Subjective well-being inequalities in and between Austria and Hungary

Subjective well-being inequalities in and between Austria and Hungary Research Report Ádám Németh, Borbála Göncz, Josef Kohlbacher, György Lengyel, Zsolt Németh, Dávid Sümeghy, Lilla Tóth, László Zöldi October 2023 FWF–NKFIH Joint Project 1 This working paper was developed in the context of the project MIGWELL – Well-being and Migration: the Hungary – Austria Migration Nexus Work Package 2: ‘Secondary Analysis of Existing Data’ MIGWELL is an FWF–NKFIH International Joint Project. In Austria the project is funded by the Austrian Science Fund. In Hungary the project has been implemented with the support provided by the National Research, Development and Innovation Fund (Ministry of Innovation and Technology of Hungary), financed under the ANN funding scheme. Project code: I 5616 (in Austria), 139465 (in Hungary) Project duration: 01.02.2022 – 31.07.2025 (in Austria), 01.12.2021 – 31.05.2025 (in Hungary) Project Partners: Institute for Urban and Regional Research at the Austrian Academy of Sciences Centre for Empirical Social Research at the Corvinus University of Budapest Department of Finno-Ugrian Studies, University of Vienna Scientific Advisory Board: Department of Sociology, University of Vienna Hungarian Demographic Research Institute Hungarian Central Statistical Office MIGWELL’s website: LINK MIGWELL’s outputs will be uploaded to: LINK Inquiries can be directed to: Institute for Urban and Regional Research at the Austrian Academy of Sciences, [email protected].at or Centre for Empirical Social Research at the Corvinus University of Budapest, [email protected] Suggested citation: Németh, Á., Göncz, B., Kohlbacher, J., Lengyel, Gy., Németh, Zs., Sümeghy, D., Tóth, L. and Zöldi, L. (2023) Subjective well-being inequalities in and between Austria and Hungary. MIGWELL Working Papers, No. 2.2. 168 p. FWF–NKFIH Joint Project 2 Table of contents 1. Introduction ................................................................................................................................................................ 5 2. Conceptual background .............................................................................................................................................. 5 2.1 OECD framework ....................................................................................................................................................... 5 2.2 WeD concept .............................................................................................................................................................. 6 2.3 The MIGWELL approach ........................................................................................................................................... 7 3. Sources and methods .................................................................................................................................................. 7 3.1 Data sources ................................................................................................................................................................ 7 3.1.1 EU-SILC ad-hoc modules ........................................................................................................................................... 8 3.1.2 Hungarian microcensus ............................................................................................................................................. 10 3.2 Methodology ............................................................................................................................................................. 11 3.3 Limitations and methodological challenges .............................................................................................................. 15 4. An overview of subjective well-being inequalities in Europe .................................................................................... 16 4.1 Overall life satisfaction ............................................................................................................................................. 16 4.2 Domain satisfaction .................................................................................................................................................. 20 4.3 Happiness.................................................................................................................................................................. 23 4.4 Eudaimonic well-being ............................................................................................................................................. 27 5. Objective and subjective well-being differences between Austria and Hungary ....................................................... 27 5.1 Material factors and the satisfaction with them ........................................................................................................ 27 5.1.1 Financial situation ..................................................................................................................................................... 28 5.1.2 Economic status and jobs .......................................................................................................................................... 35 5.1.3 Housing conditions ................................................................................................................................................... 39 5.2 Immaterial factors and the satisfaction with them..................................................................................................... 42 5.2.1 Health status ............................................................................................................................................................. 43 5.2.2 Work-life balance ..................................................................................................................................................... 44 5.2.3 Social connections .................................................................................................................................................... 48 5.2.4 External factors ......................................................................................................................................................... 55 5.3 Subjective well-being differences between Austria and Hungary ............................................................................. 57 5.3.1 Evaluative well-being (Overall life satisfaction)....................................................................................................... 57 5.3.2 Affective well-being ................................................................................................................................................. 59 5.3.3 Eudaimonic well-being ............................................................................................................................................. 61 5.3.4 Interrelations of different measures of subjective well-being ................................................................................... 62 6. Objective and subjective well-being differences between the nativeand foreign-born population in Austria ......... 66 6.1 Population by country of birth .................................................................................................................................. 66 6.2 Material factors ......................................................................................................................................................... 68 6.2.1 Financial situation ..................................................................................................................................................... 68 6.2.2 Economic status and job ........................................................................................................................................... 74 6.2.3 Housing conditions ................................................................................................................................................... 80 6.3 Immaterial factors ..................................................................................................................................................... 83 6.3.1 Health status ............................................................................................................................................................. 83 6.3.2 Work-life balance ..................................................................................................................................................... 84 6.3.3 Social connections .................................................................................................................................................... 88 6.3.4 External factors ......................................................................................................................................................... 95 6.4 Subjective well-being ............................................................................................................................................... 98 6.4.1 Evaluative well-being (life satisfaction) ................................................................................................................... 98 6.4.2 Affective well-being ............................................................................................................................................... 100 6.4.3 Eudaimonic well-being ........................................................................................................................................... 104 7. Objective and subjective well-being differences between potential stayers and potential migrants in Hungary .... 105 7.1. Socio-demographic characteristics ......................................................................................................................... 106 FWF–NKFIH Joint Project 3 7.2. Material factors ....................................................................................................................................................... 109 7.3. Immaterial factors ................................................................................................................................................... 110 7.3.1. Health status ........................................................................................................................................................... 110 7.3.2. Social connections .................................................................................................................................................. 110 7.3.3. Security ................................................................................................................................................................... 112 7.3.4. Trust in institutions ................................................................................................................................................. 112 7.4. Experiences of migration ........................................................................................................................................ 113 7.5. Subjective well-being ............................................................................................................................................. 115 7.5.1. Evaluative well-being (overall life satisfaction) ..................................................................................................... 115 7.5.2. Affective well-being ............................................................................................................................................... 117 7.5.3. Eudaimonic well-being ........................................................................................................................................... 118 8. The drivers of subjective well-being ....................................................................................................................... 119 8.1 Subjective well-being drivers in Austria ................................................................................................................. 119 8.1.1 Variables in association with overall life satisfaction ............................................................................................. 119 8.1.2 Comparative analysis: life satisfaction of people born in Austria vs. in the new EU countries .............................. 126 8.2 Subjective well-being drivers in Hungary ............................................................................................................... 129 8.2.1 Modelling “no” vs “yes” outcomes ......................................................................................................................... 129 8.2.2 Modelling “do not know” vs “yes” outcomes ......................................................................................................... 135 Literature ............................................................................................................................................................................... 138 Appendix: Methodological notes on the setup of the integrated dataset ................................................................................ 141 FWF–NKFIH Joint Project 4 MIGWELL at a glance The MIGWELL project focuses on the nexus of migration and well-being in Hungary and Austria. Using quantitative and qualitative research methods, it seeks to explore the impacts of migration on subjective well-being in the case of Hungarian immigrants in Austria as well as the effects of subjective well-being differences on emigration potential in Hungary. The approach of this project is innovative not only because it links the concepts of ‘well-being’ and ‘migration’, but also because it interprets their two-way causal relationship within one research framework. Considering that the COVID-19 pandemic might have a profound impact on both pillars, MIGWELL will also reflect on the rapidly changing socio-economic and well-being related issues that have emerged due to the epidemic throughout the life cycle of the project. The theoretical expansion of these concepts and the empirical findings of the project may contribute to more effective policies in both countries. FWF–NKFIH Joint Project 5 1. Introduction The MIGWELL project applies a mixed-methods approach including secondary analyses (literature review, migration and well-being data) and the combination of quantitative and qualitative methods: focus groups, interviews, surveys, and round-table discussions conducted in both countries. Following the presentation of the accumulated knowledge of the migration processes in and between Hungary and Austria (see our previous research report: Németh et al. 2023), the present report focuses on the subjective well-being patterns in the two countries and the factors that might be responsible for the differences. 2. Conceptual background The first research report of the MIGWELL project, entitled “Conceptual Framework for the Study of the Subjective Well-being–Migration Nexus” (Németh et al. 2022) has already provided an overview of the key definitions of well-being in general, followed by a literaturebased review of the main theories and analytical approaches. This chapter briefly summarises the most important theoretical cornerstones that are relevant for Work Package 2, “Secondary Analysis of Existing Data”. 2.1 OECD framework From the 1950s onwards, concern has been growing that the dominant frameworks in economics cannot address the challenges of our society in a rapidly changing world adequately. The insight that a narrow focus on economic factors and some widely used indicators such as GDP do not reflect people’s welfare has played a key role in the rise of the concept of wellbeing (Stiglitz et al. 2009, OECD 2011, Adler and Seligman 2016, Coulthard et al. 2018). By the beginning of the 21st century, a worldwide tendency appeared to conceive social processes and phenomena within a coherent framework of well-being. In 2011, the OECD launched the so-called Better Life Initiative to explore the general drivers of human well-being, and to enquire what needs to be done to achieve greater progress for the people. In the OECD “How’s Life” concept – described in the 2011, 2013, 2015, 2017, and 2020 How’s Life Reports – well-being is measured in terms of outcomes achieved in two broad dimensions: „Material living conditions‟ and „Quality of life‟. However, since this conceptual framework (OECD 2011) was principally designed to measure aggregated well-being scores at the level of countries, the approach to subjective well-being remained at a relatively broadbrush level. FWF–NKFIH Joint Project 6 While subjective well-being has been examined extensively in the academic literature for decades, the lack of a consistent set of questions has hampered the international comparability of data for a long time. Bridging this gap was the main motivation for the OECD (2013a) to elaborate the Guidelines on Measuring Subjective Well-being. These Guidelines offer an integrated approach and propose a solution for statistical agencies and researchers to follow a standardised survey structure with standardised methodology. Subjective well-being is taken to be good mental states, including all of the various evaluations, positive and negative, that people make of their lives, and people’s affective reactions to their experiences. This broad definition encompasses the three elements of SWB. • The life satisfaction interpretation is cognitive as well as evaluative, and requires the individual to make evaluative statements about different areas of life and about life as a whole (Boyce et al. 2010, Christoph 2010, Dumludag, 2014 etc.). Satisfaction is usually understood as a lasting state of well-being. • Happiness is the key concept of affective well-being used initially in psychology literature (Di Fabio and Palazzeschi 2015, Graham 2009, Layard 2005, etc.). Positive and negative emotions reflect a more corporeal and transitory state of well-being, which are typically surveyed with reference to a shorter timeframe, for instance the most recent two or four weeks. • The eudaimonic approach is based on the view of people regarding their living in accordance with their true selves and getting material and non-material rewards while constructing a good life (Ryan and Deci 2001). It encompasses the feeling of meaning and purpose in life, accomplishment, as well as the aspects of belonging, self-esteem, and self-actualisation (Clark et al. 2008, Di Fabio and Palazzeschi 2015, Vittersø 2016). 2.2 WeD concept Whereas the OECD Guidelines tend to focus on the individual factors of subjective well-being, including few questions on social relationships, the WeD approach places greater emphasis on the social aspects. This concept was developed by the ESRC ‘Well-being in Developing Countries’ research group at the University of Bath (Gough and McGregor 2007). In the WeD framework, material well-being encompasses the objective circumstances of life, including resources such as income or employment. However, since people’s goals and actions are always shaped by the social contexts in which they are embedded, well-being has a relational dimension too. This dimension refers to the social relationships that people must be able to enter into in order to meet human needs (Britton and Coulthard 2013). The third – subjective – dimension takes account of “what it is that people themselves regard as important for their quality of life and their assessment of their level of subjective satisfaction in their FWF–NKFIH Joint Project 7 achievement” (McGregor and Pouw 2017: 1135). 