Aug 13, 2014 - social cohesion in Dutch society is eroding (Baudet, 2011; Ministerie .... by Statistics Netherlands, in cooperation with The Netherlands Institute.
The socially excluded in the Netherlands: the development of an overall index Moniek Coumans & Hans Schmeets Accepted: 17 July 2014 / Published online: 13 August 2014 The final publication is available at link.springer.com: http://link.springer.com/article/10.1007/s11205-014-0707-6
1
Introduction
In the Netherlands there is growing concern about polarisation in society and in politics. This is reflected in allegedly lower participation rates and a move from a high-trust society in the direction of a low-trust one, with more socially excluded people, as expressed by policymakers, the media, politicians and the sentiment among the public in general (Fukuyama, 2010; Meurs, 2008). At the political level, the concerns of the Dutch population are reflected in increased support for new parties and political movements, such as the populist Party for Freedom (PVV), whose manifestos and media statements plea for fewer immigrants, more safety in the streets, withdrawal from the EU and from the process of further European unification, while also pointing out that more and more Dutch people are socially excluded and poor. After a period of depolarisation, since the end of the nineties there has been an increasing trend towards political polarisation (Aarts, Van der Kolk & Rosema, 2007). This political shift in Dutch society has been widely discussed and is often seen as an indication that social cohesion in Dutch society is eroding (Baudet, 2011; Ministerie van Economische Zaken, 2009; Meeus, 2012; Rob, 2010; Schnabel, 2010; SER, 2009). However, