Chapter 6. Non-labour market outcomes among migrants

This chapter analyses the non-labour market outcomes of migrants, examining whether and to what extent these differ from the outcomes of the native-born population. The analyses focus on self-reported health, political efficacy, interpersonal trust and volunteering. Previous analyses of data from the Survey of Adult Skills (PIAAC) have shown that literacy and numeracy skills are positively associated with many aspects of individual well-being, like health, active participation in the political process, levels of interpersonal trust, and involvement in volunteer or associative activities. This chapter examines if the association between skills and these non-labour market outcomes differs between migrants and natives, and how this connection is intertwined with education, age, gender and other individual characteristics.

    

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

The importance of non-labour market outcomes

While employment and wages are important for individual well-being, non-economic factors also contribute to well-being and to the smooth functioning of societies as a whole. These factors are becoming increasingly important in the policy discourse. The report by the Commission on the Measurement of Economic Performance and Social Progress (Stiglitz, Sen and Fitoussi, 2009[1]) is one example of the interest in developing broader measure of well-being, going beyond traditional measures of economic success, like wages (at the individual level) and GDP (at the country level). The OECD with its How’s Life initiative has been adopting the recommendations of the Commission and developed a new way to measure and benchmark countries’ performance using composite indicators reflecting well-being in a broad spectrum of economic and social dimensions.

The Survey of Adult Skills (PIAAC) collects information on four non-labour market outcomes: self-assessed health (health); the level of trust adults have in others (trust); the sense of being able to influence the political process (political efficacy); and participation in associative, religious, political or charity activities (volunteering). This chapter identifies disparities in such outcomes across native-born and foreign-born adults and examines how differences across the two groups are shaped by the socio-economic status of respondents and, crucially, by their proficiency in information-processing skills.

Examining the broad well-being of migrants is useful in identifying alternative benchmarks of integration. Labour market integration is important for migrants because it enables them to acquire economic resources, gives them a sense of purpose and provides opportunities for social bonding. It is important for host communities because it ensures that migrants contribute to the economic and social well-being of the country. However, in order to understand how and why people develop a sense of the belonging to a community it is also important to consider migrants’ broader life experiences. Measures of non-labour market outcomes are increasingly being recognised as important benchmarks in the evaluation of policy initiatives (OECD, 2013[2]).

Previous research has shown that education is one of the factors that is most strongly associated with subjective well-being, together with health status, social connectedness, being in a stable relationship with a partner, and being employed (Dolan, Peasgood and White, 2008[3]; Winkelmann and Winkelmann, 1998[4]; Kahneman and Krueger, 2006[5]; Blanchflower and Oswald, 2011[6]; Helliwell, 2008[7]). So far, however, studies have failed to capture the inter-relationship between different explanatory factors and the mechanisms that lead adults, in general (and migrants, in particular), with more education to express greater well-being. The information available from PIAAC – on participation in education and attainment, employment status and wages and on proficiency in literacy and numeracy – can elucidate some of these mechanisms.

There is a large body of empirical literature documenting the relationship between economic and non-labour market outcomes. Previous work using PIAAC data has found that proficiency in information-processing skills is positively associated with trust, volunteering, political efficacy and self-assessed good health among the general population. These relationships hold even after accounting for socio-demographic characteristics, like education, parents’ educational attainment, age and gender. The mechanisms linking economic and non-labour market outcomes, and the individual determinants of non-labour market outcomes (and, ultimately, of well-being) have been much less investigated, partly because of a lack of data, and partly because of the inherent difficulty in determining causal relationships. Non-labour market outcomes can be seen as being of inherent value and an expression of well-being, or, in light of the vast literature on the relationship between social capital and economic growth, as mediating variables in studying the relationship between skills proficiency and economic outcomes.

This chapter aims to investigate whether migrants and natives differ in non-labour market outcomes and, if so, if this can be explained by differences in observable characteristics across the two groups. The chapter also aims to identify whether education and skills play similar roles among migrants and natives in determining non-labour market outcomes.

Health

Disparities in self-reported health

Poor health is a major burden for the affected person, but also for governments. Recent estimates suggest that health expenditures account for as much as 9% of GDP across OECD countries; and in the United States, they represent as much as 18% of GDP (OECD, 2014[8]). There is a large body of evidence highlighting considerable disparities in health across population subgroups, with socio-economically disadvantaged and low-educated people disproportionately more likely to be in ill health (Grossman, 2000[9]; Grossman, 2005[10]; Schütte et al., 2013[11]; van der Kooi et al., 2013[12]; OECD/EU, 2015[13]).

Health is an important outcome in itself, but it is also a key potential determinant of differences in labour market participation and performance, and in engagement in lifelong learning activities, across adults. Adults who are highly proficient in information-processing skills might be better able to manage their health and, as a result, might be in a better position to use their skills in the labour market.

Figure 6.1 shows the percentage of native-born and migrant adults in PIAAC-participating countries who reported being in excellent or in very good health. On average across participating countries, the share of adults who reported to be in excellent or very good health is similar across the two groups,. However, in Chile, England, Ireland, Italy, New Zealand, Northern Ireland, Spain and Singapore, migrants were more likely than natives to report being in good health. By contrast, in France, Germany, Israel, the Netherlands and Sweden, they were less likely to report being in good health. In Chile, migrants were particularly more likely than natives to report being in excellent or in very good health (67% of natives but 81% of migrants reported excellent or very good health, a difference of 13 percentage points). Natives, on the other hand, were more likely to report being in excellent or very good health in Estonia (where 68% of natives but only 44% of migrants reported excellent or very good health, a difference of 25 percentage points), Israel (where 89% of natives but 75% of migrants reported excellent or very good health, a difference of 13 percentage points) and the Netherlands (where 83% of natives but 70% of migrants reported excellent or very good health, a difference of 13 percentage points).

Differences in the health status of migrants and natives could be due to differences in the background characteristics of the two populations, particularly their age and labour market status. Institutional factors, such as immigration policy and access to welfare institutions (as well as personal choice) can determine health differences between the two groups. Previous chapters in this report have indicated that migrants have poorer labour market outcomes than natives, and that their skills are underused in the labour market. Labour market penalties might lead to poorer health because migrants might have fewer economic and social resources that enable them to engage in the behaviours and to make the choices that maintain good health.

Moreover, to the extent that migrants have a lower social status than they would have had, given their background, had they not migrated, they might be more likely than natives to suffer from “status syndrome” (Marmot, 2005[14]). Status syndrome refers to the poorer health and higher mortality rates that are observed among people of lower social status compared with people of higher social status. The syndrome was first observed and described by Michael Marmot, who tracked the mortality rates and the incidence of certain health conditions among British civil servants in a Whitehall study.

Psychological factors, social support from extended family networks and welfare regimes might all contribute to differences in health across migrant populations. Differences in health status between migrants and natives might also be a “statistical artefact”, derived from the fact that, in some countries, migrants who are in poor health and who cannot work or have difficulty finding employment, might leave the host country, with the result that only migrants in good health remain. In other countries, generous welfare systems and a labour market that is less based on manual labour might attract people in poor physical health to enter and remain in the country. This selection effect might arise because legislation or personal preferences might lead migrants to return to their home country if and when they are unable to be economically active or suffer from poor health. In other host countries, comprehensive healthcare and welfare arrangements and good-quality care might eliminate this selection effect because migrants will have no reason to leave the country for health-related reasons.

Figure 6.1. Reported health by immigrant status
Percentage of migrants and natives who report being in excellent or very good health
picture

Note: Migrants are defined as those participants whose country of birth is different that the country at which they are doing the test. Statistically significant differences are marked in bold. Estimates based on a sample size less than 30 are not shown (Japan, Poland and Turkey).

Countries are ranked in descending order of the percentage of migrants who report being in excellent or very good health

Source: (OECD, 2015[15]) Survey of Adult Skills (PIAAC) (2012, 2015), Table A6.1, www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink https://doi.org/10.1787/888933846213

Table 6.A.1 indicates that in some countries, differences in the socio-demographic profile of migrants and natives lie behind the observed differences in the percentage of adults in the two groups who reported being in excellent or very good health. For example, when comparing natives and migrants of similar age, parents’ educational attainment and gender, and who speak the main language of the Survey of Adult Skills (PIAAC), only migrants in New Zealand and Singapore were more likely to report being in excellent or very good health. In Austria, Canada, Denmark, Estonia, Finland, France, Germany, Greece, Israel, the Netherlands and Sweden, migrants were less likely to report being in good health than natives, with gaps between the two groups as large as 6 percentage points in Estonia, Finland, Greece and the Netherlands.

The role of education and skills in promoting health

Changes in the nature of work, in infrastructures and healthcare have meant that non-communicable diseases that arise from people’s lifestyle choices play an increasingly important role in determining the health of individuals and disparities in health outcomes across people and communities. Prevention programmes that promote healthy lifestyles are increasingly important but present new challenges for health practitioners and policy makers. While the need for treatment in the presence of illness and disease is evident for patients, prevention programmes de facto require lifestyle changes among groups of healthy people who have to understand issues related to the risks, health benefits and psychological costs incurred at different points in time, often decades apart. As a result, and more than ever, education and proficiency in information-processing skills might be key to explaining differences in health outcomes. The expectation that individuals will become partners in the management of their health and bear responsibility for adopting healthy behaviours has increased in parallel with the growth in chronic conditions due to increases in life expectancy (Bauer et al., 2014[16]). In order to effectively manage chronic conditions individuals have to constantly communicate with health care providers and understand complex probabilistic concepts such as risk factors, learn to self-monitor parameters such as blood pressure, comply with long-term courses of drug regimens for multiple morbidities, navigate digital texts, interpret information on food and drug labels, and connect with support networks of friends and peer patients through social media. With rapidly evolving health-promoting technology products, individuals need to adapt to become perennial learners (Kakarmath et al., 2018[17]). As such, strong general literacy and numeracy proficiency have become pre-conditions for the development of health literacy (Berkman et al., 2011[18]).

Several studies have investigated the relationship between education and health outcomes and behaviours, finding a positive association between the two. The education-health link is partly explained by the higher income that more educated people earn. But it is increasingly clear that this association also stems from a direct, causal impact of education and learning on health (Lleras-Muney, 2005[19]; Lundborg, 2008[20]; Oreopoulos, 2006[21]; Silles, 2009[22]). Educated people might be more efficient at maintaining good health and, as a result, enjoy better health with the same amount of resources, all else being equal. Education might prompt adults to make better health choices, such as adopting a healthier diet, exercising and avoiding tobacco. More education generally translates into greater access to better information and greater ability to act on such information. Education might also alter the perception of risk and, by doing so, might render adults more likely to invest in their health. In addition, since it is associated with the potential for high income throughout a lifetime, education is likely to shape what individuals are willing to do to insure themselves against the risk of being in poor health and the potential associated loss of income.

Health literacy has been linked to the use of emergency health services, hospitalisation, interpretation of health communication, appropriate taking of medications and mortality in the elderly (Berkman et al., 2011[18]). The expectation that individuals will become partners in the management of their own health and bear a major responsibility for adopting health promoting behaviours has increased in parallel with the growth in life expectancy and associated chronic health conditions (Bauer et al., 2014[16]). Treatment of a chronic condition often requires that individuals communicate with health care providers and understand complex probabilistic concepts such as risk factors, learn to self-monitor parameters such as blood pressure, comply meticulously with long-term courses of drug regimens for multiple morbidities, navigate digital texts, interpret information on food and drug labels, and connect with support networks of friends and peer patients through social media. With rapidly evolving health-promoting technology products, individuals need to adapt to become perennial learners. As such, strong general literacy and numeracy proficiency have become pre-conditions for the development of health literacy.

Previous analyses of PIAAC data have indicated that information-processing skills play a key role in explaining within-country variations in self-reported health (Borgonovi and Pokropek, 2016[23]). However, little is known about the extent to which differences in the proficiency in these skills explain variations in self-reported health across natives and migrants, or the degree to which migrants and natives are likely to report that they enjoy good health if they attain similar levels of proficiency in information-processing skills.

Table 6.A.1 shows the degree to which differences in educational attainment and literacy skills explain disparities between natives and migrants in the probability of reporting that they are in excellent or very good health. Results are in line with previous work suggesting that both educational attainment and literacy levels are strongly and positively associated with adults’ self-reported health status. All else being equal, adults with a tertiary degree are more likely to report being in excellent or very good health than those who do not have an upper secondary degree, and those who have greater proficiency in literacy are more likely to report being in excellent or very good health than those who are less proficient. However, differences in the educational attainment or literacy levels between migrants and natives do not explain the disparities between migrants and natives in self-reported health status.

Figure 6.2 indicates that in the majority of participating countries, the association between self-reported health and literacy are similar among migrants and natives; but in Ireland, Israel, New Zealand, Northern Ireland, Norway, and the United States, proficiency in literacy appears to be less associated with health status among migrants than among natives. For example, in Ireland, all else being equal, a difference of 50 points in literacy proficiency is associated with a difference of around 3 percentage points in the probability that a native adult will report being in excellent or very good health; but among migrants, there is no such advantage. In the United States, a difference of 50 points in literacy proficiency is associated with a difference of around 6 percentage points in the probability that a native adult will report being in excellent or very good health; but among migrants, this difference is only 3 percentage points. Similarly, in Canada, Lithuania, Northern Ireland and the United States, the relationship between earning a tertiary degree and reporting good-to-excellent is weaker among migrants and among natives (see Table 6.A.1).

Figure 6.2. Differences between natives and migrants in the relationship between literacy and health, by migrant background
Marginal effects of literacy on the probability to report being in excellent or very good health by immigrant status
picture

Note: The returns to literacy is not statistically significant in Czech Republic, Greece and Italy and are therefore not presented on this chart. Migrants are defined as those participants whose country of birth is different that the country at which they are doing the test. Returns to literacy are based on a regression model and take account of differences associated with the following variables: age, gender, education, immigration background and parents' educational attainment (See model 4 in the source table). Statistically significant differences are maked in bold. Estimates based on a sample size less than 30 are not shown.

Countries are ranked in descending order of the returns to literacy for natives

Source: (OECD, 2015[15]) Survey of Adult Skills (PIAAC) (2012, 2015), Table 6.A.1, www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846232

Generalised trust

Generalised trust is a feeling of goodwill towards anonymous others. It allows for smooth social and economic interactions in complex societies, where people engage frequently with others whom they do not know and from whom they differ in many ways. The wealth of research on generalised trust in sociology, political science, economics and public health reflects the importance of trust in unfamiliar others in increasingly complex societies (Nannestad, 2008[24]; Newton, 2007[25]) and the social and economic benefits of generalised trust. In these contexts, the absence of trust can have negative consequences for economic activity.

Interpersonal trust, especially generalised trust, is a strong predictor of economic prosperity (Fukuyama, 1995[26]; Knack and Keefer, 1997[27]; Putnam, Leonardi and Nanetti, 1993[28]) and individual well-being (Helliwell and Wang, 2010[29]). The literature has identified a number of channels through which trust can affect economic performance (Algan and Cahuc, 2014[30]): trust is thought to be essential for the smooth functioning of financial markets; it is likely to play an important role in economic activities that involve a high degree of uncertainty (like investments in research and development, which are the sources of technological innovations) or in which contracts are difficult to enforce; and by promoting co-operation, trust can improve the organisation of firms and the quality of labour relations.