2.3 The MIGWELL approach MIGWELL has been inspired by both the OECD and the WeD concepts (Figure 1). Subjective reflection on life satisfaction, affect, and eudaimonia are our focal point – based on pre-defined EU-SILC variables that are essential for a comparative analysis – while a broader set of questions on the material and relational dimensions will provide deeper insight into the dynamics of the migration-SWB nexus. Figure 1. The relationship between the objective and subjective dimensions of well-being on a personal level according to MIGWELL Source: Németh et. al (2023: 40) 3. Sources and methods 3.1 Data sources While questions on “quality of life” or “standard of living” are relatively frequent in international surveys, only a few data sources could be suitable for a comprehensive analysis of the subjective well-being–migration nexus. Either the sample size or the number of relevant variables is small, or they do not include a background question at least on the country of birth, FWF–NKFIH Joint Project 8 allowing for the identification of the foreign-born population. If so, they typically do not include a representative sample of the immigrant population. Four potential data sources could be suitable for the MIGWELL project: the Gallup World Poll, the European Union Statistics on Income and Living Conditions survey (EU-SILC), the European Social Survey (ESS), and the OECD Survey of Adult Skills (PIAAC). The first two surveys provide the widest range of SWB variables. However, since the average number of observations for foreign-born people is significantly higher in the case of EU-SILC – 1,200 persons per country, in contrast to ca. 500 for the Gallup World Poll – (OECD 2017:128-130), we decided to use this database as the main source of our secondary data analysis. 3.1.1 EU-SILC ad-hoc modules The EU-SILC (EU Statistics on Income and Living Conditions) was initially launched in 2003 with the goal of collecting timely and comparable multidimensional microdata on income distribution, poverty, and social exclusion. This instrument has received special attention since the European Council convened in June 2010. On this occasion, the European Union endorsed a new long-term strategy, titled “Europe 2020”. Promoting social inclusion through the reduction of poverty was one of its headline targets, aiming to lift at least 20 million people out of risk of poverty and social exclusion. Therefore, measuring and monitoring the living standards of the target population 1 has got high priority. EU-SILC is a representative sample survey of private households in the European Union. Under its umbrella, Eurostat has been collecting and publishing comparable multidimensional microdata on income, poverty, social exclusion, housing, labour, education, and health from all Member States. In order to maximise data comparability, the whole procedure has been designed by a common conceptual framework, by harmonised lists of variables, by common requirements (for imputation, weighting, the calculation of sampling errors, etc.), and by harmonised classifications (ISCO, NACE, ISCED). EU-SILC also contains cross-sectional and longitudinal data, collected at two different levels: the household and the individual level. Until 2021, the ‘primary variables’ were collected every year, while the ‘secondary variables’ were collected less frequently in the so-called ad-hoc modules. These ad-hoc modules covered specific topics, e.g. intergenerational transmission of disadvantages, access to services, health, social and cultural participation, material deprivation, housing conditions, or personal well-being. 1 The target population was identified by three selected indicators: at risk of poverty, material deprivation, and jobless household (below the 60% median disposable income threshold, at or above the severe material deprivation threshold of 4, or in a household with work intensity below 20% threshold). FWF–NKFIH Joint Project 15 Table 3. Sample size of the MIGWELL database by sources. Austria Hungary Actual sample size (before weighting) Extrapolation (after weighting) Actual sample size (before weighting) Extrapolation (after weighting) EU-SILC 2013 10,940 7,057,385 21,349 8,226,678 Microcensus 2016 - - 51,281 8,085,791 EU-SILC 2018 10,633 7,282,693 14,365 7,996,609 Source of data: Eurostat, Hungarian Central Statistical Office 3.3 Limitations and methodological challenges It should be noted that several difficulties may arise by surveying and measuring personal, sensitive topics, such as subjective well-being. From the point of view of the interviewees, this is a five-step responding process: 1. understanding the question, 2. retrieving the relevant information from memory, 3. forming a judgement, 4. adapting the judgement to the answer options of the questionnaire, 5. adapting the answer before sending it to the interviewer. First of all, the whole process may differ depending on the type of the interview, i.e. whether it is conducted by telephone (CATI) or by face-to-face interviewers (CAPI). People tend to choose answers that are more in line with social norms. It is called the “social desirability bias”, which occurs when respondents provide answers to questions that they believe will make them appear good to others, concealing their true opinions or experiences. The presence of the interviewer presumably increases the risk of this kind of response bias 7 (Oismüller and Till, 2015: 943). An overview of the potential survey problems can be found in Chapter 2 of the OECD Guidelines on Measuring Subjective Well-being (OECD 2013). Regarding further methodological solutions during the setup of the integrated MIGWELL dataset, see the Appendix for further details. 7 For this reason, the 2013 module in Austria recorded which persons were present during the interview. FWF–NKFIH Joint Project 16 4. An overview of subjective well-being inequalities in Europe 4.1 Overall life satisfaction Satisfaction with life as a whole is widely considered a key variable in well-being research. This variable is expected to compress information about people’s evaluation of their lives into one single number; therefore, it can be used as a dependent variable in empirical studies. As we mentioned earlier, this Research Report focuses on the 2013, 2018, and 2021 waves of EUSILC surveys. The spatial characteristics of the 2013 results provide a good example of the so-called WestEast slope, described by Melegh (2006), with the addition that the North–South slope has at least the same importance. Going from North to South, as well as from West to East, the average values of overall life satisfaction tend to decrease step by step (Figure 2, Table 4). Among the 37 analysed countries of the EU-SILC Denmark, Finland, Switzerland, Sweden, and Iceland represented the top five (above 7.9 scores), while the lowest scores were measured in Montenegro, North Macedonia, Türkiye, Serbia, and finally in Bulgaria (4.8). While Austria was ranked seventh (fifth within the European Union), Hungary was ahead of only Bulgaria among the EU Member States. Figure 2. Overall life satisfaction in Europe by 2013, 2018 and 2021 (EU-SILC) 2013 2018 2021 Source: Eurostat, LINK FWF–NKFIH Joint Project 17 Table 4. Overall life satisfaction. National averages in the EU-SILC countries by 2013, 2018 and 2021. (In grey: non-EU countries. NA: no data. United Kingdom: not part of the EU since 2021.) 2013 2018 2021 2013 2018 2021 Albania NA 5.5 NA Lithuania 6.7 6.4 7.0 Austria 7.8 8.0 8.0 Luxembourg 7.5 7.6 7.4 Belgium 7.6 7.6 7.5 Malta 7.1 7.5 7.1 Bulgaria 4.8 5.4 5.7 Montenegro 5.7 6.5 NA Croatia 6.3 6.3 6.8 Netherlands 7.8 7.7 7.6 Cyprus 6.2 7.1 6.8 North Macedonia 5.7 6.0 NA Czechia 6.9 7.4 7.3 Norway 7.9 8.0 NA Denmark 8.0 7.8 7.3 Poland 7.3 7.8 7.5 Estonia 6.5 7.0 7.2 Portugal 6.2 6.7 7.0 Finland 8.0 8.1 7.9 Romania 7.1 7.3 7.7 France 7.1 7.3 6.8 Serbia 4.9 5.6 6.0 Germany 7.3 7.4 7.2 Slovakia 7.0 7.1 7.1 Greece 6.2 6.4 6.8 Slovenia 7.0 7.3 7.5 Hungary 6.1 6.5 6.5 Spain 6.9 7.3 7.2 Iceland 7.9 7.9 NA Sweden 7.9 7.8 7.5 Ireland 7.4 8.1 7.3 Switzerland 8.0 8.0 7.9 Italy 6.7 7.1 7.2 Türkiye 5.7 5.7 NA Kosovo NA 6.0 NA United Kingdom* 7.3 7.6 NA Latvia 6.5 6.7 6.7 EU27 (after 2020) 7.0 7.3 7.2 Source of data: Eurostat microdata and Hungarian microcensus Although the relationship between the material goods factors and subjective well-being is not necessarily linear, there is an obvious correlation in the European context between overall life satisfaction and economic situation, at least on the macro level. Figure 3 shows the share of people reporting low life satisfaction (indicated a score of 5 or lower out of 10) and the proportion of materially and socially deprived people within the population. 8 The relationship between life satisfaction and income poverty is weaker but still positive (R2 = 0.18) (Blasco and Glezies 2019: 32-33). 8 Romania is an outlier. Although it finds itself among the countries with the highest rates of material and social deprivation as well as income poverty rates, Romania is characterised by significantly higher overall satisfaction than the regional average. Exploring the reasons is beyond the scope of this study; however, the domain satisfaction scores discussed in the following subchapters will reflect on different aspects of life that Romanian people are more satisfied with compared to the surrounding countries. FWF–NKFIH Joint Project 18 Figure 3. Proportion of low satisfaction and rate of material and social deprivation for the population in 2013 Source: Blasco and Glezies 2019: 32 Considering the aggregated EU27 average, European people have become more satisfied with their lives by 2018, with the mean increasing from 7.0 to 7.3. However, the ranking has changed somewhat (Table 4). A minimal decrease was registered in four countries only: the Netherlands, Sweden, Denmark, and Lithuania. Average life satisfaction scores above 8 were measured in Ireland, Finland, Switzerland, Austria, and Norway, while the value remained below 6 in Türkiye, Serbia, Albania, and Bulgaria. Hungary's relative position has improved, with a national average of 6.5, ahead of Bulgaria, Croatia, and Lithuania. The trend had changed dramatically by 2021 (Figure 4). As a probable reflection of the impact of the Covid-19 pandemic, the EU average for overall life satisfaction has decreased by 0.1 percentage point. The group of Member States where comparable data are available between 2018 and 2021 has been split in two. Increase and decrease were measured in 14 countries each (Table 4, Figure 4 and 5). To some extent, a converging trend can be observed: satisfaction has decreased in countries with higher levels of satisfaction, and those with low life satisfaction saw an opposite trend. The greatest decrease was registered in Ireland, France, and Denmark, while overall life satisfaction increased remarkably in Lithuania, Croatia, Greece, and Romania. In 2021, life satisfaction was highest in Austria, Finland, Romania, and the Netherlands, and lowest in Bulgaria and Hungary (6.5). FWF–NKFIH Joint Project 19 Figure 4. Overall life satisfaction. National averages in the EU-SILC countries by 2013, 2018 and 2021 according to the increasing or decreasing values during the two periods (plus or minus between 2013 and 2018 / plus or minus between 2018 and 2021) Source of data: Eurostat microdata and Hungarian microcensus Figure 5. Overall life satisfaction. Change in average values between 2018 and 2021. Source: Eurostat, LINK Macro-level data showed no significant difference in life satisfaction between men and women as well as between urban and rural residents, both averaging 7.1 at the EU level. However, life satisfaction consistently decreases with age and increases with the level of education, meaning 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 EU Austria Romania Slovenia Italy Estonia Slovakia Portugal Greece Croatia Latvia Hungary Bulgaria Finland Poland Belgium Luxembourg Ireland Czechia Germany Spain Malta France Cyprus Lithuania Netherlands Sweden Denmark + / + + / - - / + - / - 2013 2018 2021 FWF–NKFIH Joint Project 20 that the higher the education level, the more satisfied people are with life, but the older they get, the less satisfied they feel (Figure 6). People in households with dependent children reported the highest levels of life satisfaction (7.3), compared with an average of 7.1 for couples living together, 7.0 for households without dependent children, and 6.7 for single-person households. The relationship between income and life satisfaction is similarly important. People with high income were more satisfied with their lives than those with the lowest earnings (7.6 and 6.5 on average for the highest and lowest income quintiles). Figure 6. Overall life satisfaction in the EU by gender, educational level and age in 2018 and 2021 Source: Eurostat, LINK 4.2 Domain satisfaction Regarding domain satisfaction, we have richer sources from the 2013 EU-SILC wave. In that year, altogether eight variables described domain satisfaction, while only three questions were used in 2018, and none of them had remained by 2021 (Figure 7 and 8). Three indicators were associated with the respondents’ material resources: satisfaction with financial situation, jobs, and housing. On the national level, the correlation between these domain satisfaction scores and overall satisfaction proved to be strongly positive. Therefore, there is no remarkable difference in the spatial characteristics of these indicators compared to the previously presented graphs. Based on the analysis of EU-SILC microdata, the correlation coefficients were significant at the individual level as well (at the 0.01 level). The correlation FWF–NKFIH Joint Project 21 was especially strong between overall life satisfaction and the satisfaction with financial situation (0.587) as well as the satisfaction with accommodation (0.559). Figure 7. Domain satisfaction (with material resources) in Europe by 2013 and 2018 (EU-SILC) 2013 2018 Satisfaction with financial situation Satisfaction with job Satisfaction with accommodation Source of data: Eurostat FWF–NKFIH Joint Project 22 Figure 8. Domain satisfaction (with non-material factors) in Europe by 2013 and 2018 (EU-SILC) 2013 2018 Satisfaction with personal relationships Satisfaction with time use Satisfaction with commuting time Satisfaction with living environment Satisfaction with recreational and green areas Source of data: Eurostat FWF–NKFIH Joint Project 23 In 2013, the non-material factors also correlated with overall life satisfaction on a national level above 0.85** values, with one exception. The satisfaction with personal relationships was likewise significant but the strength of correlation was somewhat lower at 0.673**. All correlation coefficients were significant at the individual level as well (at the 0.01 level). The strength of the correlations varied between 0.255** (overall life satisfaction and satisfaction with commuting time) and 0.499** (overall life satisfaction and satisfaction with personal relationships). 4.3 Happiness Happiness is the key variable in the affective dimension of subjective well-being. According to the Eurostat data, European people have not only become more satisfied with their life as a whole, but they have also grown happier. Taking the EU28 average, the proportion of people who have never, rarely, or only sometimes felt happy in the last four weeks has decreased by almost 5 percentage points between 2013 and 2018, while the proportion of people who have always or mostly felt happy has increased from 59.5% to 63% (Figure 9). Figure 9. Frequency of being happy in the last four weeks, EU28 average. Source of data: Eurostat microdata. However, there are remarkable differences between the European countries (Table 5). There are five countries where the proportion of people who felt happy most of the time or always was above 75% in both years: the Netherlands, Ireland, Iceland, Finland, and Switzerland. In contrast, that was true for less than 50% of the population in Italy, Romania, Greece, Lithuania, Croatia, Bulgaria, Latvia, Kosovo, and Albania. In 2018, Ireland proved to be the happiest, while Albania (and Latvia if we take only the EU Member States into account) the least happy country. Austria was 5th, and Hungary 21st on this list; 76% and 58% of the people respectively considered themselves happy “most of the time” or “always”. 0 10 20 30 40 50 60 Never Rarely Sometimes Most of the time Always Unknown Percentage of the population (%) EU28 (2013) EU28 (2018) FWF–NKFIH Joint Project 24 Table 5. Frequency of being happy in the last 4 weeks by countries by 2013 and 2018 (EU-SILC) 2013 2018 Never Rarely Sometimes Most of the time Alway s Never Rarely Sometimes Most of the time Alway s Unknown Albania NA NA NA NA NA 2.5 10.8 51.6 24.9 5.4 4.7 Austria 1.1 6.4 21.3 57.2 14.1 0.9 5.8 17.4 61.5 14.4 0.0 Belgium 1.1 5.4 21.0 57.8 14.8 1.2 4.8 17.7 59.2 16.9 0.1 Bulgaria 5.6 23.3 36.4 29.1 5.5 4.5 19.9 36.4 27.9 7.0 4.3 Croatia 1.9 13.2 47.0 33.2 4.7 2.7 12.8 41.0 36.5 5.3 1.7 Cyprus 2.5 14.7 33.0 40.6 9.2 1.8 11.3 32.1 45.9 8.8 0.2 Czechia 2.0 10.9 41.8 38.6 6.7 1.5 8.7 34.6 44.8 6.8 3.7 Denmark 0.9 6.4 16.9 60.2 15.5 2.0 8.3 19.5 55.7 14.0 0.5 Estonia 5.3 13.9 36.2 38.7 5.9 3.4 10.4 34.4 44.3 7.1 0.4 Finland 1.1 4.2 17.4 66.1 11.2 0.9 4.5 18.0 63.6 12.3 0.7 France 2.4 7.7 27.6 49.9 12.4 2.1 5.7 23.8 52.2 15.5 0.7 Germany 2.0 10.6 25.3 56.1 5.9 1.6 8.6 23.1 58.1 6.4 2.2 Greece 10.8 29.9 27.7 25.2 6.4 5.1 15.7 30.7 36.5 9.9 2.1 Hungary 2.9 15.0 26.5 47.3 8.3 1.8 12.0 28.0 48.1 9.7 0.5 Iceland 1.0 4.2 13.3 58.9 22.5 1.9 5.1 17.2 48.1 27.6 0.0 Ireland 1.2 4.7 19.0 61.6 13.6 1.6 2.9 14.7 63.3 17.4 0.0 Italy 4.5 13.2 37.7 33.4 11.3 2.2 10.0 37.3 32.7 16.4 1.5 Kosovo NA NA NA NA NA 1.3 9.0 24.0 31.3 4.5 29.9 Latvia 7.8 20.6 40.3 25.9 5.4 6.8 21.2 38.9 26.6 4.1 2.4 Lithuania 2.7 12.7 36.7 38.1 9.9 2.1 13.0 33.9 37.3 8.1 5.6 Luxembourg 1.4 5.1 18.3 61.6 13.6 2.0 4.7 18.7 59.0 14.9 0.6 Malta 1.5 8.9 22.0 52.5 15.0 2.9 9.7 24.8 56.1 5.7 0.8 Montenegro 2.7 8.7 31.5 39.1 18.0 0.7 4.9 28.4 37.3 24.6 4.0 Netherlands 1.5 3.1 13.3 61.2 21.0 1.4 3.4 18.2 60.9 15.3 0.8 N. Macedonia 3.0 12.5 35.1 39.0 10.4 0.9 7.1 35.1 45.9 6.7 4.2 Norway 0.6 6.3 24.3 56.7 12.2 1.0 8.2 25.2 53.2 12.4 0.0 Poland 1.8 9.8 20.8 54.4 13.2 1.1 7.0 21.9 51.9 16.6 1.4 Portugal 4.9 14.8 28.9 34.8 16.6 3.9 13.1 26.7 40.7 15.3 0.2 Romania 4.5 21.0 35.5 33.4 5.7 3.0 12.2 31.4 41.7 4.7 7.1 Serbia 5.6 16.5 34.4 30.8 12.7 2.2 10.4 35.2 40.6 11.1 0.6 Slovakia 1.0 8.1 32.0 51.2 7.8 1.1 6.3 27.5 48.2 9.6 7.3 Slovenia 1.6 6.4 26.6 53.7 11.7 1.9 7.1 31.6 48.8 10.1 0.5 Spain 2.0 7.0 27.7 44.1 19.1 1.9 6.1 20.6 43.0 28.5 0.0 Sweden 2.9 5.7 23.9 52.4 15.1 2.6 7.4 24.3 51.8 12.8 1.2 Switzerland 1.0 4.5 19.7 61.0 13.8 1.2 4.8 18.7 60.7 14.6 0.0 Türkiye 3.5 11.1 34.6 41.9 8.9 2.9 9.8 33.4 47.1 6.6 0.2 United Kingdom 2.0 6.4 23.0 55.4 13.2 1.5 5.8 22.3 57.5 12.7 0.2 EU27 (from 2020) 2.9 10.7 28.1 46.8 11.6 2.0 8.2 25.8 48.5 13.9 1.6 EU28 (2013-2020) 2.8 10.2 27.5 47.7 11.8 1.9 8.0 25.5 49.2 13.8 1.5 Source of data: Eurostat microdata and Hungarian microcensus FWF–NKFIH Joint Project 31 biggest problem (although the number of people affected is lower than in Hungary), while more people can afford to spend money on holidays (Figure 13). Figure 13: Changes in the proportions of each subcategory of relative deprivation in Austria and Hungary between 2013 and 2018 Source of data: Eurostat microdata and Hungarian microcensus According to the Europe 2020 strategy, the indicator of low work intensity refers to the number of persons from 0-59 years living in households where the adults (those aged 18-59, but excluding students aged 18-24) had worked a working-time maximum 20% of their total combined work-time potential during the previous year. 15 In Austria, the share of these people is relatively stable at around 5.6% and 5.4%. In Hungary, their proportion had decreased considerably from ca. 9.4% to 3.9% between 2013 and 2018 (Figure 14). Figure 14. The share of people living in households with low work intensity. (Hungary and Austria; 2013 and 2018) 15 https://ec.europa.eu/eurostat/statisticsexplained/index.php?title=Glossary:Persons_living_in_households_with_low_work_intensity FWF–NKFIH Joint Project 32 Source of data: Eurostat microdata and Hungarian microcensus The last factor we took into consideration was the financial burden of the repayment of debts from hire purchases or loans. It refers to the percentage of persons living in a dwelling where the repayment of debts from any credit card, hire purchase, or other loans (that is, excluding mortgage repayments or other loans connected with the purchase of the main dwelling) constitutes a financial burden. 