Trust, Values, and Immigration Attitudes among Managers and Workers in 14 European Countries

Trust, Values, and Immigration Attitudes among Managers and Workers in 14 European Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trust, Values, and Immigration Attitudes among Managers and Workers in 14 European Countries Hamed Ahmadinia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8251577/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This article examines how generalised trust and basic human values are associated with immigration attitudes among managers and other workers in Europe. Using data from 282,662 native-born respondents in 14 countries across 11 waves of the European Social Survey (2002–2023), we analyse both general immigration attitudes and acceptance of immigrants from different racial, ethnic, and socioeconomic backgrounds. Multilevel linear models with country and year fixed effects, and a random slope for generalised trust, show that self-transcendence values and higher trust are associated with more positive views of immigration, while conservation values are linked to more exclusionary attitudes. These patterns remain stable after adjusting for demographic factors. Managers express slightly more inclusive attitudes than other workers, although the difference is modest. Cross-national variation in the trust slope indicates that institutional contexts shape how strongly trust relates to support for immigration. We also observe small increases in immigrant acceptance around major events such as the 2015 arrivals and the COVID-19 pandemic, particularly among high-trust individuals. The study contributes to comparative migration research by linking psychological dispositions, occupational roles, and national contexts in shaping attitudes toward immigration and diversity. immigration attitudes generalised trust human values managers workers cross-national comparison multilevel modelling diversity and inclusion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The integration of immigrants into European labour markets is shaped by political developments, economic conditions, and public sentiment. Among the actors involved, organisational decision-makers—particularly managers—play a distinct role. As individuals responsible for hiring, supervision, and workplace culture, managers translate societal norms and personal values into everyday organisational practices. Understanding how they perceive immigration and diversity therefore helps connect macro-level debates about integration with meso-level labour market outcomes. Community organisations often support cultural and social adaptation but rarely influence access to employment directly (Sandberg et al., 2023), highlighting the central position of managers in shaping opportunities for immigrants. Over the past two decades, several major events have reconfigured the public and political environment in which these decisions unfold. The 2004 EU enlargement altered labour mobility patterns, the 2008 financial crisis intensified concerns about economic competition, and the 2015 asylum arrivals triggered wide public debate about borders and welfare sustainability. More recent events—including Brexit, the COVID-19 pandemic, and the arrival of Ukrainian refugees—demonstrate how crises can activate solidaristic or exclusionary responses (Indelicato & Martín, 2024; Jain et al., 2022; Martin & Bergmann, 2021). These shifts rarely affect all countries in the same way. Their impact is filtered through institutional structures, national policy regimes, media narratives, and local norms surrounding immigration. Theoretical perspectives on group conflict, relative deprivation, and intergroup contact help explain why immigration attitudes vary across social groups and contexts (Meuleman et al., 2020; Pettigrew & Tropp, 2006; Webster & Whitmeyer, 2001). These theories emphasise the role of perceived threat, symbolic boundaries, and social distance, and suggest that individual dispositions may be amplified or muted by structural conditions such as economic downturns or large-scale refugee movements. Prior research also shows that attitudes toward immigrants differ by occupation and class, with some groups perceiving greater competition or holding stronger symbolic boundaries (Nese, 2023). These theoretical perspectives guide the interpretation of our findings, although they are not directly tested in the empirical analysis. Managers have received comparatively little attention in cross-national public opinion research, despite their relevance for immigrant integration. Positioned at the intersection of organisational mandates and public expectations, managers exercise autonomy over hiring, promotion, and workplace norms (Van Riemsdijk & Basford, 2022). Their decisions can support the inclusion of immigrant workers or reinforce exclusionary practices, particularly under conditions of ambiguity or limited guidance (Farashah & Blomquist, 2019). Examining their attitudes alongside those of other workers therefore offers insight into how occupational roles intersect with broader societal debates about immigration and diversity. Two individual-level orientations are particularly relevant for understanding variation in immigration attitudes: generalised trust and personal value priorities. Generalised trust—the expectation that most people can be trusted—is consistently linked to tolerance, reduced prejudice, and social cohesion (Knack & Keefer, 1997; Rothstein & Stolle, 2008). Its role in shaping managers’ views of immigration, however, has been rarely explored (Van Hoorn, 2018). Personal value priorities, particularly self-transcendence (e.g., universalism, benevolence) and conservation (e.g., security, conformity, tradition), are among the most important predictors of attitudes toward immigrants (Davidov & Meuleman, 2012; Schwartz, 1992). While extensively studied in general populations, these orientations are less often examined among organisational actors or occupational groups. This study investigates how generalised trust and personal values relate to immigration attitudes among managers and other workers in Europe. We analyse native-born respondents from 14 countries across 11 waves of the European Social Survey (2002–2023). Two outcomes are examined. The first captures general attitudes toward immigration’s effects on the economy, culture, and quality of life. The second measures readiness to accept immigrants from racially or ethnically different groups and from poorer non-European countries. These items tap both broad sentiment and concrete inclusion thresholds. A third ESS item, referring to immigrants of the same race or ethnicity, is excluded because it reflects in-group preference rather than openness to diversity. To analyse these outcomes, we estimate multilevel models with fixed effects for survey year and country, and a random slope for generalised trust. This specification allows the influence of trust to vary across national contexts while holding constant country-level institutional differences. Values and trust are conceptualised as independent orientations rather than causal mechanisms. Occupational position is included to assess whether managers differ systematically from other workers after controlling for demographic characteristics. The analysis is structured around four research questions: RQ1: How are generalised trust and value orientations (self-transcendence and conservation) related to attitudes toward immigrants who differ by race or socioeconomic background? RQ2: Do managers hold more positive or negative immigration attitudes than other workers after accounting for values, trust, and demographic factors? RQ3: How do these relationships vary across countries and across ESS rounds? RQ4: Do predictors of general immigration attitudes differ from those shaping acceptance of specific immigrant groups? By addressing these questions, the study contributes to research on comparative migration, workplace diversity, and symbolic boundaries. It highlights how psychological orientations and occupational roles shape immigration attitudes in different national contexts and offers insights relevant for policymakers and organisational leaders concerned with immigrant integration. 2. Theoretical Framework This study draws on social capital theory and Schwartz’s theory of basic human values to explain how generalised trust and value orientations relate to immigration attitudes among European managers and other workers. While public opinion research on immigration is extensive, the views of organisational actors—who influence hiring, advancement, and workplace norms—remain less examined. An occupational perspective helps situate individual dispositions within the institutional and sociopolitical contexts in which organisations operate. In addition, three broader sociological perspectives—group conflict theory, relative deprivation, and intergroup contact—inform how external pressures may shape the salience of trust and values. These perspectives are used heuristically to interpret patterns; they are not directly tested in the empirical analysis. 2.1 Generalised Trust and Social Capital Generalised trust refers to the belief that most people can, in general, be relied upon. Within social capital theory, this orientation supports cooperation, social cohesion, and institutional legitimacy (Rothstein & Stolle, 2008). Different studies link higher generalised trust with lower prejudice and greater openness toward diversity (Knack & Keefer, 1997; Van Hoorn, 2018; Zak & Knack, 2001). Its relevance for managerial attitudes remains relatively unexplored. Managers influence how diversity policies are implemented and how immigrant employees are assessed and integrated. Behaviourally, higher trust reduces the perceived risk of interacting with unfamiliar or culturally distant others, while lower trust may heighten sensitivity to perceived threats and reliance on heuristics such as in-group preference. In hiring or team composition, these tendencies may translate into more or less inclusive practices. In this study, generalised trust is modelled as a continuous predictor with a country-level random slope, acknowledging previous findings that trust operates differently across national contexts. 2.2 Human Values and Managerial Dispositions Personal values are enduring principles that guide judgement and behaviour. According to Schwartz’s (1992) theory, two higher-order value dimensions are especially relevant for immigration research. Self-transcendence values (benevolence, universalism) emphasise empathy, equality, and concern for others and are consistently linked with more inclusive attitudes (Davidov & Meuleman, 2012). Conservation values (security, conformity, tradition) emphasise order and continuity and tend to correlate with exclusionary or restrictive immigration preferences, particularly toward outgroups perceived as culturally distant or destabilising (Davidov et al., 2020). Although widely used in public opinion research, these orientations have been less studied among organisational decision-makers. Values may shape how managers interpret the presence of immigrants in the workplace and whether diversity is viewed as an asset or a potential disruption (Farashah & Blomquist, 2019; Russo et al., 2021). In the models, self-transcendence and conservation are included as fixed effects and treated as separate predictors alongside trust. Managers are not only bearers of personal dispositions; they also operate within organisations that structure hiring norms and inclusion practices (Dobbin et al., 2011). This study therefore links psychological orientations with occupational authority to understand how attitudes toward immigrants take shape. Table 1 outlines these value dimensions and the concept of generalised trust, forming the conceptual basis for analysing managerial and worker attitudes toward immigration. Table 1: Definitions of Schwartz Human Values and Generalized Trust Concept/Category Human Value/Concept Definition Human Values Universal, trans-situational principles guiding attitudes and behaviors, categorized into four dimensions: self-transcendence, self-enhancement, openness to change, and conservation (Czymara & Eisentraut, 2020). Self-Transcendence Benevolence Concern for the well-being of close others, such as family and friends (Czymara & Eisentraut, 2020). Universalism Emphasis on understanding, tolerance, and protection for all people and the natural world (Czymara & Eisentraut, 2020). Self-Enhancement Achievement Aspiration to demonstrate competence and attain success according to social standards (Sipinen et al., 2020). Power Pursuit of social status, dominance, and authority over others (Davidov et al., 2020). Openness to Change Self-Direction Valuing independence, creativity, and freedom of thought and action (Czymara & Eisentraut, 2020). Stimulation Seeking excitement, novelty, and challenge in life (Czymara & Eisentraut, 2020). Conservation Tradition Respect for and commitment to cultural, religious, and societal customs (Davidov et al., 2020). Conformity Avoidance of behaviors or impulses that harm others or violate societal norms (Sipinen et al., 2020). Security Focus on stability, safety, and harmony within society and relationships (Sipinen et al., 2020). Generalised Trust The belief that most people in a society are honest and reliable, fostering cooperation, societal cohesion, and positive outcomes such as economic growth and reduced crime (Sipinen et al., 2020). {Table 1 here} 2.3 From General Sentiment to Immigrant Acceptance General attitudes toward immigration—such as whether immigration benefits the economy or enriches culture—capture broad sentiment but may obscure variation in whom respondents are willing to admit. To address this, the analysis includes a second outcome measuring acceptance of immigrants from racially or ethnically different groups and from poorer non-European countries. These items reflect perceived social distance and are commonly used to approximate concrete inclusion thresholds (Bell, 2021; Czaika & Di Lillo, 2018). A related ESS item that refers to immigrants of the same racial or ethnic background is excluded because it largely captures in-group preference rather than openness to diversity (Výrost & Dobeš, 2019). The two selected items form a composite index of acceptance, while the general attitude items form the basis of the broader sentiment measure. This distinction allows the analysis to compare predictors of symbolic attitudes and specific acceptance boundaries. 2.4 Contextualising Trust and Values Trust and values do not operate independently of the wider environment. Although macro-level variables are not directly modelled, several theoretical perspectives help contextualise how external conditions may shape the link between dispositions and immigration attitudes. Group conflict theory suggests that perceived threats to material or symbolic status can heighten exclusionary orientations (Meuleman et al., 2009). For managers, such perceptions may intensify during periods of economic uncertainty or demographic change. Relative deprivation theory emphasises perceived unfairness or status loss as triggers of negative attitudes (Meuleman et al., 2020). In organisational settings, diversity initiatives may be interpreted as reallocating opportunities or altering workplace traditions. Intergroup contact theory highlights how sustained, equal-status interactions can reduce prejudice and build empathy (Pettigrew & Tropp, 2006). Inclusive organisational environments may therefore amplify the effects of trust and self-transcendence, whereas ambiguous or unsupportive contexts may reinforce perceived threats. Macro-level events such as the 2015 asylum arrivals or the 2022 displacement of Ukrainians can activate existing predispositions rather than changing them fundamentally. Political discourse and media framing play an important role in shaping whether such events evoke solidarity or exclusion (McLaren & Paterson, 2020). Managers, positioned between organisational norms and public narratives, may be especially sensitive to these cues. These frameworks are used to interpret patterns in the results and to highlight the conditions under which trust and values may matter more or less. 2.5 Connecting Trust, Values, and Occupational Roles The analysis considers trust and values as separate, relatively stable psychological orientations. Although these factors may correlate—for example, trust is often higher among individuals with stronger self-transcendence values (Koivula et al., 2017)—they are not modelled as mediators or placed in a causal sequence. To examine whether occupational authority is associated with immigration attitudes, the models compare managers and other workers while controlling for demographic characteristics such as gender, age, income, and education. Manager status is included as a fixed effect in all models to assess whether any observed differences persist net of values and trust. The multilevel models incorporate fixed effects for survey year and country to account for institutional heterogeneity and use a random slope for generalised trust to capture cross-national variation in its influence. This approach balances the need to represent national context while avoiding excessive model complexity given the number of countries. Together, this framework links psychological dispositions, occupational roles, and institutional contexts in understanding immigration attitudes in Europe. It contributes to broader sociological discussions of symbolic boundaries, stratification, and organisational gatekeeping during a period of demographic and political change (Ramirez & Kim, 2024; Ziller, 2015). Figure 1 presents the conceptual framework, illustrating how trust, values, occupational status, and demographic controls feed into immigration attitudes and acceptance within a multilevel structure. (Insert Figure 1 here) 3. Data, Measurements, and Analytical Methods 3.1 Data Sources and Samples This study uses data from Rounds 1–11 (2002–2023) of the European Social Survey (ESS), a cross-national survey programme based on probability sampling, face-to-face interviews, and harmonised fieldwork procedures (Lindstrøm & Kropp, 2017 ; Schnaudt, et al., 2014 ). The ESS is widely used for comparative research on public attitudes, including migration, social trust, and personal values. We focus on 14 European countries that participated regularly and provide comparable information on immigration attitudes, generalised trust, human values, and core sociodemographic characteristics: Belgium, Finland, France, Germany, Hungary, Ireland, the Netherlands, Norway, Poland, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. The analytic sample is restricted to native-born respondents and to those who were in paid work (employed or self-employed) at the time of the survey. Native-born respondents are identified using the ESS birthplace item, and employment status is drawn from labour-force questions. Within this employed population, managers are identified using ISCO-08 occupation codes 1000–1439, in line with international classification standards (Ganzeboom & Treiman, 1996 ). All other employed respondents are treated as “other workers”, who form the reference category in occupational comparisons. This procedure yields 22,673 managers and 259,989 other workers across 11 ESS rounds and 14 countries. Managerial shares differ across countries, reflecting differences in occupational structures and sample composition, but the distribution is broadly balanced and supports meaningful cross-national comparison. Table 2 reports the number and proportion of managers and other workers by country and ESS round. Overall, the largest employed subsamples come from Germany and the United Kingdom, while smaller samples are observed in some Central and Eastern European countries. This cross-national diversity allows us to examine how trust, values, and immigration attitudes vary across institutional settings. Table 2 Country-wise Distribution of Managers and Other Workers Across ESS Rounds Group Subgroup R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 All % Manager Belgium 112 194 141 188 163 195 156 170 125 98 68 1610 7.1 Switzerland 143 120 90 129 64 134 118 105 142 109 122 1276 5.63 Germany 148 220 120 148 155 156 203 200 173 437 130 2090 9.22 Spain 61 66 118 215 115 66 90 83 106 143 145 1208 5.33 Finland 178 197 174 207 196 85 101 88 101 61 78 1466 6.47 France 99 156 152 156 179 115 61 203 36 179 118 1454 6.41 United Kingdom 226 228 287 249 287 174 183 155 195 129 180 2293 10.11 Hungary 133 104 22 78 51 74 84 38 67 79 87 817 3.6 Ireland 184 190 174 150 201 195 129 225 162 62 208 1880 8.29 Netherlands 292 199 257 264 241 184 197 144 155 130 179 2242 9.89 Norway 130 158 94 102 110 91 146 150 160 113 130 1384 6.1 Poland 190 115 158 191 307 250 172 199 174 268 190 2214 9.76 Portugal 68 150 54 60 45 38 34 51 43 106 79 728 3.21 Sweden 92 88 108 104 79 112 100 87 82 259 85 1196 5.27 Slovenia 70 63 89 56 71 67 56 56 95 82 110 815 3.59 Total 2126 2248 2038 2297 2264 1936 1830 1954 1816 2255 1909 22673 100 Other Worker Belgium 1629 1425 1504 1398 1353 1412 1386 1337 1364 1024 1311 15143 5.82 Switzerland 1553 1628 1374 1263 1091 1023 1021 999 975 977 869 12773 4.91 Germany 2557 2405 2569 2372 2590 2502 2543 2355 1883 7394 1994 31164 11.99 Spain 1589 1479 1612 2126 1578 1605 1666 1666 1354 1877 1396 17948 6.9 Finland 1759 1786 1664 1932 1617 2018 1886 1760 1570 1459 1401 18852 7.25 France 1254 1514 1639 1755 1395 1645 1633 1655 1733 1563 1469 17255 6.64 United Kingdom 1635 1498 1871 1858 1864 1846 1767 1538 1710 870 1218 17675 6.8 Hungary 1512 1361 1463 1436 1468 1915 1587 1550 1575 1743 1979 17589 6.77 Ireland 1712 1952 1390 1329 1969 2050 1948 2080 1661 1367 1481 18939 7.28 Netherlands 1916 1518 1454 1346 1447 1493 1539 1403 1340 1220 1344 16020 6.16 Norway 1773 1474 1532 1317 1286 1330 1121 1225 1097 1150 1042 14347 5.52 Poland 1889 1582 1539 1409 1419 1625 1427 1484 1314 1782 1241 16711 6.43 Portugal 1353 1793 2029 2169 1959 1981 1136 1121 881 1533 1100 17055 6.56 Sweden 1694 1675 1602 1513 1245 1501 1454 1281 1232 1746 995 15938 6.13 Slovenia 1314 1270 1282 1123 1210 1077 1070 1127 1068 1041 998 12580 4.84 Total 25139 24360 24524 24346 23491 25023 23184 22581 20757 26746 19838 259989 100 { Table 2 here} Table 3 summarises the sociodemographic profile of the pooled sample. The age distribution is skewed toward midlife cohorts, with relatively few respondents under 25 years. Women make up just over half of the sample. Educational attainment is relatively high: more than half of respondents have completed upper secondary or tertiary education. Perceived income adequacy is concentrated in the “coping” and “living comfortably” categories, though a substantial minority report financial difficulties. These patterns are broadly consistent with ESS benchmarks for employed respondents. Table 3 Detailed Sociodemographic Profile of European Managers and Employees in ESS Rounds 1–11 Variable Category R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 Age 18 & under 1435 1336 1293 1208 1288 1196 1096 994 920 1193 798 19–24 2286 2226 2014 2107 2114 2117 1842 1808 1582 2083 1502 25–34 4220 4031 3890 3852 3578 3607 3186 3156 2841 3525 2589 35–44 5170 4815 4612 4493 4108 4265 3723 3643 3327 3903 3083 45–54 4796 4532 4635 4590 4380 4663 4299 4188 3812 5133 3528 55–64 4038 4199 4320 4264 4367 4683 4426 4359 3935 5124 3783 65 & over 5320 5469 5798 6129 5920 6428 6442 6387 6156 8040 6464 Total 27265 26608 26562 26643 25755 26959 25014 24535 22573 29001 21747 Gender Male 13073 12319 12309 12525 12197 12743 12005 11840 10924 13705 10455 Female 14192 14289 14253 14118 13558 14216 13009 12695 11649 15296 11292 Total 27265 26608 26562 26643 25755 26959 25014 24535 22573 29001 21747 Education Level Less than lower secondary 4434 4730 4517 4498 4126 3836 3053 2652 2161 2075 1706 Lower secondary completed 5761 5503 5206 5015 4632 4760 4204 3826 3335 3987 3118 Upper secondary completed 10548 9796 9625 9275 9129 9786 8929 8831 8117 10517 7752 Post-secondary non-tertiary completed 542 695 826 811 1181 1333 1357 1377 1226 1871 1265 Tertiary education completed 5980 5884 6388 7044 6687 7244 7471 7849 7734 10551 7906 Total 27265 26608 26562 26643 25755 26959 25014 24535 22573 29001 21747 Income Feeling Living comfortably on present income 8752 8291 8859 8630 8190 8219 8798 9561 8923 11533 9205 Coping on present income 13700 13473 12739 12825 12200 12545 11582 11312 10259 13396 9584 Difficult on present income 3859 3826 3845 4026 3989 4564 3624 2858 2763 3246 2396 Very difficult on present income 954 1018 1119 1162 1376 1631 1010 804 628 826 562 Total 27265 26608 26562 26643 25755 26959 25014 24535 22573 29001 21747 Manager Status European Manager 2126 2248 2038 2297 2264 1936 1830 1954 1816 2255 1909 Other European Worker 25139 24360 24524 24346 23491 25023 23184 22581 20757 26746 19838 Total 27265 26608 26562 26643 25755 26959 25014 24535 22573 29001 21747 { Table 3 here} Figure 2 displays average levels of generalised trust by ESS round across the 14 countries. Trust levels show modest fluctuation over time, with dips in the mid-2000s and around the onset of the COVID-19 pandemic, and higher levels in the mid-2010s and late 2010s. These movements coincide with major political and economic events, suggesting that institutional shocks may influence trust and, in turn, attitudes toward immigration. While the multilevel models account for clustering and contextual variation, it is important to note that ESS data are cross-sectional. As such, the analysis cannot establish causal relationships and remains vulnerable to omitted-variable bias, particularly in comparative settings (Schmidt-Catran et al., 2019 ). The ESS nevertheless offers substantial advantages in terms of large samples, stable measurement instruments, and harmonised translation and fieldwork protocols. Figure 2 illustrates yearly fluctuations in generalised trust across the 14 countries from 2002 to 2023. (Insert Fig. 2 here) 3.2 Measurements The first dependent variable captures general attitudes toward immigration. It is based on three standard ESS items that ask whether immigration is bad or good for the national economy, undermines or enriches the country’s cultural life, and makes the country a worse or better place to live. Each item is measured on an 11-point scale from 0 (most negative) to 10 (most positive). The items are averaged to form a composite index, with higher scores indicating more positive views. These indicators, widely used in previous research, tap perceived economic, cultural, and societal consequences of immigration rather than specific policy preferences (Davidov & Meuleman, 2012 ; Meuleman et al., 2009 ). Item wording and coding details are provided in Appendix 1. The second dependent variable measures acceptance of socially distant immigrants. It combines two ESS items asking whether the country should allow many, some, a few, or no immigrants to come and live there if they are (a) from a different racial or ethnic group than the majority and (b) from poorer countries outside Europe. The four response categories are reverse-coded so that higher values indicate greater acceptance and then standardised and averaged. A third item about immigrants of the same race or ethnic group is intentionally omitted because it largely captures in-group preference and tends to inflate apparent support for immigration (Semyonov et al., 2006 ; Výrost & Dobeš, 2019 ). The key independent variables are generalised trust and human value orientations. Generalised trust is measured with three ESS items asking whether most people can be trusted, whether people try to be fair, and whether people are helpful. Each item is rated on an 11-point scale from 0 (lowest trust) to 10 (highest trust); the items are averaged to construct a composite trust index, which has been widely validated in studies of social capital and intergroup attitudes. Human values are measured using Schwartz’s Portrait Values Questionnaire (PVQ). Following ESS guidelines, we focus on two higher-order value dimensions: self-transcendence and conservation. Self-transcendence is captured by items that emphasise caring for others and equality, while conservation is captured by items that emphasise safety, rule-following, and tradition. All PVQ items are rated on a six-point scale from “not like me at all” to “very much like me.” In line with ESS coding recommendations, items are ipsatised, reverse-coded where necessary, and then combined into indices, which are standardised to have a mean of 0 and a standard deviation of 1. Details on item sets and coding steps are provided in Appendix 1. Control variables include gender, age (categorised into six groups), educational attainment (harmonised across rounds using ISCED-based categories), perceived income adequacy, political left–right self-placement, occupational role (manager vs other worker), country, and ESS round. 3.3 Data Cleaning and Transformation All data preparation and analysis were conducted in Python, using pandas for data handling, NumPy for numerical operations, and statsmodels and scikit-learn for modelling and scaling. The merged ESS file (Rounds 1–11) was harmonised to ensure consistent coding of variables across countries and rounds, including later waves with altered naming conventions. Where necessary, variables were mapped to a common naming scheme. To deal with item nonresponse, we used a simple single-imputation strategy. Categorical variables (such as gender and education) were imputed with the sample mode, while continuous variables (such as trust, values, and immigration attitudes) were imputed with the median after recoding standard ESS nonresponse categories (e.g., “don’t know,” “refusal,” “no answer”) to missing values. This approach prioritises sample retention in a large, pooled cross-national dataset where listwise deletion could substantially reduce statistical power and distort cross-country representation (Schmidt-Catran et al., 2019 ). This study acknowledges that more advanced techniques such as multiple imputation could, in principle, yield better estimates under certain missingness assumptions; this limitation is revisited in the discussion. Outliers on continuous variables were examined using z-scores. Observations with values beyond ± 3 standard deviations from the mean were treated as extreme. Rather than excluding these cases, we applied a winsorisation procedure, capping values at the ± 3 standard deviation thresholds. This widely used approach reduces the influence of extreme observations while maintaining the full sample, which is especially useful in large surveys with low measurement error but occasional long-tailed distributions (Cousineau & Chartier, 2010 ; Yoseph et al., 2019 ). Scale construction and rescaling followed ESS protocols. The indices for generalised trust, self-transcendence, conservation, and immigrant acceptance were standardised to have a mean of 0 and a standard deviation of 1. All continuous predictors were z-standardised prior to modelling to facilitate interpretation and comparability of coefficients. Occupational status was coded as described above, with managers distinguished from other workers based on ISCO-08 codes. The final pooled dataset contains 282,662 valid cases. 3.4 Analytical methods To examine how generalised trust and value orientations relate to immigration attitudes among managers and other workers, we estimate two multilevel linear models. Individuals (Level 1) are nested within countries (Level 2). Both models include fixed effects for country and ESS round to account for time-invariant national characteristics and common temporal shocks. All continuous predictors and outcome variables are z-standardised, so coefficients can be interpreted as effects in standard-deviation units. Models are estimated using restricted maximum likelihood (REML) with robust standard errors. A random slope for generalised trust is specified, allowing the association between trust and immigration attitudes to vary across countries. Model 1: General Immigration Attitudes The first model uses the composite index of general attitudes toward immigration (economic, cultural, and overall quality-of-life evaluations) as the outcome. At the individual level, the model includes generalised trust, self-transcendence, conservation, and the control variables (age group, gender, education, perceived income adequacy, political orientation, and occupational role: manager vs other worker). Country and round fixed effects capture macro-structural and temporal context, and a country-level random slope for trust captures cross-national variation in the strength of the trust–attitude relationship. In simplified form: $$\:{\varvec{I}\varvec{m}\varvec{m}\varvec{i}\varvec{g}\varvec{r}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}\:\varvec{A}\varvec{t}\varvec{t}\varvec{i}\varvec{t}\varvec{u}\varvec{d}\varvec{e}\varvec{s}}_{\varvec{i}\varvec{j}\:}=\:{\varvec{\beta\:}}_{0}+\:{\varvec{\beta\:}}_{1\:}{.\:\varvec{T}\varvec{r}\varvec{u}\varvec{s}\varvec{t}}_{\varvec{i}\varvec{j}}+\:{\varvec{\beta\:}}_{2}{\:.\:\varvec{C}\varvec{o}\varvec{n}\varvec{s}\varvec{e}\varvec{r}\varvec{v}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}}_{\varvec{i}\varvec{j}}+\:{\varvec{\beta\:}}_{3}{\:.\:\varvec{S}\varvec{e}\varvec{l}\varvec{f}\varvec{T}\varvec{r}\varvec{a}\varvec{n}\varvec{s}}_{\varvec{i}\varvec{j}}\:{\:+\:\varvec{\beta\:}}_{4}\:.\:{\varvec{C}\varvec{o}\varvec{n}\varvec{t}\varvec{r}\varvec{o}\varvec{l}\varvec{s}}_{\varvec{i}\varvec{j}}+\:{\varvec{\beta\:}}_{5}{\:.\:\varvec{M}\varvec{a}\varvec{n}\varvec{a}\varvec{g}\varvec{e}\varvec{r}}_{\varvec{i}\varvec{j}}+{\varvec{\gamma\:}}_{\varvec{j}}{+\:{\varvec{\delta\:}}_{\varvec{r}\:}+\:\:\varvec{u}}_{1\varvec{j}\:}.\:{\varvec{T}\varvec{r}\varvec{u}\varvec{s}\varvec{t}}_{\varvec{i}\varvec{j}}+\:{\varvec{\epsilon\:}}_{\varvec{i}\varvec{j}}$$ where: i indexes individuals j indexes countries r indexes ESS rounds • \(\:{\:\:Controls}_{ij}\:\) : age, gender, education, income adequacy • \(\:{\:Manager}_{ij}\) : binary indicator for ISCO-defined managerial status \(\:{\gamma\:}_{j}\) fixed effect for country \(\:{\delta\:}_{r\:}\) fixed effect for ESS round \(\:{u}_{1j\:}\) random slope of generalised trust by country \(\:{\epsilon\:}_{ij}\) individual-level error term Model 2: Immigrant acceptance The second model uses the composite index of acceptance of immigrants from racially/ethnically different groups and from poorer non-European countries as the outcome. The set of predictors is identical to Model 1: generalised trust, self-transcendence, conservation, sociodemographic controls, and occupational role, along with country and round fixed effects and a random slope for trust. $$\:{\varvec{A}\varvec{c}\varvec{c}\varvec{e}\varvec{p}\varvec{t}\varvec{a}\varvec{n}\varvec{c}\varvec{e}}_{\varvec{i}\varvec{j}\:}=\:=\:{\varvec{\beta\:}}_{0}+\:{\varvec{\beta\:}}_{1}\:.\:{\varvec{T}\varvec{r}\varvec{u}\varvec{s}\varvec{t}}_{\varvec{i}\varvec{j}}+\:{\varvec{\beta\:}}_{2}{\:.\:\varvec{C}\varvec{o}\varvec{n}\varvec{s}\varvec{e}\varvec{r}\varvec{v}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}}_{\varvec{i}\varvec{j}}+\:{\varvec{\beta\:}}_{3}\:.\:{\varvec{S}\varvec{e}\varvec{l}\varvec{f}\varvec{T}\varvec{r}\varvec{a}\varvec{n}\varvec{s}}_{\varvec{i}\varvec{j}}\:{\:+\:\varvec{\beta\:}}_{4}{\:.\:\varvec{C}\varvec{o}\varvec{n}\varvec{t}\varvec{r}\varvec{o}\varvec{l}\varvec{s}}_{\varvec{i}\varvec{j}}+\:{\varvec{\beta\:}}_{5}{\:.\:\varvec{M}\varvec{a}\varvec{n}\varvec{a}\varvec{g}\varvec{e}\varvec{r}}_{\varvec{i}\varvec{j}}+{\varvec{\gamma\:}}_{\varvec{j}}{+\:{\varvec{\delta\:}}_{\varvec{r}\:}+\:\:\varvec{u}}_{1\varvec{j}\:}.\:{\varvec{T}\varvec{r}\varvec{u}\varvec{s}\varvec{t}}_{\varvec{i}\varvec{j}}+\:{\varvec{\epsilon\:}}_{\varvec{i}\varvec{j}}$$ As in Model 1, the random slope term \(\:{\varvec{u}}_{1\varvec{j}}\) captures between-country variability in the effect of generalised trust, while fixed effects absorb unobserved national and temporal heterogeneity. Standardising predictors allows for straightforward comparison of effect sizes across models. 4. Results 4.1 Overview of Predictors and Patterns Across both multilevel models, generalised trust and human value orientations emerge as the strongest and most consistent predictors of immigration attitudes. Individuals with higher self-transcendence values—emphasising empathy, equality, and concern for others—hold significantly more positive attitudes toward immigration. By contrast, higher conservation values, which stress security, conformity, and tradition, are linked with more restrictive views. These associations are stable across 14 countries and 11 ESS rounds, indicating a high degree of temporal and cross-national consistency. Generalised trust, measured as a composite of interpersonal trust, fairness, and helpfulness, is also positively associated with both general immigration attitudes and immigrant acceptance. However, the magnitude of this association varies across countries. As Fig. 3 shows, the trust–attitude link is stronger in some contexts (e.g., the United Kingdom, France, Portugal) than in others (e.g., Hungary, Slovenia, Spain). This suggests that institutional and cultural environments condition how strongly trust translates into openness. Figure 3 also shows the Δβ divergence between the effect of trust on general attitudes and its effect on acceptance of socially distant immigrants. In some Western European countries, trust has a noticeably larger impact on general sentiment, whereas in several Eastern and Southern European countries the trust effects across both outcomes are more similar. These patterns align with the idea that accepting socially distant groups may require higher levels of trust in certain contexts. Sociodemographic variables display expected associations. Individuals with higher levels of education and those reporting financial comfort show more positive attitudes, while older respondents tend to be more sceptical. Gender differences are statistically detectable but substantively negligible. Because country and ESS-round fixed effects are included in all models, these findings reflect individual-level patterns net of broader institutional and historical influences. Taken together, psychological orientations—trust and values—consistently predict immigration attitudes across Europe, with further variation shaped by education, income, and age. These patterns motivate the more detailed model-specific results presented next. (Insert Fig. 3 here) 4.2 Multilevel Modelling Results 4.2.1 Model 1: General Immigration Attitudes Model 1 examines general evaluations of the economic, cultural, and societal impact of immigration. The model includes fixed effects for country and ESS round, a random slope for generalised trust, and controls for demographic characteristics and occupational role. Table 4 presents the full results. Self-transcendence values are strongly and positively associated with favourable immigration attitudes (β = 0.233, p < 0.001). Conservation values show the opposite pattern (β = − 0.323, p < .001), reflecting more sceptical orientations. Generalised trust also has a meaningful positive effect (β = 0.202, p < 0.001). The random slope variance for trust (Var = 0.002) indicates that its influence varies across countries. Among the controls—treated as background adjustments rather than key theoretical predictors—education (β = 0.114, p < 0.001) and perceived income adequacy (β = 0.102, p < 0.001) show modest positive associations, whereas older respondents express slightly more negative views (β = − 0.004, p < 0.001). Gender differences are statistically significant but substantively very small (β = − 0.016, p < 0.001). Figure 4 shows predicted immigration attitudes by occupational group across countries. Managers generally express more positive attitudes than other workers, but the size of the occupational gap varies considerably. Larger differences appear in Switzerland, Germany, the Netherlands, and Norway, whereas in Eastern and Southern European countries—such as Hungary, Poland, and Portugal—both groups hold more restrictive views and occupational gaps are smaller or even reversed. The predicted values are adjusted for all covariates, isolating managerial differences net of demographic, value, and trust factors. The pattern suggests that occupational position plays a modest but detectable role, particularly in Western and Nordic contexts. Overall, Model 1 indicates that value orientations and trust account for the largest share of variation in immigration attitudes, with structural variables and occupational role contributing additional, but smaller, effects. (Insert Fig. 4 here) 4.2.2 Model 2: Immigrant acceptance Model 2 shifts attention from general sentiment to acceptance of immigrants from different racial/ethnic groups and from poorer non-European countries. The same model structure is used, including fixed effects for country and ESS round and a random slope for generalised trust. As shown in Table 4, self-transcendence again predicts higher acceptance (β = 0.259, p < 0.001), while conservation predicts lower acceptance (β = − 0.244, p < 0.001). Generalised trust remains positively associated (β = 0.140, p < 0.001). The trust random slope variance (Var = 0.002) again indicates cross-national heterogeneity. Education (β = 0.094, p < 0.001) and gender (β = 0.031, p < 0.001) are linked to greater acceptance, although the gender effect is very small. Perceived income adequacy (β = 0.077, p < 0.001) and age (β = − 0.049, p < .001) show weaker associations. The occupational coefficient indicates a small but statistically significant difference: nonmanagers are slightly less accepting than managers (β = − 0.013, p = 0.026). While the magnitude is limited, this aligns with the pattern observed in Model 1. Figure 5 displays predicted acceptance levels for managers and workers from 2002 to 2023. Acceptance rises modestly around major events such as the 2015 refugee arrivals and the COVID-19 pandemic, aligning with short-term increases in solidaristic or humanitarian sentiment observed in other studies. The gap between occupational groups narrows slightly in the early 2020s but remains statistically significant in 2023 (0.24 for managers vs. 0.15 for other workers). Overall, Model 2 indicates that trust and value orientations remain central correlates of acceptance of socially distant immigrant groups, while occupational differences persist but are modest in magnitude. (Insert Fig. 5 here) Table 4: Multilevel Regression – Generalised Trust, Human Values, and Immigration Attitudes and Acceptance Variable 1 Model 1 Model 2 Est Se Est Se Fixed Effects Intercept -0.230*** 0.012 0.008 0.013 Sociodemographic Variables Age -0.004*** 0.001 -0.049*** 0.001 Gender -0.016*** 0.003 0.031*** 0.003 Education 0.114*** 0.001 0.094*** 0.001 Income -0.102*** 0.002 -0.077*** 0.002 Values Self-Transcendence 0.233*** 0.003 0.259*** 0.003 Conservation -0.232*** 0.003 -0.244*** 0.003 Trust Generalised Trust 0.202*** 0.010 0.140*** 0.011 Managerial Status Other European Worker -0.046*** 0.006 -0.013* 0.006 Year Effects Y.2004 -0.041*** 0.007 -0.060*** 0.007 Y.2006 -0.015* 0.007 -0.081*** 0.007 Y.2008 0.006 0.007 -0.027*** 0.007 Y.2010 -0.049*** 0.007 -0.063*** 0.007 Y.2012 0.015* 0.007 -0.005 0.007 Y.2014 -0.026*** 0.007 -0.011 0.007 Y.2016 -0.009 0.007 0.048*** 0.007 Y.2018 0.069*** 0.007 0.128*** 0.008 Y.2020 0.149*** 0.007 0.203*** 0.007 Y.2023 0.081*** 0.007 0.126*** 0.008 Country Effect s C.Switzerland 0.160*** 0.009 0.009 0.010 C.Germany 0.077*** 0.007 0.085*** 0.008 C.Spain 0.299*** 0.008 0.200*** 0.009 C.Finland 0.353*** 0.008 -0.217*** 0.009 C.France -0.070*** 0.008 -0.022** 0.009 C.Great Britain -0.075*** 0.008 -0.107*** 0.009 C.Hungary -0.217*** 0.009 -0.670*** 0.009 C.Ireland 0.196*** 0.008 0.023** 0.009 C.Netherlands 0.075*** 0.009 -0.039*** 0.009 C.Norway 0.120*** 0.009 0.190*** 0.010 C.Poland 0.429*** 0.009 0.216*** 0.009 C.Portugal 0.248*** 0.009 -0.011 0.010 C.Sweden 0.327*** 0.009 0.520*** 0.009 C.Slovenia -0.124*** 0.009 0.005 0.010 Random Effects Residual 0.599 0.7013 Random Slope Generalised Trust Slope Variance 0.002 0.000 0.002 0.001 Model Fit (Deviance) 657596.38 702177.50 1 Note: `*` p < .05, `**` p < .01, `***` p < .001. Stars indicate statistical significance of fixed effects. { Table 4 here} 5. Discussion This study examined how generalised trust and basic human values shape immigration attitudes among managers and other workers across 14 European countries over two decades. By drawing on harmonised ESS data and multilevel modelling, the analysis clarifies how psychological dispositions and occupational roles relate to public views about immigration within different national contexts. Consistent with Schwartz’s theory of basic values, self-transcendence—values emphasising empathy, equality, and concern for others—is strongly associated with both general immigration attitudes and acceptance of more socially distant immigrant groups. Conservation values, which stress security, conformity, and tradition, predict more sceptical or exclusionary views. These findings confirm earlier value-based research and show that these orientations remain highly stable across countries and survey waves (Davidov et al., 2008 ; Meuleman et al., 2020 ). Generalised trust also emerges as a robust correlate of pro-immigration attitudes, echoing prior evidence linking trust to tolerance and social cohesion (Rothstein & Stolle, 2008 ; Van Hoorn, 2018 ). The random-slope estimates indicate that the strength of the trust–attitudes relationship varies across countries, suggesting that institutional or cultural settings condition how trust is expressed. In some contexts, trust translates strongly into inclusion; in others, its effect is more muted. These cross-national differences align with research on symbolic boundaries and how national discourses shape perceptions of immigrants (Ramirez & Kim, 2024 ; Ziller, 2015 ). The results also show modest but consistent occupational differences. Managers express slightly more positive attitudes than other workers after controlling for values, trust, and sociodemographic factors. While the effect size is small, it is systematic and suggests that occupational authority may be associated with somewhat more inclusive orientations. As organisational gatekeepers, managers influence hiring, workplace norms, and daily interactions with diverse employees (Farashah & Blomquist, 2019 ; Van Riemsdijk & Basford, 2022 ). Even small attitudinal differences in this group may matter for how symbolic boundaries are enacted in workplaces. Sociodemographic patterns largely mirror previous findings. Higher education is associated with more open views, reflecting the well-established link between schooling and cosmopolitan orientations (Hainmueller & Hiscox, 2007 ). Financial comfort is associated with more positive attitudes, whereas older respondents tend to be more cautious. Gender differences are small. Temporal patterns also show slight increases in acceptance during salient events such as the 2015 refugee arrivals and the early stages of the COVID-19 pandemic, though the cross-sectional design does not allow for causal conclusions. Stronger support in countries such as Switzerland, Sweden, and Germany contrast with more restrictive attitudes in parts of Eastern and Southern Europe, reflecting differences in national discourse, policy frameworks, and historical experiences with diversity (Medvešek et al., 2022 ; Nese, 2023 ). Despite the stability of the findings, several limitations remain. First, the ESS is cross-sectional, preventing analysis of within-person change and limiting causal inference. Second, attitudes—while important—do not automatically translate into behaviour or organisational practices. Managers may support inclusion in principle yet face institutional constraints that affect hiring or promotion decisions. Future research should therefore link attitudinal data to behavioural indicators, administrative records, or field experiments. Third, the study uses single-imputation methods for handling missing data; more advanced techniques such as multiple imputation may yield more precise estimates under certain missingness assumptions. Overall, this study provides a large comparative examination of how trust, values, and occupational roles relate to immigration attitudes among European workers. The findings highlight the central role of psychological dispositions and show how these orientations intersect with occupational roles and national contexts. For policymakers and organisational leaders concerned with immigrant integration, the results point to the importance of strengthening trust, reinforcing inclusive norms, and providing clear institutional guidance—especially during periods of societal uncertainty, when symbolic boundaries are most likely to shift. 6. Contribution to the Literature and Practical Implications This study contributes to comparative migration research by linking generalised trust, personal values, and occupational roles in shaping immigration attitudes across diverse European contexts. While psychological dispositions are well-established predictors of views on immigration, they are less often examined among organisational actors. By comparing managers with other workers across 14 countries and 11 ESS rounds, the analysis provides broad descriptive evidence that value orientations and trust are associated with immigration attitudes in similar ways across occupational groups, with managers showing slightly more positive views on average. Although these differences are modest, they point to the relevance of occupational position in understanding symbolic boundaries within labour markets. Practically, the findings suggest that organisational initiatives aimed at strengthening interpersonal trust and fostering inclusive value orientations may support more positive attitudes toward immigrant colleagues. Clear institutional norms and supportive organisational cultures may be especially important during periods of uncertainty, when attitudes toward immigration can fluctuate. Future work linking attitudinal patterns with hiring, promotion, or workplace diversity practices would help clarify how these orientations are reflected in organisational behaviour. 