While institutions, such as judicial systems, are crucial in sustaining trust, education and skills policies are also likely to play an important role. Higher information-processing skills can help people better understand the motives underlying others’ behaviours, and the negative consequences of lack of co-operation. Education and cognitive skills help build the socio-emotional skills needed to engage in fruitful social relationships (Borgonovi and Burns, 2015[31]). Indirectly, societies with larger shares of skilled individuals might function more efficiently, thus helping to sustain trust.

The Survey of Adult Skills (PIAAC) allows for the creation of measures of interpersonal trust through responses to the statements: “Only few people can be trusted” and “If you are not careful, other people will take advantage of you”, to which respondents could report that they strongly disagreed, disagreed, neither agreed nor disagreed, agreed or strongly agreed. For the purpose of the analysis carried out in this section, adults who disagreed or strongly disagreed with these statements were classified as having high levels of trust.

In many countries, migration flows have increased the level of ethnic, social and religious diversity in local communities. Research on migration and generalised trust has attempted to identify the extent to which greater diversity is associated with less trust among native populations (Alesina and La Ferrara, 2002[32]; Borgonovi, 2012[33]). However, monitoring the level of generalised trust expressed by migrant communities is also a good way to identify their well-being: whether they feel safe and welcome in their communities.

Figures 6.3 and 6.4 show the percentage of migrants and natives who reported that they disagree or strongly disagree that only few people can be trusted. In 12 OECD countries, natives were more likely than migrants to report that they strongly disagree or disagree that only few people can be trusted; in Denmark and the Netherlands the differences between the two groups are particularly large. For example, in Denmark, 46% of natives, but only 32% of migrants reported that they disagree or strongly disagree that only few people can be trusted, a difference of 14 percentage points. In the Netherlands, 33% of natives but only 22% of migrants reported the same, a difference of 11 percentage points. Similarly, in 9 OECD countries, natives were more likely than migrants to report that they strongly disagree or disagree that if you are not careful, other people will take advantage of you. In Denmark, Finland, Norway and Sweden, differences between the two groups amount to at least 10 percentage points. Tables 6.A.2 and 6.A.3 suggest that differences in the profiles of migrants and natives by gender, age, language spoken at home and parents’ education do not explain differences in the levels of trust expressed by the two groups.

Figure 6.3. Percentage of adults who believe that most people can be trusted, by migrant background
Percentage of migrants and natives who report disagreeing or strong disagreeing that only few people can be trusted
picture

Note: Migrants are defined as those participants whose country of birth is different that the country at which they are doing the test. Statistically significant differences are marked in bold. Estimates based on a sample size less than 30 are not shown (Japan, Poland and Turkey).

Countries are ranked in descending order of the percentage of migrants who report disagreeing or strong disagreeing that only few people can be trusted. Statistically significant differences are marked in bold.

Source: (OECD, 2015[15]) Survey of Adult Skills (PIAAC) (2012, 2015), Table 6.A.2, www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846251

Figure 6.4. Percentage of adults who believe that others will not take advantage of them, by migrant background
Percentage of migrants and natives who report disagreeing or strong disagreeing that if you are not careful other people will take advantage of you
picture

Note: Migrants are defined as those participants whose country of birth is different that the country at which they are doing the test. Statistically significant differences are marked in bold. Estimates based on a sample size less than 30 are not shown (Japan, Poland and Turkey).

Countries are ranked in descending order of the percentage of migrants who report disagreeing or strong disagreeing that if you are not careful other people will take advantage of you. Statistically significant differences are marked in bold.

Source: (OECD, 2015[15]) Survey of Adult Skills (PIAAC) (2012, 2015), Table 6.A.3, www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846270

The role of education and skills in promoting generalised trust

The literature has identified large differences between people in levels of generalised trust, and educational attainment is one of the factors that is strongly associated with people’s propensity to trust anonymous others (Borgonovi, 2012[33]; Putnam, 2000[34]; Paxton, 2007[35]; Alesina and La Ferrara, 2002[32]; Brehm and Rahn, 1997[36]; Nannestad, 2008[24]; Merolla et al., 2013[37]);. Education could be a factor because of social sorting and cognitive processes (Nie, Junn and Stehlik-Barry, 1996[38]; Hooghe, Marien and de Vroome, 2012[39]). Better-educated adults are more likely to be active in the labour market and to command higher wages than adults with less education. As a result, better-educated adults have stronger safety nets to protect them from the negative consequences of misplacing trust. The cognitive mechanism recognises that, over time, only individuals who are not penalised for engaging in co-operative behaviours can afford to trust others. Being able to appreciate the trustworthiness of specific people in given situations is a prerequisite for people to be able to hold a general expectation about the trustworthiness of others in general (Yamagashi, 2001[40]; Sturgis, Read and Allum, 2010[41]).

Tables 6.A.2 and 6.A.3 confirm that, in the majority of PIAAC-participating countries, educational attainment and literacy proficiency are positively associated with the likelihood that individuals will trust others. For example, adults with a tertiary qualification are, on average across participating countries, 13% more likely to disagree or strongly disagree that there are only a few people that they can trust completely and, all else being equal, a difference of 50 score points in literacy proficiency is associated with a 3% greater likelihood that adults will disagree or strongly disagree that there are only a few people that can be trusted completely.

In the majority of countries, the association between educational attainment and literacy proficiency is the same among migrants and natives, but in some it is weaker among migrants. For example, in Canada, Denmark, Lithuania, New Zealand and the United States, the difference in the extent to which tertiary-educated migrants and migrants who have less than an upper secondary degree disagreed or strongly disagreed that there are only a few people you can trust completely is considerably smaller than the difference observed between natives who have a tertiary degree and those who have an upper secondary degree. Similarly, in Australia, Austria, Denmark, New Zealand and the United States, the difference in the extent to which tertiary-educated migrants and migrants who have less than an upper secondary degree disagreed or strongly disagreed that if you are not careful, other people will take advantage of you is considerably smaller than the difference observed between natives who have a tertiary degree and those who have an upper secondary degree.

In many countries, differences in self-reported trust associated with literacy skills are smaller among migrants than among natives. For example, in Australia, Austria, Canada, Denmark, Germany, New Zealand and the United States, among OECD countries, and in Singapore, adults’ reports on the extent to which they disagree or strongly disagree that there are only a few people that can be trusted completely are less associated with literacy among migrants than among natives (Table 6.A.2). Similarly, in Canada, Denmark, Estonia, Finland, Israel, the Netherlands, New Zealand and the United States, adults’ reports on the extent to which they disagree or strongly disagree that if they are not careful other people will take advantage of them are less associated with literacy among migrants than among natives (Table 6.A.3).

Political efficacy

Political efficacy helps sustain and develop successful democratic systems (Almond and Verba, 1963[42]; Macpherson, 1977[43]; Pateman, 1970[44]). It is defined as “the feeling that individual political action does have, or can have, an impact on the political process, i.e. that it is worthwhile to perform one’s civic duties” (Campbell, Gurin and Miller, 1954[45]). Political efficacy has two components that highlight different aspects of the relationship between individuals and the public sphere: internal political efficacy, which refers to feelings of personal competence “to understand and to participate effectively in politics” (Craig, Niemi and Silver, 1990[46]), and external political efficacy, which refers to people’s belief “in the responsiveness of political bodies and actors to citizens’ demands” (Balch, 1974[47]; Converse, 1972[48]).

Countries differ widely in how migrants come to acquire political rights and duties, in the range of opportunities they have to engage in the political sphere, and in the extent to which migrant communities are a primary concern for politicians at the national, regional or local level. Because political participation and representation are closely tied to citizenship and to the degree to which people feel that they belong to a community and a social system, migrants might express less political efficacy than natives. It is more difficult for migrants to acquire political rights; and developing feelings of belonging and of identification with their host country requires that migrants internalise their host community’s social mores and that their community recognises their contributions.

PIAAC respondents were presented with the following statement aimed at measuring their level of external political efficacy: “People like me do not have a say in what the government does” to which respondents could answer on a five-point Likert scale ranging from “strongly agree”, “agree”, “neither agree nor disagree”, “disagree” to “strongly disagree”. Lower values indicate less external political efficacy. The external political efficacy question has a long tradition in studies of political efficacy, dating back to the first National Election Studies in the United States in the 1950s (Lane, 1959[49]). Given the strong link between migrant background and political rights and representation, the question might lead foreign-born adults to consider their background as particularly salient when answering this question.

Figure 6.5 shows that in as many as 12 OECD countries native-born adults were more likely than foreign-born adults to report that they disagree or strongly disagree that people like them do not have any say about what the government does. In Finland the difference is particularly wide: 47% of native born but only 24% of foreign-born adults so reported, a difference of 24 percentage points. In Denmark, 52% of natives but only 35% of foreign-born adults reported that they disagree or strongly disagree that people like them do not have any say about what the government does, a difference of over 17 percentage points. Among OECD countries, differences between the two groups are wider than 10 percentage points in Denmark, Estonia, Finland, Greece, the Netherlands, Norway and Sweden. Interestingly, in Flanders (Belgium) and New Zealand, foreign-born adults were more likely than their native-born counterparts to report high levels of political efficacy. Results presented in Table A6.4 suggest that length of stay in the country is not a significant factor shaping differences in political efficacy among migrant groups.

Figure 6.5. Percentage of adults who reported high levels of political efficacy, by migrant background
Percentage of migrants and natives who report disagreeing or strong disagreeing that people like them don't have any say about what government does
picture

Note: Migrants are defined as those participants whose country of birth is different that the country at which they are doing the test. Statistically significant differences are marked in bold. Estimates based on a sample size less than 30 are not shown.

Countries are ranked in descending order of the percentage of migrants who report disagreeing or strong disagreeing that people like them don't have any say about what government does. Statistically significant differences are marked in bold.

Source: (OECD, 2015[15]) Survey of Adult Skills (PIAAC) (2012, 2015), Table A6.4, www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846289

Cultural and social reproduction theorists consider levels of political efficacy to be determined primarily by the experiences and interactions children have with important reference figures and by their experiences as they grow up. They stress the importance of socialisation processes in shaping political outcomes and civic participation (Prior, 2010[50]). During childhood, people internalise what society expects of them, but also the extent to which societal norms, and political institutions and actors will allow them to lead the lives they want and strive to achieve (Johnson and Dawes, 2016[51]; Putnam, Leonardi and Nanetti, 1993[28]; Stolle and Hooghe, 2004[52]; Uslaner, 2002[53]). To the extent that foreign-born adults might have lived under authoritarian regimes and have come to view political institutions as not responsive to local communities, they might find it difficult to develop the level of trust in institutions that will allow them to play an active and engaged role in their communities.

The policy-feedback literature has hypothesised that policies shape citizenship. Some research has examined the extent to which different types of welfare programmes, and their design, can shape people’s sense of agency, and level of civic and political engagement (Bruch, Ferree and Soss, 2016[54]; Kumlin, 2004[55]; Kumlin and Rothstein, 2005[56]). Cultural and social reproduction theories suggest that the acquisition of external political efficacy crucially depends on the experiences people have as they become adults and on the level of their parents’ political efficacy. The experientialist approach views external political efficacy as the result of positive interactions and experiences with institutions, including the government.

Political efficacy can be built and destroyed over time as individuals change, and political institutions act in ways that do (or do not) foster the well-being of the communities they serve, lack transparency or are not open to citizens’ involvement (Hardin, 2002[57]). More specifically, when communities provide few opportunities to consult with migrants, even though the migrants might not be citizens or have passive political rights, and when there are large differences in social and economic outcomes between native and migrant populations, migrants might perceive political institutions and actors as distant and unresponsive. Both the policy-feedback literature and experientialist theories suggest that the gap between migrants and natives in external political efficacy might vary greatly across countries, depending on the opportunities afforded to migrants to influence government action, and on the structure of the immigration and welfare policies that affect them.

The role of education and skills in promoting political efficacy

Educational attainment is one of the factors that is most strongly associated with political participation and involvement. Within countries and at any given time, adults who have more qualifications and who have attended school for longer are more likely to be politically active (Borgonovi, d'Hombres and Hoskins, 2010[58]; Lipset, 1959[59]; Putnam, 2001[60]; Wolfinger and Rosenstone, 1980[61]). The role of education in promoting political efficacy could stem from knowledge about political institutions, an understanding of economic and social affairs, and also from the greater information-processing skills that better-educated adults have developed. In fact, feelings of efficacy depend on people’s ability to make use of the information in their environment to hold political institutions accountable for respecting the mandate given to them by the electorate. While voting is a key form of political participation, people have other means to ensure that they play an active role in making local, regional and national governments respond to their needs, protect their rights and promote their well-being.

Table 6.A.4 indicates that, in most PIAAC-participating countries, educational attainment and literacy proficiency are strongly and positively associated with external political efficacy. On average across participating countries, tertiary-educated graduates were 16% more likely than adults without an upper secondary degree to report disagreeing or strongly disagreeing that people like them do not have any say about what the government does. In Austria, Chile, Finland, Ireland, the Netherlands, Norway and the United States, the difference between tertiary-educated adults and adults without an upper secondary degree is at least 20 percentage points. Similarly, a difference of 50 score points in literacy proficiency is associated with a higher likelihood that adults will report disagreeing or strongly disagreeing that people like them do not have any say about what the government does. Among OECD countries, the change in political efficacy that is associated with literacy is particularly steep in Australia, Canada, Chile, Denmark, Greece, New Zealand, Norway, Sweden and the United States.

Figure 6.6 and Table 6.A.4 suggest that in a few countries educational attainment and literacy proficiency moderate disparities in political efficacy related to migrant background. For example, in Canada, England (UK), Estonia, and Germany, literacy proficiency is less strongly associated with political efficacy among migrants than among natives. In Denmark, Flanders (Belgium), Ireland, the Netherlands, New Zealand, Norway and the United States, educational attainment is less strongly associated with political efficacy among migrants than among natives. In the majority of countries, estimated differences between the two groups suggest a weaker relationship among migrants, although small sample sizes lead to imprecise estimates and therefore it is not possible to reject the null hypothesis of similarity in effects across the two groups at conventional levels (p<5%).

These results could indicate that while access to and ability to use information are key to explaining disparities in political efficacy among native-born adults, other factors might be at play for migrants. For example, structural impediments to political participation and involvement, and feeling that their voices, needs and concerns are of secondary importance to politicians might better explain why migrants report less political efficacy. Most research on the effects of migration flows on political participation and involvement focuses on the impact that a large population of migrants has on the political views, perceptions and feelings of efficacy among natives. But if political systems are to represent the interests and needs of local communities and promote social cohesion in among diverse populations, then they must ensure that foreign-born individuals feel that institutions are responsive to their needs and that their voices are heard and respected.

Figure 6.6. Differences in the effect of literacy proficiency on political efficacy, by migrant background
Marginal effects of literacy on the probability to report disagreeing or strong disagreeing that people like them don't have any say about what government does
picture

Note: The returns to literacy are not statistically significant in France and are therefore not presented on this chart. Migrants are defined as those participants whose country of birth is different that the country at which they are doing the test. Returns to literacy are based on a regression model and take account of differences associated with the following variables: age, gender, education, immigration background and parents' educational attainment (See model 4 in the source table). Statistically significant differences are marked in bold. Estimates based on a sample size less than 30 are not shown.

Countries are ranked in descending order of the returns to literacy for natives. Statistically significant differences are marked in bold.

Source: (OECD, 2015[15]) Survey of Adult Skills (PIAAC) (2012, 2015), Table A6.4, www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846308

Volunteering

Volunteering is the donation of time, and sometimes expertise, by an individual to benefit a group or a cause (Wilson, 2000[62]). Although it shares several common features with other helping behaviours, volunteering is proactive and organised rather than reactive and spontaneous. Volunteering directly benefits those who engage in the activity: people who volunteer enjoy higher levels of mental and physical well-being than those who do not volunteer (Li and Ferraro, 2005[63]; Post, 2005[64]; Whiteley, 2014[65]). In addition, volunteering indicates social integration and community spirit.