16 The group of people without debt makes up about four-fifths of the whole population in both countries, but their proportion decreased by ca. 2 percentage points during the analysed period (in 2018 ca. 79.5% in Austria, 78.4% in Hungary). However, the trend is positive regarding the perceived financial burden of the debt. In 2018 more respondents reported no or only a moderate degree of burden in both countries, while the repayment of debts still presents a problem for about 3.2% of the Austrian and 6.4% of the Hungarian population (Figure 15). Figure 15. The share of people with or without the financial burden of the repayment of debts from hire purchases or loans (Hungary and Austria; 2013 and 2018) 16 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=EU_statistics_on_income_and_living_conditions_(EUSILC)_methodology_-_economic_strain_linked_to_dwelling#Description FWF–NKFIH Joint Project 33 Source of data: Eurostat microdata and Hungarian microcensus The objective income gap is clearly mirrored in people’s subjective perceptions of their financial situation (Figure 16). The average scores were significantly higher in Austria during the whole analysed period. Nevertheless, the improving living standards resulted in a slight increase in the mean values of satisfaction with the household’s financial situation in both countries (rose from 6.9 to 7.3 in Austria, and from 5.2 to 5.5 in Hungary). However, despite the fact that Hungary started to catch up with Austria in terms of the objective indicators mentioned above, the subjective well-being gap between the two countries regarding people’s satisfaction with their economic situation had stagnated (1.7 in 2013; 1.8 in 2018). While every second person in Austria (54%) seemed to be very satisfied with their financial capacities (8-10 scores) according to the 2018 EU-SILC survey, less than every fifth respondent formulated the same opinion in Hungary. Their number and proportion are almost equal to the group of respondents who replied with only 0-3 scores to the same question. It means that approximately 1.2 million people (almost 17% of the total Hungarian population) are still extremely unsatisfied with their financial situation. In Austria, the same ratio is only 5.7% (Table 7, Figure 17). Figure 16. Satisfaction with financial situation: mean values (Hungary and Austria; 2013, 2016 and 2018) AT HU 2013 2018 2013 2018 0 25 50 75 100 Household debts No debts No burden Minor burden Heavy burden FWF–NKFIH Joint Project 34 Source of data: Eurostat microdata and Hungarian microcensus Table 7. Satisfaction with financial situation: share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded). (Hungary and Austria; 2013, 2016 and 2018) AT 2013 AT 2018 HU 2013 HU 2016 HU 2018 0 2.2 1.1 3.2 2.7 2.8 1 1.0 0.5 3.8 2.6 1.8 2 1.7 1.3 8.0 6.2 4.0 3 3.7 2.8 10.8 9.5 8.2 4 3.8 3.2 10.1 9.9 10.5 5 13.3 11.2 18.3 22.6 21.5 6 9.6 8.6 14.7 15.0 16.4 7 16.6 16.8 12.9 15.7 15.8 8 23.1 26.1 12.1 18.1 13.4 9 10.8 13.4 3.7 7.7 3.7 10 14.2 15.0 2.5 6.9 2.0 Source of data: Eurostat microdata and Hungarian microcensus. FWF–NKFIH Joint Project 35 Figure 17. Satisfaction with financial situation: share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded). (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus 5.1.2 Economic status and jobs In the OECD concept, the employment rate, the long-term unemployment rate and the average gross annual earnings of full-time employees were the key indicators of measuring and comparing selected countries regarding the well-being domain titled “jobs and earnings”. However, as the OECD How’s Life Report (2013: 29) underlined, the gaps in this field of statistics are huge, and no reliable and internationally comparable databases on employment quality exist. Since the MIGWELL project aims to study the SWB-migration nexus from a micro-perspective, instead of using aggregated macro-data, we focus on the individual level. These objective, personal attributes are directly related to people’s satisfaction with their jobs, and they also affect people’s subjective well-being as a whole. • Self-defined current economic status, • Occupation (ISCO 08 categories), • Highest level of education. The economically active population comprises everyone within the working-age population who is either an employee, self-employed, or an unemployed person. According to the representative EU-SILC surveys, the proportion of employed and self-employed people increased from 52% to 55% in Austria and from 46% to 54% in Hungary between 2013 and 2018. At the same time, the number of unemployed persons had decreased considerably: in Austria from 5% to 4.5%, in Hungary from 8.5% to 4%. The retired persons and the students 0.0 5.0 10.0 15.0 20.0 25.0 30.0 0 1 2 3 4 5 6 7 8 9 10 AT 2013 AT 2018 HU 2013 HU 2016 HU 2018 FWF–NKFIH Joint Project 36 make up the vast majority of the inactive population in both countries, and their proportion proved to be relatively stable at around 27% and 7-8% (Figure 18). Figure 18. The share of people by current economic status. (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus After excluding the missing cases (respondents who did not answer the question), one can observe different tendencies in the occupational transitions in the two countries. While in Hungary the occupational structure did not change significantly during the analysed period (typically within 1 percentage point, plus or minus), in Austria the share of managers, professionals, and associate professionals and technicians (the top 3 categories) increased by 7.5 percentage points. The relative weight of all other categories shrank, particularly the proportion of craft and trade workers (Figure 19, Table 8). In 2018, almost every second person in Hungary (47%), and every third in Austria (32%) had an “elementary occupation” or was employed as an agricultural, forestry, fishery or craft worker, a plant and machine operator, or an assembler. The ratio was quite the opposite in the case of the occupations with high-income skills: managers, professionals and technicians. FWF–NKFIH Joint Project 37 Figure 19. The share of people by the International Standard Classification of Occupations (ISCO 08). (Hungary and Austria; 2013, 2016 and 2018). Category names: see Table 8 below. Source of data: Eurostat microdata and Hungarian microcensus Table 8. The share of people by the International Standard Classification of Occupations (ISCO 08). 100% = valid answers. Cases with missing values are excluded. (Hungary and Austria; 2018) Austria Hungary 1. Managers 7.5 3.5 2. Professionals 18.3 13.4 3. Technicians and associate professionals 16.8 12.6 4. Clerical support workers 8.3 8.5 5. Service and sales workers 17.0 14.4 6. Skilled agricultural, forestry, and fishery workers 5.1 4.4 7. Craft and related trades workers 11.9 14.8 8. Plant and machine operators, assemblers 6.5 13.4 9. Elementary occupations 8.3 14.4 0. Armed forces occupations 0.2 0.5 Source of data: Eurostat microdata Regarding the highest level of education, it can be seen that the proportion of people with primary education (or less) is slightly decreasing, while the share of people with tertiary-level qualifications is constantly increasing in both countries. This category encompasses various education forms, such as bachelor’s, master’s and doctoral degrees at universities as well as post-secondary technical and vocational certificates. The leap in the proportion of people with a tertiary education in Austria (from 17% to 31%) is particularly noteworthy (Figure 20). FWF–NKFIH Joint Project 38 Figure 20. The share of people by the highest level of education. (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus It is important to underline again that the lack of a reliable, standardised dataset on objective employment quality hampers the international comparability of this well-being aspect. Still, the measurement of job satisfaction is a standard question in all SWB-related surveys. The mean values proved to be very stable, with 8.0 in Austria and 7.1 in Hungary (Figure 21). While completely unsatisfied persons (scores 0-3) are rare in both countries, there is a great difference in the percentage of people who are very satisfied (scores 8-10) with their jobs: 67% and 68% in Austria, compared to 50% and 48% in Hungary in 2013 and 2018 respectively (Figure 22). Figure 21. Job satisfaction (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus FWF–NKFIH Joint Project 39 Figure 22. Job satisfaction: the share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded). (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus 5.1.3 Housing conditions In the OECD How’s Life framework, housing conditions were captured according to three headline indicators: the number of rooms per person, housing costs, and the number of dwellings lacking basic facilities (OECD 2013). Since the lack of basic facilities does not pose a widespread problem in Hungarian and Austrian households, we focus on the reported housing problems which directly affect people’s satisfaction with their housing situation in this Research Report. Therefore, this well-being domain has been operationalised according to the following indicators: • Number of rooms available to the household • Housing problems: Leaking roof, damp walls/floors/foundation, or rot in window frames or floor • Housing problems: too dark, not enough light • Housing problems: noise from neighbours or from the street • Housing problems: pollution, filth, or other environmental problems • Housing problems: crime, violence, or vandalism in the area. During the analysed period, the Hungarian households nearly caught up with the Austrian standard regarding both the average number of rooms (2.9 in 2013, 3.7 in 2018) and its average number per person (from 1.22 to 1.65; in Austria from 1.66 to 1.68 between 2013 and 2018) (Figure 23). 0.0 5.0 10.0 15.0 20.0 25.0 30.0 0 1 2 3 4 5 6 7 8 9 10 AT 2013 AT 2018 HU 2013 HU 2016 HU 2018 FWF–NKFIH Joint Project 40 Figure 23. Number of rooms available to the household. (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus However, respondents in Hungary more frequently reported different types of internal housing problems. Leaking roof, rot in window frames and floor, damp walls/floors or foundation (22% vs. 10%), and dark rooms (8.5% vs. 5.5%) are almost twice as frequent in Hungary as in Austria. On the other hand, the external negative factors, such as noise from the streets, crime and vandalism in the area, pollution, filth, and other environmental problems seem to be stronger in Austria (Figure 24). Generally speaking, the internal factors of the housing situation seem to determine people’s satisfaction with their accommodations. Since these housing conditions depend heavily on the households’ financial capacities, the difference between the mean values of satisfaction with accommodation (in Hungary 6.8, in Austria 8.2) reflects their general satisfaction with their economic resources (Section 5.1.1). Whereas three out of four people in Austria are very satisfied with their housing conditions (scores 8-10), less than half of the respondents in Hungary reported similar opinions (Figure 25, 26). FWF–NKFIH Joint Project 47 Figure 31. Satisfaction with time use: mean values (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC/Microcensus question: “Overall, how satisfied are you with …? The amount of time you have to do things you like doing” Figure 32. Satisfaction with time use: the share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded). (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC/Microcensus question: “Overall, how satisfied are you with …? The amount of time you have to do things you like doing” Differences in satisfaction with time use and access to leisure in the two countries might also be dependent on another aspect of an acceptable work-life balance: commuting time. Although no objective measures are available, there are measures of satisfaction with commuting time in the two countries in 2013. Answers to this question among those affected by commuting FWF–NKFIH Joint Project 48 reveal that respondents in Austria are indeed more satisfied with this aspect of life than in Hungary (with an average of 8 vs. 7 on a scale of 0-10). Half of the respondents in Austria indicated a 9 or a 10 on the scale, whereas only one-third of the respondents was satisfied to this degree in Hungary. This nevertheless does not provide information on the actual length of commuting time. 5.2.3 Social connections As well-being and people’s aspirations are always shaped by the social contexts in which they are embedded, we also take into account the relational dimension in our research. This dimension refers to the social relationships that people must be able to enter into in order to meet human needs. It has objective as well as more subjective indicators. Micro-level objective indicators of social connections include: • Marital status • Living with a partner • Household size • Presence of small children. Indicators of the reliability of social connections include: • Meeting with family and friends • Having someone to discuss personal matters • Access to help (material, non-material). A more subjective evaluation the own position and relation to others include: • Trust in others • Satisfaction with personal relationships. Overall, very similar tendencies characterise Austrian and Hungarian respondents in terms of those with whom they live. In Austria, 60-61% of the respondents live with their partner, which is only slightly lower in Hungary: 54-57% (Figure 33). Respondents in the two countries are also very similar regarding their marital status. Around one-third of respondents has never married, 44-51% are married, and 10-13% are divorced. There is only a minor difference in the share of widows: in Hungary there is slightly more widowed among the respondents (11-12%) than in Austria (6-7%) (Figure 34). FWF–NKFIH Joint Project 49 Figure 33. Share of respondents living with a partner (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus Figure 34. Marital status of respondents (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus The average household size is similar in the two countries: in 2018, 2.7 in Austria and 2.8 in Hungary (Figure 35). While the Austrian household size remained quite stable from 2013 to 2018, the Hungarian household structure grew closer to the Austrian one, with households becoming smaller. 18-21% of the respondents in Austria live in a one-person household. In Hungary, this proportion was 13% in 2013, increasing to 18% in 2018. The share of respondents living in a two-person household is 36-39% in Austria, whereas this proportion grew from 29% to 36% in Hungary. Around 17-21% of respondents are living in a household of three in both countries and across the different points of time. 15-17% of the respondents live in a household of four in Austria. In Hungary, this decreased from 21% in 2013 to 15% in 2018, similar to households of five. Whereas 7-9% of respondents live in a household of five in Austria, this FWF–NKFIH Joint Project 50 was 16% in Hungary in 2013, decreasing to 12% in 2018. The proportion of respondents living with a small child under the age of 6 is quite stable across the countries and time points: 1416% - although this proportion has decreased somewhat in Hungary in 2018 (12%). Figure 35. Size of households in which respondents live (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus Regarding social connections, Austrian respondents seemed to be in a better situation, as nearly everyone (89-90%) said that they were able to get together with friends/family (relatives) for a drink/meal at least once a month. This proportion is much lower in Hungary (38% in 2013 and 63% in 2018), although there has been an important increase over the period. This difference is partly due to financial deprivation, when people cannot meet with friends and family for financial reasons. This has decreased over the period and concerned 40% of the respondents in 2013, but only 22% in 2018. In 2018 there were still 15% (21% in 2013) of the respondents who did not meet with friends and relatives at least once a month due to other reasons – these are perhaps cases of social exclusion, which concern only about 7% of the respondents in Austria (Figure 36). Figure 36. Percentage of people meeting friends and family at least once a month (Hungary and Austria; 2013 and 2018) FWF–NKFIH Joint Project 51 Source of data: Eurostat microdata and Hungarian microcensus EU-SILC/Microcensus question: “Could you tell me if you have or do the following? Get-together with friends/family (relatives) for a drink/meal at least once a month?” Despite the differences in the frequency of contact with friends and family, a similarly high proportion of respondents in Austria (80%) and Hungary (85%) report that they do indeed have someone with whom they could discuss personal matters. This proportion was even higher (94%) in Hungary in 2016. Nevertheless, this might eventually be due to the fact that in the 2016 Microcensus in Hungary, the share of non-responses was only 1% for this question, whereas this rate was 12% in Austria and 17% in Hungary for the 2013 EU-SILC. Figure 37. Percentage of people having someone with whom to discuss personal matters (Hungary and Austria; 2013 and 2016) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC/Microcensus question: “Do you have anyone to discuss personal matters with? FWF–NKFIH Joint Project 52 In line with having someone to discuss personal matters, despite the differences in the frequency of contact with friends and family, a similarly high proportion of respondents in Austria (85%) and Hungary (80%) report that they can ask for help from relatives, friends, or neighbours. The slight difference might come from higher non-response in Hungary. The question was not formulated in the same way in the 2013 and 2018 waves of the EU-SILC. In 2013, the question addressed help in general, whereas there were in fact two questions in 2018, one concerning material and the other non-material help. The latter two questions were recoded (if any kind of help was mentioned) to make them comparable to the 2013 question. We know, however, that non-material help is more widespread and seems to correspond to that which was initially mentioned as help in general (with similar proportions, 85% in Austria and 80% in Hungary). Asking for financial help seems to be a more sensitive issue: a somewhat lower share of respondents would be able to ask for it, 72% in Austria and 69% in Hungary (Figure 38). Figure 38. Percentage of people being able to ask for help from relatives, friends, or neighbours (Hungary and Austria; 2013 and 2018) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC 2013 question: “Do you have any relatives, friends or neighbours that you can ask for help?” EU-SILC 2018 questions: “Do you feel that if you needed material help (e.g. money, loan or an object) you could receive it from relatives, friends, neighbours or other persons you know?” and “Do you feel that if you needed non-material help (e.g. somebody to talk to, help with doing something or collecting something) you could receive it from relatives, friends, neighbours or other persons you know?” In terms of trusting other people, there are notable differences. The level of trust is lower in Hungary than in Austria: the mean level of trust on a 0-10 scale is 5.8-5.9 in Austria as opposed to 4.7-5.3 in Hungary. Higher trust (7-10 on a 0-10 scale) is mentioned by 43-44% of the respondents in Austria, whereas the share of respondents with similarly positive answers is only 26-30% in Hungary. Lower trust (0-4 on a 0-10 scale), on the other hand, is mentioned by only 18-21% of Austrian respondents and 30-34% of Hungarian ones. The 2016 Microcensus FWF–NKFIH Joint Project 53 produced even more negative results, with 40% of respondents not really trusting (0-4) and only 20% trusting (7-10) other people (Figure 39, 40). Figure 39. Trust in other people: mean values (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC 2013 question: “Would you say that most people can be trusted?” EU-SILC 2018 question: “To what extent do you trust other people?” Figure 40. Trust in other people: the share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded) (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC 2013 question: “Would you say that most people can be trusted?” EU-SILC 2018 question: “To what extent do you trust other people?” FWF–NKFIH Joint Project 54 Despite similar tendencies in terms of having someone with whom to discuss personal matters and similar access to help from others, respondents in Hungary are much less satisfied with their personal relationships than Austrian respondents are. This might reflect the lower level of trust in others and the lower frequency of meeting friends and relatives in the former case. Whereas the average satisfaction in Austria is 8.5-8.6 (on a scale of 0-10), the mean satisfaction is 7.3-7.6 in Hungary. Many more (58-59%) of the Austrian respondents gave positive answers (9-10) than Hungarians did (32-34%) (Figure 41, 42). Figure 41. Satisfaction with personal relationships: mean values (Hungary and Austria; 2013, 2016 and 2018) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC question: “Overall, how satisfied are you with …? Your personal relationships” Figure 42. Satisfaction with personal relationships: share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded) (Hungary and Austria; 2013, 2016, 2018) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC question: “Overall, how satisfied are you with …? Your personal relationships” FWF–NKFIH Joint Project 55 5.2.4 External factors The quality of the living and working environment directly contributes to quality of life, but it also has an effect on people’s health or the activities in which they take part (raising children, leisure activities, etc.). Similarly, living in a secure environment contributes to one’s wellbeing. These aspects of well-being will be explored through the subjective indicators of: • Satisfaction with living environment (2013, 2016) • Satisfaction with recreational or green areas (2013) • Perception of personal security. The satisfaction with one’s living environment and access to green areas follow similar tendencies (Figure 43, 44). Not taking into account those respondents – 12% in Austria and 1720% in Hungary – who could not answer the question, 55% reported a satisfaction rate of 9-10 on a scale of 0-10 in Austria and only 15% reported the same in Hungary. The respective averages are 8.4 and 8.2 (respectively for satisfaction with the living environment and access to green areas) in Austria, and 6.5 and 6.2 in Hungary. The results of the Microcensus in 2016 in Hungary confirm these tendencies, however, with the share of non-answers being lower. The average satisfaction is somewhat higher (6.9) in the case of satisfaction with the living environment. Figure 43. Satisfaction with the quality of the living environment: the share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded). (Hungary and Austria; 2013 and 2016) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC/Microcensus question: “Overall, how satisfied are you with …? The quality of your living environment” FWF–NKFIH Joint Project 56 Figure 44. Satisfaction with the recreational or green areas in the place of living: the share of respondents by the scores on a 0-10 scale (100% = valid answers. Cases with missing values are excluded). (Hungary and Austria; 2013) Source of data: Eurostat microdata and Hungarian microcensus EU-SILC/Microcensus question: “Overall, how satisfied are you with …? The recreational or green areas in the place where you live” In terms of perceived personal safety, many (up to 20%) of the respondents could not answer, especially in the EU-SILC survey in Hungary. If we take only the valid answers into account, the main tendencies did not change in Hungary between 2013 and 2016: 69% of the respondents feel fairly or very safe and 28% feel a bit or very unsafe. There was a slight decrease over time in the share of those declaring that they felt very unsafe (from 12% to 6%). Nevertheless, Hungarian respondents felt less safe overall than respondents in Austria, where only 17% felt a bit or very unsafe and over 80% felt fairly or very safe. The difference is most notable among those feeling very safe when walking alone in their area after dark: 43% vs 17% of the respondents (Figure 45). FWF–NKFIH Joint Project 63 (eudaimonic well-being) implies higher overall satisfaction with life. Nevertheless, happiness and a life seen as worthwhile are only weakly connected. Beside satisfaction with the household’s financial situation, overall satisfaction is positively correlated (at a moderate level) with satisfaction with accommodation, actual work, and personal relations, among the other elements of evaluative well-being. Furthermore, it is also positively associated with positive elements of affective well-being and negatively associated with negative elements of affective well-being. Satisfaction with other domains in life are not connected to overall satisfaction but can be linked among themselves. Satisfaction with the living environment, for instance, is connected to satisfaction with accommodation, whereas satisfaction with time use is connected to satisfaction with personal relationships, or satisfaction with access to green areas is connected to satisfaction with the living environment. The different emotional states seem to be more strongly connected: positive states such as being happy or feeling calm and peaceful go together, while being very nervous, feeling downhearted or depressed, or feeling down in the dumps oppose positive feelings. The sentiment of solitude seems to be only loosely connected to either of them. The perception of having a life worth living is related to overall life satisfaction and satisfaction with one’s current work. Table 9. Interrelations of different measures of subjective well-being in Austria in 2013 and 2018. (Pearson’s correlation coefficients. Cases with missing values are excluded) FWF–NKFIH Joint Project 64 Source of data: Eurostat microdata and Hungarian microcensus In Hungary, on the other hand, the different elements of subjective well-being seem to be more interlinked than in Austria, with stronger correlation coefficients. Similarly to Austria, higher happiness (affective well-being) and the feeling that one’s life is worthwhile (eudaimonic wellbeing) also means higher overall satisfaction with life. However, in Hungary, happiness and a life seen as worthwhile are also connected (Table 10). ..life overall ..household’s financial situation ..accommodation ..job ..time use ..personal relationships ..living environment ..commuting time ..recreational or green areas Being very nervous Feeling calm and peaceful Feeling downhearted or Being happy Feeling down in the dumps 2013 0,58 2018 0,55 2013 0,40 0,42 2018 2013 0,41 0,31 0,31 2018 0,40 0,29 2013 0,30 0,26 0,27 0,33 2018 0,27 0,23 0,35 2013 0,40 0,30 0,33 0,32 0,44 2018 0,44 0,31 0,31 0,40 2013 0,31 0,29 0,45 0,24 0,24 0,29 2018 2013 0,20 0,16 0,20 0,28 0,21 0,20 0,19 2018 2013 0,29 0,27 0,38 0,22 0,27 0,30 0,68 0,17 2018 2013 -0,31 -0,23 -0,15 -0,21 -0,23 -0,19 -0,14 -0,06 -0,14 2018 -0,33 -0,22 -0,20 -0,19 -0,20 2013 0,32 0,25 0,16 0,25 0,23 0,22 0,15 0,08 0,16 -0,52 2018 0,33 0,22 0,22 0,21 0,23 -0,52 2013 -0,42 -0,30 -0,20 -0,21 -0,18 -0,25 -0,17 -0,07 -0,15 0,47 -0,42 2018 -0,41 -0,27 -0,20 -0,14 -0,25 0,49 -0,42 2013 0,46 0,33 0,24 0,27 0,24 0,33 0,20 0,13 0,20 -0,36 0,45 -0,50 2018 0,49 0,32 0,27 0,20 0,34 -0,36 0,45 -0,49 2013 -0,42 -0,31 -0,18 -0,24 -0,19 -0,25 -0,16 -0,09 -0,14 0,51 -0,47 0,59 -0,46 2018 -0,43 -0,29 -0,24 -0,15 -0,26 0,51 -0,46 0,60 -0,46 2013 2018 -0,33 -0,22 -0,13 -0,08 -0,30 0,27 -0,21 0,41 -0,37 2013 0,46 0,32 0,29 0,42 0,19 0,33 0,30 0,20 0,26 -0,18 0,22 -0,26 0,34 -0,26 2018 Source of data: Eurostat microdata Strong positive Moderate positive Moderate negative Strong negative Pearson's correlation coefficients Feeling down in the dumps Feeling lonely Life is worthwhile Evaluative - satisfation with… Affective Euda imon ic ..commuting time ..recreational or green areas Being very nervous Feeling calm and peaceful Feeling downhearted or depressed Being happy ..household’s financial situation ..accommodation ..job ..time use ..personal relationships ..living environment FWF–NKFIH Joint Project 65 Table 10. Interrelations of different measures of subjective well-being in Hungary in 2013, 2016 and 2018. (Pearson’s correlation coefficients. Cases with missing values are excluded) Source of data: Eurostat microdata and Hungarian microcensus ..life overall ..household’s financial situation ..accommodation ..job ..time use ..personal relationships ..living environment ..commuting time ..recreational or green areas ..health ..personal income Being very nervous Feeling calm and peaceful Feeling downhearted or depressed Being happy Feeling down in the dumps 2013 0,66 2016 0,71 2018 0,56 2013 0,49 0,57 2016 0,55 0,59 2018 2013 0,50 0,45 0,45 2016 0,55 0,50 0,44 2018 0,47 0,47 2013 0,43 0,40 0,43 0,42 2016 0,41 0,40 0,38 0,37 2018 0,54 0,38 0,37 2013 0,43 0,32 0,41 0,36 0,45 2016 0,53 0,43 0,45 0,46 0,41 2018 0,49 0,31 0,39 0,46 2013 0,34 0,38 0,47 0,30 0,34 0,37 2016 0,45 0,45 0,57 0,40 0,37 0,55 2018 2013 0,32 0,28 0,31 0,48 0,41 0,30 0,28 2016 0,43 0,43 0,34 0,54 0,24 0,37 0,35 2018 2013 0,33 0,35 0,40 0,29 0,37 0,37 0,75 0,28 2016 2018 2013 2016 0,54 0,44 0,34 0,41 0,31 0,48 0,37 0,42 2018 2013 2016 0,51 0,65 0,41 0,66 0,32 0,32 0,36 0,49 0,36 2018 2013 -0,43 -0,34 -0,24 -0,29 -0,28 -0,24 -0,17 -0,15 -0,18 2016 -0,30 -0,26 -0,22 -0,22 -0,22 -0,25 -0,23 -0,17 -0,25 -0,20 2018 -0,32 -0,22 -0,25 -0,30 -0,22 2013 0,45 0,36 0,28 0,29 0,30 0,28 0,21 0,15 0,21 -0,61 2016 0,37 0,31 0,25 0,24 0,28 0,29 0,24 0,17 0,29 0,24 -0,36 2018 0,42 0,29 0,26 0,35 0,30 -0,50 2013 -0,49 -0,37 -0,28 -0,29 -0,28 -0,30 -0,19 -0,15 -0,19 0,59 -0,55 2016 -0,43 -0,35 -0,26 -0,28 -0,24 -0,33 -0,25 -0,25 -0,39 -0,26 0,48 -0,37 2018 -0,54 -0,41 -0,32 -0,35 -0,36 0,42 -0,44 2013 0,51 0,36 0,30 0,31 0,27 0,36 0,22 0,17 0,21 -0,45 0,59 -0,52 2016 0,48 0,37 0,27 0,27 0,24 0,37 0,27 0,24 0,40 0,28 -0,24 0,48 -0,44 2018 0,54 0,34 0,26 0,34 0,35 -0,34 0,59 -0,47 2013 -0,50 -0,36 -0,28 -0,31 -0,27 -0,30 -0,20 -0,16 -0,20 0,64 -0,56 0,72 -0,51 2016 2018 -0,51 -0,32 -0,30 -0,35 -0,34 0,56 -0,48 0,65 -0,52 2013 2016 -0,32 -0,24 -0,22 -0,19 -0,13 -0,34 -0,22 -0,20 -0,27 -0,16 0,33 -0,20 0,45 -0,32 2018 -0,38 -0,23 -0,23 -0,21 -0,36 0,23 -0,31 0,47 -0,43 0,46 2013 2016 -0,29 -0,25 -0,22 -0,22 -0,28 -0,24 -0,21 -0,13 -0,24 -0,19 0,59 -0,43 0,47 -0,26 2018 2013 0,56 0,43 0,43 0,55 0,45 0,51 0,41 0,39 0,39 -0,28 0,34 -0,36 0,42 -0,38 2016 0,56 0,47 0,46 0,55 0,46 0,61 0,49 0,41 0,49 0,39 -0,24 0,30 -0,35 0,38 2018 Source of data: Eurostat microdata and Hungarian microcensus Affective Eudaim onic Being happy Feeling down in the dumps Feeling lonely Feeling stressed Life is worthwhile ..recreational or green areas ..health ..personal income Being very nervous Feeling calm and peaceful Feeling downhearted or depressed ..household’s financial situation ..accommodati on ..job ..time use ..personal relationships ..living environment ..commuting time Evaluative - satisfation with… FWF–NKFIH Joint Project 66 Among the evaluative elements, besides satisfaction with the household’s financial situation, with accommodation, actual work, and personal relations, overall satisfaction is also positively correlated with satisfaction with time use, health, and personal income (the latter two were not measured in Austria) among the other elements of evaluative well-being. Furthermore, in the case of Hungary there is a stronger positive connection with positive elements of affective wellbeing and a stronger negative connection with negative elements of affective well-being. In terms of satisfaction with other domains in life, beyond the interconnections existing in Austria, there is a stronger relation between satisfaction with accommodation and household finances and satisfaction with the current job. There is a stronger link between respondents’ satisfaction with their employment and commuting time, and interestingly, between personal relations and the environment where they lived. This eventually highlights the human aspect of a neighbourhood. The different emotional states seem to be strongly connected, as it is the case in Austria. The sentiment of having a life worth living is related to overall life satisfaction and satisfaction with one’s current work, as it is in Austria. However, satisfaction with personal relations is also strongly associated with it, as is satisfaction with other domains in life to some extent. 6. Objective and subjective well-being differences between the nativeand foreign-born population in Austria In the following, we present EU-SILC survey data from 2013 and 2018, with each analysed variable broken down by country-of-birth categories. The variables are evaluated in the same order as in Chapter 5, which contains the Hungary-Austria comparison. The detailed methodological descriptions of the variables can be found in Chapter 3.2 as well as in the Appendix. It is important, however, to stress once again that the survey responses have been analysed after weighting. In this way, we can provide representative information that can be extrapolated to the whole population. However, within the country-of-birth groups we cannot speak about representativeness. Consequently, the values of some of the variables that are observed as based on the survey may differ from the statistical databases based on censuses and registers. We will try to address these issues in the light of the available data. 6.1 Population by country of birth As the analysis focuses on the SWB characteristics of nativeor foreign-born people classified into pre-defined categories, it is crucial first to consider the size of these groups as well as the extent to which they were represented in the EU-SILC survey. The largest group in both 2013 and 2018 was the Austrian-born population, but their share has fallen by almost three FWF–NKFIH Joint Project 67 percentage points, i. e. from 83.4% to 80.5%, during the analysed period. In 2018, the highest proportion of foreigners was constituted by people born in Germany, followed by Bosnia and Herzegovina, Türkiye, Serbia, and Romania (Figure 54). Figure 54. Population stock by the Top 10 countries of birth at national level in Austria (number of people, 2002-2022) Source: Németh et al. 2023: 24 According to the official statistics as of 1 January 2014, 16.6% of the population of Austria was born abroad, whereas their share was 19.5% on 1 January 2019. The EU-SILC survey slightly overestimated the immigrant proportion at 17.8% and 20.7% respectively, but overall, the surveys are in line with the official register-based data (Table 11). The analysis below follows the classification logic of Statistics Austria. For the sake of simplicity, “EU15” refers to the “old” Member States of the European Union (who have joined before 2004) as well as the EFTA countries (Iceland, Norway, Switzerland, and Liechtenstein), while the “EU12” label covers the countires that joined the EU in 2004 and 2007. In this classification, Croatia belongs to the category of “former Yugoslavian countries, excluding Slovenia”. It is important to emphasize again that we have held strictly to the country-of-birth variable. Therefore, these numbers do not include people with an “immigration background”, a term which is often used in statistical publications (e.g., "Migration & Integration: Zahlen. Daten. Indikatoren", published yearly by the Statistics Austria). FWF–NKFIH Joint Project 68 Table 11. The share of foreign-born people in Austria according to the official statistics and the EU-SILC survey 2013 2018 Statistics Austria EU-SILC Statistics Austria EU-SILC “EU15”: “old” EU Member States + EFTA countries 3.6% 3.8% 3.7% 4.7% “EU12”: “new” EU Member States (without Croatia) 3.7% 3.4% 4.7% 4.7% “YUG”: former Yugoslavian countries (including Croatia, without Slovenia) 4.5% 4.9% 4.7% 4.0% “TUR”: Türkiye 1.9% 2.4% 1.8% 2.3% Data source: Statistics Austria, Eurostat. Own table. 6.2 Material factors 6.2.1 Financial situation According to the surveys, the Austrian-born population had the highest median personal income at €21,070 in 2013 and €22,887 in 2018. (The questionnaire had in fact enquired after the income in the previous year. These numbers thus refer to the situation in 2012 and 2017.) Austrian-born persons in 2013 were closely followed by EU15 immigrants (€20,154), but their median earnings had dropped to 81.8 of the 2013 value (€16,500) by 2018. However, if we take the mean instead of the median, we also see an income gain for this group (€26,349 to €29,018) and the mean value is now slightly higher than that of Austrians (€27,190). Since the mean is higher than the median – and this phenomenon was not observed for any other group – we can speak of a right-skewed distribution in which a few individuals with very high incomes distort the mean. There has also been a drop in the income of people born in the former territory of Yugoslavia (excluding Slovenia), where the 2018 survey shows a net income of €14,820, which totals only 85.2 of the €17,384 recorded in 2013. This means that the formerly third-highest income group has become the fifth-highest in five years. On the other hand, increases are observed for people born in the EU12 countries (+22.9 ), in Türkiye (+24.05 ) as well as in other countries (+28.03 ). The median income of those born in Türkiye is higher than that of those born in the EU12 in both years under review, but the mean income was more favourable for the EU12 group in 2013. Despite the rapid increase in the incomes of the “other countries” group, their median income in 2019 is still only 50.8% of that of the Austrian-born (Figure 55). FWF–NKFIH Joint Project 69 Figure 55. Personal incomes in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata In terms of median household income, there is a decreasing order in 2013 for people born in Austria – the old Member States (EU15) – the former Yugoslavia – the new Member States (EU12) – Türkiye – and other countries of the world. By 2018, the positions of the EU12 and Yugoslavia had reversed (Figure 56). In contrast to personal income, the EU15 does not display the same decline in 2018, with a median household income close to that of the Austrian-born at 94.8%. While all groups show an increase in income (the largest increase is +21.7% for the group labelled “other countries”), the EU15 displays a decrease similar to personal income (97% of the previous figure, which is not as marked a difference as for personal income). Beside having experienced a rapid increase, the median household income of the “other countries” group is not as far behind the value of that in Austria – 67.1% – as in the case of personal income. This may suggest that mixed marriages have created mixed-ethnicity households, where the higher income of an Austrianborn person offsets the lower income of a first-generation immigrant. 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 20000 40000 60000 80000 Personal income in EUR FWF–NKFIH Joint Project 70 Figure 56. Household incomes in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata Although the at-risk-of-poverty rate in Austria has generally not changed substantially (see Chapter 5.1), this is no longer true for all sub-groups by country of birth. In 2018, the rate was lowest in the case of Austrian-born people (9.6%; decreased by 0.9%) and people born in the old Member States (19.5%; decreased by 2.6%). A larger decrease is observed for those born in Türkiye (from 27.4 to 22.3 ) and in other countries (from 46.3 to 37.1 ). On the other hand, the proportion of people at risk of poverty has slightly increased (from 25% to 26.2%) in the case of people from the new Member States, and almost doubled for those born in Yugoslavia (from 17.5% to 30.7%) (Figure 57). Remarkable differences can be observed in the extent to which social benefits reduce the number of people at risk of poverty. Social benefits tend to improve living standards and decrease the risk of poverty most significantly for the people born in Türkiye (a decrease to 48% of the value before benefits) as well as in Yugoslavia (a decrease to 54%). In the case of the category “other countries” and the Old Member States, the social benefits are less effective (a decrease to 77% and 68% respectively) (Statistik Austria, 2019). 