contact - who email, or through the internet and social that it aims at parties who networks? contacted the respondent 1. SPÖ by mail, email or online, 2. ÖVP but not by any other 3. FPÖ means. 4. FP Kärnten 5. BZÖ 6. Greens 7. KPÖ 8. NEOS 9. Team Stronach 10. Pirates

11. Other Party

12. No party/nobody 26

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CSES AUTNES Label Differences Explanation Variable Variable Name Name Q20a q58 Political The next few questions are a quiz about A short introduction was Information 1 Austrian politics. We are interested in added to avoid that recording the current level of information of respondents feel like they the public. If you are uncertain about a are in a test situation. question, please say it. We will then continue with the next question. Which of the following persons was Finance Minister before the national parliamentary election? Josef Pröll, Johanna Mikl-Leitner, oder ? Q20b q59 Political Different calculation of the unemployment The unemployment rate Information 2 rate: used for this item is the September 2013: unemployment rate by 1. 2.9% national calculation since 2. 6.9% this number is reported 3. 8.9% more often in the media. 4. 9.9% Because we wanted to avoid confusion with the EUROSTAT calculation, we changed the categories into [UR – 4%], [UR], [UR+2%], [UR+3%]. Q20d q61 Political One name was replaced. Kurt Waldheim was Information 4 replaced by Javier Perez 1. Kofi Annan de Cuellar as Waldheim 2. Javier Perez de Cuellar is a former Austrian 3. Ban Ki-Moon president, who died a few 4. Boutros Boutros-Ghali years ago. It thus seemed sensible to replace the name as most Austrians are likely to know that Waldheim is an incorrect answer.

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CSES AUTNES Label Differences Explanation Variable Variable Name Name Q22a q72 Ownership: Question Q22a split in two questions In Austria a larger residence 1 Owning an apartment or house? proportion of people owns a small second home such as a holiday flat. This is why the original question provided by the CSES was split into two separate questions in order to avoid bias. Q22a q73 Ownership: Question Q22a split in two questions In Austria a larger residence 2 Owning a weekend home? proportion of people owns a small second home such as a holiday flat, This is why the original question provided by the CSES was split into two separate questions in order to avoid bias. Q22b q74 Ownership: Livestock was excluded from this question It did not seem relevant business, in the Austrian context to property, include this. Whenever farm, someone answered that livestock they owned a farm this included livestock to some extent.

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CSES AUTNES Label Differences Explanation Variable Variable Name Name D3 q78* Education Answer categories were adapted to Austrian It seemed sensible to educational system amend the highest degree of education to Survey categories correspond to ISCED the Austrian context. categories as follows: ISCED Levels are 1 did not go to school indicated to ensure 2 did not finish school comparability. 3 Elementary school or lower (ISCED 1) 4 Modern secondary school or general secondary school lower grad 4 (ISCED 2) 5 Special school (ISCED 2) 6 Polytechnic school, vocational middle school (ISCED 3) 7 Apprenticeship, vocational school (ISCED 3) 8 General secondary school with diploma (ISCED 3) 9 Vocational secondary school with diploma (ISCED 4) 10 University related institution (ISCED 5) 11 College (ISCED 5) 12 Bachelor (ISCED 5) 13 Master/Graduated engineer/ University of Applied Science Degree (ISCED 5) 14 Doctorate/ PhD (ISCED 6) 15 Other, namely… D10 q83* Current Some codes were cut: It was adjusted to the employment 1. employed fulltime (more than 32 Austrian context, so status hours/week) codes that were shown to 2. employed part time (Between 15 and 32 be unimportant were cut. hours/week) 3. employed less than 15hours / week; 4. helping family member 5. apprenticeship

6. unemployed or looking for work

7. maternity leave or others 29

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CSES AUTNES Label Differences Explanation Variable Variable Name Name D12 q84* Socio Some codes were added: The Austrian context has Economic 1. white collar a more differentiated status 2. worker scale of socio-economic 3. public sector / official status. 4. public sector / contract worker 5. self-employed without employees 6. self-employed with employees 7. independent contractor 8. farmer D14 q86 Industrial Codes cut: The answering codes sector Do you work in agriculture or forestry, in an were reduced as the industrial enterprise, or in the service and original ones were fairly public administration sector? long. It did not change 1. agriculture and forestry the sense of the question 2. industrial enterprise and respondents picked 3. service and public administration the sectors easily without 4. 0ther more information on the codes. D15 q94 Current Some codes were cut: It was adjusted to the employment 1. employed fulltime (more than 32 Austrian context, so status – hours/week) codes that were shown to partner/spou 2. employed part time (Between 15 and 32 be unimportant were cut. se hours/week) 3. employed less than 15hours / week; 4. helping family member 5. apprentice 6. unemployed or looking for work 7. maternity leave or other D17 q95 Socio Some codes were added: The Austrian context has Economic 1. white collar a more differentiated status 2. worker scale of socio-economic partner/spou 3. public sector / official status. se 4. public sector / contract worker 5. self-employed without employees

6. self-employed with employees

7. independent contractor 30 8. farmer

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CSES AUTNES Label Differences Explanation Variable Variable Name Name D19 q97 Industrial Codes cut down: The answering codes sector Do you work in agriculture or forestry, in an were reduced as the partner/ industrial enterprise, or in the service and original ones were fairly spouse public administration sector? long. It did not change 1. agriculture and forestry the sense of the question 2. industrial enterprise and respondents picked 3. service and public administration the sectors easily without 4. 0ther more information codes. D20 q104 Household Actual ranges indicated: Instead of providing income 1. less than 1.200 Euros quintiles, actual ranges 2. 1.200 to 2.000 Euros are indicated. 3. 2.000 to 2.800 Euros 4. 2.800 to 3.600 Euros 5. more than 3.600 Euro s D24 q108* Religious Codes amended to the five largest religious Amended to the Austrian denomination groups in Austria: context. 1. Roman-Catholic 2. Protestant 3. Islam/Muslim 4. Eastern Orthodox Church 5. Jewish 6. other D25 q110* Language And in which language or languages do you This was asked as a usually speak? follow up to a question spoken at that was added and home captured whether respondent spoke another language than German at home (See Section 5.3.1 q109). Only respondents who mentioned in q109 that they predominantly speak another language than German were asked

this question.

31

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CSES AUTNES Label Differences Explanation Variable Variable Name Name D26 q113 Region Question wording deviates: It was asked where the In which region is your main residence? respondents’ main (‘Bundesländer’) residence is. 1. Burgenland 2. Carinthia 3. Lower Austria 4. Upper Austria 5. Salzburg 6. Styria 7. Tyrol 8. Vorarlberg 9. Vienna D29 q114* Rural or Coding differs: Another code for small urban 1. rural area or village towns was introduced. residence 2. small town Note also that the code 3. mid-sized town for “large town or city” 4. large town or city and “suburbs of a large 5. suburbs of a large town or city town or city” changed. D31 q116* Country of List provided: Instead of asking an birth 1. Austria open-ended question, a 2. Germany list with the most 3. Turkey common countries of 4. Serbia origin was provided. 5. Croatia 6. Bosnia and Herzegovina 7. Slovenia 8. Poland 9. Czech Republic 10. Russia 11. Hungary 12. Other

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5.3.3 Dropped Questions

The following questions were dropped from the Austrian CSES questionnaire as they are not applicable to the Austrian context:

CSES Label Q5P1-a Turnout (president) Q5P1-b Vote choice (president) Q5P2-a Turnout (president 2nd round) Q5P2-b Vote Choice (president 2nd round) Q5LH-c Vote Choice (current Lower House election district candidate) Q6c Previous election: vote choice – district candidate Q13a – Q13i Additional 0-10 scale parties Q14 Additional 0-10 scale self D7 Business or employers association membership D8 Farmers association membership D9 Professional association membership D13 Employment type – public or private D18 Spouse employment type – public or private D27 Race D28 Ethnicity

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6. Codebook

Variable Label Name Value Label RESPONDENT ID id number AUSTRIAN CITIZENSHIP q1 1 yes 2 no YEAR OF BIRTH q2 number MONTH OF BIRTH q2_2 number GENDER q3 1 male 2 female PUBLIC EXPENDITURE: HEALTH q4_1 1 much more than now PUBLIC EXPENDITURE: EDUCATION q4_2 2 somewhat more than now PUBLIC EXPENDITURE: UNEMPLOYMENT BENEFITS q4_3 3 the same as now PUBLIC EXPENDITURE: DEFENCE q4_4 4 somewhat less than now PUBLIC EXPENDITURE: OLD-AGE PENSIONS q4_5 5 much less than now PUBLIC EXPENDITURE: BUSINESS AND INDUSTRY q4_6 PUBLIC EXPENDITURE: POLICE & LAW ENFORCEMENT q4_7 PUBLIC EXPENDITURE: WELFARE BENEFITS q4_8 IMPROVING STANDARD OF LIVING q5 1 very likely 2 somewhat likely 3 somewhat unlikely 4 very unlikely CHANGES IN THE ECONOMIC SITUATION IN AUSTRIA (PAST q6 1 got better 12 MONTHS): BETTER/SAME/WORSE 2 stayed the same 3 got worse CHANGES IN THE ECONOMIC SITUATION IN AUSTRIA (PAST 12 q7 1 much better MONTHS): MUCH BETTER/SOMEW 2 somewhat better CHANGES IN THE ECONOMIC SITUATION IN AUSTRIA (PAST 12 q8 1 much worse MONTHS): MUCH WORSE/SOMEWHAT WORSE 2 somewhat worse CHANGES IN THE ECONOMIC SITUATION IN THE REGION (IN q9 1 got better COMPARISON TO AUSTRIA): BETTER/SAME/WORSE 2 stayed the same 3 got worse CHANGES IN THE ECONOMIC SITUATION IN THE REGION (IN q10 1 much better COMPARISON TO AUSTRIA): MUCH BETTER/SOMEWHAT 2 somewhat better

BETTER

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Variable Label Name Value Label CHANGES IN THE ECONOMIC SITUATION IN THE REGION (IN q11 1 much worse COMPARISON TO AUSTRIA): MUCH WORSE/SOMEWHAT 2 somewhat worse WORSE CHANGES IN THE PERSONAL ECONOMIC SITUATION (PAST 1-2 q12 1 got better YEARS): BETTER/SAME/WORSE 2 stayed the same 3 got worse CHANGES IN THE PERSONAL ECONOMIC SITUATION (PAST 1-2 q13 1 much better YEARS): MUCH BETTER/SOMEWHAT BETTER 2 somewhat better CHANGES IN THE PERSONAL ECONOMIC SITUATION (PAST 1-2 q14 1 much worse YEARS): MUCH WORSE/SOMEWHAT WORSE 2 somewhat worse GOVERNMENT ACTION - DIFFERENCES IN INCOME LEVELS q15 1 strongly agree 2 somewhat agree 3 neither agree, nor disagree 4 somewhat disagree 5 strongly disagree VOTING: TURNOUT q16 1 I did not vote in the national parliamentary election on 29 September 2013 2 I thought about voting, but did not do it this time 3 I usually vote, but I did not this time 4 I am sure I voted on 29 September 5 I voted by mail VOTING: VOTE CHOICE q17 1 SPOE 2 OEVP 3 FPOE 4 FP Kaernten 5 BZOE 6 Greens 7 KPOE 8 NEOS 9 Team Stronach 10 Pirates 11 other party

12 cast invalid ballot

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Variable Label Name Value Label PREFERENTIAL VOTE ON FEDERAL LIST (NO/YES) q18 1 Yes, I cast a preferential vote 2 No, I did not cast a preferential vote 3 I cast an invalid preferential vote IF R. WOULD HAVE VOTED: VOTE CHIOCE q19 1 SPOE 2 OEVP 3 FPOE 4 FP Kaernten 5 BZOE 6 Greens 7 KPOE 8 NEOS 9 Team Stronach 10 Pirates 11 other party 12 cast invalid ballot PREVIOUS ELECTION: ELECTORAL PARTICIPATION q20 1 I surely did not vote in the national parliamentary election September 2008 2 I am not sure, but I think that I probably did not vote 3 I am not sure, but I think that I probably voted 4 I surely voted in the national parliamentary election in September 2008 PREVIOUS ELECTION: ELECTORAL PARTICIPATION q21 1 SPOE 2 OEVP 3 FPOE [HC Strache] 4 BZOE [Joerg Haider] 5 Greens 6 FRITZ 7 KPOE 8 LIF 9 other party 10 invalid vote

11 did not vote

12 not eligible to vote 36

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Variable Label Name Value Label WHO IS IN POWER CAN MAKE A DIFFERENCE q22 1 It doesn't make any difference who is in power 5 It make a big difference who is in power