Participation in volunteer activities is a strong indicator of the extent to which people are part of formal social networks and activities (Putnam, 2001[60]). Volunteering can be a way for migrants to form strong connections both with other migrants and with the wider community. As such, volunteering can be a way for migrants to mediate some of the adverse consequences that are typically linked with relocation, such as loss of social and cultural capital. Volunteering can also be an effective way for migrants to upgrade and practice language skills without having to sustain some of the costs that are typically associated with participation in language courses – essentially exchanging work for the possibility of practicing the language of the host country (Dudley, 2007[66]).

Volunteering can also be a way for migrants to improve their employment opportunities and improve their likelihood of integrating into the labour market because it can act as a proxy for work experience (Aycan and Berry, 1996[67]; Couton, 2002[68]; Dudley, 2007[66]). Employers can regard volunteering as a productive activity that gives them relevant information on the job-relevant skills and attitudes of migrants who lack work experience in their host country and whose education qualifications might be a poor indicator of human capital. As a result, participation in volunteer activities can help improve migrants’ psychological well-being (because of the positive social network effect) and can result in better jobs or higher wages (Dicken and Blomberg, 1988[69]; Hackl, Halla and Pruckner, 2007[70]; Prouteau and Wolff, 2006[71]). Those migrants who volunteer in religious organisations, social welfare organisations or for groups that support migrants might also benefit psychologically from knowing that these organisations can assist them, too.

At the same time, migrants might volunteer less because they have fewer bonds in the host community, and many migrants, either out of necessity or choice, devote all of their efforts and energy to being productive members of the labour force. In addition, while the perception of discrimination against and local attitudes towards migrants might encourage migrants to volunteer for organisations that support migrant communities, they might also discourage migrants from volunteering for broader causes, which might help them forge strong links with the local community.

Few studies examine patterns of volunteering among migrant populations and whether they differ from those of native-born populations. Studies generally find that migrants are less likely to volunteer than natives but that, when they do volunteer, they tend to contribute a similar amount of time. Migrants appear to be more involved in volunteering for religious organisations and for community groups that provide programmes and services for migrants (Dechief, 2005[72]). This finding is consistent with the notion that migrants attempt to build an informal social welfare system that will insulate them from adversity.

Figure 6.7. Percentage of adults who reported that they had volunteered, by migrant background
Percentage of migrants and natives who report participating in voluntary work for charity or non-profit organisations at least once a month
picture

Note: Migrants are defined as those participants whose country of birth is different that the country at which they are doing the test. Statistically significant differences are marked in bold. Estimates based on a sample size less than 30 are not shown.

Countries are ranked in descending order of the percentage of migrants who report participating in voluntary work for charity or non-profit organisations at least once a month. Statistically significant differences are marked in bold.

Source: (OECD, 2015[15]) Survey of Adult Skills (PIAAC) (2012, 2015), Table A6.5, www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846327

The Survey of Adult Skills (PIAAC) asked respondents the following question: “In the last 12 months, how often, if at all, did you do voluntary work, including unpaid work for a charity, political party, trade union or other non-profit organisation?” Respondents could answer: “never”, “less than once a month”, “less than once a week but at least once a month”, “at least once a week but not every day”, or “every day”.

On average across participating countries, native adults were more likely to report having participated in voluntary work, including unpaid work for a charity, political party, trade union or other non-profit organisation in the year before they participated in PIAAC. Some 36% of native adults, but 27% of migrant adults reported that they had volunteered in the previous year, a difference of eight percentage points. Differences between the two groups are particularly pronounced in Germany, where 38% of natives but only around 18% of migrants reported volunteering, and in Austria, where 38% of natives but only 20% of migrants so reported. Gaps between the two groups are observed in 20 of the 25 OECD countries with available data. Among OECD countries, no such difference is observed in Chile the Czech Republic, England (UK), Greece and New Zealand.

Differences in the socio-demographic profile of natives and migrants are unrelated to both the observed differences in volunteering rates and the propensity to volunteer. In Norway and the United States, volunteering is most prevalent among natives; as many as 60% of natives (compared with 42% of migrants) in Norway and 58% of natives (compared with 45%) of migrants) in the United States reported having volunteered at least once in the year prior to the PIAAC survey. In Spain, only around 19% of natives reported having volunteered –a share 8 percentage points larger than the share of migrants who so reported.

The role of education and skills in promoting volunteering

In all countries, higher proficiency in literacy is associated with a greater likelihood of engaging in voluntary work for non-profit organisations (e.g. political, charity or religious organisations). Participation in this kind of activity is likely to be a good proxy for altruism and civic engagement, whose link with skills has been attributed to civic education. Like trust, altruism can also be beneficial for economic performance, in that it may foster co-operation (Bowles and Polania-Reyes, 2012[73]). Literacy proficiency is not equally associated with the probability that native-born and foreign-born individuals will engage in volunteering activities. In some countries, including Australia, Chile, England (UK), Flanders (Belgium), Germany, Lithuania, New Zealand, Slovenia and the United States the increase in the probability of volunteering associated with higher literacy proficiency is lower among migrants than natives (see Table 6.A.5). In the remaining countries the opposite is true. Table 6.A.5 does not reveal differences across migrants and natives in how the probability of volunteering differs depending on educational attainment.

Conclusions and policy implications

The aim of this chapter was to present a picture of the broader well-being outcomes of migrants. International comparisons of migrants’ well-being present numerous challenges, as the size and characteristics of the migrant population can differ in important ways across countries (OECD, 2017[74]). This means that cross-country comparisons of migrants’ well-being outcomes need to be interpreted with caution and with an awareness of both the differences in the composition of migrant populations as well as the differences in the historical impact of migration policies across countries.

Results suggest that in some countries migrants report lower levels of health than natives. Migrants (especially undocumented migrants and asylum seekers) often face legal restrictions on entitlements to health care. Other barriers include user fees, language, lack of familiarity with rights, entitlements and the overall health system, underdeveloped health literacy, administrative obstacles, social exclusion, and direct and indirect discrimination. Health services should consider the specific challenges and needs of migrant populations to promote their health (OECD, 2017[74]). Furthermore, since stress is a major risk factor for a variety of diseases, migrants may be particularly exposed to a number of stressors, including pre-migration stressors such as refugee camp internment and catastrophic experiences, as well as post-migration stressors such as separation from family, unemployment, poverty, homesickness, acculturation stress, guilt, isolation, marginality and discrimination (Fenta, Hyman and Noh, 2004[75]; Prilleltensky, 2008[76]). Factors reducing the stress of adapting to a new country include strong social support networks within family and community, coping skills and knowledge of the new language and culture (Bhugra et al., 2011[77]; Hovey, 2000[78]; Hovey and King, 1997[79]; OECD, 2017[74]).

This chapter also identified that in some countries, migrants report lower levels of generalised trust, political efficacy and volunteering. Understanding migrants’ experiences of civic and political engagement is particularly important as they may often be excluded from certain forms of civic expression or from certain public services depending on their legal status (e.g. citizenship, type of residence permit) and their ability to navigate government bureaucracy and procedures. Developing ways to ensure that migrants are able to fully feel part of their communities, that there are ways for them to feel represented by national and local governments and that they are empowered to contribute their time and energy to promote the well-being of their communities is crucial to promote social cohesion.

References

[32] Alesina, A. and E. La Ferrara (2002), “Who trusts others?”, Journal of Public Economics, Vol. 85, pp. 207-34.

[30] Algan, Y. and P. Cahuc (2014), “Trust, growth, and well-being: New evidence and policy implications”, in Aghion, P. and S. Durlauf (eds.), Handbook of Economic Growth, Vol. 2A, Elsevier, Amsterdam.

[42] Almond, G. and S. Verba (1963), The Civic Culture: Political Attitudes and Democracy in Five Nations, Princeton University Press, Princeton, NJ.

[67] Aycan, Z. and J. Berry (1996), “Impact of employment-related experiences on migrants’ psychological well-being and adaptation to Canada”, Canadian Journal of Behavioural Science, Vol. 28/3, pp. 240-251.

[47] Balch, G. (1974), “Multiple indicators in survey research: the concept of ‘sense of political efficacy’”, Political Methodology, Vol. 1/2 (Spring 1974), pp. 1-43.

[16] Bauer, U. et al. (2014), “Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA.”, Lancet (London, England), Vol. 384/9937, pp. 45-52, http://dx.doi.org/10.1016/S0140-6736(14)60648-6.

[18] Berkman, N. et al. (2011), “Low health literacy and health outcomes: An updated systematic review”, Annals of Internal Medicine, Vol. 155/2, p. 97, http://dx.doi.org/10.7326/0003-4819-155-2-201107190-00005.

[77] Bhugra, D. et al. (2011), “WPA guidance on mental health and mental health care in migrants”, World Psychiatry, Vol. 10/1, pp. 2-10.

[6] Blanchflower, D. and A. Oswald (2011), “International happiness”, Measurement, Vol. 25/1, pp. 23-30, http://dx.doi.org/10.5465/AMP.2011.59198446.

[33] Borgonovi, F. (2012), “The relationship between education and levels of trust and tolerance in Europe1”, The British Journal of Sociology, Vol. 63/1, pp. 146-167, http://dx.doi.org/10.1111/j.1468-4446.2011.01397.x.

[31] Borgonovi, F. and T. Burns (2015), “The educational roots of trust”, OECD Education Working Papers, No. 119.

[58] Borgonovi, F., B. d'Hombres and B. Hoskins (2010), “Voter turnout, information acquisition and education: evidence from 15 European countries”, The B.E. Journal of Economic Analysis & Policy, Vol. 10/1, http://dx.doi.org/10.2202/1935-1682.2463.

[23] Borgonovi, F. and A. Pokropek (2016), “Education and self-reported health: Evidence from 23 countries on the role of years of schooling, cognitive skills and social capital.”, PloS one, Vol. 11/2, p. e0149716, http://dx.doi.org/10.1371/journal.pone.0149716.

[73] Bowles, S. and S. Polania-Reyes (2012), “Economic incentives and social preferences: Substitutes or complements?”, Journal of Economic Literature, Vol. 50, pp. 368-425.

[36] Brehm, J. and W. Rahn (1997), “Individual-level evidence for the causes and consequences of social capital”, American Journal of Political Science, Vol. 41, pp. 999-1023.

[54] Bruch, S., M. Ferree and J. Soss (2016), “From policy to polity: democracy, paternalism, and the incorporation of disadvantaged citizens”, American Sociological Review , Vol. 75/2, pp. 205-226.

[45] Campbell, A., G. Gurin and W. Miller (1954), The Voter Decides, Row, Peterson and Company.

[48] Converse, P. (1972), “Change in the American electorate”, in Campbell, A. and P. Converse (eds.), The Human Meaning of Social Change, Russell Sage Foundation, New York.

[68] Couton, P. (2002), “Highly skilled migrants: Recent trends and issues”, ISUMA-Canadian Journal of Policy Research, Vol. 3/2, pp. 114-23.

[46] Craig, S., R. Niemi and G. Silver (1990), “Political efficacy and trust: a report on the NES Pilot study items”, Political Behavior, Vol. 12, pp. 289-314.

[72] Dechief, D. (2005), “Recent migrants as an &quot;alternate civic core&quot;: How VCN provides internet services and Canadian experiences”, CRACIN Working Paper, No. 8, Canadian Research Alliance for Community Innovation and Networking, Toronto.

[69] Dicken, B. and R. Blomberg (1988), “Evaluating volunteer experience of women reentering the work force”, Personnel Journal, Vol. 67/12, pp. 94-99.

[3] Dolan, P., T. Peasgood and M. White (2008), “Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being”, Journal of Economic Psychology, Vol. 29/1, pp. 94-122, http://dx.doi.org/10.1016/j.joep.2007.09.001.

[66] Dudley, L. (2007), “Integrating volunteering into the adult migrant second language experience”, Canadian Modern Language Review, Vol. 63/4, pp. 539-561.

[75] Fenta, H., I. Hyman and S. Noh (2004), “Determinants of depression among Ethiopian immigrants and refugees in Toronto”, The Journal of Nervous and Mental Disease , Vol. 192/5, pp. 363-372.

[26] Fukuyama, F. (1995), Trust: Social Virtues and the Creation of Prosperity, Free Press, New York.

[10] Grossman, M. (2005), “Education and nonmarket outcomes”, in Hanushek, E. and F. Welch (eds.), Handbook of the Economics of Education, North Holland, Amsterdam.

[9] Grossman, M. (2000), “The human capital model”, in Culyer, A. and J. Newhouse (eds.), Handbook of Health Economics, North Holland, Amsterdam.

[70] Hackl, F., M. Halla and G. Pruckner (2007), “Volunteering and income - the fallacy of the good samaritan?”, Kyklos, Vol. 60/1, pp. 77-104.

[57] Hardin, R. (2002), Trust and Trustworthiness, Russel Sage Foundation, New York.

[7] Helliwell, J. (2008), “Life satisfaction and quality of development”, Working Paper Series, No. 14507, National Bureau of Economic Research, http://dx.doi.org/10.3386/w14507.

[29] Helliwell, J. and S. Wang (2010), “Trust and well-being”, Working Paper Series, No. 15911, National Bureau of Economic Research, http://dx.doi.org/10.3386/w15911.

[39] Hooghe, M., S. Marien and T. de Vroome (2012), “The cognitive basis of trust. The relation between education, cognitive ability, and generalized and political trust”, Intelligence, Vol. 40/6, pp. 604-613.

[78] Hovey, J. (2000), “Acculturative stress, depression, and suicidal ideation in Mexican immigrants”, Cultural Diversity and Ethnic Minority Psychology, Vol. 6/2, pp. 134-151.

[79] Hovey, J. and C. King (1997), “Suicidality among acculturating Mexican Americans: Current knowledge and directions for research”, Suicide and Life-Threatening Behavior, Vol. 27/1, pp. 92-103.

[51] Johnson, T. and C. Dawes (2016), “Do parents’ life experiences affect the political and civic participation of their children? The case of draft-induced military service”, Political Behavior, Vol. 38/4, pp. 793-816.

[5] Kahneman, D. and A. Krueger (2006), “Developments in the measurement of subjective well-being”, Journal of Economic Perspectives, Vol. 20/1, pp. 3-24, http://dx.doi.org/10.1257/089533006776526030.

[17] Kakarmath, S. et al. (2018), “Association between literacy and self-rated poor health in 33 high-and upper-middle-income countries”, OECD Education Working Papers, No. 165, OECD Publishing, Paris, http://dx.doi.org/10.1787/7aaeac27-en.

[27] Knack, S. and P. Keefer (1997), “Does social capital have an economic payoff? A cross-country investigation”, The Quarterly Journal of Economics, Vol. 112/4, pp. 1251-1288.

[55] Kumlin, S. (2004), The Personal and the Political: How Personal Welfare State Experiences Affect Political Trust and Ideology, Palgrave Macmillan, New York.

[56] Kumlin, S. and B. Rothstein (2005), “Making and breaking social capital. The impact of welfare-state institutions”, Comparative Political Studies, Vol. 38/4, pp. 339-365, https://doi.org/10.1177/0010414004273203.

[49] Lane, R. (1959), Political Life: Why People Get Involved In Politics, Markham , Chicago.