6. Contribution to the Literature and Practical Implications This research reaffirms existing studies that correlate values of conservation and self-transcendence with attitudes towards immigration and broadens these findings by adding an occupational perspective with a focus on managers. Previous studies have investigated mainly the general public (Davidov & Meuleman, 2012 ), and this analysis reveals how value orientations play a role in decision-making situations. By examining how these values interact with generalised trust, the study advances the understanding of how psychological dispositions shape inclusion in organisational settings. The findings also deepen social capital theory by demonstrating that trust amplifies the effects of prosocial values on openness toward diversity (Rothstein & Stolle, 2008 ; Van Hoorn, 2018 ). These results are also consistent with sociological explanations of the ways in which external threats and intergroup relations produce responses to immigration, especially in the context of low-trust and high-conservation values. By positioning managerial attitudes at the intersection of internal orientations and sociopolitical forces, this research provides a better understanding of symbolic boundary-making and inclusion in the workplace. 7. Limitations and Future Research Directions Although this study provides comparative evidence on how generalised trust, value orientations, and occupational roles relate to immigration attitudes among European workers, several limitations should be noted. First, the analysis is based on repeated cross-sectional ESS data. While the large samples and harmonised design strengthen the reliability of the findings, the data do not allow for causal inference or within-person change over time. Multilevel models and fixed effects reduce some sources of bias, but unmeasured factors may still influence the results. Future research using panel data, longitudinal surveys, or experimental designs would help clarify how trust and values evolve in response to political or organisational developments. Second, the study focuses on native-born managers and workers. Immigrant managers may hold different attitudes shaped by their own migration experiences, labour market trajectories, or exposure to discrimination (Birinci et al., 2023 ; Quaranta, 2025 ). Including immigrant organisational actors in future work would deepen understanding of within-occupation heterogeneity. Third, despite covering 14 European countries, the analysis excludes several European and non-European contexts. Extending the design to additional regions—such as Southern and Central Eastern Europe, North America, or East Asia—would allow researchers to assess the generalisability of trust- and value-based explanations across different integration regimes and labour market institutions (Green et al., 2020 ). Fourth, the models adjust for individual characteristics but cannot account for organisational context. Factors such as industry sector, firm size, workplace diversity norms, and HR practices likely shape how managers form and express immigration attitudes (Ho & Turk-Ariss, 2018 ). Linking survey data to employer-level information or administrative records could help determine whether attitudinal patterns correspond to hiring, promotion, or diversity outcomes. Fifth, the study does not capture information on media exposure or misinformation. Emerging evidence suggests that news framing, digital media environments, and algorithmic filtering influence perceived threats and can trigger or dampen exclusionary sentiments, especially during periods of crisis (Ausat, 2023 ). Experimental research on media consumption could clarify how these dynamics interact with trust and values. Sixth, the analysis does not differentiate between immigrant groups by origin, religion, or skill level. Public opinion research shows that attitudes vary considerably depending on perceived cultural distance, economic contribution, or group stereotypes (Ahmad, 2020a , 2020b ; Ford & Mellon, 2020 ). Future studies could incorporate more fine-grained distinctions to assess conditional forms of inclusion. Finally, cross-national survey research faces challenges related to contextual and measurement heterogeneity. Differences in translation, interpretation, or national discourse may influence responses despite ESS harmonisation efforts (Davidov et al., 2018 ; Goerres et al., 2019 ). Mixed-method approaches—such as interviews, focus groups, or organisational ethnographies—could complement survey evidence and illuminate context-specific understandings of immigration. Person-centred approaches may also help capture multidimensional or ambivalent attitudes beyond simple pro–anti divisions (Goubin & Ruelens, 2024 ). 8. Conclusion This study examined how generalised trust, personal values, and occupational roles relate to immigration attitudes among workers and managers across 14 European countries. Using harmonised ESS data and multilevel models, the analysis shows that self-transcendence, conservation, and trust remain strong correlates of attitudes toward immigration, while occupational differences are modest but systematic. These patterns underscore the importance of psychological dispositions in shaping symbolic boundaries and highlight how national contexts condition their expression. The findings offer comparative evidence relevant to debates on migration, organisational diversity, and workplace inclusion. As immigration continues to reshape European labour markets, understanding how trust and values influence attitudes across different occupational groups remains essential. Further research linking attitudes to organisational practices would help clarify how these dispositions translate into concrete outcomes in hiring, promotion, and diversity management. Declarations Author Contribution Title: Trust, Values, and Immigration Attitudes among Managers and Workers in 14 European CountriesAuthor: Hamed Ahmadinia1, 2 & 31 University of Turku, Economic Sociology, Social Research Department, Assistentinkatu 7, 20500, Turku2Åbo Akademi University, Information Studies, Vänrikinkatu 3B, 20500, Turku, Finland3 Migration Institute of Finland, Hämeenkatu 13, 20500, Turku, Finland.Corresponding author:Hamed AhmadiniaEmail: [email protected] : University of Turku, Social Research Department, Turku, Finland.DeclarationsFundingThis work was supported by the Strategic Research Council at the Research Council of Finland (2021–2027) as part of the Mobile Futures project. Decision numbers: 364420, 364422.Availability of data and materialsThe data are publicly available from the European Social Survey (https://ess.sikt.no/). Python scripts used for data cleaning and analysis will be made available upon publication or upon request via the author’s GitHub repository (currently private).Ethics approval and consent to participateNot applicable.Consent for publicationNot applicable.Competing interestsThe author declares no competing interests.Authors’ contributions (CRediT)Hamed Ahmadinia: Conceptualization; Data curation; Formal analysis; Methodology; Writing – original draft; Writing – review & editing. Acknowledgement This study was funded by the Strategic Research Council (2021–2027; decision numbers 364420 and 364422) as part of the Mobile Futures project at the Research Council of Finland. 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The Economic Journal , 111 (470), 295–321. https://doi.org/10.1111/1468-0297.00609 Ziller, C. (2015). Ethnic Diversity, Economic and Cultural Contexts, and Social Trust: Cross-Sectional and Longitudinal Evidence from European Regions, 2002–2010. Social Forces , 93 (3), 1211–1240. https://doi.org/10.1093/sf/sou088 Additional Declarations No competing interests reported. Supplementary Files OnlineAppendix2122025.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":84084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalytical Framework Linking Trust, Values, and Occupational Role to Immigration Attitudes and Acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e The conceptual framework shows how generalised trust (random slope), self-transcendence and conservation values (fixed effects), occupational role (manager versus worker), and demographic controls relate to two multilevel outcomes: immigration attitudes and immigrant acceptance. The models include country and ESS-year fixed effects.\u003c/p\u003e","description":"","filename":"Figure1AnalyticalFrameworkLinkingTrustValuesandOccupationalRoletoImmigrationAttitudesandAcceptance.png","url":"https://assets-eu.researchsquare.com/files/rs-8251577/v1/10a759630238bd8b4d8bd08e.png"},{"id":100140876,"identity":"c10470b0-5a2b-4d8b-8b81-b0a2b51790ac","added_by":"auto","created_at":"2026-01-13 11:30:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":231661,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Generalised Trust Across Europe (2002–2023).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Annual average generalised trust scores across 14 European countries with LOESS and linear trend lines. Values range from −0.050 to 0.048. Fluctuations correspond to major sociopolitical events such as the 2004 EU expansion, the 2015 refugee arrivals, and the COVID-19 pandemic.\u003c/p\u003e","description":"","filename":"Figure2TrendsinGeneralisedTrustAcrossEurope20022023.png","url":"https://assets-eu.researchsquare.com/files/rs-8251577/v1/07f67ee5833dc1abdd84d5e7.png"},{"id":100366274,"identity":"c2a1b268-9088-411c-a713-84ba48b60f77","added_by":"auto","created_at":"2026-01-16 07:56:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":605529,"visible":true,"origin":"","legend":"\u003cp\u003eCountry-Level Effects of Generalised Trust on Immigration Attitudes and Acceptance\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Random-slope estimates from multilevel models, showing variation in the effect of generalised trust across countries for both outcomes. Δβ refers to the difference in the estimated trust effect between general immigration attitudes and acceptance of immigrants from socially distant groups.\u003c/p\u003e","description":"","filename":"Figure3CountryLevelEffectsofGeneralisedTrustonImmigrationAttitudesandAcceptance.png","url":"https://assets-eu.researchsquare.com/files/rs-8251577/v1/1b1a62f863b6a3b2e3e221f2.png"},{"id":100140873,"identity":"1ac60cb7-0933-4f29-ae07-cf09b13a9886","added_by":"auto","created_at":"2026-01-13 11:30:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":311512,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Immigration Attitudes by Country and Occupational Group\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Predicted values from Model 1 for managers and other workers across 14 countries. Predictions adjust for trust, values, education, income adequacy, age, gender, country, and ESS round.\u003c/p\u003e","description":"","filename":"Figure4PredictedImmigrationAttitudesandAcceptancebyCountryandOccupationalGroup.png","url":"https://assets-eu.researchsquare.com/files/rs-8251577/v1/bc75c37d65d496523dff752d.png"},{"id":100140878,"identity":"c650d08f-bcc1-465d-aa2f-b56b5d7caf63","added_by":"auto","created_at":"2026-01-13 11:30:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":575659,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Immigrant Acceptance Across ESS Rounds 1–11 (2002–2023)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Predicted acceptance scores from Model 2 for managers and other workers, based on attitudes toward admitting immigrants from different racial/ethnic groups and from poorer countries outside Europe. Predictions adjust for trust, values, education, income adequacy, age, gender, country, and ESS round.\u003c/p\u003e","description":"","filename":"Figure5PredictedImmigrationAttitudesandAcceptanceAcrossESSRounds11120022023.png","url":"https://assets-eu.researchsquare.com/files/rs-8251577/v1/5f2d222ec6e96a5a04b1e421.png"},{"id":103506206,"identity":"5c6b33de-ffdc-4e9f-9af5-c7a34ee064b8","added_by":"auto","created_at":"2026-02-26 13:34:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3267829,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8251577/v1/8f0dd103-7bdd-469c-a3cd-7bce76d0b930.pdf"},{"id":100140880,"identity":"b15bf0dc-4d15-46c2-b828-917381ba6328","added_by":"auto","created_at":"2026-01-13 11:30:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":238285,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineAppendix2122025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8251577/v1/6cf40a29e9fd920c99d6c22c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTrust, Values, and Immigration Attitudes among Managers and Workers in 14 European Countries\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe integration of immigrants into European labour markets is shaped by political developments, economic conditions, and public sentiment. Among the actors involved, organisational decision-makers\u0026mdash;particularly managers\u0026mdash;play a distinct role. As individuals responsible for hiring, supervision, and workplace culture, managers translate societal norms and personal values into everyday organisational practices. Understanding how they perceive immigration and diversity therefore helps connect macro-level debates about integration with meso-level labour market outcomes. Community organisations often support cultural and social adaptation but rarely influence access to employment directly (Sandberg et al., 2023), highlighting the central position of managers in shaping opportunities for immigrants.\u003c/p\u003e\n\u003cp\u003eOver the past two decades, several major events have reconfigured the public and political environment in which these decisions unfold. The 2004 EU enlargement altered labour mobility patterns, the 2008 financial crisis intensified concerns about economic competition, and the 2015 asylum arrivals triggered wide public debate about borders and welfare sustainability. More recent events\u0026mdash;including Brexit, the COVID-19 pandemic, and the arrival of Ukrainian refugees\u0026mdash;demonstrate how crises can activate solidaristic or exclusionary responses (Indelicato \u0026amp; Mart\u0026iacute;n, 2024; Jain et al., 2022; Martin \u0026amp; Bergmann, 2021). These shifts rarely affect all countries in the same way. Their impact is filtered through institutional structures, national policy regimes, media narratives, and local norms surrounding immigration.\u003c/p\u003e\n\u003cp\u003eTheoretical perspectives on group conflict, relative deprivation, and intergroup contact help explain why immigration attitudes vary across social groups and contexts (Meuleman et al., 2020; Pettigrew \u0026amp; Tropp, 2006; Webster \u0026amp; Whitmeyer, 2001). These theories emphasise the role of perceived threat, symbolic boundaries, and social distance, and suggest that individual dispositions may be amplified or muted by structural conditions such as economic downturns or large-scale refugee movements. Prior research also shows that attitudes toward immigrants differ by occupation and class, with some groups perceiving greater competition or holding stronger symbolic boundaries (Nese, 2023). These theoretical perspectives guide the interpretation of our findings, although they are not directly tested in the empirical analysis.\u003c/p\u003e\n\u003cp\u003eManagers have received comparatively little attention in cross-national public opinion research, despite their relevance for immigrant integration. Positioned at the intersection of organisational mandates and public expectations, managers exercise autonomy over hiring, promotion, and workplace norms (Van Riemsdijk \u0026amp; Basford, 2022). Their decisions can support the inclusion of immigrant workers or reinforce exclusionary practices, particularly under conditions of ambiguity or limited guidance (Farashah \u0026amp; Blomquist, 2019). Examining their attitudes alongside those of other workers therefore offers insight into how occupational roles intersect with broader societal debates about immigration and diversity.\u003c/p\u003e\n\u003cp\u003eTwo individual-level orientations are particularly relevant for understanding variation in immigration attitudes: generalised trust and personal value priorities. Generalised trust\u0026mdash;the expectation that most people can be trusted\u0026mdash;is consistently linked to tolerance, reduced prejudice, and social cohesion (Knack \u0026amp; Keefer, 1997; Rothstein \u0026amp; Stolle, 2008). Its role in shaping managers\u0026rsquo; views of immigration, however, has been rarely explored (Van Hoorn, 2018). Personal value priorities, particularly self-transcendence (e.g., universalism, benevolence) and conservation (e.g., security, conformity, tradition), are among the most important predictors of attitudes toward immigrants (Davidov \u0026amp; Meuleman, 2012; Schwartz, 1992). While extensively studied in general populations, these orientations are less often examined among organisational actors or occupational groups.\u003c/p\u003e\n\u003cp\u003eThis study investigates how generalised trust and personal values relate to immigration attitudes among managers and other workers in Europe. We analyse native-born respondents from 14 countries across 11 waves of the European Social Survey (2002\u0026ndash;2023). Two outcomes are examined. The first captures general attitudes toward immigration\u0026rsquo;s effects on the economy, culture, and quality of life. The second measures readiness to accept immigrants from racially or ethnically different groups and from poorer non-European countries. These items tap both broad sentiment and concrete inclusion thresholds. A third ESS item, referring to immigrants of the same race or ethnicity, is excluded because it reflects in-group preference rather than openness to diversity.\u003c/p\u003e\n\u003cp\u003eTo analyse these outcomes, we estimate multilevel models with fixed effects for survey year and country, and a random slope for generalised trust. This specification allows the influence of trust to vary across national contexts while holding constant country-level institutional differences. Values and trust are conceptualised as independent orientations rather than causal mechanisms. Occupational position is included to assess whether managers differ systematically from other workers after controlling for demographic characteristics.\u003c/p\u003e\n\u003cp\u003eThe analysis is structured around four research questions:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRQ1:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;How are generalised trust and value orientations (self-transcendence and conservation) related to attitudes toward immigrants who differ by race or socioeconomic background?\u003cbr\u003e\u003c/em\u003e\u003cstrong\u003e\u003cem\u003eRQ2:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Do managers hold more positive or negative immigration attitudes than other workers after accounting for values, trust, and demographic factors?\u003cbr\u003e\u003c/em\u003e\u003cstrong\u003e\u003cem\u003eRQ3:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;How do these relationships vary across countries and across ESS rounds?\u003cbr\u003e\u003c/em\u003e\u003cstrong\u003e\u003cem\u003eRQ4:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Do predictors of general immigration attitudes differ from those shaping acceptance of specific immigrant groups?\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBy addressing these questions, the study contributes to research on comparative migration, workplace diversity, and symbolic boundaries. It highlights how psychological orientations and occupational roles shape immigration attitudes in different national contexts and offers insights relevant for policymakers and organisational leaders concerned with immigrant integration.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Theoretical Framework","content":"\u003cp\u003eThis study draws on social capital theory and Schwartz\u0026rsquo;s theory of basic human values to explain how generalised trust and value orientations relate to immigration attitudes among European managers and other workers. While public opinion research on immigration is extensive, the views of organisational actors\u0026mdash;who influence hiring, advancement, and workplace norms\u0026mdash;remain less examined. An occupational perspective helps situate individual dispositions within the institutional and sociopolitical contexts in which organisations operate. In addition, three broader sociological perspectives\u0026mdash;group conflict theory, relative deprivation, and intergroup contact\u0026mdash;inform how external pressures may shape the salience of trust and values. These perspectives are used heuristically to interpret patterns; they are not directly tested in the empirical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Generalised Trust and Social Capital\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralised trust refers to the belief that most people can, in general, be relied upon. Within social capital theory, this orientation supports cooperation, social cohesion, and institutional legitimacy (Rothstein \u0026amp; Stolle, 2008). Different studies link higher generalised trust with lower prejudice and greater openness toward diversity (Knack \u0026amp; Keefer, 1997; Van Hoorn, 2018; Zak \u0026amp; Knack, 2001).\u003c/p\u003e\n\u003cp\u003eIts relevance for managerial attitudes remains relatively unexplored. Managers influence how diversity policies are implemented and how immigrant employees are assessed and integrated. Behaviourally, higher trust reduces the perceived risk of interacting with unfamiliar or culturally distant others, while lower trust may heighten sensitivity to perceived threats and reliance on heuristics such as in-group preference. In hiring or team composition, these tendencies may translate into more or less inclusive practices. In this study, generalised trust is modelled as a continuous predictor with a country-level random slope, acknowledging previous findings that trust operates differently across national contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Human Values and Managerial Dispositions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePersonal values are enduring principles that guide judgement and behaviour. According to Schwartz\u0026rsquo;s (1992) theory, two higher-order value dimensions are especially relevant for immigration research. Self-transcendence values (benevolence, universalism) emphasise empathy, equality, and concern for others and are consistently linked with more inclusive attitudes (Davidov \u0026amp; Meuleman, 2012). Conservation values (security, conformity, tradition) emphasise order and continuity and tend to correlate with exclusionary or restrictive immigration preferences, particularly toward outgroups perceived as culturally distant or destabilising (Davidov et al., 2020).\u003c/p\u003e\n\u003cp\u003eAlthough widely used in public opinion research, these orientations have been less studied among organisational decision-makers. Values may shape how managers interpret the presence of immigrants in the workplace and whether diversity is viewed as an asset or a potential disruption (Farashah \u0026amp; Blomquist, 2019; Russo et al., 2021). In the models, self-transcendence and conservation are included as fixed effects and treated as separate predictors alongside trust.\u003c/p\u003e\n\u003cp\u003eManagers are not only bearers of personal dispositions; they also operate within organisations that structure hiring norms and inclusion practices (Dobbin et al., 2011). This study therefore links psychological orientations with occupational authority to understand how attitudes toward immigrants take shape.\u0026nbsp;Table 1 outlines these value dimensions and the concept of generalised trust, forming the conceptual basis for analysing managerial and worker attitudes toward immigration.\u003c/p\u003e\n\u003cp\u003eTable 1: Definitions of Schwartz Human Values and Generalized Trust\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcept/Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman Value/Concept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman Values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eUniversal, trans-situational principles guiding attitudes and behaviors, categorized into four dimensions: self-transcendence, self-enhancement, openness to change, and conservation (Czymara \u0026amp; Eisentraut, 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSelf-Transcendence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eBenevolence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eConcern for the well-being of close others, such as family and friends (Czymara \u0026amp; Eisentraut, 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eUniversalism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eEmphasis on understanding, tolerance, and protection for all people and the natural world (Czymara \u0026amp; Eisentraut, 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSelf-Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eAchievement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eAspiration to demonstrate competence and attain success according to social standards (Sipinen et al., 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ePower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003ePursuit of social status, dominance, and authority over others (Davidov et al., 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 102px;\"\u003e\n \u003cp\u003eOpenness to Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSelf-Direction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eValuing independence, creativity, and freedom of thought and action (Czymara \u0026amp; Eisentraut, 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eStimulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eSeeking excitement, novelty, and challenge in life (Czymara \u0026amp; Eisentraut, 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 102px;\"\u003e\n \u003cp\u003eConservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eTradition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eRespect for and commitment to cultural, religious, and societal customs (Davidov et al., 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eConformity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eAvoidance of behaviors or impulses that harm others or violate societal norms (Sipinen et al., 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003eFocus on stability, safety, and harmony within society and relationships (Sipinen et al., 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneralised Trust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 474px;\"\u003e\n \u003cp\u003eThe belief that most people in a society are honest and reliable, fostering cooperation, societal cohesion, and positive outcomes such as economic growth and reduced crime (Sipinen et al., 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e{Table 1 here}\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 From General Sentiment to Immigrant Acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneral attitudes toward immigration\u0026mdash;such as whether immigration benefits the economy or enriches culture\u0026mdash;capture broad sentiment but may obscure variation in whom respondents are willing to admit. To address this, the analysis includes a second outcome measuring acceptance of immigrants from racially or ethnically different groups and from poorer non-European countries. These items reflect perceived social distance and are commonly used to approximate concrete inclusion thresholds (Bell, 2021; Czaika \u0026amp; Di Lillo, 2018).\u003c/p\u003e\n\u003cp\u003eA related ESS item that refers to immigrants of the same racial or ethnic background is excluded because it largely captures in-group preference rather than openness to diversity (V\u0026yacute;rost \u0026amp; Dobe\u0026scaron;, 2019). The two selected items form a composite index of acceptance, while the general attitude items form the basis of the broader sentiment measure. This distinction allows the analysis to compare predictors of symbolic attitudes and specific acceptance boundaries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Contextualising Trust and Values\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrust and values do not operate independently of the wider environment. Although macro-level variables are not directly modelled, several theoretical perspectives help contextualise how external conditions may shape the link between dispositions and immigration attitudes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGroup conflict theory\u003c/strong\u003e suggests that perceived threats to material or symbolic status can heighten exclusionary orientations (Meuleman et al., 2009). For managers, such perceptions may intensify during periods of economic uncertainty or demographic change. \u003cstrong\u003eRelative deprivation theory\u003c/strong\u003e emphasises perceived unfairness or status loss as triggers of negative attitudes (Meuleman et al., 2020). In organisational settings, diversity initiatives may be interpreted as reallocating opportunities or altering workplace traditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntergroup contact theory\u003c/strong\u003e highlights how sustained, equal-status interactions can reduce prejudice and build empathy (Pettigrew \u0026amp; Tropp, 2006). Inclusive organisational environments may therefore amplify the effects of trust and self-transcendence, whereas ambiguous or unsupportive contexts may reinforce perceived threats. Macro-level events such as the 2015 asylum arrivals or the 2022 displacement of Ukrainians can activate existing predispositions rather than changing them fundamentally. Political discourse and media framing play an important role in shaping whether such events evoke solidarity or exclusion (McLaren \u0026amp; Paterson, 2020). Managers, positioned between organisational norms and public narratives, may be especially sensitive to these cues. These frameworks are used to interpret patterns in the results and to highlight the conditions under which trust and values may matter more or less.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Connecting Trust, Values, and Occupational Roles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis considers trust and values as separate, relatively stable psychological orientations. Although these factors may correlate\u0026mdash;for example, trust is often higher among individuals with stronger self-transcendence values (Koivula et al., 2017)\u0026mdash;they are not modelled as mediators or placed in a causal sequence. To examine whether occupational authority is associated with immigration attitudes, the models compare managers and other workers while controlling for demographic characteristics such as gender, age, income, and education. Manager status is included as a fixed effect in all models to assess whether any observed differences persist net of values and trust.\u003c/p\u003e\n\u003cp\u003eThe multilevel models incorporate fixed effects for survey year and country to account for institutional heterogeneity and use a random slope for generalised trust to capture cross-national variation in its influence. This approach balances the need to represent national context while avoiding excessive model complexity given the number of countries. Together, this framework links psychological dispositions, occupational roles, and institutional contexts in understanding immigration attitudes in Europe. It contributes to broader sociological discussions of symbolic boundaries, stratification, and organisational gatekeeping during a period of demographic and political change (Ramirez \u0026amp; Kim, 2024; Ziller, 2015). Figure 1 presents the conceptual framework, illustrating how trust, values, occupational status, and demographic controls feed into immigration attitudes and acceptance within a multilevel structure.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(Insert Figure 1 here)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e"},{"header":"3. Data, Measurements, and Analytical Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data Sources and Samples\u003c/h2\u003e\n \u003cp\u003eThis study uses data from Rounds 1\u0026ndash;11 (2002\u0026ndash;2023) of the European Social Survey (ESS), a cross-national survey programme based on probability sampling, face-to-face interviews, and harmonised fieldwork procedures (Lindstr\u0026oslash;m \u0026amp; Kropp, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schnaudt, et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The ESS is widely used for comparative research on public attitudes, including migration, social trust, and personal values.