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 20000 40000 60000 Household income in EUR FWF–NKFIH Joint Project 71 Figure 57. Share of people at risk of poverty in Austria (2013 and 2018), categorised by country of birth Source of data: Eurostat microdata Indicators of material deprivation also point to an improvement between 2013 and 2018 (Figure 58). The deprivation rate increased from 1.3% to 2.3% among the EU15 group, ie. immigrants from the old Member States. However, this is still the second lowest value after the Austrian-born population (1.6%). In all other groups, the deprivation rate decreased (-0.4% in the case of immigrants from the former Yugoslavia, -2.7% from Türkiye, -3.7% from the new Member States). In the case of other foreign-born people from all around the world there was an even sharper decrease: from 18% in 2013 to 7.1% in 2018. This 2018 value is already lower than the indicator for the people born in Türkiye and in Yugoslavia. Figure 58. Share of materially deprived people in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 At risk of poverty No Yes 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Material deprivation No Yes FWF–NKFIH Joint Project 72 Although the Austrian population in general does not have serious problems with debt repayments, there are differences between the levels of burden among immigrant groups. Approximately 80% of the people in the Austrian-born, EU15, and “other countries” groups have no debt, which is 2-3% lower than the 2013 figures for the respective groups. Within the EU15 group, the proportion of people who have a moderate or a heavy credit burden has increased over the five years. For a heavy burden, there has been an increase to 2.6 times the previous value in this group, but even this increased value at 2.4% in 2018 is low, equalling the indicator for those born in Austria (Figure 59). The trend for the Austrian-born and the “other countries” group is similar. The number of people for whom credit constitutes a heavy burden has fallen, whereas the proportion with no burden and the proportion experiencing a moderate burden has risen. For those born in the EU12, there is also a decrease in the share of people who reported a heavy burden (almost halving from 8.4% to 4.3%) and an increase in the proportion of those who reported a moderate burden. This group furthermore shows an increase in the proportion who have no debt (albeit only by a marginal 0.8%). For the latter statement, this is the only such group. Among the immigrants born in the former territory of Yugoslavia, the number of people without debt had decreased from 81.4% to 73.1%, while both the “somewhat of a burden (minor burden)” (12.1%) and the “heavy burden” (10.5%) options were selected by more persons in 2018 than was the “no burden” option (4.3%). This had already been the case in 2013, but the proportional differences have increased between the “no burden” and the other two response options. Among the total population, the share of debt-free persons is by far the lowest among the Turkish-born people (67.2% and 54.4% in 2013 and 2018 respectively), while “high burden” is the most typical among this group (16.3% and 11.9% in the two years). Still, for the majority within this group, debt repayments are either “somewhat burdensome” (21.7% – the highest of all groups) or “no burden” at all (12% – the highest of all groups). FWF–NKFIH Joint Project 79 Figure 63. Share of people according to their highest level of education in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata The mean values of the responses to subjective job satisfaction are high for all groups, displaying a convergence compared to 2013 (the previous difference of one unit between the highest and lowest group mean values has been reduced to 0.5). The trend prevails, however, that those born in Austria remain the group that is most satisfied with their job (mean of 8), while those born in Türkiye are the least satisfied (mean of 7.5). All other groups have likewise reached the level of 8, similar to those born in Austria (0.5 unit improvement since 2013). The median for each group is 8 (Figure 64). Figure 64. Satisfaction with job in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Highest level of education Primary or less Secondary Tertiary 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Satisfaction with job FWF–NKFIH Joint Project 80 6.2.3 Housing conditions There has been no change in the median number of rooms in the housing units compared to 2013. In terms of averages, the only notable decreases are for the groups born in Türkiye and Yugoslavia (0.3 unit decreases in both cases). In 2018, the sample group of respondents born in Austria has the largest number of rooms at their disposal (4), while the sample born in Yugoslavia has the least (2.9) (Figure 65). Figure 65. Number of rooms in the dwellings in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata In general, housing conditions have improved over five years, with fewer housing-related problems (Figure 66). The proportion of people living in a dwelling prone to dampness/leaking/rot has decreased substantially among the population born in Türkiye. In 2013, this group had the highest proportion (23.3%), but by 2018, the second lowest proportion of people with dampness/leaking/rot is now the group born in Türkiye (9.9 ), following the group born in Austria (9.2%). There has also been a great improvement in the proportion of people living in insufficiently lit accommodation among those born in Türkiye (from 17.1 to 3.1 ), which is even more remarkable due to the increase in the proportion of people living in such conditions among all other groups. Within the EU15 group, it has almost doubled (from 5.7% to 10.8%). For 2018, the EU15 group is now the most affected, followed by the EU12 (10.3%) and those born in Yugoslavia (9.6%). 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 2 4 6 Number of rooms in the dwelling FWF–NKFIH Joint Project 81 Figure 66. Share of people whose accommodation was affected by leaking (A), dark dwelling (B), noise pollution from the street (C), environmental problems (D), and those whose neighbourhood is affected by crime (E) in Austria (2013 and 2018) categorised by country of birth A B C D E Source of data: Eurostat microdata Within the group born in Türkiye, the proportion living under conditions of noise pollution has also decreased (from 27.9% to 21.9%), but the largest percentage point decrease was observed in the “other countries” group (from 25.7% to 17.9%). The only group to have seen an increase is the EU12 (from 22.9% to 25.5%), which is now the group most affected by the problem. This group is followed in decreasing order by those born in Türkiye and Yugoslavia (20%), while those born in Austria are the least affected by noise pollution (16.9%). 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Leaking roof No Yes Unknown 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Dark dwelling No Yes 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Noise from street No Yes Unknown 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Environmental problems No Yes Unknown 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Crime in the area No Yes Unknown FWF–NKFIH Joint Project 82 The proportion of people affected by environmental problems has decreased for those born in Austria, Türkiye, Yugoslavia and the “other countries” group, while it has increased for the EU12 and EU15. The most marked decrease was observed for those born in Yugoslavia (from 16.9% to 7.3%). Consequently, by 2018, this group is the least affected. By contrast, the groups most exposed to the problem are those born in Türkiye (13.6 ), the EU15 (11.5 ), and the EU12 (11.5%). There is a clear decrease in living in crime-prone neighbourhoods in all categories, and the rate of decrease is relatively stable (highest for those born in Yugoslavia and the EU12 group). In 2018, despite the decrease, the problem of living in a crime-prone environment was still most prevalent among those born in Yugoslavia (11.4%), the EU12 (10.2%), and the Austrian-born group (9.6%). Those born in Türkiye are considered to live in the safest neighbourhoods (only 5% report that crime is a problem) (Figure 66). In relation to data on housing and living environment, it should be noted that a higher proportion of those born in Austria live in rural, sparsely urbanised areas (43.9% in 2018), while those born in Yugoslavia (61%) or the “other countries” group (62.6%) tend to live in densely populated areas. 39.8% of those born outside Austria lived in the capital, Vienna, in 2019 (Statistik Austria, 2019). Figure 67. Share of people according to the urbanity of their settlements in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata Satisfaction with housing conditions was not captured in the 2018 EU-SILC survey, so that we do not have data on how the general improvements described above (especially for those born in Türkiye) translate into subjective perceptions. Based on the 2013 data, those born in Türkiye were the least satisfied with their accommodation situation (mean of 6.7), 0.6 lower than the 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Degree of urbanisation Thinly Intermediate Densely FWF–NKFIH Joint Project 83 second-lowest score for the EU12. The most satisfied group is constituted by those born in Austria (mean of 8.4), followed by the EU15 (8.1), and lagging behind were those born in Yugoslavia (7.4) (Figure 68). Although this analysis does not focus on ownership, it does have a small impact on the subjective well-being of the Austrian population. Those who own or use their dwelling free of charge have higher life satisfaction (Angel & Gregory, 2023). Ownership affects satisfaction through subjective perceptions of housing costs, according to the authors’ results. Furthermore, there are large differences in ownership attitudes in Austria related to ethnic background (Buchegger-Traxler & Sirsch, 2012). Figure 68. Satisfaction with accommodation in Austria (2013) categorised by country of birth Source of data: Eurostat microdata 6.3 Immaterial factors 6.3.1 Health status Austrian society in general enjoyed a good health status in 2018, with a “very good” or “good” response rate above 50% in all groups (Figure 69). In 2013, this was not the case for people born in Türkiye. In 2018, those born in Yugoslavia (6.4 ) were the most likely to have very bad health, followed by the “other countries” group (3.9%) and the group born in Türkiye (3.1%). Moreover, the “other countries” group has seen a drastic change in its proportions over five years, rising from 0.7% to 3.9%. The increase in the proportion of people in very bad health is observed in all groups except those born in Austria (0.3% decrease). However, the proportion of those in bad health has fallen everywhere except in the EU15 group, with a particularly striking decline for those born in Türkiye (from 19.3 to 11 ). For 2018, this 11% is the second highest rate after those born in Yugoslavia (12.8%). Between 1% and 2% more people 2013 NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Satisfaction with accommodation FWF–NKFIH Joint Project 84 responded with a good health status in 2018 than in 2013 for the EU15, EU12, and Austrianborn groups. The “other countries” group shows a moderate positive change (from 37.1% to 43.3%), but for those born in Türkiye, there is an increase of 10.2 (from 27.5 to 37.7 ). The negative figures for those born in Yugoslavia are further worsened by the decrease in the proportion of people in good health (from 36.3% to 32%). This is offset to a degree by an increase in the proportion of respondents in very good health among those born in Yugoslavia (from 19.8% to 21.9%). At 21.9%, this figure still only exceeded the score of those born in Türkiye (17.3 ) in 2018, as in 2013. Over the five years, not only has the proportion of those born in Yugoslavia with very good health increased, but the same has happened in all groups. The most notable increase took place within the EU12 group (from 23% to 33.3%). As health status becomes more polarised – with an increase both in the “very bad” and “very good” categories – the intermediate, “fair” category has declined everywhere. Overall, the population born in Yugoslavia and Türkiye is in the worst health, while the health statuses of the rest of the groups are equally good. In addition to suffering frequently from physical pain, Austrian immigrants who speak Turkish are also affected by a higher proportion of psychological problems (Wimmer-Puchinger et al, 2006). Both the physical and mental health statuses are therefore poor for this group. Figure 69. Share of people according to their self-reported health status in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata 6.3.2 Work-life balance Compared to 2013, the mean number of working hours per week has decreased in all the groups surveyed. The largest absolute decrease occurred in the group born in Türkiye (from 38.6 to 30.8). In 2018, this large decrease resulted in the group born in Türkiye having the lowest mean, 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Self-reported health status Very bad Bad Fair Good Very good Unknown FWF–NKFIH Joint Project 85 while in 2013, those born in Yugoslavia had the lowest mean (change from 38 to 31.4). If we look at the median rather than the mean, Yugoslavia has the lowest value (38), followed by Türkiye (39) and the remaining groups (all of them have a value of 40). The smallest decrease was in the EU15 group mean (from 41.2 to 36.3), with this group working the most on average in 2013 and 2018. The mean for the group born in Austria was the second highest in both years (40 and 33.7) (Figure 70). Figure 70. Working hours per week in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata The proportion of people participating in leisure activities has increased in all groups under survey, compared to 2013. In percentage points, the largest increase was in the EU12 (from 48.5% to 66.3%), whereas the smallest increase was in the group born in Yugoslavia (from 46.1% to 51.2%). The gap between Yugoslavia-EU12 (2.4% gap) and Yugoslavia-other (4.1%), which was narrow back in 2013, has widened remarkably (15.1% and 10.7% respectively). The groups with the highest levels of leisure activity are those born in Austria (75.9%) and the EU15 (77.3%). The increase in leisure-time activity in the Austrian-born, EU15, and Yugoslavianborn groups is mainly due to a decrease in the “no – other reason” responses (decreases vary between 7-10%) rather than to people being more able to afford it. In addition, among the EU15 and those born in Yugoslavia, the proportion of those who selected the option “no – I cannot afford it” has increased (+2.7 and +2.9 ). For those born in Türkiye and the “other countries” group, the proportion of “yes” answers has increased mainly because the proportion of “no – I cannot afford it” had decreased (decrease of 13.2% and 11.9%). Among the EU12 group, the “no – other reason” and “no – I cannot afford it” response rates have also decreased by almost the same amount (10.8% and 7.2%). As the “no – other reason” responses are almost evenly balanced, with a difference of 9.1% between the minimum and maximum, the real 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 20 40 60 Working hours / week FWF–NKFIH Joint Project 86 differentiating factor is the respondents’ financial situation. For those born in Austria (6.5%) and the EU15 (8.1%), the number of people who cannot afford leisure-time activities is small, for those born in the EU12 (18 ), Türkiye (21.7 ), and the “other countries” group (21.4%) it is a greater problem, whereas the highest proportion is found in the group of those born in Yugoslavia (27.7%) (Figure 71). Figure 71. Share of people according to their participation in leisure activities in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata Subjective satisfaction with the use of time also reveals the same trend as does job satisfaction, namely that the differences between groups observed in 2013 are decreasing, except for the group born in Yugoslavia. In 2018, as in 2013, satisfaction is highest for the group born in Austria, with a mean of 7.4, but this value is stagnating. The EU15 (7.2), EU12 (7.2) and “other countries” (7.1) groups seem to be catching up. In particular, the 0.5 score improvement in the EU12 mean is impressive. However, the mean for those born in Yugoslavia has fallen by 0.4. The satisfaction of those born in Yugoslavia (6.3) is not only below that of the “other countries” group, but also below that of those born in Türkiye (6.7), who, thanks to a small improvement (+0.2), are catching up with the leading groups, but still display a considerable gap (0.5 gap with the next group, namely the “other countries”). In all groups, the mean subjective score of those who cannot afford leisure-time activities is lower than not only those who participate in leisure activities but also those who have other reasons for not taking part in those activities. For those born in Yugoslavia, the means are 5.27 (“no – cannot afford it”), followed by 6.85 (“no – other reasons”) and finally 7 (“yes”). Similarly, for those born in Türkiye, there is a difference of 1.07 between the two sub-categories of “no” responses (Figure 72). 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Leisure No - other reason No - cannot afford it Yes Unknown FWF–NKFIH Joint Project 87 Figure 72. Satisfaction with time use in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata A measure of satisfaction with commuting time is missing from the 2018 survey, so we can only present findings for 2013. The population born in Austria displays the highest degree of satisfaction (mean 8.1), while the population born in Türkiye has the lowest (7.5). The interval between the two extremes is therefore not remarkable for this question. The remaining groups can be ranked as follows: Yugoslavia (7.6), EU12 (7.7), “other countries” (7.8), and EU15 (7.9) (Figure 73). Figure 73. Satisfaction with commuting time in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Satisfaction with time use 2013 NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Satisfaction with commute time FWF–NKFIH Joint Project 88 6.3.3 Social connections In 2013, those born in Austria had the lowest proportion (56.9%) of people living with a partner. By 2018, there was a small increase (+2.2%), while the proportion of people in a relationship has decreased from 62.4% to 58.6% among the EU15 group, which is now the group with the lowest proportion of people in a relationship. There has also been a drop in their share for the groups born in Türkiye (7.4 ) and Yugoslavia (7.5 ), so that the overall gap has narrowed over the five years. Despite the decline, those born in Türkiye still display the highest proportion of people in a partnership (75%), although this group is no longer followed by those born in Yugoslavia, but by the “other countries” (69.4%) and EU12 (67.5%) groups (Figure 74). Figure 74. Share of people according to their partnership status in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata In terms of marital status, the highest proportion (34.2%) of those who had never married by 2013 was observable among the Austrian-born population (Figure 75). Their share had increased slightly (+0.8%) by 2018. Whereas in the previous analysis, there had been groups in which the proportion of single people had decreased over the five years, the proportion of nevermarried has increased in all groups. This contrast may suggest a declining propensity to marry among cohabiting partners in general. There has been a spectacular increase in the proportion of never-married persons among those born in Yugoslavia (+5.6 ), Türkiye (+6.9 ), the EU12 (+12.2%) and the EU15 (+8.9%), the latter group now displaying the largest unmarried population segment at 42.2%. The share of married persons has fallen in line with these trends in all groups, except for a small increase of 0.2% for those born in Austria. As of 2013, the highest share of married persons remained among those born in Türkiye (71%), followed by 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Partner No Yes Unknown FWF–NKFIH Joint Project 95 Figure 82. Satisfaction with personal relationships in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata 6.3.4 External factors In terms of satisfaction with recreational and green spaces in 2013, the Austrian-born (mean 8.4) and EU15 (8.3) groups stand out clearly from the rest, followed by the EU12 group (7.5). The group born in Türkiye (7.2) and the “other countries” group (7.2) share the lowest mean, while the group born in Yugoslavia (7.4) has a mean closer to that of the EU12. Compared with the question on environmental problems analysed in the housing section, it is striking how much worse the EU12 and the “other countries” group rate their satisfaction with green spaces compared to the low percentage of environmental problems (a reminder: the occurrence of environmental problems for those groups is similarly low to that within the Austrian-born and EU15 groups) (Figure 83). Figure 83. Satisfaction with green areas in Austria (2013) categorised by country of birth 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Satisfaction with personal relationships FWF–NKFIH Joint Project 96 Source of data: Eurostat microdata The 2013 results for satisfaction with the living environment show a similar trend, with the highest mean for the Austrian-born (8.5) and the EU15 group (8.5). The difference, however, is that the mean for the “other countries” group is no longer the lowest, but the third highest (7.8, tied with those born in Yugoslavia). The mean for those born in Türkiye (7.6) is the lowest on this question (Figure 84). The higher satisfaction of the “other countries” group is more in line with the fact that only a low percentage of them live in neighbourhoods with environmental problems. Figure 84. Satisfaction with living environment in Austria (2013) categorised by country of birth Source of data: Eurostat microdata Institutional trust is analysed on the basis of the variables of trust in politics, the legal system, the police, the authorities, and the media, which are only available for 2013. The data reveal 2013 NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Satisfaction with green areas 2013 NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Satisfaction with living environment FWF–NKFIH Joint Project 97 the seemingly paradoxical situation that for all questions, the value for those born in Austria is generally lower than the scores of most of the foreign-born groups. For the questions on trust in politics, in the legal system, and in the police (although in a threefold tie), the value of the Austrian-born group is actually the lowest. Several studies have already shown that immigrants have higher trust in institutions than natives, and the reason may be that immigrants come from countries where these institutions perform poorly, so that their expectations are low (Röder & Mühlau, 2012). The mean of those born in Austria is highest for trust in the police (7.1) and they are even more trusting of the media (4.6) than of politics (4.2). The variable of trust in the police is where the means of the groups are closest – there is a 0.6 difference between the two extremes – while the interval between the minimum and maximum is wider for trust in the media and politicians (1.5 in both cases). In all categories of institutional trust, the “other countries” and Yugoslavia-born groups display the mean values with the highest levels of trust (“other countries” and Yugoslavia in order: politics: 5.7 and 5.5; police: 7.6 and 7.7; media: 5.7 and 5.6) (Figure 85). Figure 85. Trust in police in Austria (2013) categorised by country of birth Source of data: Eurostat microdata For the question on perceived safety, the proportion of respondents who did not answer is noteworthy. The largest proportion occurred within the “other countries” group (21.2%), while those born in Yugoslavia had the highest proportion of responses to this question (11.4% refrained from answering). The “very unsafe” response option is most prevalent within the EU12 (8.8%) group, a striking difference compared to the EU15 group (2.4%, the lowest rate). The proportion of furthter response options is balanced within the EU12 group (19.7% a bit unsafe, 27.1% fairly safe, 26.5 very safe). The same is observed for those born in Türkiye (21.8% – 26.7% – 26.5%). Within the “other countries” group, the most common responses are 2013 NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Trust in the police FWF–NKFIH Joint Project 98 fairly safe (33.9%) and very safe (25.7%), similar to those born in Austria (33% and 38.1%). The proportion of those feeling “a bit unsafe” is lower for the Austrian-born group than for the “other countries” group. The EU15 group has the highest perception of safety, with 41.7% responding that they felt very safe when walking around their neighbourhood (Figure 86). Figure 86. Population shares according to perceived safety in Austria (2013) categorised by country of birth Source of data: Eurostat microdata 6.4 Subjective well-being 6.4.1 Evaluative well-being (life satisfaction) In terms of overall satisfaction, the EU15 population displayed the highest mean scores in 2013 and 2018 (8 and 8.2 respectively). There has also been a similar increase of 0.2% for the Austrian-born population (7.9 – 8.1), maintaining their second place. All other groups, with the exception of the group born in Yugoslavia, have also seen an improvement, with a higher increase than that of the EU15 and Austrian-born groups. The “other countries” group had experienced an increase of 0.5 (from 7.3 to 7.8), while the EU12 (7.1 – 7.7) and the group born in Türkiye (6.2 – 6.8) both recorded an increase of 0.6. Although the mean satisfaction of the group born in Yugoslavia remained higher in 2018 (7) than that of the group born in Türkiye, the trend that the mean satisfaction of this group has fallen by 0.2 and that the gap with Türkiye has narrowed by 0.8 is worrying (Figure 87). Figure 87. Overall satisfaction in Austria (2013 and 2018) categorised by country of birth 2013 NAT EU15 EU12 TUR YUG OTH 0 25 50 75 100 Feel safe Very unsafe A bit unsafe Fairly safe Very safe Unknown FWF–NKFIH Joint Project 99 Source of data: Eurostat microdata We have also analysed the domains with which each group was most satisfied according to the two data sets (2013 and 2018). This shows that in 2013, satisfaction with the living environment and social relations ranked highest. Second last in all groups is satisfaction with time use, while satisfaction with finances ranked last altogether. The low satisfaction with finances correlates with the results of previous research (e.g. Delhey, 2004), also covering Austria. The intermediate rankings are shared by satisfaction with commuting time, green spaces, accommodation, and employment. It is interesting to note that for those born in Austria, green space is ranked third and employment sixth, while for those born in Yugoslavia, the reverse is true. In 2013, fewer subjective domains were surveyed. Two clusters are observed, the Austrian-born group and the EU15, where satisfaction with finances is higher than satisfaction with time use for 2018. Admittedly, the difference is minimal, only 0.1 and 0.2 respectively. All other groups show the same order of satisfaction with time and then finances as in 2013. In the absence of questions on living environment and green spaces, the order of relationships and employment is quite clear. Although the low number of items in this sample means that we cannot make any confident statements about gender differences, it is still worthwhile to talk about them, as there may be gender gaps within each group that could be useful for policy-making purposes. Research by Özlü-Erkilic and colleagues (2015) within the Turkish community in Vienna showed that men display higher satisfaction in areas such as health, income, and relationships, while the reverse is true for work, friends, and housing conditions, to the benefit of Turkish women. 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Overall satisfaction FWF–NKFIH Joint Project 100 6.4.2 Affective well-being There are no major differences in the happiness levels of each group. In 2013, there was only a 0.4 difference between the highest groups (born in Austria, EU15, and “other countries” – 3.8) and the lowest (born in Türkiye – 3.4), but by 2018, the difference had decreased to 0.3 (being the difference between 3.9 for those born in Austria and 3.6 for those born in Yugoslavia). Mean happiness levels have stagnated for the EU15 and the “other countries” group but increased for the remaining groups. The largest increase (0.3) is observed for those born in Türkiye. In their case, the proportion of those who are happy most of the time or all of the time has increased by 21% (Figure 88). Figure 88. Happiness levels in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata For the question on calmness and relaxation, similar values are observed as for happiness. Here, however, most of the groups have stagnated, with only the EU12 group showing an increase in mean (from 3.6 to 3.7), while the mean for the “other countries” group had decreased (from 3.8 to 3.6). Although there was a large increase in happiness, this was not observed for calmness within the group born in Türkiye, and they are still the group with the lowest mean as in 2013 (3.4). Only 53.79% of the group born in Türkiye were calm most of the time or all of the time. The group born in Austria and the EU15 had the highest mean in 2018 (3.8) (Figure 89). 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 1 2 3 4 5 Being happy FWF–NKFIH Joint Project 101 Figure 89. Calmness levels in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata Among the negative affective states, the highest mean scores were found for nervousness, which is therefore the most common problem. The previously observed increase in calmness is accompanied by a decrease in nervousness for the EU12 group (from 2.6 to 2.4), which is close to the best values for the group born in Austria (2.2), the EU15 and the “other countries” group (2.3). The group born in Türkiye and Yugoslavia display the worst values in both 2013 and 2018, but while the former shows a decrease (from 2.7 to 2.6), the latter stagnates (2.8). The highest rates of being most or constantly nervous are not found among those born in Türkiye (14.48%) but among those born in Yugoslavia (18.08%). The EU15 is the only group where there has been an increase, with a rate of 0.1. In this group, feelings of depression and downheartedness had stagnated over five years, with a stable mean score of 2, which is still a good result. The lowest mean is 1.9, measured in 2018 for the groups born in Austria and in the EU12. In the latter group, this is a big leap compared to 2013, with a decrease of 0.4. A similar decrease of 0.3 is observed within the group born in Türkiye, but their value of 2.3 remains the lowest, although they have already caught up with the stagnating Yugoslavian group (Figure 90). 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 1 2 3 4 5 Feel calm FWF–NKFIH Joint Project 102 Figure 90. Nervousness levels in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata In percentage terms, the proportion of people who were depressed most of the time or always has decreased from 14.97% to 8.96%. For those born in Yugoslavia, it has changed from 13.69% to 11.43% in five years (representing a mere 2.26% drop) (Figure 91). Figure 91. Levels of feeling downhearted in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata The least typical negative affect is feeling down in the dumps. In the case of this variable, an improving trend characteristic of the group born in the EU12 (from 2.1 to 1.7) and Türkiye (from 2.4 to 1.9) can be observed, while unfavourable processes are observable for those born in Yugoslavia (the mean increased from 2.1 to 2.2 and the proportion of those who were down most of the time or always has increased from 9.81% to 9.96%). Feeling down characterised 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 1 2 3 4 5 Being nervous 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 1 2 3 4 5 Feel downhearted FWF–NKFIH Joint Project 103 the group born in Austria the least in both years under scrutiny (the mean scores were 1.8 and 1.6); in 2018 the proportion of people who were in such a state most of the time or always comprised only 3.28% (Figure 92). Figure 92. Levels of feeling down in Austria (2013 and 2018) categorised by country of birth Source of data: Eurostat microdata The loneliness variable was only included in the 2018 survey, on the basis of which an order was created in line with the previous findings, namely: born in Austria (1.5), EU15, EU12, and the “other countries” group with the same value (1.6), those born in Türkiye (1.7), followed by the group born in Yugoslavia with a wide gap (2). Among those born in Austria, 2.77% are lonely most of the time or always, while this proportion is 11.07% among those born in Yugoslavia (Figure 93). Figure 93. Loneliness levels in Austria (2018) categorised by country of birth Source of data: Eurostat microdata 2013 2018 NAT EU15 EU12 TUR YUG OTH NAT EU15 EU12 TUR YUG OTH 1 2 3 4 5 Feel down 2018 NAT EU15 EU12 TUR YUG OTH 1 2 3 4 5 Feel lonely FWF–NKFIH Joint Project 104 6.4.3 Eudaimonic well-being The questions about eudaimonic well-being were only included in the 2013 survey, so that it is not possible to establish improving, stagnant, or deteriorating trends. Looking at the means, the group born in Austria (8) is the most likely to think that the things they do in their lives have meaning. They are followed by the EU15 (7.8) and EU12 (7.5) groups. The group born in Yugoslavia is interesting from the point of view that although their mean is low (7.3), the 75% quantile value is 10 (the only other group where this occurs is the group born in Austria) and the proportion of non-responders is low too (10.3 ). Those born in Türkiye (7.1) have the lowest mean value, as with the majority of the subjective variables, but from the previous results it can be assumed that by 2018, this position in the ranking would be different. Only 45.9% of those born in Türkiye answered with a value of 8, 9 or 10, compared to 62.05% of those born in Austria (Figure 94). Figure 94. Perceptions on meaning of life in Austria (2013) categorised by country of birth Source of data: Eurostat microdata In terms of optimism, the race for the lead is close: the EU15 group has the highest value (4), but the pack consisting of the group born in Austria, the EU15, and the “other countries” group lags behind only 0.1 points, with a score of 3.9. The lowest value (3.6) belongs to the group of people born in Türkiye in this category, too (Figure 95). The same can be established in the case of the free life variable for the born in Türkiye group, where their value is 3.9. In the case of this question, there is a different pattern for those born in Yugoslavia, as their value of 4.2 is the same as that of the EU12 group and higher than that of the “other countries” group at 4.1. The highest value is scored by the Austrian group (4.4) (Figure 96). 2013 NAT EU15 EU12 TUR YUG OTH 0.0 5.2 0.5 5.7 0.10 Meaning of life FWF–NKFIH Joint Project 111 of those who are planning to move abroad and those who are undecided do not have a partner (57%), while only 40% of stayers are in a similar situation. On the other hand, there was no difference in the average household size among the different target groups: each group displayed an average household size of 3 persons. Similarly, there was no difference in terms of the number of children in the households (Figure 105). Figure 105. Migration intention by share of respondents living with a partner (Hungary 2016) Source of data: Hungarian microcensus The level of trust towards other people is a measure of social capital, which eventually influences both individual well-being and migration intentions. Microcensus data, however, do not show important differences between the different target groups in this respect (Figure 106). Figure 106. Migration intention by mean values of trust in other people (Hungary 2016) Source of data: Hungarian microcensus FWF–NKFIH Joint Project 112 7.3.3. Security The perception of safety, how much one feels safe or unsafe, is also related to well-being and eventually to migration intentions . According to 2016 microcensus data, and considering the different target groups, the main, but still small, difference was between those who have migration plans and those who are undecided, with the latter feeling safer (73% of them feeling fairly or very safe as opposed to 67% of the former group). It seems that feeling less safe contributed to more defined migration plans. It also seems that those with migration plans have slightly more polarised attitudes on the matter than the other groups, with the highest share of people feeling very safe is found within that group (21%) (Figure 107). Figure 107. Migration intention by subjective feeling of security (Hungary 2016) Source of data: Hungarian microcensus 7.3.4. Trust in institutions The level of trust in institutions also constitutes a difference between those who plan to move abroad and those who are undecided, the latter generally having a lower level of trust. In general, people trust the military armed forces most, followed by the police, while the legal and the political system generate the least trust. The latter two institutions are significantly less trusted among those with migration intentions (Figure 108, Table 12). FWF–NKFIH Joint Project 113 Figure 108. Migration intention by mean values of trust in institutions (0-10 scale) (Hungary 2016) Source of data: Hungarian microcensus Table 12. Trust in institutions: Migration intention by mean values (0-10 scale) (Hungary 2016) Trust in… Migration intention No Do not know Yes the military 5.5 5.3 4.7 the police 5.4 5.0 4.3 the legal system 4.4 4.3 3.4 the political system 3.8 3.6 2.6 7.4. Experiences of migration Beside the reviewed elements, previous migration experiences are very important determinants of further migration intention. This experience can be either direct, when one has already lived abroad, or indirect, when someone from one’s immediate network is living abroad. Indeed, a gradual increase of the share of people with such experiences across the different target groups is seen from the data: while only 5.6% of stayers have lived abroad in the past or 4.5% have a relative living abroad, 11.6% of those planning to move have lived abroad before and 10.6% of them have a relative living abroad (Figure 109, 110). FWF–NKFIH Joint Project 114 The knowledge accumulated by people while living abroad is of key importance for future migration: as the amount of time spent abroad increases, the uncertainty related to moving is reduced. Another element of these experiences is the network surrounding the individual, which is fundamental to the migration decision, to the preparation, and to the implementation of emigration. The term “migration bubble” or “migration envelope” has been proposed in previous studies for such personal relations (Sik 2018). Figure 109. Migration intention by direct experiences with migration (Hungary 2016) Source of data: Hungarian microcensus Figure 110. Migration intention by indirect experiences with migration (Hungary 2016) Source of data: Hungarian microcensus FWF–NKFIH Joint Project 115 7.5. Subjective well-being 7.5.1. Evaluative well-being (overall life satisfaction) In terms of the evaluative elements of subjective well-being, there is a difference between those who would stay, those who are undecided, and those who are planning to move abroad. Those who would stay and those are undecided reported similar levels of overall satisfaction and across the different domains, while those with migration intentions were less satisfied, especially with personal incomes. The only exception was satisfaction with personal health: on average, while representing a younger population, the indecisive and the mover groups were both more satisfied with their health than those who had decided to stay. Beside different levels of satisfaction with health, those who didn’t know whether they would move were also the most satisfied group out of the three with their life in general, pointing to an eventual lack of motivation to move abroad. Overall satisfaction was 6.7 on average in their case (on a scale of 0-10), while it was 6.4 in the case of stayers and 6.1 for those intending to migrate (Table 13, Figure 111). Table 13. Migration intention by satisfaction with different aspects of life: mean values on a 0-10 scale (Hungary 2016) Satisfaction with… Migration intention No Do not know Yes personal income 5.2 5.1 4.4 financial situation of the household 5.8 5.9 5.3 accommodation 7.0 6.9 6.5 job 6.9 6.7 6.2 health 6.5 7.6 7.2 personal relationships 7.3 7.5 7.2 living environment 6.9 6.8 6.4 commuting environment 6.1 6.3 6.0 time use 5.9 5.8 5.4 Overall satisfaction 6.4 6.7 6.1 Source of data: Hungarian microcensus FWF–NKFIH Joint Project 116 Figure 111. Migration intention by mean values of satisfaction with different aspects of life on a 0-10 scale (Hungary 2016) Source of data: Hungarian microcensus FWF–NKFIH Joint Project 117 7.5.2. Affective well-being There was no notable difference between the level of affective well-being of the different target groups. It seems that affective well-being had no influence on intentions to move abroad. Nevertheless, although the average level of happiness was similar to that of the other groups, there was much less variance in the degree of happiness among those who did not know whether they would migrate. On the other hand, although the average levels of reported stress were similar, its variance was much more marked among those who planned to move abroad (Figure 112, Table 14). Figure 112. Migration intention by mean values of affective well-being on a 1-5 scale (Hungary 2016) Source of data: Hungarian microcensus FWF–NKFIH Joint Project 118 Table 14. Migration intention by affective well-being: mean values on a 1-5 scale (Hungary 2016) How much of the time over the past four weeks: Have you been... Migration intention No Do not know Yes Happy 3.7 3.9 3.8 Feeling calm 3.6 3.6 3.5 Feeling stressed 2.8 2.8 3.0 Nervous 2.6 2.5 2.8 Feeling downhearted 2.8 2.6 2.8 Feeling lonely 2.2 2.1 2.3 Source of data: Hungarian microcensus 7.5.3. Eudaimonic well-being In terms of having a life worth living on a scale of 0-10, people who wouldn’t move abroad had an average score of 7.1. Those who didn’t know whether they would move had a very similar score of 7.2, while people who would move abroad scored slightly lower at 6.9, with a higher variance than in the case of the previous groups. Accordingly, people who intend to migrate were slightly less convinced that they were leading a life that is worth living (Figure 113). Figure 113. Migration intention by subjective feeling that life is worthwhile: mean values on a 1-10 scale (Hungary 2016) Source of data: Hungarian microcensus The category of optimism, measured on a similar 0-10 scale, reveals somewhat different tendencies. While stayers scored 6.0 on average, the highest degree of optimism was reported by those who were undecided, with an average score of 6.5. Those with migration intentions scored the lowest at an average of 5.8, again with a higher variance (Figure 114). Figure 114. Migration intention by optimism: mean values on a 1-10 scale (Hungary 2016) FWF–NKFIH Joint Project 119 Source of data: Hungarian microcensus It seems that there is a younger, male-dominated, more educated, and rather urbanised group of people among which it is the level of perceived safety, trust in the legal and political system, satisfaction with different domains of life (especially with personal incomes), and optimism that decides whether they report intentions of migration or remain indecisive about it. People with lower levels of perceived safety, trust, satisfaction, and optimism are the ones that have better defined migration plans. They are also more likely to have previous direct or indirect experiences with migration. 