[59] Lipset, S. (1959), “Some social requisites of democracy: Economic development and political legitimacy”, American Political Science Review, Vol. 53/1, pp. 69-105.

[63] Li, Y. and K. Ferraro (2005), “Volunteering and depression in later life: Social benefit or selection processes?”, Journal of Health and Social Behavior, Vol. 46/1, pp. 68-84, https://doi.org/10.1177/002214650504600106.

[19] Lleras-Muney, A. (2005), “The relationship between education and adult mortality in the United States”, The Review of Economic Studies, Vol. 72/1, pp. 189-221.

[20] Lundborg, P. (2008), “The health returns to education: What can we learn from twins?”, IZA Discussion Papers , No. 3399, Institute for the Study of Labor (IZA).

[43] Macpherson, C. (1977), The Life and Times of Liberal Democracy, Oxford University Press, New York.

[14] Marmot, M. (2005), The Status Syndrome: How Social Standing Affects Our Health and Longevity, Holt Paperbacks.

[37] Merolla, J. et al. (2013), “Oxytocin and the biological basis for interpersonal and political trust”, Political Behavior, Vol. 35/4, pp. 753-776.

[24] Nannestad, P. (2008), “What have we learned about generalized trust, if anything?”, Annual Review of Political Science, Vol. 11, pp. 413-436.

[25] Newton, K. (2007), “Social and political trust”, in Dalton, R. and H. Klingemann (eds.), The Oxford Handbook of Political Behavior, Oxford University Press, New York.

[38] Nie, N., J. Junn and K. Stehlik-Barry (1996), Education and Democratic Citizenship in America, University of Chicago Press, Chicago.

[74] OECD (2017), “Chapter 3: Migrants' well-being: Moving to a better life?”, in How's Life? 2017: Measuring Well-being., OECD Publishing, Paris.

[15] OECD (2015), Survey of Adult Skills (PIAAC) (database), http://www.oecd.org/skills/piaac/publicdataandanalysis.

[8] OECD (2014), National Accounts at a Glance 2014, OECD Publishing, Paris.

[2] OECD (2013), How's Life? 2013: Measuring Well-being, OECD Publishing, Paris.

[13] OECD/EU (2015), Indicators of Immigrant Integration 2015: Settling In, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264234024-en.

[21] Oreopoulos, P. (2006), “Estimating average and local average treatment effects of education when compulsory schooling laws really matter”, The American Economic Review, Vol. 96/1, pp. 152-175.

[80] Pallas, A. (2000), “The effects of schooling on individual lives”, in Hallinan, M. (ed.), Handbook of the Sociology of Education, Springer, Boston, MA.

[44] Pateman, C. (1970), Participation and Democratic Theory, Cambridge University Press, Cambridge.

[35] Paxton, P. (2007), “Association memberships and generalised trust: A multilevel model across 31 countries”, Social Forces, Vol. 86/1, pp. 47-76.

[64] Post, S. (2005), “Altruism, happiness, and health: It’s good to be good”, International Journal of Behavioral Medicine, Vol. 12/2, pp. 66-77.

[76] Prilleltensky, I. (2008), “Migrant well-being is a multilevel, dynamic, value dependent phenomenon”, American Journal of Community Psychology, Vol. 42/3-4, pp. 359-364.

[50] Prior, M. (2010), “You’ve either got it or you don’t? The stability of political interest over the life cycle”, The Journal of Politics, Vol. 72/3, pp. 747-766.

[71] Prouteau, L. and F. Wolff (2006), “Does volunteer work pay off in the labor market?”, Journal of Behavioral and Experimental Economics, Vol. 35/6, pp. 992-1013.

[60] Putnam, R. (2001), “Social capital: Measurement and consequences”, Canadian Journal of Policy Research, pp. 41-51.

[34] Putnam, R. (2000), Bowling Alone. The Collapse and Revival of American Community, Simon & Schuster, New York.

[28] Putnam, R., R. Leonardi and R. Nanetti (1993), Making Democracy Work : Civic Traditions in Modern Italy, Princeton University Press, Princeton.

[11] Schütte, S. et al. (2013), “Social differences in self-reported health among men and women in 31 countries in Europe”, Scandinavian Journal of Public Health, Vol. 41/1, pp. 51-57, http://dx.doi.org/10.1177/1403494812469854.

[22] Silles, M. (2009), “The causal effect of education on health: Evidence from the United Kingdom”, Economics of Education Review, Vol. 28/1, pp. 122-128.

[1] Stiglitz, J., A. Sen and J. Fitoussi (2009), Report by the Commission on the Measurement of Economic Performance and Social Progress, Comission on the Measurement of Economic Performance and Social Progress, Paris, http://www.stiglitz-sen-fitoussi.fr/en/index.htm.

[52] Stolle, D. and M. Hooghe (2004), “The roots of social capital: Attitudinal and network mechanisms in the relation between youth and adult indicators of social capital”, Acta Politica, Vol. 39/4, pp. 422-441, http://dx.doi.org/10.1057/palgrave.ap.5500081.

[41] Sturgis, P., S. Read and N. Allum (2010), “Does intelligence foster generalized trust? An empirical test using the UK birth cohort studies”, Intelligence, Vol. 38/1, pp. 45-54.

[53] Uslaner, E. (2002), The Moral Foundations of Trust, Cambridge University Press, Cambridge, http://dx.doi.org/10.1017/CBO9780511614934.

[12] van der Kooi, A. et al. (2013), “The modifying influence of country development on the effect of individual educational attainment on self-rated health”, American Journal of Public Health, Vol. 103/11, pp. e49-e54, http://dx.doi.org/10.2105/AJPH.2013.301593.

[65] Whiteley, P. (2014), The art of happiness: Is volunteering the blueprint for bliss?, Economic and Social Research Council , London.

[62] Wilson, J. (2000), “Volunteering”, Annual Review of Sociology, Vol. 26, pp. 215-240.

[4] Winkelmann, L. and R. Winkelmann (1998), “Why are the unemployed so unhappy? Evidence from panel data”, Economica, Vol. 65/257, pp. 1-15.

[61] Wolfinger, R. and S. Rosenstone (1980), Who Votes?, Yale University Press, New Haven, CT.

[40] Yamagashi, T. (2001), “Trust as a form of social intelligence”, in Cook, K. (ed.), Trust in Society , Russel Sage Foundation , New York.

Annex 6.A. Tables
Annex Table 6.A.1. Differences in self-reported health, by migrant status and individual characteristics

% in Excellent or very good health

Model 1 - Migrant gap controlling for age, gender and parents' educational attainment

Model 2- Migrant gap controlling for age, gender, parents' educational attainment and educational attainment

 

Natives

Migrants

Diff. (Natives-migrants)

Migrant gap

Education (Tertiary minus lower than upper secondary)

 

%

S.E.

%

S.E.

% dif.

S.E.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

85.6

(0.5)

84.8

(0.9)

0.8

(0.0)

0.477

0.09

0.924

0.99

0.307

10.32

0.000

Austria

84.0

(0.5)

81.1

(1.6)

2.9

(0.0)

0.095

3.69

0.020

3.19

0.036

15.46

0.000

Canada

89.1

(0.4)

87.7

(0.7)

1.5

(0.0)

0.112

1.65

0.028

2.24

0.002

9.87

0.000

Chile

67.3

(1.4)

80.8

(6.9)

-13.5

(0.1)

0.039

-6.19

0.362

-4.76

0.496

28.13

0.000

Czech Republic

88.8

(0.6)

86.2

(3.5)

2.6

(0.0)

0.454

1.55

0.525

0.48

0.853

16.92

0.000

Denmark

82.9

(0.5)

82.5

(1.0)

0.4

(0.0)

0.667

2.44

0.026

1.59

0.138

18.66

0.000

England (UK)

84.8

(0.6)

88.1

(1.4)

-3.3

(0.0)

0.029

-1.40

0.408

-1.22

0.669

11.05

0.000

Estonia

68.4

(0.4)

43.6

(1.6)

24.8

(0.0)

0.000

6.34

0.000

6.88

0.000

22.11

0.000

Finland

82.0

(0.5)

79.4

(2.6)

2.6

(0.0)

0.664

6.03

0.013

4.99

0.032

14.64

0.000

Flanders (Belgium)

85.9

(0.5)

83.4

(2.0)

2.6

(0.0)

0.292

1.65

0.181

1.29

0.280

9.62

0.000

France

81.9

(0.4)

76.1

(1.4)

5.8

(0.0)

0.000

2.93

0.011

2.02

0.058

13.02

0.000

Germany

89.3

(0.5)

84.4

(1.6)

5.0

(0.0)

0.007

3.53

0.005

2.62

0.054

10.37

0.000

Greece

87.9

(0.6)

85.3

(2.1)

2.7

(0.0)

0.236

6.30

0.001

6.07

0.001

11.54

0.000

Ireland

88.2

(0.5)

90.6

(1.0)

-2.5

(0.0)

0.033

0.20

0.881

1.06

0.441

10.27

0.000

Israel

88.5

(0.5)

75.4

(1.2)

13.1

(0.0)

0.000

5.57

0.000

6.49

0.000

14.51

0.000

Italy

81.5

(0.8)

87.6

(1.6)

-6.2

(0.0)

0.001

-1.33

0.526

-1.54

0.469

10.22

0.000

Lithuania

67.0

(0.7)

57.8

(4.6)

9.1

(0.0)

0.054

-4.45

0.287

-4.27

0.336

20.50

0.000

Netherlands

83.4

(0.5)

70.3

(2.1)

13.1

(0.0)

0.000

10.30

0.000

9.78

0.000

13.41

0.000

New Zealand

86.4

(0.7)

90.1

(0.9)

-3.7

(0.0)

0.001

-2.95

0.015

-1.78

0.161

8.59

0.000

Northern Ireland (UK)

82.0

(0.8)

88.6

(2.3)

-6.6

(0.0)

0.008

-5.31

0.118

-4.64

0.212

15.47

0.000

Norway

82.9

(0.7)

83.3

(1.7)

-0.4

(0.0)

0.875

1.62

0.372

2.04

0.252

16.39

0.000

Singapore

74.0

(0.7)

80.1

(1.1)

-6.1

(0.0)

0.000

-4.49

0.004

-3.91

0.013

14.40

0.000

Slovenia

82.6

(0.6)

79.8

(1.6)

2.8

(0.0)

0.091

-1.73

0.213

-3.08

0.030

15.87

0.000

Spain

77.4

(0.8)

84.9

(1.3)

-7.5

(0.0)

0.000

-2.86

0.165

-3.70

0.060

10.47

0.000

Sweden

84.7

(0.7)

80.0

(1.5)

4.7

(0.0)

0.006

4.61

0.002

3.22

0.024

15.59

0.000

United States

85.4

(0.7)

83.6

(1.2)

1.8

(0.0)

0.294

-1.34

0.430

-2.40

0.105

19.29

0.000

Average

82.4

(0.1)

80.6

(0.5)

1.8

(0.0)

0.171

15.47

0.212

15.47

0.193

15.47

0.000

 

Model 3 - Migrant gap controlling for age, gender, parents' educational attainment, educational attainment and literacy proficiency

Model 4 - Moderating role of literacy

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Literacy

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

-0.43

0.664

7.42

0.000

0.06

0.000

3.08

0.599

7.45

0.000

0.06

0.000

0.01

0.546

Austria

0.12

0.936

10.72

0.000

0.11

0.000

1.96

0.783

10.69

0.000

0.11

0.000

0.01

0.792

Canada

0.78

0.274

6.93

0.000

0.05

0.000

-6.04

0.101

6.93

0.000

0.06

0.000

-0.03

0.059

Chile

-6.08

0.368

20.47

0.000

0.13

0.000

-12.18

0.752

20.47

0.000

0.13

0.000

-0.03

0.857

Czech Republic

0.38

0.887

15.79

0.000

0.02

0.258

-5.30

0.741

15.81

0.000

0.02

0.226

-0.02

0.737

Denmark

-3.17

0.013

13.80

0.000

0.10

0.000

-1.03

0.883

13.84

0.000

0.10

0.000

0.01

0.620

England (UK)

-3.76

0.061

7.14

0.000

0.09

0.000

-9.16

0.354

7.10

0.000

0.09

0.000

-0.02

0.600

Estonia

5.50

0.000

19.23

0.000

0.07

0.000

4.29

0.553

19.21

0.000

0.07

0.000

0.00

0.981

Finland

2.08

0.413

12.31

0.000

0.05

0.002

-8.29

0.336

12.15

0.000

0.06

0.002

-0.04

0.184

Flanders (Belgium)

0.07

0.777

7.53

0.000

0.04

0.005

-7.47

0.334

7.47

0.000

0.04

0.004

-0.03

0.264

France

-0.79

0.593

8.15

0.000

0.09

0.000

2.30

0.563

8.17

0.000

0.09

0.000

0.01

0.462

Germany

0.67

0.574

5.48

0.002

0.09

0.000

-0.94

0.880

5.49

0.002

0.09

0.000

-0.01

0.971

Greece

5.87

0.002

10.73

0.000

0.03

0.119

9.78

0.366

10.71

0.000

0.02

0.191

0.02

0.708

Ireland

0.34

0.823

7.69

0.000

0.05

0.000

-16.20

0.020

7.44

0.000

0.07

0.000

-0.07

0.012

Israel

5.00

0.000

9.40

0.000

0.09

0.000

-1.55

0.225

9.38

0.000

0.10

0.000

-0.03

0.011

Italy

-2.08

0.339

9.38

0.000

0.02

0.229

-12.92

0.362

9.35

0.000

0.03

0.161

-0.05

0.437

Lithuania

-5.36

0.246

17.22

0.000

0.11

0.000

-29.00

0.468

17.10

0.000

0.11

0.000

-0.09

0.525

Netherlands

7.15

0.000

10.18

0.000

0.07

0.000

13.69

0.075

10.26

0.000

0.06

0.000

0.03

0.376

New Zealand

-3.17

0.014

5.50

0.000

0.07

0.000

-18.44

0.004

5.33

0.001

0.08

0.000

-0.06

0.014

Northern Ireland (UK)

-6.05

0.091

10.60

0.000

0.10

0.000

-35.58

0.036

10.40

0.000

0.11

0.000

-0.12

0.045

Norway

-0.64

0.753

14.01

0.000

0.06

0.000

-13.19

0.036

13.82

0.000

0.07

0.000

-0.05

0.045

Singapore

-5.51

0.001

6.76

0.002

0.09

0.000

-4.13

0.497

6.77

0.002

0.09

0.000

0.01

0.804

Slovenia

-3.94

0.007

12.59

0.000

0.06

0.000

-19.36

0.006

12.37

0.000

0.08

0.000

-0.07

0.018

Spain

-6.54

0.001

5.27

0.005

0.11

0.000

-9.41

0.253

5.22

0.005

0.11

0.000

-0.01

0.719

Sweden

-1.29

0.474

11.53

0.000

0.08

0.000

-2.59

0.875

11.49

0.000

0.08

0.000

-0.01

0.989

United States

-5.30

0.000

12.88

0.000

0.10

0.000

-22.91

0.000

13.09

0.000

0.12

0.000

-0.08

0.001

Average

15.47

0.320

15.47

0.000

15.47

0.024

15.47

0.388

15.47

0.000

15.47

0.022

15.47

0.453

 

Model 5 - Moderating role of education

Model 6 - Migrant gap controlling for individual background characteristics as well as length of stay in the country

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Tertiary education

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Length of stay

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

-0.57

0.652

7.58

0.000

0.06

0.000

-0.48

0.853

m

m

m

m

m

m

m

m

Austria

-0.51

0.775

11.63

0.000

0.11

0.000

-4.26

0.294

-5.34

0.110

10.39

0.000

0.11

0.000

-0.25

0.039

Canada

-1.19

0.313

8.49

0.000

0.05

0.000

-4.86

0.001

-1.34

0.274

6.77

0.000

0.05

0.000

-0.10

0.036

Chile

-10.39

0.394

21.10

0.000

0.13

0.000

-13.77

0.421

-19.06

0.264

20.80

0.000

0.13

0.000

-1.18

0.211

Czech Republic

0.79

0.761

15.50

0.000

0.02

0.256

7.31

0.269

-6.06

0.252

15.55

0.000

0.02

0.258

-0.18

0.178

Denmark

-3.86

0.009

14.16

0.000

0.10

0.000

-2.57

0.264

-9.50

0.000

13.53

0.000

0.11

0.000

-0.31

0.000

England (UK)

-3.69

0.152

7.12

0.000

0.09

0.000

0.20

0.739

-9.97

0.002

6.86

0.000

0.09

0.000

-0.29

0.007

Estonia

5.21

0.004

19.34

0.000

0.07

0.000

-0.66

0.801

2.92

0.461

19.25

0.000

0.07

0.000

-0.07

0.511

Finland

2.40

0.382

12.24

0.000

0.05

0.002

1.40

0.797

-9.62

0.042

12.05

0.000

0.06

0.000

-0.74

0.000

Flanders (Belgium)

-0.43

0.971

7.73

0.000

0.04

0.005

-2.40

0.621

-3.85

0.214

7.49

0.000

0.04

0.003

-0.20

0.063

France

-1.79

0.241

9.08

0.000

0.09

0.000

-5.36

0.090

0.96

0.555

8.16

0.000

0.09

0.000

0.06

0.279

Germany

0.02

0.924

6.15

0.002

0.09

0.000

-3.23

0.333

-0.31

0.901

5.38

0.003

0.09

0.000

-0.05

0.591

Greece

5.17

0.010

11.21

0.000

0.03

0.116

-3.41

0.406

2.28

0.537

10.72

0.000

0.03

0.104

-0.13

0.220

Ireland

-0.18

0.886

8.17

0.000

0.05

0.000

-1.92

0.382

-3.07

0.112

7.55

0.000

0.05

0.000

-0.18

0.015

Israel

3.65

0.001

10.84

0.000

0.09

0.000

-3.80

0.248

13.08

0.000

10.06

0.000

0.08

0.000

0.24

0.000

Italy

-1.42

0.528

8.80

0.001

0.02

0.236

13.38

0.124

0.56

0.880

9.39

0.000

0.02

0.255

0.14

0.450

Lithuania

-8.74

0.094

17.82

0.000

0.11

0.000

-14.41

0.027

-10.73

0.486

17.19

0.000

0.11

0.000

-0.13

0.712

Netherlands

8.35

0.000

9.24

0.000

0.06

0.000

5.23

0.222

7.85

0.009

10.20

0.000

0.06

0.000

0.03

0.757

New Zealand

-4.33

0.012

6.29

0.000

0.07

0.000

-2.81

0.265

-7.58

0.000

4.98

0.001

0.07

0.000

-0.24

0.000

Northern Ireland (UK)

-15.46

0.001

12.60

0.000

0.10

0.000

-24.94

0.001

-5.80

0.364

10.61

0.000

0.10

0.000

0.01

0.912

Norway

-1.64

0.430

14.53

0.000

0.06

0.000

-3.32

0.232

-6.68

0.009

13.73

0.000

0.06

0.000

-0.35

0.001

Singapore

-3.46

0.086

5.84

0.010

0.09

0.000

4.16

0.151

-9.22

0.001

6.58

0.003

0.09

0.000

-0.17

0.067

Slovenia

-4.51

0.010

13.14

0.000

0.06

0.000

-7.28

0.225

-10.68

0.002

12.66

0.000

0.07

0.000

-0.22

0.048

Spain

-7.08

0.001

5.53

0.004

0.11

0.000

-3.13

0.471

-8.05

0.004

5.27

0.005

0.11

0.000

-0.11

0.425

Sweden

-2.53

0.189

12.85

0.000

0.08

0.000

-5.55

0.089

-7.00

0.046

11.11

0.000

0.09

0.000

-0.25

0.014

United States

-6.73

0.000

14.12

0.000

0.10

0.000

-7.00

0.026

-11.01

0.006

12.73

0.000

0.11

0.000

-0.24

0.069

Average

15.47

0.301

15.47

0.001

15.47

0.024

15.47

0.321

15.47

0.221

15.47

0.000

15.47

0.025

15.47

0.224

Note: Marginal probabilities are multiplied by 100. Differences are based on a regression model and take account of differences associated with the following variables: age, gender, education, immigration background and parents' educational attainment. Only the score-point differences between two contrast categories are shown, which is useful for showing the relative significance of each socio-demographic variable vis-a-vis observed score-point differences. Estimates based on a sample size less than 30 are not shown.

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846745

Annex Table 6.A.2. Differences in the percentage of individuals who report disagreeing or strongly disagreeing that there are only a few people they can trust completely, by migrant status and individual characteristics

 

% with high trust (disagree or strongly disagree that there are only a few people you can trust completely)

Model 1 - Migrant gap controlling for age, gender and parents' educational attainment

Model 2- Migrant gap controlling for age, gender, parents' educational attainment and educational attainment

 

Natives

Migrants

Diff. (Natives-migrants)

Migrant gap

Education (Tertiary minus lower than upper secondary)

 

%

S.E.

%

S.E.

% dif.

S.E.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

23.9

(0.9)

20.5

(1.0)

3.4

(0.0)

0.011

5.44

0.000

7.10

0.000

15.01

0.000

Austria

22.9

(0.7)

18.7

(1.4)

4.2

(0.0)

0.009

4.14

0.021

4.31

0.014

13.64

0.000

Canada

25.8

(0.5)

22.2

(0.9)

3.6

(0.0)

0.005

3.99

0.000

4.98

0.000

9.89

0.000

Chile

14.1

(0.8)

13.4

(4.1)

0.7

(0.0)

0.871

1.40

0.770

1.65

0.739

3.16

0.162

Czech Republic

7.1

(0.5)

7.2

(2.7)

-0.1

(0.0)

0.958

-0.03

0.993

0.94

0.746

4.71

0.008

Denmark

46.5

(0.6)

32.4

(1.2)

14.1

(0.0)

0.000

14.62

0.000

14.37

0.000

28.15

0.000

England (UK)

19.1

(0.8)

16.7

(1.4)

2.4

(0.0)

0.153

3.15

0.064

3.74

0.025

13.65

0.000

Estonia

9.9

(0.4)

8.8

(1.0)

1.0

(0.0)

0.387

1.10

0.381

1.04

0.417

3.70

0.001

Finland

33.7

(0.6)

24.2

(2.8)

9.5

(0.0)

0.014

10.10

0.005

8.30

0.019

21.49

0.000

Flanders (Belgium)

18.2

(0.6)

22.0

(2.4)

-3.8

(0.0)

0.196

-3.64

0.044

-4.19

0.027

18.61

0.000

France

10.1

(0.3)

10.1

(1.0)

0.0

(0.0)

0.558

-0.72

0.754

-0.99

0.634

7.79

0.000

Germany

15.0

(0.6)

13.7

(1.5)

1.4

(0.0)

0.397

0.64

0.733

0.54

0.802

9.68

0.000

Greece

7.8

(0.5)

8.7

(1.8)

-0.9

(0.0)

0.625

0.26

0.882

-0.09

0.959

3.85

0.017

Ireland

16.5

(0.6)

14.2

(1.0)

2.3

(0.0)

0.037

3.59

0.004

3.97

0.001

9.54

0.000

Israel

30.2

(0.9)

23.6

(1.3)

6.6

(0.0)

0.000

8.92

0.000

9.66

0.000

16.19

0.000

Italy

8.5

(0.5)

11.4

(1.9)

-3.0

(0.0)

0.127

-2.93

0.072

-3.50

0.031

8.18

0.000

Lithuania

18.0

(0.7)

21.3

(4.6)

-3.2

(0.0)

0.491

-3.48

0.416

-3.42

0.434

10.33

0.000

Netherlands

32.7

(0.7)

22.1

(1.7)

10.6

(0.0)

0.000

10.73

0.000

10.00

0.000

22.11

0.000

New Zealand

25.2

(0.8)

22.1

(1.3)

3.1

(0.0)

0.051

5.03

0.002

6.47

0.000

11.58

0.000

Northern Ireland (UK)

16.0

(0.8)

18.5

(2.4)

-2.4

(0.0)

0.343

-2.00

0.401

-1.71

0.510

14.89

0.000

Norway

34.7

(0.7)

29.0

(2.1)

5.7

(0.0)

0.022

4.89

0.038

5.80

0.013

21.74

0.000

Singapore

17.1

(0.6)

23.4

(1.1)

-6.3

(0.0)

0.000

-5.28

0.000

-4.94

0.000

3.38

0.075

Slovenia

12.1

(0.5)

11.5

(1.6)

0.6

(0.0)

0.755

-1.10

0.504

-2.49

0.137

13.70

0.000

Spain

22.2

(0.6)

15.1

(1.4)

7.1

(0.0)

0.000

9.33

0.000

7.57

0.000

11.94

0.000

Sweden

35.0

(0.8)

26.6

(1.6)

8.4

(0.0)

0.000

8.39

0.000

8.07

0.000

19.95

0.000

United States

23.4

(0.8)

15.7

(1.6)

7.7

(0.0)

0.000

7.20

0.001

7.63

0.000

12.63

0.001

Average

21.0

(0.1)

18.2

(0.4)

2.8

(0.0)

0.231

3.22

0.234

3.26

0.212

12.67

0.010

 

Model 3 - Migrant gap controlling for age, gender, parents' educational attainment, educational attainment and literacy proficiency

Model 4 - Moderating role of literacy

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Literacy

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

4.77

0.000

9.72

0.000

0.13

0.000

-12.03

0.153

9.43

0.000

0.15

0.000

-0.06

0.039

Austria

1.34

0.455

7.84

0.001

0.14

0.000

-27.30

0.023

7.92

0.001

0.16

0.000

-0.11

0.015

Canada

2.20

0.020

4.43

0.004

0.10

0.000

-15.27

0.015

4.38

0.008

0.12

0.000

-0.07

0.004

Chile

1.51

0.761

2.29

0.261

0.02

0.421

-12.27

0.495

2.27

0.267

0.02

0.397

-0.06

0.381

Czech Republic

0.70

0.802

3.37

0.077

0.03

0.066

17.45

0.381

3.35

0.078

0.03

0.119

0.06

0.412

Denmark

8.90

0.000

21.86

0.000

0.14

0.000

-7.83

0.320

21.68

0.000

0.15

0.000

-0.07

0.027

England (UK)

1.78

0.255

9.97

0.000

0.08

0.000

-13.99

0.231

9.79

0.000

0.09

0.000

-0.06

0.167

Estonia

0.87

0.505

3.37

0.005

0.01

0.468

-5.88

0.430

3.31

0.006

0.01

0.362

-0.03

0.342

Finland

6.34

0.076

19.64

0.000

0.04

0.062

-6.90

0.703

19.53

0.000

0.05

0.040

-0.05

0.435

Flanders (Belgium)

-4.86

0.012

17.27

0.000

0.03

0.057

1.50

0.752

17.27

0.000

0.02

0.114

0.03

0.772

France

-1.52

0.343

6.58

0.000

0.02

0.014

-2.54

0.603

6.58

0.000

0.03

0.045

0.00

0.481

Germany

-1.89

0.180

5.03

0.007

0.10

0.000

-11.52

0.001

5.09

0.004

0.10

0.000

-0.04

0.002

Greece

-0.15

0.933

3.66

0.023

0.01

0.633

-8.22

0.346

3.67

0.022

0.01

0.474

-0.03

0.343

Ireland

3.49

0.003

8.12

0.000

0.03

0.108

-4.80

0.673

7.92

0.000

0.04

0.048

-0.03

0.452

Israel

7.96

0.000

12.21

0.000

0.09

0.000

-1.90

0.297

12.15

0.000

0.10

0.000

-0.04

0.051

Italy

-4.46

0.010

6.69

0.000

0.04

0.008

-15.24

0.105

6.64

0.000

0.05

0.004

-0.04

0.223

Lithuania

-3.95

0.365

8.54

0.003

0.06

0.009

19.56

0.626

8.61

0.003

0.06

0.013

0.09

0.538

Netherlands

5.36

0.035

15.84

0.000

0.13

0.000

-20.07

0.135

15.58

0.000

0.15

0.000

-0.10

0.053

New Zealand

4.38

0.009

6.27

0.003

0.12

0.000

-13.90

0.107

5.90

0.004

0.14

0.000

-0.06

0.039

Northern Ireland (UK)

-2.74

0.276

12.07

0.000

0.06

0.008

-21.95

0.104

11.84

0.000

0.07

0.005

-0.07

0.130

Norway

0.76

0.757

16.51

0.000

0.13

0.000

-14.83

0.184

16.24

0.000

0.14

0.000

-0.06

0.150

Singapore

-5.60

0.000

-0.07

0.955

0.04

0.005

-18.14

0.002

-0.32

0.862

0.06

0.001

-0.05

0.025

Slovenia

-3.03

0.077

11.82

0.000

0.04

0.014

-19.10

0.018

11.79

0.000

0.05

0.004

-0.06

0.045

Spain

6.57

0.001

9.98

0.000

0.04

0.017

11.99

0.256

10.04

0.000

0.04

0.037

0.02

0.618

Sweden

2.19

0.348

13.63

0.000

0.13

0.000

5.26

0.640

13.65

0.000

0.12

0.000

0.01

0.765

United States

6.01

0.004

8.98

0.001

0.07

0.000

-6.23

0.071

9.13

0.000

0.07

0.000

-0.05

0.015

Average

1.42

0.240

9.45

0.052

0.07

0.073

-7.85

0.295

9.36

0.048

0.08

0.064

-0.04

0.251

 

Model 5 - Moderating role of education

Model 6 - Migrant gap controlling for individual background characteristics as well as length of stay in the country

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Tertiary education

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Length of stay

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

2.36

0.214

10.92

0.000

0.13

0.000

-4.69

0.079

m

m

m

m

m

m

m

m

Austria

0.04

0.985

8.64

0.000

0.14

0.000

-4.69

0.195

2.16

0.524

3.26

0.001

0.14

0.000

0.04

0.761

Canada

-1.49

0.700

5.85

0.001

0.10

0.000

-6.24

0.003

5.72

0.002

5.16

0.003

0.10

0.000

0.15

0.028

Chile

-0.48

0.957

2.51

0.212

0.02

0.427

-4.51

0.644

2.77

0.523

4.66

0.274

0.02

0.421

0.12

0.726

Czech Republic

5.10

0.083

2.98

0.096

0.03

0.063

7.21

0.149

3.86

0.343

5.02

0.077

0.03

0.069

0.14

0.308

Denmark

6.21

0.004

22.61

0.000

0.14

0.000

-6.05

0.044

3.35

0.254

2.22

0.000

0.14

0.000

-0.31

0.008

England (UK)

-0.18

0.906

10.44

0.000

0.08

0.000

-3.44

0.426

2.23

0.325

6.86

0.000

0.08

0.000

0.03

0.731

Estonia

-0.33

0.867

3.59

0.004

0.01

0.484

-2.07

0.387

2.44

0.476

5.00

0.005

0.01

0.470

0.05

0.581

Finland

3.72

0.410

19.96

0.000

0.04

0.063

-6.58

0.322

10.30

0.204

4.85

0.000

0.04

0.079

0.24

0.526

Flanders (Belgium)

-5.96

0.005

17.54

0.000

0.03

0.053

-3.02

0.185

-2.77

0.440

3.24

0.000

0.03

0.069

0.16

0.264

France

-1.68

0.532

6.65

0.000

0.02

0.014

-0.44

0.917

-1.62

0.494

3.52

0.000

0.02

0.014

-0.02

0.906

Germany

-1.81

0.319

4.99

0.006

0.10

0.000

0.22

0.964

-5.55

0.035

2.61

0.011

0.10

0.000

-0.18

0.145

Greece

1.01

0.688

3.30

0.044

0.01

0.653

3.34

0.338

3.15

0.394

5.55

0.024

0.01

0.683

0.13

0.287

Ireland

1.10

0.513

8.98

0.000

0.03

0.109

-4.90

0.073

5.37

0.001

4.21

0.000

0.03

0.135

0.13

0.165

Israel

4.98

0.011

13.22

0.000

0.09

0.000

-5.42

0.374

22.62

0.000

11.91

0.000

0.09

0.000

0.50

0.000

Italy

-4.83

0.014

6.91

0.000

0.04

0.008

-2.85

0.456

-2.74

0.329

2.60

0.000

0.04

0.009

0.09

0.458

Lithuania

-8.39

0.114

8.97

0.002

0.06

0.008

-15.41

0.038

-13.34

0.632

2.78

0.004

0.06

0.009

-0.24

0.708

Netherlands

1.40

0.639

16.94

0.000

0.13

0.000

-10.23

0.061

11.32

0.014

5.47

0.000

0.13

0.000

0.25

0.135

New Zealand

-0.99

0.536

8.48

0.000

0.12

0.000

-9.02

0.002

3.08

0.199

6.93

0.003

0.12

0.000

-0.08

0.364

Northern Ireland (UK)

-2.10

0.515

11.94

0.000

0.06

0.007

1.42

0.706

0.10

0.936

1.14

0.000

0.06

0.009

0.17

0.235

Norway

-0.45

0.884

16.82

0.000

0.13

0.000

-2.51

0.544

1.18

0.765

5.86

0.000

0.13

0.000

0.03

0.895

Singapore

-6.97

0.000

0.54

0.823

0.04

0.004

-2.32

0.302

-7.45

0.001

0.99

0.916

0.04

0.004

-0.09

0.265

Slovenia

-4.46

0.019

12.39

0.000

0.04

0.012

-6.27

0.087

-3.01

0.414

3.22

0.000

0.04

0.014

0.00

0.997

Spain

6.30

0.012

10.05

0.000

0.04

0.017

-0.86

0.827

2.90

0.409

5.73

0.000

0.04

0.013

-0.29

0.165

Sweden

-0.16

0.970

14.75

0.000

0.13

0.000

-6.09

0.104

0.52

0.873

5.20

0.000

0.13

0.000

-0.08

0.596

United States

-0.64

0.718

11.12

0.000

0.07

0.000

-14.79

0.002

2.23

0.439

4.02

0.001

0.07

0.000

-0.18

0.110

Average

-0.33

0.447

10.04

0.046

0.07

0.074

-4.24

0.316

1.95

0.361

4.48

0.053

0.07

0.080

0.03

0.415

Note: Marginal probabilities are multiplied by 100. Differences are based on a regression model and take account of differences associated with the following variables: age, gender, education, immigration background and parents' educational attainment. Only the score-point differences between two contrast categories are shown, which is useful for showing the relative significance of each socio-demographic variable vis-a-vis observed score-point differences. Estimates based on a sample size less than 30 are not shown.

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846764

Annex Table 6.A.3. Differences in the percentage of individuals who report disagreeing or strongly disagreeing that if they are not careful other people will take advantage of them, by migrant status and individual characteristics

 

% who disagree or strongly disagree that if you are not careful, other people will take advantage of you

Model 1 - Migrant gap controlling for age, gender and parents' educational attainment

Model 2- Migrant gap controlling for age, gender, parents' educational attainment and educational attainment

 

Natives

Migrants

Diff. (Natives-migrants)

Migrant gap

Education (Tertiary minus lower than upper secondary)

 

%

S.E.

%

S.E.

% dif.

S.E.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

14.0

(0.6)

14.5

(1.0)

-0.6

(0.0)

0.573

1.15

0.260

2.23

0.032

11.14

0.000

Austria

18.3

(0.7)

15.5

(1.3)

2.9

(0.0)

0.052

2.95

0.064

3.26

0.038

13.50

0.000

Canada

16.1

(0.4)

14.1

(0.7)

1.9

(0.0)

0.126

1.95

0.008

2.65

0.001

5.81

0.000

Chile

10.2

(0.6)

6.8

(3.7)

3.4

(0.0)

0.345

3.94

0.544

4.05

0.530

0.10

0.955

Czech Republic

5.0

(0.5)

5.8

(2.7)

-0.8

(0.0)

0.766

-0.80

0.748

-0.23

0.925

3.56

0.003

Denmark

40.0

(0.6)

25.1

(1.2)

15.0

(0.0)

0.000

16.15

0.000

15.88

0.000

30.24

0.000

England (UK)

13.3

(0.6)

10.8

(1.4)

2.5

(0.0)

0.123

2.68

0.149

3.12

0.073

9.30

0.000

Estonia

9.9

(0.3)

5.9

(0.7)

4.0

(0.0)

0.000

5.08

0.000

5.03

0.000

2.85

0.021

Finland

38.9

(0.6)

23.9

(2.8)

15.0

(0.0)

0.003

13.29

0.001

12.27

0.002

15.70

0.000

Flanders (Belgium)

18.6

(0.6)

20.8

(1.9)

-2.1

(0.0)

0.172

-3.60

0.129

-4.15

0.056

16.73

0.000

France

14.4

(0.4)

11.6

(1.0)

2.8

(0.0)

0.017

2.10

0.120

1.99

0.128

8.75

0.000

Germany

8.5

(0.5)

6.5

(0.9)

2.1

(0.0)

0.035

1.70

0.150

1.62

0.175

7.39

0.000

Greece

4.5

(0.4)

7.0

(1.6)

-2.5

(0.0)

0.126

-1.76

0.141

-1.97

0.100

2.65

0.035

Ireland

12.4

(0.5)

12.4

(1.1)

0.1

(0.0)

0.967

0.69

0.583

1.01

0.415

7.17

0.000

Israel

24.2

(0.6)

21.3

(1.2)

2.9

(0.0)

0.033

5.99

0.000

6.59

0.000

14.80

0.000

Italy

6.9

(0.5)

7.6

(1.4)

-0.7

(0.0)

0.645

-0.57

0.693

-1.29

0.365

8.88

0.000

Lithuania

8.3

(0.5)

9.6

(2.7)

-1.3

(0.0)

0.638

-0.88

0.744

-0.86

0.751

2.39

0.239

Netherlands

25.7

(0.6)

18.2

(1.8)

7.5

(0.0)

0.000

7.60

0.002

7.12

0.003

19.39

0.000

New Zealand

16.2

(0.6)

15.0

(1.0)

1.1

(0.0)

0.358

1.93

0.127

3.07

0.026

8.79

0.000

Northern Ireland (UK)

10.3

(0.7)

10.2

(2.1)

0.1

(0.0)

0.967

0.33

0.884

0.70

0.782

7.19

0.000

Norway

31.2

(0.7)

20.8

(1.6)

10.4

(0.0)

0.000

10.94

0.000

11.68

0.000

22.43

0.000

Singapore

11.6

(0.5)

16.9

(1.2)

-5.3

(0.0)

0.000

-4.94

0.000

-4.95

0.000

-5.98

0.000

Slovenia

5.4

(0.3)

4.2

(0.9)

1.2

(0.0)

0.236

0.81

0.481

0.27

0.813

4.98

0.000

Spain

17.1

(0.6)

14.7

(1.3)

2.4

(0.0)

0.050

3.03

0.058

1.84

0.290

10.84

0.000

Sweden

43.5

(0.8)

31.7

(1.9)

11.8

(0.0)

0.000

11.65

0.000

11.78

0.000

14.36

0.000

United States

10.6

(0.5)

12.5

(1.3)

-1.9

(0.0)

0.141

-2.42

0.019

-2.45

0.018

8.13

0.000

Average

16.7

(0.1)

14.0

(0.3)

2.8

(0.0)

0.245

15.47

0.227

15.47

0.212

15.47

0.048

 

Model 3 - Migrant gap controlling for age, gender, parents' educational attainment, educational attainment and literacy proficiency

Model 4 - Moderating role of literacy

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Literacy

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

1.61

0.121

9.77

0.000

0.03

0.005

-2.69

0.716

9.69

0.000

0.04

0.010

-0.02

0.555

Austria

0.94

0.535

8.91

0.000

0.11

0.000

-20.85

0.073

8.95

0.000

0.13

0.000

-0.08

0.061

Canada

2.05

0.006

4.68

0.001

0.02

0.047

-10.30

0.107

4.72

0.001

0.04

0.002

-0.05

0.044

Chile

4.18

0.522

0.86

0.574

-0.01

0.525

16.88

0.620

0.89

0.562

-0.01

0.459

0.05

0.735

Czech Republic

-0.43

0.855

2.19

0.070

0.03

0.031

24.22

0.065

2.16

0.072

0.03

0.068

0.08

0.056

Denmark

10.49

0.000

23.88

0.000

0.14

0.000

-15.67

0.080

23.61

0.000

0.16

0.000

-0.10

0.004

England (UK)

2.33

0.156

7.88

0.000

0.03

0.033

-17.90

0.127

7.67

0.000

0.05

0.006

-0.08

0.070

Estonia

4.23

0.000

1.32

0.256

0.04

0.002

-21.21

0.008

1.14

0.319

0.05

0.000

-0.10

0.002

Finland

12.17

0.003

15.64

0.000

0.00

0.965

-28.42

0.136

15.30

0.000

0.02

0.367

-0.16

0.028

Flanders (Belgium)

-5.39

0.008

14.24

0.000

0.05

0.010

-10.84

0.031

14.25

0.000

0.05

0.004

-0.03

0.095

France

0.80

0.548

6.44

0.000

0.05

0.000

0.21

0.722

6.44

0.000

0.05

0.000

0.00

0.634

Germany

0.07

0.986

4.52

0.005

0.06

0.000

-2.30

0.201

4.54

0.004

0.06

0.000

-0.01

0.194

Greece

-1.82

0.130

3.21

0.013

-0.02

0.120

2.54

0.702

3.19

0.013

-0.02

0.082

0.02

0.513

Ireland

0.41

0.745

5.22

0.001

0.04

0.007

-8.45

0.276

5.00

0.001

0.05

0.004

-0.03

0.243

Israel

5.15

0.000

11.40

0.000

0.08

0.000

-2.12

0.012

11.37

0.000

0.09

0.000

-0.03

0.001

Italy

-1.67

0.233

8.26

0.000

0.02

0.187

-4.29

0.691

8.25

0.000

0.02

0.171

-0.01

0.801

Lithuania

-1.22

0.650

1.19

0.592

0.04

0.005

37.21

0.157

1.25

0.571

0.04

0.014

0.14

0.118

Netherlands

3.21

0.172

14.12

0.000

0.11

0.000

-28.72

0.020

13.80

0.000

0.14

0.000

-0.12

0.012

New Zealand

1.76

0.212

5.48

0.007

0.08

0.000

-16.65

0.045

5.11

0.014

0.10

0.000

-0.06

0.026

Northern Ireland (UK)

0.43

0.878

6.53

0.001

0.01

0.411

-3.21

0.811

6.49

0.001

0.02

0.391

-0.01

0.770

Norway

5.70

0.007

15.82

0.000

0.16

0.000

-14.26

0.182

15.53

0.000

0.18

0.000

-0.07

0.063

Singapore

-4.56

0.000

-4.02

0.010

-0.02

0.063

-9.37

0.033

-4.10

0.008

-0.02

0.223

-0.02

0.277

Slovenia

-0.23

0.840

2.78

0.008

0.05

0.000

-11.86

0.065

2.80

0.008

0.06

0.000

-0.05

0.069

Spain

1.51

0.427

10.17

0.000

0.01

0.358

-2.85

0.083

10.09

0.000

0.02

0.143

-0.02

0.056

Sweden

5.71

0.038

7.95

0.002

0.13

0.000

7.58

0.538

7.96

0.003

0.13

0.000

0.01

0.886

United States

-2.13

0.044

8.89

0.000

-0.01

0.198

-7.43

0.001

9.00

0.000

-0.01

0.605

-0.02

0.002

Average

15.47

0.312

15.47

0.059

15.47

0.114

15.47

0.250

15.47

0.061

15.47

0.098

15.47

0.243

 

Model 5 - Moderating role of education

Model 6 - Migrant gap controlling for individual background characteristics as well as length of stay in the country

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Tertiary education

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Length of stay

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

-0.98

0.546

11.08

0.000

0.03

0.004

-4.88

0.037

m

m

m

m

m

m

m

m

Austria

-1.06

0.549

10.03

0.000

0.11

0.000

-6.71

0.045

3.54

0.229

9.01

0.000

0.11

0.000

0.14

0.290

Canada

-0.87

0.915

5.78

0.000

0.02

0.046

-4.84

0.056

1.47

0.251

4.85

0.001

0.02

0.036

-0.05

0.349

Chile

0.97

0.882

1.17

0.441

-0.01

0.514

-8.62

0.126

6.52

0.422

0.81

0.605

-0.01

0.525

0.22

0.405

Czech Republic

1.79

0.495

1.94

0.095

0.03

0.030

3.96

0.361

-4.70

0.108

1.97

0.096

0.03

0.030

-0.25

0.021

Denmark

4.56

0.075

25.17

0.000

0.14

0.000

-12.31

0.000

7.44

0.009

23.82

0.000

0.14

0.000

-0.17

0.139

England (UK)

-0.31

0.968

8.47

0.000

0.03

0.034

-4.71

0.232

1.30

0.553

7.84

0.000

0.03

0.030

-0.06

0.592

Estonia

3.85

0.027

1.36

0.246

0.04

0.002

-0.66

0.772

12.13

0.018

1.35

0.244

0.04

0.002

0.22

0.081

Finland

12.14

0.047

15.67

0.000

0.00

0.965

-0.24

0.983

4.41

0.589

15.53

0.000

0.00

0.821

-0.49

0.249

Flanders (Belgium)

-6.51

0.013

14.52

0.000

0.05

0.009

-3.16

0.512

-7.78

0.020

14.24

0.000

0.05

0.010

-0.15

0.315

France

0.68

0.668

6.48

0.000

0.05

0.000

-0.31

0.900

1.06

0.611

6.44

0.000

0.05

0.000

0.01

0.795

Germany

-1.28

0.373

4.98

0.003

0.06

0.000

-3.23

0.206

-1.71

0.502

4.42

0.008

0.06

0.000

-0.09

0.474

Greece

-1.27

0.415

2.98

0.018

-0.02

0.116

1.63

0.472

3.86

0.212

3.13

0.016

-0.02

0.079

0.22

0.030

Ireland

-2.17

0.227

6.24

0.000

0.04

0.007

-5.22

0.058

-1.07

0.562

5.15

0.001

0.04

0.006

-0.10

0.266

Israel

2.62

0.236

12.26

0.000

0.08

0.000

-4.57

0.172

15.54

0.000

11.72

0.000

0.07

0.000

0.38

0.000

Italy

-2.39

0.114

8.56

0.000

0.02

0.176

-5.07

0.105

-1.01

0.697

8.25

0.000

0.02

0.196

0.04

0.728

Lithuania

-1.68

0.653

1.25

0.573

0.04

0.005

-1.33

0.792

-13.10

0.075

1.16

0.600

0.04

0.005

-0.31

0.102

Netherlands

0.19

0.962

14.87

0.000

0.11

0.000

-7.17

0.168

0.29

0.954

14.06

0.000

0.11

0.000

-0.13

0.386

New Zealand

-3.02

0.130

7.43

0.000

0.07

0.000

-7.83

0.005

0.64

0.798

5.38

0.009

0.08

0.000

-0.07

0.438

Northern Ireland (UK)

1.43

0.702

6.39

0.001

0.01

0.398

1.93

0.708

-2.40

0.486

6.43

0.001

0.02

0.348

-0.19

0.205

Norway

5.43

0.064

15.87

0.000

0.16

0.000

-0.52

0.902

-0.25

0.933

15.61

0.000

0.17

0.000

-0.38

0.015

Singapore

-6.50

0.000

-2.86

0.081

-0.02

0.064

-3.82

0.065

-7.98

0.000

-4.25

0.006

-0.02

0.080

-0.16

0.023

Slovenia

-0.33

0.822

2.80

0.008

0.05

0.000

-0.31

0.906

-1.11

0.675

2.79

0.008

0.05

0.000

-0.03

0.701

Spain

-0.01

0.825

10.71

0.000

0.01

0.352

-5.66

0.094

-3.76

0.100

10.19

0.000

0.02

0.279

-0.41

0.023

Sweden

4.89

0.141

8.39

0.002

0.13

0.000

-2.33

0.572

2.56

0.558

7.77

0.004

0.13

0.000

-0.15

0.291

United States

-4.82

0.005

10.23

0.000

-0.01

0.235

-6.34

0.018

-5.47

0.010

8.77

0.000

-0.01

0.237

-0.15

0.135

Average

15.47

0.417

15.47

0.057

15.47

0.114

15.47

0.356

15.47

0.375

15.47

0.064

15.47

0.107

15.47

0.282

Note: Marginal probabilities are multiplied by 100. Differences are based on a regression model and take account of differences associated with the following variables: age, gender, education, immigration background and parents' educational attainment. Only the score-point differences between two contrast categories are shown, which is useful for showing the relative significance of each socio-demographic variable vis-a-vis observed score-point differences. Estimates based on a sample size less than 30 are not shown.

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846783

Annex Table 6.A.4. Differences in self-reported political efficacy, by migrant status and individual characteristics

 

% who disagree or strongly disagree that people like me don't have any say about what the government does

Model 1 - Migrant gap controlling for age, gender and parents' educational attainment

Model 2- Migrant gap controlling for age, gender, parents' educational attainment and educational attainment

 

Natives

Migrants

Diff. (Natives-migrants)

Migrant gap

Education (Tertiary minus lower than upper secondary)

 

%

S.E.

%

S.E.

% dif.

S.E.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

32.4

(0.6)

34.3

(1.2)

-1.8

(0.0)

0.136

0.89

0.536

2.79

0.056

19.02

0.000

Austria

32.0

(0.7)

25.3

(1.6)

6.7

(0.0)

0.000

6.00

0.002

5.96

0.003

20.03

0.000

Canada

34.9

(0.5)

34.8

(0.9)

0.2

(0.0)

0.943

0.98

0.450

2.07

0.069

14.34

0.000

Chile

59.2

(1.4)

64.6

(7.1)

-5.4

(0.1)

0.466

0.74

0.899

0.93

0.878

21.85

0.000

Czech Republic

21.4

(0.9)

14.8

(3.1)

6.6

(0.0)

0.063

7.42

0.078

8.02

0.059

6.42

0.040

Denmark

51.9

(0.8)

34.7

(1.2)

17.2

(0.0)

0.000

17.23

0.000

17.06

0.000

17.32

0.000

England (UK)

30.8

(0.9)

33.2

(2.0)

-2.4

(0.0)

0.093

-3.23

0.157

-2.45

0.248

16.75

0.000

Estonia

29.2

(0.6)

13.1

(0.9)

16.1

(0.0)

0.000

14.18

0.000

14.38

0.000

12.57

0.000

Finland

47.5

(0.7)

23.5

(2.7)

23.9

(0.0)

0.000

23.08

0.000

21.21

0.000

24.76

0.000

Flanders (Belgium)

33.2

(0.7)

39.3

(2.1)

-6.0

(0.0)

0.018

-6.12

0.000

-6.64

0.000

16.42

0.000

France

9.8

(0.4)

8.6

(0.9)

1.2

(0.0)

0.660

0.25

0.848

0.18

0.926

3.97

0.000

Germany

25.8

(0.6)

17.3

(1.8)

8.6

(0.0)

0.000

7.55

0.004

6.78

0.011

14.42

0.000

Greece

72.0

(1.0)

57.8

(3.2)

14.2

(0.0)

0.000

13.52

0.000

12.59

0.000

11.46

0.000

Ireland

28.3

(0.8)

25.1

(1.6)

3.2

(0.0)

0.107

5.05

0.015

6.06

0.004

20.33

0.000

Israel

29.5

(0.8)

32.5

(1.7)

-3.0

(0.0)

0.094

-1.18

0.553

-0.36

0.879

17.01

0.000

Italy

18.1

(0.9)

11.3

(1.9)

6.8

(0.0)

0.001

8.12

0.005

7.11

0.015

13.06

0.000

Lithuania

71.0

(0.9)

73.5

(5.1)

-2.5

(0.1)

0.644

3.98

0.493

3.98

0.485

16.18

0.000

Netherlands

41.7

(0.7)

29.7

(2.4)

11.9

(0.0)

0.000

11.80

0.000

10.97

0.000

24.35

0.000

New Zealand

41.7

(0.8)

44.8

(1.4)

-3.1

(0.0)

0.042

-1.98

0.254

0.06

0.860

17.03

0.000

Northern Ireland (UK)

23.2

(0.8)

25.6

(2.9)

-2.4

(0.0)

0.457

-2.17

0.487

-1.66

0.661

18.15

0.000

Norway

50.9

(0.8)

37.0

(2.0)

14.0

(0.0)

0.000

13.60

0.000

14.54

0.000

26.56

0.000

Singapore

25.5

(0.6)

27.8

(1.2)

-2.3

(0.0)

0.099

-1.12

0.430

-0.49

0.734

13.53

0.000

Slovenia

13.2

(0.5)

9.4

(1.1)

3.8

(0.0)

0.004

1.97

0.191

1.37

0.370

6.57

0.000

Spain

23.6

(0.6)

21.2

(1.6)

2.4

(0.0)

0.434

1.82

0.172

0.62

0.482

10.47

0.000

Sweden

46.8

(0.9)

34.0

(2.0)

12.8

(0.0)

0.000

11.06

0.000

10.61

0.000

16.28

0.000

United States

45.0

(0.9)

36.3

(1.9)

8.7

(0.0)

0.000

7.63

0.001

7.13

0.002

20.44

0.000

Average

36.1

(0.2)

31.1

(0.5)

5.0

(0.0)

0.164

5.43

0.215

5.49

0.259

16.13

0.002

 

Model 3 - Migrant gap controlling for age, gender, parents' educational attainment, educational attainment and literacy proficiency

Model 4 - Moderating role of literacy

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Literacy

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

-0.26

0.849

12.14

0.000

0.17

0.000

-12.38

0.194

11.95

0.000

0.18

0.000

-0.04

0.195

Austria

3.06

0.131

14.54

0.000

0.13

0.000

-2.46

0.846

14.55

0.000

0.13

0.000

-0.02

0.664

Canada

-1.81

0.084

6.58

0.002

0.14

0.000

-8.64

0.013

6.57

0.003

0.15

0.000

-0.03

0.022

Chile

-0.96

0.878

10.32

0.008

0.21

0.000

15.25

0.545

10.31

0.008

0.20

0.000

0.07

0.509

Czech Republic

7.46

0.072

2.63

0.406

0.09

0.008

37.09

0.256

2.57

0.416

0.09

0.011

0.10

0.368

Denmark

11.65

0.000

11.01

0.000

0.14

0.000

-1.86

0.833

10.86

0.000

0.15

0.000

-0.05

0.105

England (UK)

-5.52

0.017

11.18

0.000

0.13

0.000

-37.67

0.001

10.80

0.000

0.15

0.000

-0.12

0.003

Estonia

11.75

0.000

7.53

0.000

0.13

0.000

-19.50

0.104

7.36

0.000

0.14

0.000

-0.12

0.011

Finland

17.28

0.000

20.44

0.000

0.10

0.000

-0.08

0.997

20.31

0.000

0.10

0.000

-0.07

0.413

Flanders (Belgium)

-8.74

0.000

12.65

0.000

0.07

0.001

-0.14

0.171

12.66

0.000

0.07

0.001

0.04

0.681

France

0.01

0.949

3.65

0.002

0.01

0.506

-2.49

0.169

3.65

0.002

0.01

0.308

-0.01

0.198

Germany

4.17

0.122

9.41

0.000

0.10

0.000

-5.99

0.069

9.46

0.000

0.11

0.000

-0.04

0.031

Greece

11.10

0.000

6.56

0.011

0.17

0.000

19.80

0.180

6.53

0.012

0.17

0.000

0.04

0.540

Ireland

5.18

0.016

17.16

0.000

0.07

0.007

13.02

0.192

17.33

0.000

0.06

0.017

0.03

0.437

Israel

-2.22

0.218

12.19

0.000

0.11

0.000

-3.56

0.145

12.18

0.000

0.12

0.000

-0.01

0.228

Italy

5.53

0.067

10.81

0.000

0.06

0.006

-1.55

0.925

10.78

0.000

0.06

0.006

-0.03

0.621

Lithuania

2.70

0.625

12.39

0.000

0.13

0.000

21.93

0.592

12.46

0.000

0.13

0.000

0.08

0.624

Netherlands

5.93

0.034

17.65

0.000

0.14

0.000

-8.58

0.504

17.49

0.000

0.15

0.000

-0.05

0.253

New Zealand

-3.19

0.072

9.12

0.000

0.18

0.000

-7.72

0.555

9.02

0.000

0.18

0.000

-0.02

0.753

Northern Ireland (UK)

-2.97

0.388

14.52

0.000

0.08

0.004

-7.79

0.662

14.48

0.000

0.08

0.005

-0.02

0.761

Norway

6.23

0.007

17.92

0.000

0.21

0.000

-10.08

0.409

17.67

0.000

0.23

0.000

-0.06

0.170

Singapore

-2.03

0.163

5.41

0.013

0.10

0.000

-10.97

0.132

5.25

0.016

0.11

0.000

-0.03

0.216

Slovenia

0.99

0.525

5.27

0.001

0.03

0.065

-19.94

0.044

5.22

0.001

0.04

0.016

-0.09

0.031

Spain

-0.49

0.939

8.19

0.000

0.05

0.014

-4.37

0.976

8.11

0.000

0.05

0.023

-0.02

0.988

Sweden

3.61

0.198

8.61

0.002

0.15

0.000

-5.41

0.643

8.53

0.002

0.16

0.000

-0.03

0.428

United States

3.62

0.155

12.35

0.000

0.14

0.000

5.12

0.961

12.32

0.000

0.14

0.000

0.01

0.711

Average

2.77

0.250

10.78

0.017

0.12

0.024

-2.27

0.428

10.71

0.02

0.12

0.015

-0.02

0.383

 

Model 5 - Moderating role of education

Model 6 - Migrant gap controlling for individual background characteristics as well as length of stay in the country

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Tertiary education

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Length of stay

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

-3.17

0.130

14.01

0.000

0.16

0.000

-6.33

0.054

m

m

m

m

m

m

m

m

Austria

1.27

0.559

15.74

0.000

0.13

0.000

-6.86

0.202

4.70

0.284

14.61

0.000

0.13

0.000

0.09

0.621

Canada

-2.96

0.065

7.07

0.001

0.14

0.000

-2.02

0.315

-0.24

0.885

6.61

0.001

0.14

0.000

0.09

0.181

Chile

4.32

0.368

9.46

0.014

0.21

0.000

17.96

0.120

8.69

0.142

10.03

0.008

0.21

0.000

1.11

0.097

Czech Republic

7.63

0.115

2.60

0.400

0.09

0.008

0.48

0.959

11.65

0.173

2.80

0.377

0.09

0.008

0.18

0.503

Denmark

7.20

0.002

12.36

0.000

0.14

0.000

-11.02

0.002

19.81

0.000

11.32

0.000

0.14

0.000

0.45

0.001

England (UK)

-8.96

0.009

12.28

0.000

0.13

0.000

-6.92

0.120

-6.48

0.032

11.14

0.000

0.13

0.000

-0.05

0.671

Estonia

9.65

0.001

7.87

0.000

0.13

0.000

-4.27

0.317

19.67

0.003

7.57

0.000

0.13

0.000

0.23

0.201

Finland

17.69

0.001

20.40

0.000

0.10

0.000

0.97

0.884

18.53

0.013

20.45

0.000

0.10

0.000

0.07

0.819

Flanders (Belgium)

-12.53

0.000

13.58

0.000

0.07

0.001

-12.08

0.005

-16.01

0.000

12.53

0.000

0.08

0.001

-0.27

0.095

France

-0.45

0.659

3.84

0.002

0.01

0.504

-1.41

0.504

-2.97

0.150

3.66

0.002

0.01

0.429

-0.12

0.079

Germany

3.23

0.305

9.78

0.000

0.10

0.000

-2.84

0.498

3.62

0.476

9.39

0.000

0.10

0.000

-0.02

0.887

Greece

10.74

0.001

6.75

0.013

0.17

0.000

-1.77

0.771

29.06

0.000

6.29

0.015

0.16

0.000

0.78

0.001

Ireland

2.23

0.408

18.36

0.000

0.07

0.008

-6.33

0.021

5.53

0.072

17.14

0.000

0.07

0.008

0.03

0.832

Israel

-1.50

0.613

11.83

0.000

0.12

0.000

1.54

0.668

5.30

0.111

12.49

0.000

0.11

0.000

0.29

0.004

Italy

4.70

0.161

11.11

0.000

0.06

0.006

-6.94

0.328

5.68

0.244

10.80

0.000

0.06

0.006

0.01

0.955

Lithuania

4.53

0.475

12.12

0.000

0.13

0.000

9.94

0.378

30.55

0.142

12.37

0.000

0.13

0.000

0.72

0.138

Netherlands

0.80

0.817

19.37

0.000

0.14

0.000

-14.39

0.014

11.46

0.045

17.70

0.000

0.14

0.000

0.24

0.236

New Zealand

-8.34

0.004

11.83

0.000

0.18

0.000

-9.53

0.018

-4.93

0.057

8.92

0.000

0.18

0.000

-0.11

0.302

Northern Ireland (UK)

-4.69

0.337

14.88

0.000

0.08

0.004

-3.97

0.562

-2.41

0.662

14.58

0.000

0.08

0.004

0.03

0.807

Norway

1.08

0.740

19.43

0.000

0.21

0.000

-11.68

0.009

10.18

0.005

18.09

0.000

0.21

0.000

0.25

0.160

Singapore

-3.40

0.156

5.93

0.010

0.10

0.000

-2.21

0.474

-1.87

0.479

5.42

0.014

0.10

0.000

0.01

0.924

Slovenia

0.74

0.674

5.37

0.001

0.03

0.064

-1.25

0.790

7.02

0.064

5.17

0.001

0.03

0.077

0.22

0.066

Spain

0.54

0.484

7.76

0.000

0.05

0.014

3.98

0.197

0.11

0.968

8.17

0.000

0.05

0.014

0.00

0.996

Sweden

4.87

0.150

7.92

0.006

0.15

0.000

3.73

0.443

8.41

0.051

8.86

0.002

0.15

0.000

0.23

0.130

United States

-0.14

0.790

14.02

0.000

0.14

0.000

-9.70

0.021

4.34

0.346

12.50

0.000

0.14

0.000

0.04

0.787

Average

1.35

0.308

11.37

0.02

0.12

0.023

-3.19

0.334

6.78

0.216

10.74

0.017

0.11

0.022

0.18

0.420

Note: Marginal probabilities are multiplied by 100. Differences are based on a regression model and take account of differences associated with the following variables: age, gender, education, immigration background and parents' educational attainment. Only the score-point differences between two contrast categories are shown, which is useful for showing the relative significance of each socio-demographic variable vis-a-vis observed score-point differences. Estimates based on a sample size less than 30 are not shown.