WHO PEOPLE VOTE FOR MAKES A DIFFERENCE q23 1 Who people vote for doesn't make a difference 5 Who people vote for makes a big difference DEMOCRACY SATISFACTION IN AUSTRIA q24 1 very satisfied 2 fairly satisfied 3 not very satisfied 4 not at all satisfied CLOSENESS TO A POLITICAL PARTY q25 1 yes 2 no CLOSER TO ONE PARTY THAN TO ANOTHER q26 1 yes 2 no PARTY R FEELS CLOSER TO q27 1 SPOE 2 OEVP 3 FPOE 4 FP Kaernten 5 BZOE 6 Greens 7 KPOE 8 NEOS 9 Team Stronach 10 Pirates 11 other party DEGREE OF CLOSENESS TO THIS PARTY q29_1 1 very close 2 somewhat close 3 not very close LIKE - DISLIKE: SPOE q29_1 0 strongly dislike to LIKE - DISLIKE: OEVP q29_2 10 strongly like LIKE - DISLIKE: FPOE q29_3 77 don't know this party LIKE - DISLIKE: BZOE q29_4

LIKE - DISLIKE: GREENS q29_5

LIKE - DISLIKE: TEAM STRONACH q29_6 37 LIKE - DISLIKE: NEOS q29_7

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Variable Label Name Value Label LIKE - DISLIKE: WERNER FAYMANN q30_1 0 strongly like to LIKE - DISLIKE: MICHAEL SPINDELEGGER q30_2 10 strongly dislike LIKE - DISLIKE: HEINZ-CHRISTIAN STRACHE q30_3 77 don't know this candidate LIKE - DISLIKE: JOSEF BUCHER q30_4 LIKE - DISLIKE: EVA GLAWISCHNIG q30_5 LIKE - DISLIKE: FRANK STRONACH q30_6 LIKE - DISLIKE: MATTHIAS STROLZ q30_7 LEFT - RIGHT PLACEMENT: SPOE q31_1 0 left to LEFT - RIGHT PLACEMENT: OEVP q31_2 10 right LEFT - RIGHT PLACEMENT: FPOE q31_3 77 don't know this party LEFT - RIGHT PLACEMENT: BZOE q31_4 LEFT - RIGHT PLACEMENT: GREENS q31_5 LEFT - RIGHT PLACEMENT: TEAM STRONACH q31_6 LEFT - RIGHT PLACEMENT: NEOS q31_7 LEFT - RIGHT SELF-PLACEMENT q32 0 Left 10 Right R. CONTACTED BY ANY PARTY/CANDIDATE IN PERSON q33 1 yes (NO/YES) 2 no R. CONTACTED IN PERSON (NO/YES) q34 1 yes 2 no PERSONAL CONTACT: SPOE q35_1 0 not mentioned PERSONAL CONTACT: OEVP q35_2 1 mentioned PERSONAL CONTACT: FPOE q35_3 PERSONAL CONTACT: FP KAERNTEN q35_4 PERSONAL CONTACT: BZOE q35_5 PERSONAL CONTACT: THE GREENS q35_6 PERSONAL CONTACT: KPOE q35_7 PERSONAL CONTACT: NEOS/LIF/JULIS q35_8 PERSONAL CONTACT: TEAM STRONACH q35_9 PERSONAL CONTACT: PIRATES q35_10 PERSONAL CONTACT: OTHER PARTY q35_11 PERSONAL CONTACT: NO PARTY/NO ONE q35_12 PERSONAL CONTACT: DON'T KNOW q35_88 PERSONAL CONTACT: REFUSED q35_99 R. RECEIVED A CALL DURING CAMPAIGN (NO/YES) q36 1 yes

2 no

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Variable Label Name Value Label CALL FROM: SPOE q37_1 0 not mentioned CALL FROM: OEVP q37_2 1 mentioned CALL FROM: FPOE q37_3 CALL FROM: FP KAERNTEN q37_4 CALL FROM: BZOE q37_5 CALL FROM: THE GREENS q37_6 CALL FROM: KPOE q37_7 CALL FROM: NEOS/LIF/JULIS q37_8 CALL FROM: TEAM STRONACH q37_9 CALL FROM: PIRATES q37_10 CALL FROM: OTHER PARTY q37_11 CALL FROM: NO PARTY/NO ONE q37_12 CALL FROM: DON'T KNOW q37_88 CALL FROM: REFUSED q37_99 R. RECEIVED A TEXT MESSAGE DURING CAMPAIGN (NO/YES) q38 1 yes 2 no TEXT MESSAGE FROM: SPOE q39_1 0 not mentioned TEXT MESSAGE FROM: OEVP q39_2 1 mentioned TEXT MESSAGE FROM: FPOE q39_3 TEXT MESSAGE FROM: FP KAERNTEN q39_4 TEXT MESSAGE FROM: BZOE q39_5 TEXT MESSAGE FROM: THE GREENS q39_6 TEXT MESSAGE FROM: KPOE q39_7 TEXT MESSAGE FROM: NEOS/LIF/JULIS q39_8 TEXT MESSAGE FROM: TEAM STRONACH q39_9 TEXT MESSAGE FROM: PIRATES q39_10 TEXT MESSAGE FROM: OTHER PARTY q39_11 TEXT MESSAGE FROM: NO PARTY/NO ONE q39_12 TEXT MESSAGE FROM: DON'T KNOW q39_88 TEXT MESSAGE FROM: REFUSED q39_99 R. RECEIVED LETTER DURING CAMPAIGN (NO/YES) q40 1 yes 2 no R. RECEIVED E-MAIL DURING CAMPAIGN (NO/YES) q41 1 yes 2 no R. CONTACTED THROUGH SOCIAL NETWORK (NO/YES) q42 1 yes

2 no

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Variable Label Name Value Label LETTER/E-MAIL/SOCIAL NETWORK: SPOE q43_1 0 not mentioned LETTER/E-MAIL/SOCIAL NETWORK: OEVP q43_2 1 mentioned LETTER/E-MAIL/SOCIAL NETWORK: FPOE q43_3 LETTER/E-MAIL/SOCIAL NETWORK: FP KAERNTEN q43_4 LETTER/E-MAIL/SOCIAL NETWORK: BZOE q43_5 LETTER/E-MAIL/SOCIAL NETWORK: THE GREENS q43_6 LETTER/E-MAIL/SOCIAL NETWORK: KPOE q43_7 LETTER/E-MAIL/SOCIAL NETWORK: NEOS/LIF/JULIS q43_8 LETTER/E-MAIL/SOCIAL NETWORK: TEAM STRONACH q43_9 LETTER/E-MAIL/SOCIAL NETWORK: PIRATES q43_10 LETTER/E-MAIL/SOCIAL NETWORK: OTHER PARTY q43_11 LETTER/E-MAIL/SOCIAL NETWORK: NO PARTY/NO ONE q43_12 LETTER/E-MAIL/SOCIAL NETWORK: DON'T KNOW q43_88 LETTER/E-MAIL/SOCIAL NETWORK: REFUSED q43_99 R. SAW TV COMMERCIAL BY ANY PARTY (NO/YES) q44 1 yes 2 no SAW TV COMMERCIAL: SPOE q45_1 0 not mentioned SAW TV COMMERCIAL: OEVP q45_2 1 mentioned SAW TV COMMERCIAL: FPOE q45_3 SAW TV COMMERCIAL: FP KAERNTEN q45_4 SAW TV COMMERCIAL: BZOE q45_5 SAW TV COMMERCIAL: THE GREENS q45_6 SAW TV COMMERCIAL: KPOE q45_7 SAW TV COMMERCIAL: NEOS/LIF/JULIS q45_8 SAW TV COMMERCIAL: TEAM STRONACH q45_9 SAW TV COMMERCIAL: PIRATES q45_10 SAW TV COMMERCIAL: OTHER PARTY q45_11 SAW TV COMMERCIAL: NO PARTY/NO ONE q45_12 SAW TV COMMERCIAL: DON'T KNOW q45_88 SAW TV COMMERCIAL: REFUSED q45_99 PERSUASION: R TRIED TO PERSUADE: CLOSE FAMILY q46 1 yes PERSUASION: R TRIED TO PERSUADE: FRIENDS q47 2 no PERSUASION: R TRIED TO PERSUADE: COLLEAGUES q48 PERSUASION: R TRIED TO PERSUADE: NEIGHBOURS q49

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Variable Label Name Value Label MOBILISATION: PERSONAL CONTACT q50 1 yes MOBILISATION: FACE-TO-FACE q51 2 no MOBILISATION: BY MAIL q52 MOBILISATION: BY PHONE q53 MOBILISATION: BY TEXT MESSAGE q54 MOBILISATION: BY EMAIL q55 MOBILISATION: THROUGH SOCIAL NETWORK q56 MOBILISATION: SIGN UP FOR ONLINE INFORMATION OR q57 ALERTS POLITCAL INFORMATION ITEM - 1ST: FINANCIAL MINISTER q58 1 Josef Proell 2 Johanna Mikl-Leitner 3 Maria Fekter 4 Rudolf Hundstorfer POLITCAL INFORMATION ITEM - 2ND: UNEMPLOYMENT q59 1 2,9% RATELAST MONTH 2 6,9% 3 8,9% 4 9,9% POLITCAL INFORMATION ITEM - 3RD: PARTY SECOND IN q60 1 FPOE SEATS 2 Greens 3 OEVP 4 SPOE POLITCAL INFORMATION ITEM - 4TH: UN SECRETARY q61 1 Kofi Annan GENERAL 2 Javier Perez de Cuellar 3 Ban Ki-Moon 4 Boutros Boutros-Ghali LIKELIHOOD OF DECREASE IN HOUSEHOLD INCOME (NEXT 12 q62 1 very likely MONTH) 2 fairly likely 3 fairly unlikely 4 very unlikely COMPETENCE: WERNER FAYMANN q63_1 1 very competent COMPETENCE: MICHAEL SPINDELEGGER q63_2 2 fairly competent COMPETENCE: HEINZ-CHRISTIAN STRACHE q63_3 3 a little competent COMPETENCE:JOSEF BUCHER q63_4 4 not at all competent COMPETENCE:EVA GLAWISCHNIG q63_5 COMPETENCE: FRANK STRONACH q63_6 COMPETENCE: MATTHIAS STROLZ q63_7

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Variable Label Name Value Label HONESTY: WERNER FAYMANN q64_1 1 very honest HONESTY: MICHAEL SPINDELEGGER q64_2 2 fairly honest HONESTY: HEINZ-CHRISTIAN STRACHE q64_3 3 a little honest HONESTY: JOSEF BUCHER q64_4 4 not at all honest HONESTY: EVA GLAWISCHNIG q64_5 HONESTY: FRANK STRONACH q64_6 HONESTY: MATTHIAS STROLZ q64_7 ASSERTIVENESS: WERNER FAYMANN q65_1 1 very assertive ASSERTIVENESS: MICHAEL SPINDELEGGER q65_2 2 fairly assertive ASSERTIVENESS: HEINZ-CHRISTIAN STRACHE q65_3 3 a little assertive ASSERTIVENESS: JOSEF BUCHER q65_4 4 not at all assertive ASSERTIVENESS: EVA GLAWISCHNIG q65_5 ASSERTIVENESS: FRANK STRONACH q65_6 ASSERTIVENESS: MATTHIAS STROLZ q65_7 CHARISMA: WERNER FAYMANN q66_1 1 very charismatic CHARISMA: MICHAEL SPINDELEGGER q66_2 2 fairly charismatic CHARISMA: HEINZ-CHRISTIAN STRACHE q66_3 3 a little charismatic CHARISMA: JOSEF BUCHER q66_4 4 not at all charismatic CHARISMA: EVA GLAWISCHNIG q66_5 CHARISMA: FRANK STRONACH q66_6 CHARISMA: MATTHIAS STROLZ q66_7 SPLIT A OR SPLIT B split A B SPLIT A: NUMBER OF ACTIVITIES PARTICIPATED IN (PAST 3 Q67 number YEARS) SPLIT B: NUMBER OF ACTIVITIES PARTICIPATED IN (PAST 3 Q68 YEARS) NUMBER OF PERSONS IN HOUSEHOLD q69 number NUMBER OF PERSONS IN HOUSEHOLD (<18 YEARS OLD) q70 NUMBER OF PERSONS IN HOUSEHOLD (<6 YEARS OLD) q71 OWNERSHIP: RESIDENCE q72 1 yes

OWNERSHIP: HOLIDAY HOME/FLAT q73 2 no OWNERSHIP: BUSINESS OR PROPERTY OR FARM OR q74 LIVESTOCK OWNERSHIP: RENTAL PROPERTY q75

OWNERSHIP: STOCKS OR BONDS q76

OWNERSHIP: SAVINGS q77 42

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Variable Label Name Value Label HIGHEST LEVEL OF EDUCATION q78 1 did not go to school 2 did not finish school 3 Elementary school or lower 4 Modern secondary school or general secondary school lower grad 4 5 Special school 6 Polytechnic school, vocational middle school 7 Apprenticeship, vocational school 8 General secondary school with diploma 9 Vocational secondary school with diploma 10 University related institution 11 College 12 Bachelor 13 Master/Graduated engineer/ University of Applied Science Degree 14 Doctorate/ PhD 15 Other, namely: HIGHEST LEVEL OF EDUCATION - OTHER (String) q78_15 string MARITAL STATUS q79 1 married or lives with partner 2 widowed 3 divorced or separated 4 single UNION MEMBERSHIP q80 1 yes 2 no UNION MEMBERSHIP: OTHER PERSON IN HOUSEHOLD q81 CURRENT EMPLOYMENT STATUS: EMPLOYED/UNEMPLOYED q82 1 yes 2 no CURRENT EMPLOYMENT SITUATION q83 1 employed, more than 35h weekly 2 employed, 15 - 35h weekly 3 employed, less than 15h weekly 4 employed by a family member

5 in vocational training

6 unemployed/ looking for work

7 on leave 43

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Variable Label Name Value Label CURRENT OCCUPATION q84 1 employee 2 manual worker 3 civil servant with employment guarantee 4 civil servant on contract basis 5 self-employed without employees 6 self-employed with employees 7 independent contractor 8 farmer MAIN OCCUPATION q85 string INDUSTRIAL SECTOR q86 1 primary sector: agricultural, forestry, fisheries 2 secondary sector: industry, mining, construction manufacturing 3 tertiary sector: transportation, communication and other public utilities, trade, finance etc. 4 other LIKELIHOOD OF FINDING NEW INCOME IF R LOST JOB q87 1 Very easy 2 Somewhat easy 3 Somewhat difficult 4 Very difficult CURRENT SITUATION q88 1 retired 2 student at school 3 student at university 4 other training 5 on leave 6 house wife/ house husband 7 doing military or alternative civilian service, or voluntary social year 8 unfit for work 9 unemployed or seeking for work 10 other PREVIOUS EMPLOYMENT STATUS q89 1 full-time or part-time 2 neither, nor

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Variable Label Name Value Label REVIOUS OCCUPATION q90 1 employee 2 manual worker 3 civil servant with employment guarantee 4 civil servant on contract basis 5 self-employed without employees 6 self-employed with employees 7 independent contractor 8 farmer PREVIOUS MAIN OCCUPATION q91 String PREVIOUS INDUSTRIAL SECTOR q92 1 primary sector: agricultural, forestry, fisheries 2 secondary sector: industry, mining, construction manufacturing 3 tertiary sector: transportation, communication and other public utilities, trade, finance etc. 4 other SPOUSE: CURRENT EMPLOYMENT STATUS: q93 1 yes EMPLOYED/UNEMPLOYED 2 no SPOUSE: CURRENT EMPLOYMENT STATUS q94 1 employed, more than 35h weekly 2 employed, 15 - 35h weekly 3 employed, less than 15h weekly 4 employed by a family member 5 in vocational training 6 unemployed/ looking for work 7 on leave SPOUSE: CURRENT OCCUPATION q95 1 employee 2 manual worker 3 civil servant with employment guarantee 4 civil servant on contract basis 5 self-employed without employees 6 self-employed with employees 7 independent contractor 8 farmer

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Variable Label Name Value Label SPOUSE: MAIN OCCUPATION q96 String SPOUSE: INDUSTRIAL SECTOR q97 1 primary sector: agricultural, forestry, fisheries 2 secondary sector: industry, mining, construction manufacturing 3 tertiary sector: transportation, communication and other public utilities, trade, finance etc. 4 other SPOUSE: LIKELIHOOD OF FINDING NEW INCOME IF SPOUSE q98 1 Very easy LOST JOB 2 Somewhat easy 3 Somewhat difficult 4 Very difficult SPOUSE: CURRENT STATUS q99 1 retired 2 employed 3 a student at school 4 a student at university 5 other training 6 on leave 7 doing military or alternative civilian service, or voluntary social year 8 unfit for work 9 unemployed or seeking for work 10 other SPOUSE: PREVIOUS EMPLOYMENT STATUS q100 1 full-time or part-time 2 neither, nor SPOUSE: PREVIOUS MAIN OCCUPATION q101 1 employee 2 manual worker 3 civil servant with employment guarantee 4 civil servant on contract basis 5 self-employed without employees 6 self-employed with employees 7 independent contractor 8 farmer

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Variable Label Name Value Label SPOUSE: PREVIOUS MAIN OCCUPATION q102 string SPOUSE: PREVIOUS INDUSTRIAL SECTOR q103 1 primary sector: agricultural, forestry, fisheries 2 secondary sector: industry, mining, construction manufacturing 3 tertiary sector: transportation, communication and other public utilities, trade, finance etc. 4 other NET INCOME q104 1 up to 1200 Euros 2 more than 1200 up to 2000 Euros 3 more than 2000 up to 2800 Euros 4 more than 3600 Euros RELIGIOUS SERVICE ATTENDANCE q105 1 never 2 once a year 3 2 to 11 times a year 4 once a month 5 two times a month or more often 6 once a week or more often DEGREE OF RELIGIOSITY q106 1 Not at all religious 2 Not very religious 3 Somewhat religious 4 Very religious RELIGIOUS GROUP q107 1 yes 2 no RELIGIOUS DENOMINATION q108 1 Roman-Catholic church 2 Protestant church 3 Islam/Muslim 4 Christian-Orthodox church 5 Judaism/Mosaic 6 Other OTHER LANGUAGE THAN GERMAN SPOKEN AT HOME YES/NO q109 1 yes 2 no

LANGUAGE q110 String

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Variable Label Name Value Label MAIN RESIDENCE: STATE q113 1 Burgenland 2 Carinthia 3 Lower Austria 4 Upper Austria 5 Salzburg 6 Styria 7 Tyrol 8 Vorarlberg 9 Vienna DESCRIPTION OF RESIDENTIAL AREA q114 1 village 2 small town 3 mid-sized town 4 centre of large city 5 suburbs of large city YEARS LIVED IN RESIDENTIAL AREA q115 number COUNTY OF BIRTH q116 1 Austria 2 Germany 3 Turkey 4 Serbia 5 Croatia 6 Bosnia 7 Slovenia 8 Poland 9 Czech Republic 10 Russia 11 Hungary 12 Other, namely: COUNTRY OF BIRTH: OTHER (String) q116_12 string YEAR MOVED TO AUSTRIA q117 number INTERVIEW: MOBILE PHONE/ LANDLINE q118 1 mobile phone 2 landline INTERVIEW: R. WHERE CONTACTED q119 1 at home 2 at work or at place of apprenticeship 3 out, on my own 4 out, together with others

5 elsewhere

INTERVIEW: R. ALSO HAS LANDLINE q120 1 yes 48 2 no

INTERVIEW: R. USES ADDITIONAL MOBILE PHONE q121 Page Page

Variable Label Name Value Label INTERVIEW: R. HAS ONE OR MORE MOBILE PHONES q122 1 yes, one 2 yes, more than one 3 no INTERVIEWER ID i123 abbreviation INTERVIEWER'S GENDER i123_1 1 male 2 female INTERVIEWER'S YEAR OF BIRTH i123_2 Number INTERVIEWER'S LEVEL OF EDUCATION i123_3 1 compulsory education 2 apprenticeship 3 BMS 4 Matura 5 university degree R.'S COMMAND OF GERMAN i124 0 very bad 10 very good PHONE BOOK OR RDD i125 1 telephone book 2 RDD WEIGHT gew number DATE QUESTIONNAIRE ADMINISTERED - MONTH month number DATE QUESTIONNAIRE ADMINISTERED - DAY day number DATE QUESTIONNAIRE ADMINISTERED - YEAR year number PRIMARY ELECTORAL DISTRICT rwk number SIZE OF MUNICIPALITY gemgr_gr 1 <2.000 2 2.000-<5.000 3 5.000-<10.000 4 10.000-<20.000 5 20.000-<50.000 6 50.000-<1.000.000 7 >1.000.000

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