8. The drivers of subjective well-being 8.1 Subjective well-being drivers in Austria 8.1.1 Variables in association with overall life satisfaction After presenting the descriptive data, the next step is to examine the extent to which each variable is associated with subjective well-being, and more specifically, with life satisfaction. For this purpose, different models have been created using linear regression, two of which are presented in greater detail and one of which is mentioned only briefly. It is important to emphasise that this method in itself is not capable of detecting causal relationships, but only of identifying links and correlations. Therefore, it is not possible to say whether the individual variables actually influence well-being. We can only report whether we could observe a relationship between the variable and well-being. FWF–NKFIH Joint Project 120 Model 1 and 2. In the modelling, both the 2013 and 2018 EU-SILC samples were used, with the year variable included as a dummy variable in the model. Since many questions were only surveyed among the employed population only (e.g., number of hours worked, ISCO categories), we created two models: a model for the full sample (Model 1) and another model for the sub-sample of employed individuals (Model 2). Figure 115 is a visual representation of the results of the two main models. The model run on the full sample is shown in light blue, while the one run on the working individuals’ subsample is shown in orange. The year dummy variable was found to be significant, holding all other variables constant, so that, compared to 2013, a respondent has a life satisfaction surplus in 2018 of 0.111 or 0.128 (according to the two models: full sample and workers). Socio-economic varables Among the socio-economic variables, age and the squared terms for age are also significant, so that the U-shaped relationship described in the literature appears for both models. This implies that life satisfaction decreases with age, but this decrease is transformed into an increase over time. Thus, the satisfaction of younger and older people is higher than that of middle-aged individuals. In the case of education, only the comparison between those with secondary and primary education reveals a significant difference for the total sample. The sub-sample of workers (9,106) comprises less than half of the total sample (19,524), making it more difficult to detect significant relationships. For the full sample, an individual with a secondary education reported a 0.067 higher well-being factor on average than an individual with a primary education. In the tertiary vs. primary level relation, this difference is 0.052, which is not significant. There is also a highly significant gender difference in favour of women. For the full sample, the coefficient of the variable is 0.151, while for the workers’ sample it is lower, namely 0.091. The last socio-economic variable examined is the number of children under 6 years of age per household. The more children a person has, the higher his or her subjective well-being is, but the effect is negligible and not significant. Migration-related variables The next block contains the variables of greatest interest to the research, namely those which relate to migration by country of birth. The results show that, when contrasted with having been born in Austria, all other categories are associated with lower life satisfaction. Although the coefficients for the EU15 and the “other countries” groups are negative for the full sample as well as for the worker sample, their value is minimal and cannot be considered significant, except for the “other countries” group in the model ran on the worker sample (where the FWF–NKFIH Joint Project 127 EU in 2004) as the main subject of the present project. Variables that were found to be significant in the formerly presented regression analysis were included in this analysis. The difference in overall life satisfaction between the two groups is 0.471, of which 0.268 can be explained, i.e., 56.9% of the total difference can be accounted for. These are due to the fact that individuals in the EU12 group are significantly less well positioned on variables that are closely related to the level of subjective well-being. Such variables include economic, housing, and social relations as well as spatial elements. The main contributor to the difference in subjective well-being is the much lower proportion of people in very good health in the EU12 group compared to the Austrian-born group. This is counterbalanced to a degree by the fact that good health is more common in the EU12 group, but this effect is not significant. Seeing that the regression analysis reveals a very close link between health and life satisfaction, this is certainly a worrying gap within the EU12 group. In general, there is also a shortfall for the EU12 group in terms of social relations. The Austrianborn group is more likely to participate in leisure activities, to meet friends, and to receive help from others. This group furthermore has a higher level of trust in people. These differences all contribute to the gap in subjective well-being between the two groups. Economic reasons include lower household incomes in the EU12 and higher rates of unemployment, deprivation, and poverty risk. Housing problems related to quality of life also affect the EU12 population to a greater extent, especially water leakage in the dwellings and noise pollution. Regional differences play a role: a greater proportion of the Austrian-born population live in Tyrol and Salzburg, two provinces displaying higher levels of subjective well-being. A difference of 0.203 is not explained by the different characteristics of the two groups. The literature provides two main explanations for this difference. One is an external cause: discrimination, negative attitudes and stereotypes. Discrimination fundamentally undermines overall life satisfaction, as shown in Haindorfer's (2020) study, which partly examines Hungarian cross-border commuters. Not only does discrimination hinder immigrants’ socioeconomic achievement on the labour and housing markets, but it also seems to undermine their anticipation in terms of life opportunities and suppress their happiness level. The effect of negative attitudes and stereotypes towards immigrants as a predictor of lower psychological well-being was demonstrated in a longitudinal sample in Austria by Weber et al. (2020). The second explanation is internal and it originates in the immigrants’ personality type and their culture, which is partly determined by their country of origin. Economic migrants are typically more extrinsically oriented (e.g., more oriented towards work, achievement, and power) and less intrinsically oriented (e.g., valuing family and friends) compared to stayers. Furthermore, migrants’ happiness continues to depend on their home-country conditions. FWF–NKFIH Joint Project 128 Figure 116. Explained differences in the EU12 - Austrian-born overall life satisfaction gap (Notes: Two-fold decomposition, group weight is -1, 500 bootstrapping replicates) Data source: Eurostat. Own figure. FWF–NKFIH Joint Project 129 8.2 Subjective well-being drivers in Hungary After the descriptive analyses, models were constructed to answer the following research question: “Which variables are related to the different degrees of migration intention and in which direction do these relationships hold?” Migration intentions were analysed in two ways: “no” versus “yes” (Figure 117) and “do not know” versus “yes” comparisons (Figure 118) using binary logistic regression. Both models were run on the full sample and on a subsample, the latter including only those respondents, who were employed or self-employed at the time of the survey. In the following sections we refer those as workers. The subsample model also includes extra variables which are available only for workers. We thus created a total of four different models. The data source for the models was the 2016 Hungarian microcensus. To facilitate the interpretation of the models, the resulting coefficients were converted into odds ratios and were plotted visually. 8.2.1 Modelling “no” vs “yes” outcomes As a first step, we examined socio-economic variables. It is clear that there is a fairly strong relationship between the level of education and migration intention. For the present variable, in contrast to the reference group of persons holding primary education only, having a secondary education (likeliness to migrate is greater by 32%) and a tertiary education (61%) is associated with a significantly higher intention to migrate. This association is also observed in the subsample of worker respondents, but the association is weaker there. Compared to the lowskilled active population, the probability of expressing the intent to emigrate is 17% higher among those with a secondary level of education, while the corresponding figure is 44% for the tertiary educated. Regarding the gender variable, the general phenomenon observed in international migration, namely that women have a lower migration potential than men, is also true for the Hungarian sample. For women, the probability that a respondent has the intention to migrate is only 0.74 times that of a man. The odds of migration among worker women are even lower compared to worker men (0.69). A negative relationship is also found for the age variable. With each additional year of age, the odds of emigration amounts to 0.947 of the previous odds in the case of the full sample and to 0.943 for the worker subsample. Having children is negatively related to the intention to migrate, and the more children there are, the less likely a respondent parent is to migrate. For each additional child under 18, the intention to migrate changes to 0.921 of the intention observed for those with one child less. The relationship is even stronger for children under five years of age, with an odds ratio of 0.83. It can therefore be concluded that the intention to migrate is lower especially for those with a minor child or children. If the children are older, the propensity to migrate is slightly higher, but not as high as for those without or with fewer children. In the sample reduced to the worker population, there is no significant relationship with the number of children under 18. FWF–NKFIH Joint Project 130 There is a positive correlation between willingness to migrate and direct or indirect experiences of migration. Those who have a history of migration and have already resided in one or more countries are 2.291 times more likely to migrate again (i.e., 129% more likely) than those who have only lived in Hungary. For the workers, the association is also strong, with an odds ratio of 1.955. It is interesting to note that, in contrast to direct experiences of migration, the association with intention to migrate is stronger for those with an indirect experience of migration, i.e., having someone living abroad. Those who have an acquaintance living abroad are 3 times more likely to migrate compared to those who do not have such a person. This is even more significant among workers, where the odds are 3.214 times higher. The main reason for this may be the prospect of finding work with the help of an expatriate relative. Among the economic variables, economic status was analysed only for the full sample, while the variable of high social status was analysed only for the sample restricted to the worker population. For economic status, the reference group was the employed group. Compared to them, a minimally lower intention to emigrate and a non-significant relationship was found both among the self-employed and students. However, belonging to the group of pensioners and the group of disabled showed a strong negative relationship with migration intention. As expected, these groups have lower intentions to emigrate. A retired person is only half as likely to migrate as is an active employee, and is only 0.3 times as likely to migrate as is a member of the reference group. Belonging to the unemployed group, on the other hand, is associated with a higher migration potential. Compared to an employed person, an unemployed person is 48.6% more likely to migrate. This is a surprising result, as the unemployed population is usually described as a group with few resources, lacking the financial background, skills, and social connections to move abroad. However, if unemployment is explained by poor economic opportunities in the country and a tight labour market, unemployed persons may indeed be of the opinion that moving abroad can increase their economic room for manoeuvre. Of course, it is unclear how many of the unemployed persons who had indicated their intention to migrate in the survey eventually did so. The odds ratio for those with a high social status is 0.995, and the relationship is not significant. Among the social relationships, the variables of trust and of having a partner were analysed. For the former, we consider relationships that are not necessarily social, but are related to society. The results show that having a partner is negatively linked to emigration intention. Among those who have a partner, the odds of moving are only 0.812 times that of singles, and is even lower for the subsample of workers (0.799). This may be because individuals would be reluctant to leave their partner behind if they were moving alone, and if the respondent is planning to move together with his/her partner, the partner may not be as receptive to moving. For the variable of trust in other people, a weak negative significant relationship is observed in the full sample, which disappears in the reduced subsample. The odds ratio is 0.977. Thus, if a person ranks one place higher on the scale measuring trust, the probability of emigration FWF–NKFIH Joint Project 131 decreases. This is an interesting relationship, since people who migrate are thought to be cosmopolitan, sociable individuals who trust others. Moreover, this low level of trust can be a significant barrier to integration in the new country. Concerning the other variables, those who intend to emigrate are less trusting of institutions in Hungary. In the case of the model run on the subsample derived for the worker population, there is a significant negative relationship with the levels of trust in politics, the police, and the legal system. For the full sample, this statement holds only for politics and police. On the scale of trust in politics, marking a value higher by one is associated with a lower chance of emigration. In that case the probability of emigration is reduced to 0.892 of the probability that occurs at a one-point lower marked trust value (0.894 in the model for the worker population). For the variable of trust in the police, the decrease in the odds is not as drastic, with odds ratios of 0.948 and 0.955 for the two models. However, the difference between the two models is that trust in the legal system is only significant in the model of the worker population, with an odds ratio of 0.985 for this variable. Among the variables representing spatiality, we first discuss the result of the regional NUTS2 classification. For this variable, the reference group is Budapest, the most developed region in Hungary. Of the other categories, only living in the Pest region is associated with an odds ratio greater than 1 and a greater intention to emigrate compared to Budapest, but this difference is not significant. Only two of the negative associations are significant, namely that of living in the Northern Great Plain region or the Southern Great Plain region. Both are associated with lower migration intention compared to living in Budapest. For the Northern Great Plain the odds are 0.729 times those for Budapest, while for the Southern Great Plain, migration intention measures at 0.811 compared to Budapest. This may be due to the fact that these regions are situated in the immediate vicinity of countries that are also underdeveloped by European standards (Romania, Serbia, Ukraine), so the promise of a nearby move is not very attractive. In the model run on the worker population, the significant relationship with the Southern Great Plain disappears and the odds ratio for the Northern Great Plain becomes even smaller (0.68). Staying with the worker subsample, in addition to the regional distribution, the type of immediate settlement also shows a relationship with the intention to migrate. Compared to living in a sparsely populated settlement, even living in a settlement with a medium population density is associated with a higher likelihood to emigrate (+17%). For those living in a densely populated settlement, the increase is even larger: plus 41.6% compared to the reference group of those living in sparsely populated regions. For the total sample, only the latter comparison is significant. The intention to migrate from a densely populated municipality is 1.34 times greater than that of the respondents from sparsely populated municipalities. The relationship between life satisfaction and migration potential has been the subject of numerous studies. Ostrashchenko & Popova (2014), for example, found that in Central and Eastern Europe, people with lower life satisfaction are more likely to indicate a desire to migrate. In contrast, in Hungary, Lengyel (2012) found no significant relationship between FWF–NKFIH Joint Project 132 overall satisfaction and exit potential. Among the variables measuring satisfaction with each domain of subjective well-being, we first elaborate on the items related to income and work. For the full sample and the reduced subsample, it is clear that there is a negative relationship between satisfaction with the financial situation and intentions to migrate. Each higher value changes the odds of a pre-existing probability by a factor of 0.941 (or 0.965 for the worker subsample). However, no significant relationship is observed for income satisfaction, a variable which was only assessed for the subsample of the group of employed or self-employed individuals. It can also be observed that the proportion of those who intend to migrate is higher among those who are dissatisfied with their job. The odds ratio for this variable is 0.947. Neither satisfaction with commuting time nor time use related to the work-life balance indicate a significant link with the intention to migrate. Similarly, satisfaction with accommodation does not display a relationship with the intention to migrate, but a significant negative relationship with satisfaction with the living environment is found in both models. For the full sample, the increase in satisfaction by one is accompanied by a reduction in the previous odds of migration by a factor of 0.932 (0.923 for the subsample). This satisfaction may also be linked to living in densely populated settlements (where previously we found a higher intention to move) with a higher concentration of environmental problems. A positive association with intention to migrate is observed for two variables: satisfaction with health and satisfaction with relationships. An increase of one score in health satisfaction is accompanied by a 2.4% increase in the propensity to migrate (1.9% in the worker sample, which is not significant). This suggests that it is not those suffering from the shortcomings of the Hungarian health system but those in good health who would migrate. This is logical, since in the new country, fit and healthy individuals are more likely to find work for themselves, and the move itself can be a physically demanding process, which those in very poor health are less likely to undertake (especially if they need supervision because of their poor health). For satisfaction with social relationships, a one-point increase on the scale changes the odds of emigrating to 1.078 times the previous odds (1.086 for the worker sample). This is surprising because the previous results suggest that those in a relationship and those with children had lower intentions to move. Among the affective well-being variables, significant relationships were found only in the positive direction. The weakest such relationship was found between happiness level and intention to emigrate, and this was significant only for the total sample. This is a new finding compared to Lengyel’s (2012) research in Hungary. A happiness level of one score higher is associated with a 6% increase in the likelihood of emigration. Successful migration requires a certain degree of happiness that renders the individual more confident about their plans. Happier individuals are more likely to seek challenges and are more adventurous and optimistic (Polgreen & Simpson, 2011). According to Ivlevs (2015), it is mainly in poorer countries that happier people migrate, as they are more likely to find work in the new country. Our result support this, but does not support the finding that relative unhappiness with individuals of the FWF–NKFIH Joint Project 133 same socio-economic background leads individuals to migrate (Graham & Markowitz, 2011). For the factor of stress, a larger odds ratio is observed (1.095 for the full sample and 1.07 for the subsample of worker individuals). This suggests that individuals who are more frequently stressed have a higher propensity to emigrate. This stress may also be triggered by the previously described dissatisfaction with the financial situation and job. Among the affective variables, the strongest relationship is found for loneliness. A one-point increase on the scale is associated with a 13.3% higher probability of emigration (15.7% for the worker subsample). This result is in line with the results for the children and partner variables but stands in contrast to the findings for subjective satisfaction with relationships. There is a lower proportion of people who want to move away among those who are more nervous, or calmer, or more often feel themselves downhearted, but these relationships are not significant. No significant relationship is found between the two variables of eudaimonic well-being (meaningfulness of life and optimism) and intention to migrate, but the direction of the relationships is negative, so those who consider their life meaningful or are optimistic are slightly less likely to migrate. Figure 117. Visual representation of the coefficients and the confidence intervals for the models treating migration intention as the dependent variable. “No” versus “yes”. FWF–NKFIH Joint Project 134 Data source: Eurostat. Own figure. FWF–NKFIH Joint Project 135 8.2.2 Modelling “do not know” vs “yes” outcomes In the following, we analyse only the “do not know” or “yes” answers to the question regarding migration intention. Compared to the results of the “no-vs-yes” model, an important difference is that, compared to having low education, having a secondary education is not significantly related to whether a person indicates “yes” or “do not know”. However, a positive relationship is observed for tertiary education. Having a tertiary education is associated with a 34.8% higher chance of “yes” compared to the reference group. Furthermore, the significant relationship found in the “no vs yes” model is no longer present for gender. The age variable retained its significant effect, but it is weakened somewhat. One additional year of age is associated with 0.992-fold change in the odds of a “yes” response for the full sample (with an equal value for the subsample). Although the number of children under 18 retained its significant negative effect (more children is linked to a lower chance of responding “yes” even compared to “do not know”), the variable for the number of children under 6, which previously had a very strong relationship, has retained the negative sign, but lost its significance. Direct and indirect experiences of migration are also strongly significantly related to the choice between “do not know” and “yes”. Having previous experience of migration is associated with a higher chance of a “yes” by 34.8% (31.6% in the worker subsample), while having a friend living abroad is associated with a higher probability of a “yes” by 82.8% (99.9% in the worker subsample). Again, the evidence shows that indirect experience could be particularly important for workers. In terms of economic activity, the fact of being unemployed compared to belonging to the reference group (employed) is associated with a higher probability of “yes” answers even in this model. Although it should be noted that the relationship has weakened, there is now only a 27% increase in the odds. The negative significant relationship observed in the previous model has disappeared among the retired and those who are disabled. In the topic of relationships, the existence of a partner is no longer significantly associated with the difference between “do not know” and “yes” answers. In the full sample, the association remains that those with a higher level of trust in people are less likely to answer with “yes” (the association is no longer significant in the worker subsample). Indeed, a higher level of trust is associated with a 0.961-fold modification of the odds of saying yes. The variables of institutional trust, except for trust in the police, maintained their negative significant relationships. In the “do not know” and “yes” comparison, trust in the legal system is also significant for the full sample, not only for the subsample. The odds ratio is 0.953 for the full sample and 0.924 for the subsample. If someone has high trust in the legal system, he/she is therefore less likely to choose the “yes” answer. The same is true for those who trust politics. On the full sample, a response of one point higher on the scale for the legal system causes a 0.953-fold change in the odds of a “yes” answer, while the same movement on the scale for trust in politics brings about a 0.923-fold change in the odds for the confident migration intention response. FWF–NKFIH Joint Project 136 Looking at the NUTS2 regions, it can be observed that, compared to the Budapest reference group, living in any other region except Pest does not reveal a significant relationship with the two currently examined response options for migration intention in the model reduced to the worker subsample. In the full model, the Northern Great Plain retains its significant effect. That is, residents of the Northern Great Plain are less likely to choose “yes” as opposed to “do not know”, compared to Budapest residents. The odds ratio is 0.724. The same is also true for the Pest region (odds ratio is 0.731 times that of a Budapest resident), which is a novelty compared to the models presented earlier, where the previous results showed that living in Pest was associated with a greater tendency towards migration, although not significantly so, compared to Budapest. The Southern Great Plain region, which previously showed a significant negative relationship, is not significant in the “yes” and “do not know” comparisons. Among the subjective well-being domains measuring satisfaction, satisfaction with finances and satisfaction with relationships are significantly related to the difference between “yes” and “do not know” responses, as they were in the “yes” and “no” comparisons. In the full sample, if satisfaction is one point higher on the scale for the financial question, then it is associated with a 0.965-fold change in the odds of a “yes” answer (0.968 in the worker sample, but the relationship is not significant). An increase in satisfaction with relationships is associated with a 5% increase in the odds towards a “yes” (4.9% in the worker sample). The previously observed role of satisfaction with health turns negative, i.e., those who are satisfied with their health have a decrease in the odds of answering with “yes” (odds ratio 0.964 in the full sample, 0.948 in the worker sample). Thus, while satisfaction with health is associated with a higher possibility of “yes” in the “no and yes” comparisons, it is associated with a lower chance of a “yes” in the “do not know vs yes” models. Among the affective well-being variables, the present models also show a significant relationship between experiencing stress or loneliness and an increased intention to migrate. An increase in the frequency of these negative affective states is associated with a higher probability of emigration. The role of negative emotions is furthermore reinforced by the fact that the effect of nervousness is positively significant in the “do not know” and “yes” comparisons, but the role of happiness is not (based on previous models, a higher degree of happiness seemed to be associated with an increase in the odds of emigration). The strongest relationship is found for nervousness. An increase of 10.4% in the odds of a “yes” response is associated with a one-degree higher level of nervousness on the scale. No significant relationship is observed for the eudaimonic variables. FWF–NKFIH Joint Project 143 Unemployed Unemployed Pupil, student, further training, unpaid work experience Student In retirement or in early retirement or has given up business Retired Permanently disabled or/and unfit to work Disabled In compulsory military community or service Other inactive Fulfilling domestic tasks and care responsibilities Other inactive person Occupation MC 2016 Source variables Integrated dataset Occupation (FOGLKOD1) Occupation (ISCO08_1) ISCO08 Classification, 1st level ISCO8 Classification, 1st level excluding level 0 (armed forces) EU-SILC 2013, 2018 Source variables Integrated dataset Occupation (PL051) Occupation (ISCO08_1) ISCO08 Classification, 2nd level ISCO8 Classification, 1st level excluding level 0 (armed forces) Total weekly hours in paid work MC 2016 Source variables Integrated dataset Weekly hours in paid work (MUNKIDO) Total weekly hours in paid work (HOURS) Numeric value >= 0 Numeric value >= 0 EU-SILC 2013, 2018 Source variables Integrated dataset Number of hours usually worked per week in main job (PL060) The number of hours usually worked in second, third....jobs (PL100) Total hours in paid work Numeric value >= 0 Numeric value >= 0 PL060 + PL100 Regular leisure activity MC 2016 Not available EU-SILC 2013, 2018 FWF–NKFIH Joint Project 144 Source variables Integrated dataset Regularly participate in a leisure activity (PD060) Regular leisure activity (LEISURE2) Yes Yes No - cannot afford it No - cannot afford it No - other reason No - other reason Education MC 2016 Source variables Integrated dataset Highest completed educational level (LISKV) Education (EDUC) Did not complete the first grade of primary school Primary or less Primary school 1st–3rd grade Primary school 4th–5th grade Primary school 6th–7th grade Primary school 8th grade Secondary school without graduation, with a vocational certificate Secondary Secondary school graduation University, college, etc., with a degree Tertiary EU-SILC 2013, 2018 Source variables Integrated dataset Highest ISCED level attained Education (EDUC) ISCED <= 200 Primary or less 300 <= ISCED < 500 Secondary ISCED >= 500 Tertiary Sex MC 2016 Source variables Integrated dataset Sex (NEME) Sex (GENDER) Male Male Female Female EU-SILC 2013, 2018 Source variables Integrated dataset Sex (PB150) Sex (GENDER) Male Male Female Female FWF–NKFIH Joint Project 145 Age MC 2016 Source variables Integrated dataset Age (KEV) Age (AGE) Numeric values >= 0 Numeric values >= 0 EU-SILC 2013, 2018 Source variables Integrated dataset Age (RX010) Age (AGE) Numeric values >= 0 Numeric values >= 0 Region (NUTS1) MC 2016 Source variables Integrated dataset Region NUTS1 (REGIO) Region NUTS1 (NUTS1) HU11 Central Hungary HU12 HU21 HU22 Transdanubia HU23 HU31 Great Plain and North HU32 HU33 EU-SILC 2013, 2018 Source variables Integrated dataset Region NUTS1 (DB040) Region NUTS1 (NUTS1) AT1 Eastern Austria AT2 Southern Austria AT3 Western Austria HU1 Central Hungary HU2 Transdanubia HU3 Great Plain and North Region (NUTS2) MC 2016 FWF–NKFIH Joint Project 146 Source variables Integrated dataset Region (REGIO) Region NUTS2 (NUTS2) HU11 Budapest HU12 Pest HU21 Central Transdanubia HU22 Western Transdanubia HU23 Southern Transdanubia HU31 Northern Hungary HU32 Northern Great Plain HU33 Southern Great Plain EU-SILC 2013, 2018 (Not available for Hungary) Source variables Integrated dataset Region NUTS2 (BUNDESLD) Region NUTS2 (NUTS2) AT11 Burgenland AT12 Lower Austria AT13 Vienna AT21 Carinthia AT22 Styria AT31 Upper Austria AT32 Salzburg AT33 Tyrol AT34 Vorarlberg Degree of urbanization MC 2016 Source variables Integrated dataset Administrative rank of settlement (IGRANG) Degree of urbanization (URBAN) Budapest kerületei Densely Megyeszékhely Densely Megyei jogú város Densely Város Intermediate Nagyközség Intermediate Község Thinly EU-SILC 2013, 2018 Source variables Integrated dataset Degree of urbanization (DB100) Degree of urbanization (URBAN) FWF–NKFIH Joint Project 147 Densely populated area Densely Intermediate area Intermediate Thinly populated area Thinly Country of birth MC 2016 Source variables Integrated dataset Country of birth (SZ_EU28) Country of birth (CBIRTH1) Hungary Native Country in the European Union (EU28) EU28 Other country Other EU-SILC 2013, 2018 Source variables Integrated dataset Country of birth (PB210) Country of birth (CBIRTH1) LOC Native EU EU28 OTH Other Detailed country of birth MC 2016 Not available EU-SILC 2013, 2018 (Not available for Hungary) Source variables Integrated dataset Country of birth (P110000nu) Detailed Country of birth (CBIRTH2) Austria Native EU15, EFTA EU15, EFTA EU12 EU12 Yugoslavia without Slovenia Yugoslavia without Slovenia Türkiye Türkiye Other Other Citizenship MC 2016 Source variables Integrated dataset FWF–NKFIH Joint Project 148 Citizenship (ALLAMP) Citizenship (CTZSHIP) Hungary Native Other Foreign EU-SILC 2013, 2018 Source variables Integrated dataset Country of birth (PB220A) Citizenship (CTZSHIP) LOC Native EU Foreign OTH Foreign Year of immigration MC 2016 Not available EU-SILC 2013, 2018 Source variables Integrated dataset Year of immigration (RB031) Year of immigration (YIMMIG) Numeric values between 1938 and 2018 Numeric values between 1938 and 2018. If the individual was born in the country, the missing value is replaced by the value of AGE. Duration of stay (years since the immigration) MC 2016 Not available EU-SILC 2013, 2018 The variable DURATION is calculated as the difference between the survey year (YEAR) and the year of immigration (YIMMIG). Household size MC 2016 The variable HSIZE is calculated as the sum of unique personal identification numbers (PID) with the same household identification number (HID). EU-SILC 2013, 2018 FWF–NKFIH Joint Project 149 Source variables Integrated dataset Household size (HX040) Household size (HSIZE) Numeric values >= 1 Numeric values >= 1 Number of children in the household (younger than 6) MC 2016 The variable CHILDREN5 is derived by summing the number of individuals in the same household (HID) whose age (AGE) is below six years. EU-SILC 2013, 2018 The variable CHILDREN5 is derived by summing the number of individuals in the same household (HID) whose age (AGE) is below six years. Number of children in the household (between 6 and 17) MC 2016 The variable CHILDREN18 is derived by summing the number of individuals in the same household (HID) whose age (AGE) is between 6 and 17 years. EU-SILC 2013, 2018 The variable CHILDREN18 is derived by summing the number of individuals in the same household (HID) whose age (AGE) is between 6 and 17 years. Migration intention MC 2016 Source variables Integrated dataset Planning to move abroad within the next 2 years for work, study, or other reasons (WTERV) Migration intention (MIGINTENTION) No No Do not know Do not know Yes Yes EU-SILC 2013, 2018 Not available FWF–NKFIH Joint Project 150 Migration history MC 2016 Source variables Integrated dataset Lived outside the current territory of Hungary for at least one continuous year (KULLAHE) Migration history (MIGHISTORY) No No Yes Yes EU-SILC 2013, 2018 Not available Relatives living abroad MC 2016 Source variables Integrated dataset Household member living abroad on a permanent basis (TKESZ) Household members temporarily living abroad (AKESZJAV) RELATABROAD = TKESZ + AKESZJAV Numeric value >= 0 Numeric value >= 0 Numeric value >= 0 EU-SILC 2013, 2018 Not available Number of rooms in the dwelling MC 2016 Source variables Integrated dataset Size of the dwelling bases on the number of rooms (LASZOB) Number of rooms (ROOMS) 1 room without kitchen 1 1 room 1 2 rooms without kitchen 2 2 rooms 2 3 rooms 3 4 rooms 4 5 rooms 5 6 rooms or more 6 EU-SILC 2013, 2018 FWF–NKFIH Joint Project 151 Source variables Integrated dataset Number of rooms available to the household (HH030) Household size (HSIZE) Numeric values between 1 and 6 Numeric values between 1 and 6 Leaking roof, damp, or rot MC 2016 Not available EU-SILC 2013, 2018 Source variables Integrated dataset Leaking roof, damp, or rot (HH040) Leaking roof, damp, or rot (LEAKING) No No Yes Yes Dark rooms MC 2016 Not available EU-SILC 2013, 2018 Source variables Integrated dataset Dark rooms (HH160) Dark rooms (DARK) No No Yes Yes Noise pollution MC 2016 Not available EU-SILC 2013, 2018 Source variables Integrated dataset Noise pollution in the area (HH170) Noise pollution in the area (NOISE) No No Yes Yes FWF–NKFIH Joint Project 152 Pollution in the area MC 2016 Not available EU-SILC 2013, 2018 Source variables Integrated dataset Pollution in the area (HH170) Pollution in the area (ENV_PROBLEM) No No Yes Yes Crime in the area MC 2016 Not available EU-SILC 2013, 2018 Source variables Integrated dataset Crime in the area (HH170) Crime in the area (CRIME) No No Yes Yes Total personal income (gross, current EUR) MC 2016 Not available EU-SILC 2013, 2018 Cash from employment (PY010G) + Cash from self-employment (PY050G) + Pension from individual private plan (PS080G) + Unemployment benefit (PY090G) + Old-age benefit (PY100G) + Survivors benefit (PY110G) + Sickness benefit (PY120G) + Disability benefit (PY130G)