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846802

Annex Table 6.A.5. Differences in self-reported volunteering, by migrant status and individual characteristics

 

% who reported participating in the last 12 months, how often, if at all, did you do voluntary work, including unpaid work for a charity, volonteer party, trade union or other non-profit organisation

Model 1 - Migrant gap controlling for age, gender and parents' educational attainment

Model 2- Migrant gap controlling for age, gender, parents' educational attainment and educational attainment

 

Natives

Migrants

Diff. (Natives-migrants)

Migrant gap

Education (Tertiary minus lower than upper secondary)

 

%

S.E.

%

S.E.

% dif.

S.E.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

41.4

(1.1)

34.6

(1.3)

6.7

(0.0)

0.000

8.96

0.000

10.54

0.000

16.80

0.000

Austria

38.4

(0.7)

20.3

(1.4)

18.1

(0.0)

0.000

19.89

0.000

19.69

0.000

16.59

0.000

Canada

52.1

(0.6)

38.7

(1.1)

13.4

(0.0)

0.000

14.00

0.000

15.42

0.000

12.69

0.000

Chile

31.8

(1.6)

35.9

(4.5)

-4.1

(0.0)

0.386

-1.44

0.756

-1.26

0.780

10.53

0.000

Czech Republic

17.6

(0.8)

20.2

(4.8)

-2.6

(0.0)

0.590

-2.14

0.632

-1.84

0.677

6.91

0.012

Denmark

45.6

(0.7)

32.8

(1.5)

12.7

(0.0)

0.000

12.54

0.000

12.17

0.000

13.85

0.000

England (UK)

31.5

(0.9)

28.2

(1.7)

3.3

(0.0)

0.094

4.32

0.037

4.89

0.013

20.35

0.000

Estonia

28.1

(0.5)

25.0

(1.5)

3.1

(0.0)

0.046

1.03

0.540

1.38

0.416

16.75

0.000

Finland

44.2

(0.7)

34.4

(3.1)

9.8

(0.0)

0.009

10.19

0.006

8.99

0.015

14.08

0.000

Flanders (Belgium)

35.2

(0.9)

22.1

(2.0)

13.1

(0.0)

0.000

11.55

0.000

10.93

0.000

17.87

0.000

France

26.5

(0.5)

16.5

(1.3)

9.9

(0.0)

0.000

11.50

0.000

9.93

0.000

18.88

0.000

Germany

37.6

(0.9)

17.6

(1.7)

20.0

(0.0)

0.000

21.19

0.000

19.99

0.000

18.07

0.000

Greece

20.1

(0.8)

18.3

(2.4)

1.7

(0.0)

0.487

3.94

0.129

2.78

0.276

14.03

0.000

Ireland

40.8

(0.9)

30.4

(1.5)

10.4

(0.0)

0.000

11.54

0.000

12.53

0.000

17.79

0.000

Israel

34.7

(0.7)

24.0

(1.3)

10.7

(0.0)

0.000

11.71

0.000

11.85

0.000

4.67

0.033

Italy

22.3

(0.8)

14.1

(1.8)

8.2

(0.0)

0.000

9.43

0.001

8.60

0.002

11.46

0.000

Lithuania

10.3

(0.6)

15.7

(4.2)

-5.5

(0.0)

0.209

-6.73

0.030

-6.75

0.033

0.80

0.690

Netherlands

42.3

(0.7)

30.4

(2.0)

11.9

(0.0)

0.000

11.96

0.000

11.42

0.000

13.44

0.000

New Zealand

52.7

(0.9)

50.3

(1.4)

2.4

(0.0)

0.162

3.37

0.062

5.52

0.003

15.78

0.000

Northern Ireland (UK)

34.0

(1.1)

25.1

(3.2)

9.0

(0.0)

0.015

9.75

0.024

10.40

0.008

27.56

0.000

Norway

59.6

(0.7)

42.4

(2.0)

17.2

(0.0)

0.000

17.70

0.000

17.85

0.000

12.08

0.000

Singapore

34.1

(0.7)

34.8

(1.3)

-0.6

(0.0)

0.703

0.86

0.602

2.18

0.175

23.78

0.000

Slovenia

35.1

(0.9)

19.1

(1.5)

16.1

(0.0)

0.000

13.63

0.000

12.06

0.000

16.84

0.000

Spain

19.1

(0.6)

11.3

(1.1)

7.8

(0.0)

0.000

9.83

0.000

8.43

0.000

13.63

0.000

Sweden

38.4

(0.8)

25.7

(1.6)

12.8

(0.0)

0.000

13.63

0.000

12.68

0.000

12.65

0.000

United States

57.7

(0.9)

44.4

(2.3)

13.3

(0.0)

0.000

8.94

0.000

8.73

0.001

25.96

0.000

Average

35.8

(0.2)

27.4

(0.5)

8.4

(0.0)

0.104

15.47

0.108

8.81

0.092

15.15

0.028

 

Model 3 - Migrant gap controlling for age, gender, parents' educational attainment, educational attainment and literacy proficiency

Model 4 - Moderating role of literacy

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Literacy

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

7.29

0.000

9.71

0.000

0.17

0.000

-0.86

0.366

9.60

0.042

0.18

0.443

-0.03

0.000

Austria

17.69

0.000

13.01

0.000

0.08

0.000

43.85

0.001

12.95

0.000

0.07

0.031

0.10

0.000

Canada

10.29

0.000

2.59

0.090

0.18

0.000

8.25

0.170

2.56

0.000

0.19

0.616

-0.01

0.000

Chile

-1.88

0.666

6.75

0.008

0.07

0.005

-10.64

0.176

6.74

0.728

0.07

0.757

-0.04

0.000

Czech Republic

-2.30

0.594

3.82

0.191

0.07

0.002

2.64

0.192

3.81

0.103

0.07

0.805

0.02

0.000

Denmark

7.17

0.000

7.98

0.000

0.13

0.000

16.67

0.064

8.07

0.004

0.12

0.308

0.04

0.000

England (UK)

1.36

0.498

13.93

0.000

0.15

0.000

-10.37

0.022

13.77

0.317

0.15

0.391

-0.04

0.000

Estonia

-0.41

0.814

13.32

0.000

0.09

0.000

-11.48

0.546

13.22

0.000

0.10

0.362

-0.04

0.000

Finland

2.99

0.406

8.60

0.000

0.12

0.000

10.09

0.432

8.66

0.000

0.12

0.723

0.03

0.000

Flanders (Belgium)

6.97

0.003

10.91

0.000

0.14

0.000

1.21

0.001

10.91

0.029

0.14

0.446

-0.02

0.000

France

6.69

0.000

12.55

0.000

0.13

0.000

21.84

0.015

12.53

0.000

0.12

0.107

0.06

0.000

Germany

15.92

0.000

10.24

0.000

0.16

0.000

12.14

0.160

10.27

0.919

0.16

0.800

-0.01

0.000

Greece

2.43

0.337

12.76

0.000

0.04

0.038

26.37

0.386

12.71

0.985

0.04

0.117

0.09

0.000

Ireland

11.00

0.000

13.12

0.000

0.10

0.000

14.10

0.193

13.15

0.480

0.10

0.798

0.01

0.000

Israel

10.17

0.000

0.65

0.795

0.09

0.000

9.59

0.494

0.68

0.449

0.09

0.225

0.00

0.000

Italy

6.71

0.015

8.63

0.000

0.08

0.002

8.33

0.171

8.61

0.276

0.08

0.916

0.01

0.000

Lithuania

-7.17

0.026

-0.92

0.600

0.07

0.001

-23.63

0.319

-0.99

0.036

0.07

0.369

-0.06

0.000

Netherlands

6.98

0.004

7.67

0.001

0.12

0.000

17.15

0.302

7.77

0.056

0.11

0.390

0.04

0.000

New Zealand

3.32

0.063

10.49

0.000

0.12

0.000

-3.86

0.172

10.38

0.462

0.13

0.473

-0.03

0.000

Northern Ireland (UK)

8.40

0.025

22.07

0.000

0.12

0.000

49.91

0.382

22.41

0.011

0.11

0.073

0.15

0.000

Norway

12.43

0.000

6.72

0.004

0.13

0.000

20.59

0.951

6.84

0.053

0.12

0.499

0.03

0.000

Singapore

-0.16

0.925

11.85

0.000

0.15

0.000

10.20

0.886

12.04

0.060

0.14

0.229

0.04

0.000

Slovenia

11.47

0.000

14.88

0.000

0.04

0.028

2.78

0.577

14.83

0.000

0.05

0.070

-0.04

0.000

Spain

7.02

0.000

10.80

0.000

0.06

0.000

20.73

0.491

10.93

0.987

0.05

0.168

0.05

0.000

Sweden

5.98

0.015

5.53

0.051

0.14

0.000

4.86

0.111

5.52

0.005

0.14

0.880

0.00

0.000

United States

4.67

0.081

16.56

0.000

0.16

0.000

0.24

0.224

16.63

0.382

0.17

0.175

-0.02

0.000

Average

5.96

0.172

9.78

0.067

0.11

0.003

9.26

0.300

9.79

0.246

0.11

0.430

0.01

0.000

Model 5 - Moderating role of education

Model 6 - Migrant gap controlling for individual background characteristics as well as length of stay in the country

 

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Migrant*Tertiary education

Migrant gap

Education (Tertiary minus lower than upper secondary)

Literacy

Length of stay

 

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Marg. Prob.

p-value

Australia

4.94

0.052

11.23

0.000

0.17

0.000

-5.30

0.068

m

m

m

m

m

m

m

m

Austria

20.15

0.000

11.58

0.000

0.08

0.000

8.96

0.082

21.50

0.00

13.13

0.00

0.08

0.00

0.20

0.23

Canada

7.90

0.000

3.59

0.016

0.18

0.000

-4.25

0.102

13.30

0.00

2.78

0.07

0.18

0.00

0.15

0.02

Chile

2.49

0.695

6.18

0.013

0.07

0.006

10.34

0.127

-0.58

0.93

6.71

0.01

0.07

0.01

0.13

0.76

Czech Republic

-4.00

0.368

4.20

0.133

0.07

0.002

-5.43

0.554

3.67

0.60

4.18

0.13

0.07

0.00

0.25

0.24

Denmark

9.22

0.001

7.38

0.000

0.13

0.000

5.00

0.177

13.58

0.00

8.22

0.00

0.13

0.00

0.35

0.00

England (UK)

4.60

0.163

13.05

0.000

0.15

0.000

6.06

0.159

4.39

0.11

14.10

0.00

0.14

0.00

0.17

0.15

Estonia

-0.18

0.937

13.27

0.000

0.09

0.000

0.45

0.895

-8.49

0.09

13.21

0.00

0.09

0.00

-0.24

0.06

Finland

-0.80

0.858

9.15

0.000

0.12

0.000

-11.06

0.108

9.15

0.25

8.55

0.00

0.12

0.00

0.35

0.38

Flanders (Belgium)

6.16

0.024

11.05

0.000

0.14

0.000

-2.16

0.983

23.45

0.00

11.07

0.00

0.13

0.00

0.71

0.00

France

9.03

0.000

11.80

0.000

0.13

0.000

6.31

0.071

4.85

0.16

12.56

0.00

0.13

0.00

-0.07

0.44

Germany

17.30

0.000

9.81

0.000

0.16

0.000

3.95

0.408

19.24

0.00

10.42

0.00

0.16

0.00

0.16

0.37

Greece

1.94

0.524

12.89

0.000

0.04

0.037

-1.58

0.780

10.78

0.05

12.64

0.00

0.04

0.05

0.34

0.07

Ireland

11.91

0.000

12.73

0.000

0.10

0.000

2.10

0.577

17.05

0.00

13.22

0.00

0.09

0.00

0.40

0.00

Israel

9.57

0.000

0.85

0.813

0.09

0.000

-1.11

0.942

22.82

0.00

1.04

0.67

0.09

0.00

0.45

0.00

Italy

6.64

0.019

8.70

0.000

0.08

0.002

-1.03

0.898

16.67

0.00

8.53

0.00

0.07

0.00

0.53

0.01

Lithuania

-9.14

0.051

-0.70

0.684

0.07

0.001

-5.55

0.360

-9.73

0.75

-0.93

0.60

0.07

0.00

-0.06

0.92

Netherlands

8.91

0.005

7.01

0.002

0.12

0.000

5.60

0.279

5.97

0.22

7.68

0.00

0.12

0.00

-0.04

0.80

New Zealand

-1.48

0.435

13.25

0.000

0.12

0.000

-9.27

0.005

10.14

0.00

11.37

0.00

0.11

0.00

0.43

0.00

Northern Ireland (UK)

7.75

0.167

22.17

0.000

0.12

0.000

-1.51

0.949

17.57

0.00

22.31

0.00

0.11

0.00

0.54

0.01

Norway

14.54

0.000

5.98

0.013

0.13

0.000

5.12

0.224

16.52

0.00

6.90

0.00

0.12

0.00

0.27

0.08

Singapore

0.71

0.782

11.53

0.000

0.15

0.000

1.33

0.689

1.00

0.66

11.88

0.00

0.15

0.00

0.06

0.51

Slovenia

12.90

0.000

14.33

0.000

0.04

0.030

8.01

0.090

25.85

0.00

14.64

0.00

0.04

0.04

0.47

0.00

Spain

8.02

0.001

10.56

0.000

0.06

0.000

2.96

0.443

5.37

0.09

10.81

0.00

0.06

0.00

-0.12

0.45

Sweden

8.66

0.016

4.23

0.150

0.14

0.000

7.28

0.162

4.34

0.33

5.43

0.06

0.14

0.00

-0.08

0.61

United States

1.87

0.689

17.95

0.000

0.16

0.000

-7.58

0.069

8.31

0.05

16.63

0.00

0.16

0.00

0.19

0.22

Average

6.14

0.223

9.76

0.070

0.11

0.003

0.68

0.392

10.27

0.172

9.88

0.062

0.11

0.004

0.22

0.254

Note: Marginal probabilities are multiplied by 100. Differences are based on a regression model and take account of differences associated with the following variables: age, gender, education, immigration background and parents' educational attainment. Only the score-point differences between two contrast categories are shown, which is useful for showing the relative significance of each socio-demographic variable vis-a-vis observed score-point differences. Estimates based on a sample size less than 30 are not shown.

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846821

End of the section – Back to iLibrary publication page