\u003c/p\u003e\n \u003cp\u003eWe focus on 14 European countries that participated regularly and provide comparable information on immigration attitudes, generalised trust, human values, and core sociodemographic characteristics: Belgium, Finland, France, Germany, Hungary, Ireland, the Netherlands, Norway, Poland, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. The analytic sample is restricted to native-born respondents and to those who were in paid work (employed or self-employed) at the time of the survey. Native-born respondents are identified using the ESS birthplace item, and employment status is drawn from labour-force questions.\u003c/p\u003e\n \u003cp\u003eWithin this employed population, managers are identified using ISCO-08 occupation codes 1000\u0026ndash;1439, in line with international classification standards (Ganzeboom \u0026amp; Treiman, \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). All other employed respondents are treated as \u0026ldquo;other workers\u0026rdquo;, who form the reference category in occupational comparisons. This procedure yields 22,673 managers and 259,989 other workers across 11 ESS rounds and 14 countries. Managerial shares differ across countries, reflecting differences in occupational structures and sample composition, but the distribution is broadly balanced and supports meaningful cross-national comparison.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reports the number and proportion of managers and other workers by country and ESS round. Overall, the largest employed subsamples come from Germany and the United Kingdom, while smaller samples are observed in some Central and Eastern European countries. This cross-national diversity allows us to examine how trust, values, and immigration attitudes vary across institutional settings.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCountry-wise Distribution of Managers and Other Workers Across ESS Rounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eManager\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHungary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIreland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Worker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHungary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIreland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e{\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003ehere}\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the sociodemographic profile of the pooled sample. The age distribution is skewed toward midlife cohorts, with relatively few respondents under 25 years. Women make up just over half of the sample. Educational attainment is relatively high: more than half of respondents have completed upper secondary or tertiary education. Perceived income adequacy is concentrated in the \u0026ldquo;coping\u0026rdquo; and \u0026ldquo;living comfortably\u0026rdquo; categories, though a substantial minority report financial difficulties. These patterns are broadly consistent with ESS benchmarks for employed respondents.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed Sociodemographic Profile of European Managers and Employees in ESS Rounds 1\u0026ndash;11\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR11\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 \u0026amp; under\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 \u0026amp; over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than lower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower secondary completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper secondary completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-secondary non-tertiary completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary education completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome Feeling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving comfortably on present income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoping on present income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifficult on present income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery difficult on present income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eManager Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther European Worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e{\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cstrong\u003ehere}\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFigure 2 displays average levels of generalised trust by ESS round across the 14 countries. Trust levels show modest fluctuation over time, with dips in the mid-2000s and around the onset of the COVID-19 pandemic, and higher levels in the mid-2010s and late 2010s. These movements coincide with major political and economic events, suggesting that institutional shocks may influence trust and, in turn, attitudes toward immigration.\u003c/p\u003e\n \u003cp\u003eWhile the multilevel models account for clustering and contextual variation, it is important to note that ESS data are cross-sectional. As such, the analysis cannot establish causal relationships and remains vulnerable to omitted-variable bias, particularly in comparative settings (Schmidt-Catran et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The ESS nevertheless offers substantial advantages in terms of large samples, stable measurement instruments, and harmonised translation and fieldwork protocols. Figure\u0026nbsp;2 illustrates yearly fluctuations in generalised trust across the 14 countries from 2002 to 2023.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Insert Fig. 2 here)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Measurements\u003c/h2\u003e\n \u003cp\u003eThe first dependent variable captures general attitudes toward immigration. It is based on three standard ESS items that ask whether immigration is bad or good for the national economy, undermines or enriches the country\u0026rsquo;s cultural life, and makes the country a worse or better place to live. Each item is measured on an 11-point scale from 0 (most negative) to 10 (most positive). The items are averaged to form a composite index, with higher scores indicating more positive views. These indicators, widely used in previous research, tap perceived economic, cultural, and societal consequences of immigration rather than specific policy preferences (Davidov \u0026amp; Meuleman, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meuleman et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Item wording and coding details are provided in Appendix 1.\u003c/p\u003e\n \u003cp\u003eThe second dependent variable measures acceptance of socially distant immigrants. It combines two ESS items asking whether the country should allow many, some, a few, or no immigrants to come and live there if they are (a) from a different racial or ethnic group than the majority and (b) from poorer countries outside Europe. The four response categories are reverse-coded so that higher values indicate greater acceptance and then standardised and averaged. A third item about immigrants of the same race or ethnic group is intentionally omitted because it largely captures in-group preference and tends to inflate apparent support for immigration (Semyonov et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; V\u0026yacute;rost \u0026amp; Dobe\u0026scaron;, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe key independent variables are generalised trust and human value orientations. Generalised trust is measured with three ESS items asking whether most people can be trusted, whether people try to be fair, and whether people are helpful. Each item is rated on an 11-point scale from 0 (lowest trust) to 10 (highest trust); the items are averaged to construct a composite trust index, which has been widely validated in studies of social capital and intergroup attitudes.\u003c/p\u003e\n \u003cp\u003eHuman values are measured using Schwartz\u0026rsquo;s Portrait Values Questionnaire (PVQ). Following ESS guidelines, we focus on two higher-order value dimensions: self-transcendence and conservation. Self-transcendence is captured by items that emphasise caring for others and equality, while conservation is captured by items that emphasise safety, rule-following, and tradition. All PVQ items are rated on a six-point scale from \u0026ldquo;not like me at all\u0026rdquo; to \u0026ldquo;very much like me.\u0026rdquo; In line with ESS coding recommendations, items are ipsatised, reverse-coded where necessary, and then combined into indices, which are standardised to have a mean of 0 and a standard deviation of 1. Details on item sets and coding steps are provided in Appendix 1.\u003c/p\u003e\n \u003cp\u003eControl variables include gender, age (categorised into six groups), educational attainment (harmonised across rounds using ISCED-based categories), perceived income adequacy, political left\u0026ndash;right self-placement, occupational role (manager vs other worker), country, and ESS round.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Data Cleaning and Transformation\u003c/h2\u003e\n \u003cp\u003eAll data preparation and analysis were conducted in Python, using pandas for data handling, NumPy for numerical operations, and statsmodels and scikit-learn for modelling and scaling. The merged ESS file (Rounds 1\u0026ndash;11) was harmonised to ensure consistent coding of variables across countries and rounds, including later waves with altered naming conventions. Where necessary, variables were mapped to a common naming scheme.\u003c/p\u003e\n \u003cp\u003eTo deal with item nonresponse, we used a simple single-imputation strategy. Categorical variables (such as gender and education) were imputed with the sample mode, while continuous variables (such as trust, values, and immigration attitudes) were imputed with the median after recoding standard ESS nonresponse categories (e.g., \u0026ldquo;don\u0026rsquo;t know,\u0026rdquo; \u0026ldquo;refusal,\u0026rdquo; \u0026ldquo;no answer\u0026rdquo;) to missing values. This approach prioritises sample retention in a large, pooled cross-national dataset where listwise deletion could substantially reduce statistical power and distort cross-country representation (Schmidt-Catran et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). This study acknowledges that more advanced techniques such as multiple imputation could, in principle, yield better estimates under certain missingness assumptions; this limitation is revisited in the discussion.\u003c/p\u003e\n \u003cp\u003eOutliers on continuous variables were examined using z-scores. Observations with values beyond \u0026plusmn;\u0026thinsp;3 standard deviations from the mean were treated as extreme. Rather than excluding these cases, we applied a winsorisation procedure, capping values at the \u0026plusmn;\u0026thinsp;3 standard deviation thresholds. This widely used approach reduces the influence of extreme observations while maintaining the full sample, which is especially useful in large surveys with low measurement error but occasional long-tailed distributions (Cousineau \u0026amp; Chartier, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yoseph et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eScale construction and rescaling followed ESS protocols. The indices for generalised trust, self-transcendence, conservation, and immigrant acceptance were standardised to have a mean of 0 and a standard deviation of 1. All continuous predictors were z-standardised prior to modelling to facilitate interpretation and comparability of coefficients. Occupational status was coded as described above, with managers distinguished from other workers based on ISCO-08 codes. The final pooled dataset contains 282,662 valid cases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Analytical methods\u003c/h2\u003e\n \u003cp\u003eTo examine how generalised trust and value orientations relate to immigration attitudes among managers and other workers, we estimate two multilevel linear models. Individuals (Level 1) are nested within countries (Level 2). Both models include fixed effects for country and ESS round to account for time-invariant national characteristics and common temporal shocks. All continuous predictors and outcome variables are z-standardised, so coefficients can be interpreted as effects in standard-deviation units. Models are estimated using restricted maximum likelihood (REML) with robust standard errors. A random slope for generalised trust is specified, allowing the association between trust and immigration attitudes to vary across countries.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1: General Immigration Attitudes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe first model uses the composite index of general attitudes toward immigration (economic, cultural, and overall quality-of-life evaluations) as the outcome. At the individual level, the model includes generalised trust, self-transcendence, conservation, and the control variables (age group, gender, education, perceived income adequacy, political orientation, and occupational role: manager vs other worker). Country and round fixed effects capture macro-structural and temporal context, and a country-level random slope for trust captures cross-national variation in the strength of the trust\u0026ndash;attitude relationship.\u003c/p\u003e\n \u003cp\u003eIn simplified form:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{\\varvec{I}\\varvec{m}\\varvec{m}\\varvec{i}\\varvec{g}\\varvec{r}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}\\:\\varvec{A}\\varvec{t}\\varvec{t}\\varvec{i}\\varvec{t}\\varvec{u}\\varvec{d}\\varvec{e}\\varvec{s}}_{\\varvec{i}\\varvec{j}\\:}=\\:{\\varvec{\\beta\\:}}_{0}+\\:{\\varvec{\\beta\\:}}_{1\\:}{.\\:\\varvec{T}\\varvec{r}\\varvec{u}\\varvec{s}\\varvec{t}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\beta\\:}}_{2}{\\:.\\:\\varvec{C}\\varvec{o}\\varvec{n}\\varvec{s}\\varvec{e}\\varvec{r}\\varvec{v}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\beta\\:}}_{3}{\\:.\\:\\varvec{S}\\varvec{e}\\varvec{l}\\varvec{f}\\varvec{T}\\varvec{r}\\varvec{a}\\varvec{n}\\varvec{s}}_{\\varvec{i}\\varvec{j}}\\:{\\:+\\:\\varvec{\\beta\\:}}_{4}\\:.\\:{\\varvec{C}\\varvec{o}\\varvec{n}\\varvec{t}\\varvec{r}\\varvec{o}\\varvec{l}\\varvec{s}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\beta\\:}}_{5}{\\:.\\:\\varvec{M}\\varvec{a}\\varvec{n}\\varvec{a}\\varvec{g}\\varvec{e}\\varvec{r}}_{\\varvec{i}\\varvec{j}}+{\\varvec{\\gamma\\:}}_{\\varvec{j}}{+\\:{\\varvec{\\delta\\:}}_{\\varvec{r}\\:}+\\:\\:\\varvec{u}}_{1\\varvec{j}\\:}.\\:{\\varvec{T}\\varvec{r}\\varvec{u}\\varvec{s}\\varvec{t}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{j}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ewhere:\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003ei indexes individuals\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003ej indexes countries\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003er indexes ESS rounds\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\:Controls}_{ij}\\:\\)\u003c/span\u003e\u003c/span\u003e: age, gender, education, income adequacy\u003c/p\u003e\n \u003cp\u003e\u0026bull; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Manager}_{ij}\\)\u003c/span\u003e\u003c/span\u003e: binary indicator for ISCO-defined managerial status\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{j}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e fixed effect for country\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{r\\:}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e fixed effect for ESS round\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{1j\\:}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003erandom slope of generalised trust by country\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{ij}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003eindividual-level error term\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2: Immigrant acceptance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe second model uses the composite index of acceptance of immigrants from racially/ethnically different groups and from poorer non-European countries as the outcome. The set of predictors is identical to Model 1: generalised trust, self-transcendence, conservation, sociodemographic controls, and occupational role, along with country and round fixed effects and a random slope for trust.\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{\\varvec{A}\\varvec{c}\\varvec{c}\\varvec{e}\\varvec{p}\\varvec{t}\\varvec{a}\\varvec{n}\\varvec{c}\\varvec{e}}_{\\varvec{i}\\varvec{j}\\:}=\\:=\\:{\\varvec{\\beta\\:}}_{0}+\\:{\\varvec{\\beta\\:}}_{1}\\:.\\:{\\varvec{T}\\varvec{r}\\varvec{u}\\varvec{s}\\varvec{t}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\beta\\:}}_{2}{\\:.\\:\\varvec{C}\\varvec{o}\\varvec{n}\\varvec{s}\\varvec{e}\\varvec{r}\\varvec{v}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\beta\\:}}_{3}\\:.\\:{\\varvec{S}\\varvec{e}\\varvec{l}\\varvec{f}\\varvec{T}\\varvec{r}\\varvec{a}\\varvec{n}\\varvec{s}}_{\\varvec{i}\\varvec{j}}\\:{\\:+\\:\\varvec{\\beta\\:}}_{4}{\\:.\\:\\varvec{C}\\varvec{o}\\varvec{n}\\varvec{t}\\varvec{r}\\varvec{o}\\varvec{l}\\varvec{s}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\beta\\:}}_{5}{\\:.\\:\\varvec{M}\\varvec{a}\\varvec{n}\\varvec{a}\\varvec{g}\\varvec{e}\\varvec{r}}_{\\varvec{i}\\varvec{j}}+{\\varvec{\\gamma\\:}}_{\\varvec{j}}{+\\:{\\varvec{\\delta\\:}}_{\\varvec{r}\\:}+\\:\\:\\varvec{u}}_{1\\varvec{j}\\:}.\\:{\\varvec{T}\\varvec{r}\\varvec{u}\\varvec{s}\\varvec{t}}_{\\varvec{i}\\varvec{j}}+\\:{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{j}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAs in Model 1, the random slope term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{u}}_{1\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e captures between-country variability in the effect of generalised trust, while fixed effects absorb unobserved national and temporal heterogeneity. Standardising predictors allows for straightforward comparison of effect sizes across models.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Overview of Predictors and Patterns\u003c/h2\u003e\n \u003cp\u003eAcross both multilevel models, generalised trust and human value orientations emerge as the strongest and most consistent predictors of immigration attitudes. Individuals with higher self-transcendence values\u0026mdash;emphasising empathy, equality, and concern for others\u0026mdash;hold significantly more positive attitudes toward immigration. By contrast, higher conservation values, which stress security, conformity, and tradition, are linked with more restrictive views. These associations are stable across 14 countries and 11 ESS rounds, indicating a high degree of temporal and cross-national consistency.\u003c/p\u003e\n \u003cp\u003eGeneralised trust, measured as a composite of interpersonal trust, fairness, and helpfulness, is also positively associated with both general immigration attitudes and immigrant acceptance. However, the magnitude of this association varies across countries. As Fig.\u0026nbsp;3 shows, the trust\u0026ndash;attitude link is stronger in some contexts (e.g., the United Kingdom, France, Portugal) than in others (e.g., Hungary, Slovenia, Spain). This suggests that institutional and cultural environments condition how strongly trust translates into openness.\u003c/p\u003e\n \u003cp\u003eFigure 3 also shows the \u0026Delta;\u0026beta; divergence between the effect of trust on general attitudes and its effect on acceptance of socially distant immigrants. In some Western European countries, trust has a noticeably larger impact on general sentiment, whereas in several Eastern and Southern European countries the trust effects across both outcomes are more similar. These patterns align with the idea that accepting socially distant groups may require higher levels of trust in certain contexts.\u003c/p\u003e\n \u003cp\u003eSociodemographic variables display expected associations. Individuals with higher levels of education and those reporting financial comfort show more positive attitudes, while older respondents tend to be more sceptical. Gender differences are statistically detectable but substantively negligible. Because country and ESS-round fixed effects are included in all models, these findings reflect individual-level patterns net of broader institutional and historical influences.\u003c/p\u003e\n \u003cp\u003eTaken together, psychological orientations\u0026mdash;trust and values\u0026mdash;consistently predict immigration attitudes across Europe, with further variation shaped by education, income, and age. These patterns motivate the more detailed model-specific results presented next.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Insert Fig. 3 here)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Multilevel Modelling Results\u003c/h2\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Model 1: General Immigration Attitudes\u003c/h2\u003e\n \u003cp\u003eModel 1 examines general evaluations of the economic, cultural, and societal impact of immigration. The model includes fixed effects for country and ESS round, a random slope for generalised trust, and controls for demographic characteristics and occupational role.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the full results. Self-transcendence values are strongly and positively associated with favourable immigration attitudes (\u0026beta;\u0026thinsp;=\u0026thinsp;0.233, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conservation values show the opposite pattern (\u0026beta; = \u0026minus;\u0026thinsp;0.323, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), reflecting more sceptical orientations. Generalised trust also has a meaningful positive effect (\u0026beta;\u0026thinsp;=\u0026thinsp;0.202, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The random slope variance for trust (Var\u0026thinsp;=\u0026thinsp;0.002) indicates that its influence varies across countries.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003eAmong the controls\u0026mdash;treated as background adjustments rather than key theoretical predictors\u0026mdash;education (\u0026beta;\u0026thinsp;=\u0026thinsp;0.114, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and perceived income adequacy (\u0026beta;\u0026thinsp;=\u0026thinsp;0.102, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) show modest positive associations, whereas older respondents express slightly more negative views (\u0026beta; = \u0026minus;\u0026thinsp;0.004, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Gender differences are statistically significant but substantively very small (\u0026beta; = \u0026minus;\u0026thinsp;0.016, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eFigure 4 shows predicted immigration attitudes by occupational group across countries. Managers generally express more positive attitudes than other workers, but the size of the occupational gap varies considerably. Larger differences appear in Switzerland, Germany, the Netherlands, and Norway, whereas in Eastern and Southern European countries\u0026mdash;such as Hungary, Poland, and Portugal\u0026mdash;both groups hold more restrictive views and occupational gaps are smaller or even reversed.\u003c/p\u003e\n \u003cp\u003eThe predicted values are adjusted for all covariates, isolating managerial differences net of demographic, value, and trust factors. The pattern suggests that occupational position plays a modest but detectable role, particularly in Western and Nordic contexts. Overall, Model 1 indicates that value orientations and trust account for the largest share of variation in immigration attitudes, with structural variables and occupational role contributing additional, but smaller, effects.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Insert Fig. 4 here)\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e4.2.2 Model 2: Immigrant acceptance\u003c/h2\u003e\n \u003cp\u003eModel 2 shifts attention from general sentiment to acceptance of immigrants from different racial/ethnic groups and from poorer non-European countries. The same model structure is used, including fixed effects for country and ESS round and a random slope for generalised trust.\u003c/p\u003e\n \u003cp\u003eAs shown in Table 4, self-transcendence again predicts higher acceptance (\u0026beta;\u0026thinsp;=\u0026thinsp;0.259, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while conservation predicts lower acceptance (\u0026beta; = \u0026minus;\u0026thinsp;0.244, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Generalised trust remains positively associated (\u0026beta;\u0026thinsp;=\u0026thinsp;0.140, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The trust random slope variance (Var\u0026thinsp;=\u0026thinsp;0.002) again indicates cross-national heterogeneity.\u003c/p\u003e\n \u003cp\u003eEducation (\u0026beta;\u0026thinsp;=\u0026thinsp;0.094, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and gender (\u0026beta;\u0026thinsp;=\u0026thinsp;0.031, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are linked to greater acceptance, although the gender effect is very small. Perceived income adequacy (\u0026beta;\u0026thinsp;=\u0026thinsp;0.077, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and age (\u0026beta; = \u0026minus;\u0026thinsp;0.049, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) show weaker associations.\u003c/p\u003e\n \u003cp\u003eThe occupational coefficient indicates a small but statistically significant difference: nonmanagers are slightly less accepting than managers (\u0026beta; = \u0026minus;\u0026thinsp;0.013, p\u0026thinsp;=\u0026thinsp;0.026). While the magnitude is limited, this aligns with the pattern observed in Model 1.\u003c/p\u003e\n \u003cp\u003eFigure 5 displays predicted acceptance levels for managers and workers from 2002 to 2023. Acceptance rises modestly around major events such as the 2015 refugee arrivals and the COVID-19 pandemic, aligning with short-term increases in solidaristic or humanitarian sentiment observed in other studies. The gap between occupational groups narrows slightly in the early 2020s but remains statistically significant in 2023 (0.24 for managers vs. 0.15 for other workers). Overall, Model 2 indicates that trust and value orientations remain central correlates of acceptance of socially distant immigrant groups, while occupational differences persist but are modest in magnitude.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Insert Fig. 5 here)\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4: \u0026nbsp;Multilevel Regression \u0026ndash; Generalised Trust, Human Values, and Immigration Attitudes and Acceptance\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"522\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.230***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.004***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.049***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.016***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.031***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.114***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.094***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.077***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValues\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eSelf-Transcendence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.233***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.259***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eConservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.232***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.244***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eGeneralised Trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.202***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.140***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eManagerial Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eOther European Worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.046***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.041***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.060***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.081***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.027***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.049***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.063***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.026***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.048***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.069***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.128***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.149***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.203***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eY.2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.081***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.126***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry Effect\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Switzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.160***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Germany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.077***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.085***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.299***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.200***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Finland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.353***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.217***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.070***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.022**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Great Britain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.075***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.107***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Hungary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.217***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.670***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Ireland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.196***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.023**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Netherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.075***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.039***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Norway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.120***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.190***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Poland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.429***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.216***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Portugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.248***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Sweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.327***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.520***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eC.Slovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.124***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.7013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Slope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eGeneralised Trust Slope Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Fit (Deviance)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e657596.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e702177.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003ca href=\"#_ftnref1\" name=\"_ftn1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003eNote: `*` p \u0026lt; .05, `**` p \u0026lt; .01, `***` p \u0026lt; .001. Stars indicate statistical significance of fixed effects.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e{\u003c/strong\u003eTable 4 \u003cstrong\u003ehere}\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study examined how generalised trust and basic human values shape immigration attitudes among managers and other workers across 14 European countries over two decades. By drawing on harmonised ESS data and multilevel modelling, the analysis clarifies how psychological dispositions and occupational roles relate to public views about immigration within different national contexts.\u003c/p\u003e \u003cp\u003eConsistent with Schwartz\u0026rsquo;s theory of basic values, self-transcendence\u0026mdash;values emphasising empathy, equality, and concern for others\u0026mdash;is strongly associated with both general immigration attitudes and acceptance of more socially distant immigrant groups. Conservation values, which stress security, conformity, and tradition, predict more sceptical or exclusionary views. These findings confirm earlier value-based research and show that these orientations remain highly stable across countries and survey waves (Davidov et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Meuleman et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeneralised trust also emerges as a robust correlate of pro-immigration attitudes, echoing prior evidence linking trust to tolerance and social cohesion (Rothstein \u0026amp; Stolle, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Van Hoorn, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The random-slope estimates indicate that the strength of the trust\u0026ndash;attitudes relationship varies across countries, suggesting that institutional or cultural settings condition how trust is expressed. In some contexts, trust translates strongly into inclusion; in others, its effect is more muted. These cross-national differences align with research on symbolic boundaries and how national discourses shape perceptions of immigrants (Ramirez \u0026amp; Kim, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ziller, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results also show modest but consistent occupational differences. Managers express slightly more positive attitudes than other workers after controlling for values, trust, and sociodemographic factors. While the effect size is small, it is systematic and suggests that occupational authority may be associated with somewhat more inclusive orientations. As organisational gatekeepers, managers influence hiring, workplace norms, and daily interactions with diverse employees (Farashah \u0026amp; Blomquist, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Van Riemsdijk \u0026amp; Basford, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Even small attitudinal differences in this group may matter for how symbolic boundaries are enacted in workplaces.\u003c/p\u003e \u003cp\u003eSociodemographic patterns largely mirror previous findings. Higher education is associated with more open views, reflecting the well-established link between schooling and cosmopolitan orientations (Hainmueller \u0026amp; Hiscox, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Financial comfort is associated with more positive attitudes, whereas older respondents tend to be more cautious. Gender differences are small. Temporal patterns also show slight increases in acceptance during salient events such as the 2015 refugee arrivals and the early stages of the COVID-19 pandemic, though the cross-sectional design does not allow for causal conclusions. Stronger support in countries such as Switzerland, Sweden, and Germany contrast with more restrictive attitudes in parts of Eastern and Southern Europe, reflecting differences in national discourse, policy frameworks, and historical experiences with diversity (Medvešek et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nese, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the stability of the findings, several limitations remain. First, the ESS is cross-sectional, preventing analysis of within-person change and limiting causal inference. Second, attitudes\u0026mdash;while important\u0026mdash;do not automatically translate into behaviour or organisational practices. Managers may support inclusion in principle yet face institutional constraints that affect hiring or promotion decisions. Future research should therefore link attitudinal data to behavioural indicators, administrative records, or field experiments. Third, the study uses single-imputation methods for handling missing data; more advanced techniques such as multiple imputation may yield more precise estimates under certain missingness assumptions.\u003c/p\u003e \u003cp\u003eOverall, this study provides a large comparative examination of how trust, values, and occupational roles relate to immigration attitudes among European workers. The findings highlight the central role of psychological dispositions and show how these orientations intersect with occupational roles and national contexts. For policymakers and organisational leaders concerned with immigrant integration, the results point to the importance of strengthening trust, reinforcing inclusive norms, and providing clear institutional guidance\u0026mdash;especially during periods of societal uncertainty, when symbolic boundaries are most likely to shift.\u003c/p\u003e"},{"header":"6.\tContribution to the Literature and Practical Implications","content":"\u003cp\u003eThis study contributes to comparative migration research by linking generalised trust, personal values, and occupational roles in shaping immigration attitudes across diverse European contexts. While psychological dispositions are well-established predictors of views on immigration, they are less often examined among organisational actors. By comparing managers with other workers across 14 countries and 11 ESS rounds, the analysis provides broad descriptive evidence that value orientations and trust are associated with immigration attitudes in similar ways across occupational groups, with managers showing slightly more positive views on average. Although these differences are modest, they point to the relevance of occupational position in understanding symbolic boundaries within labour markets.\u003c/p\u003e \u003cp\u003ePractically, the findings suggest that organisational initiatives aimed at strengthening interpersonal trust and fostering inclusive value orientations may support more positive attitudes toward immigrant colleagues. Clear institutional norms and supportive organisational cultures may be especially important during periods of uncertainty, when attitudes toward immigration can fluctuate. Future work linking attitudinal patterns with hiring, promotion, or workplace diversity practices would help clarify how these orientations are reflected in organisational behaviour.\u003c/p\u003e\n\u003ch3\u003e6. Contribution to the Literature and Practical Implications\u003c/h3\u003e\n\u003cp\u003eThis research reaffirms existing studies that correlate values of conservation and self-transcendence with attitudes towards immigration and broadens these findings by adding an occupational perspective with a focus on managers. Previous studies have investigated mainly the general public (Davidov \u0026amp; Meuleman, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and this analysis reveals how value orientations play a role in decision-making situations. By examining how these values interact with generalised trust, the study advances the understanding of how psychological dispositions shape inclusion in organisational settings. The findings also deepen social capital theory by demonstrating that trust amplifies the effects of prosocial values on openness toward diversity (Rothstein \u0026amp; Stolle, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Van Hoorn, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These results are also consistent with sociological explanations of the ways in which external threats and intergroup relations produce responses to immigration, especially in the context of low-trust and high-conservation values. By positioning managerial attitudes at the intersection of internal orientations and sociopolitical forces, this research provides a better understanding of symbolic boundary-making and inclusion in the workplace.\u003c/p\u003e"},{"header":"7. Limitations and Future Research Directions","content":"\u003cp\u003eAlthough this study provides comparative evidence on how generalised trust, value orientations, and occupational roles relate to immigration attitudes among European workers, several limitations should be noted.\u003c/p\u003e \u003cp\u003eFirst, the analysis is based on repeated cross-sectional ESS data. While the large samples and harmonised design strengthen the reliability of the findings, the data do not allow for causal inference or within-person change over time. Multilevel models and fixed effects reduce some sources of bias, but unmeasured factors may still influence the results. Future research using panel data, longitudinal surveys, or experimental designs would help clarify how trust and values evolve in response to political or organisational developments.\u003c/p\u003e \u003cp\u003eSecond, the study focuses on native-born managers and workers. Immigrant managers may hold different attitudes shaped by their own migration experiences, labour market trajectories, or exposure to discrimination (Birinci et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Quaranta, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Including immigrant organisational actors in future work would deepen understanding of within-occupation heterogeneity.\u003c/p\u003e \u003cp\u003eThird, despite covering 14 European countries, the analysis excludes several European and non-European contexts. Extending the design to additional regions\u0026mdash;such as Southern and Central Eastern Europe, North America, or East Asia\u0026mdash;would allow researchers to assess the generalisability of trust- and value-based explanations across different integration regimes and labour market institutions (Green et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFourth, the models adjust for individual characteristics but cannot account for organisational context. Factors such as industry sector, firm size, workplace diversity norms, and HR practices likely shape how managers form and express immigration attitudes (Ho \u0026amp; Turk-Ariss, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Linking survey data to employer-level information or administrative records could help determine whether attitudinal patterns correspond to hiring, promotion, or diversity outcomes.\u003c/p\u003e \u003cp\u003eFifth, the study does not capture information on media exposure or misinformation. Emerging evidence suggests that news framing, digital media environments, and algorithmic filtering influence perceived threats and can trigger or dampen exclusionary sentiments, especially during periods of crisis (Ausat, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Experimental research on media consumption could clarify how these dynamics interact with trust and values.\u003c/p\u003e \u003cp\u003eSixth, the analysis does not differentiate between immigrant groups by origin, religion, or skill level. Public opinion research shows that attitudes vary considerably depending on perceived cultural distance, economic contribution, or group stereotypes (Ahmad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Ford \u0026amp; Mellon, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Future studies could incorporate more fine-grained distinctions to assess conditional forms of inclusion.\u003c/p\u003e \u003cp\u003eFinally, cross-national survey research faces challenges related to contextual and measurement heterogeneity. Differences in translation, interpretation, or national discourse may influence responses despite ESS harmonisation efforts (Davidov et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Goerres et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mixed-method approaches\u0026mdash;such as interviews, focus groups, or organisational ethnographies\u0026mdash;could complement survey evidence and illuminate context-specific understandings of immigration. Person-centred approaches may also help capture multidimensional or ambivalent attitudes beyond simple pro\u0026ndash;anti divisions (Goubin \u0026amp; Ruelens, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThis study examined how generalised trust, personal values, and occupational roles relate to immigration attitudes among workers and managers across 14 European countries. Using harmonised ESS data and multilevel models, the analysis shows that self-transcendence, conservation, and trust remain strong correlates of attitudes toward immigration, while occupational differences are modest but systematic. These patterns underscore the importance of psychological dispositions in shaping symbolic boundaries and highlight how national contexts condition their expression. The findings offer comparative evidence relevant to debates on migration, organisational diversity, and workplace inclusion. As immigration continues to reshape European labour markets, understanding how trust and values influence attitudes across different occupational groups remains essential. Further research linking attitudes to organisational practices would help clarify how these dispositions translate into concrete outcomes in hiring, promotion, and diversity management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTitle: Trust, Values, and Immigration Attitudes among Managers and Workers in 14 European CountriesAuthor: Hamed Ahmadinia1, 2 \u0026amp; 31 University of Turku, Economic Sociology, Social Research Department, Assistentinkatu 7, 20500, Turku2\u0026Aring;bo Akademi University, Information Studies, V\u0026auml;nrikinkatu 3B, 20500, Turku, Finland3 Migration Institute of Finland, H\u0026auml;meenkatu 13, 20500, Turku, Finland.Corresponding author:Hamed AhmadiniaEmail: [email protected]: University of Turku, Social Research Department, Turku, Finland.DeclarationsFundingThis work was supported by the Strategic Research Council at the Research Council of Finland (2021\u0026ndash;2027) as part of the Mobile Futures project. Decision numbers: 364420, 364422.Availability of data and materialsThe data are publicly available from the European Social Survey (https://ess.sikt.no/). Python scripts used for data cleaning and analysis will be made available upon publication or upon request via the author\u0026rsquo;s GitHub repository (currently private).Ethics approval and consent to participateNot applicable.Consent for publicationNot applicable.Competing interestsThe author declares no competing interests.Authors\u0026rsquo; contributions (CRediT)Hamed Ahmadinia: Conceptualization; Data curation; Formal analysis; Methodology; Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded by the Strategic Research Council (2021\u0026ndash;2027; decision numbers 364420 and 364422) as part of the Mobile Futures project at the Research Council of Finland. The revisions, which required substantial time and effort, were completed after the researcher, no longer working for the project, joined a new research project at the University of Turku. Conducting this study would not have been possible without the guidance, consultation, and support of Associate Professor Marja Tiilikainen, Research Director at the Migration Institute of Finland, as well as Dr. Elina Turjanmaa and Dr. Outi K\u0026auml;h\u0026auml;ri from the University of Oulu.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are publicly available from the European Social Survey (https://ess.sikt.no/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmad, A. (2020a). 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Ethnic Diversity, Economic and Cultural Contexts, and Social Trust: Cross-Sectional and Longitudinal Evidence from European Regions, 2002\u0026ndash;2010. \u003cem\u003eSocial Forces\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e(3), 1211\u0026ndash;1240. https://doi.org/10.1093/sf/sou088\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"immigration attitudes, generalised trust, human values, managers, workers, cross-national comparison, multilevel modelling, diversity and inclusion","lastPublishedDoi":"10.21203/rs.3.rs-8251577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8251577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article examines how generalised trust and basic human values are associated with immigration attitudes among managers and other workers in Europe. Using data from 282,662 native-born respondents in 14 countries across 11 waves of the European Social Survey (2002\u0026ndash;2023), we analyse both general immigration attitudes and acceptance of immigrants from different racial, ethnic, and socioeconomic backgrounds. Multilevel linear models with country and year fixed effects, and a random slope for generalised trust, show that self-transcendence values and higher trust are associated with more positive views of immigration, while conservation values are linked to more exclusionary attitudes. These patterns remain stable after adjusting for demographic factors. Managers express slightly more inclusive attitudes than other workers, although the difference is modest. Cross-national variation in the trust slope indicates that institutional contexts shape how strongly trust relates to support for immigration. We also observe small increases in immigrant acceptance around major events such as the 2015 arrivals and the COVID-19 pandemic, particularly among high-trust individuals. The study contributes to comparative migration research by linking psychological dispositions, occupational roles, and national contexts in shaping attitudes toward immigration and diversity.\u003c/p\u003e","manuscriptTitle":"Trust, Values, and Immigration Attitudes among Managers and Workers in 14 European Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 11:30:26","doi":"10.21203/rs.3.rs-8251577/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cfc38ae6-c73b-4c41-9b81-1867e5adc978","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T05:10:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 11:30:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8251577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8251577","identity":"rs-8251577","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}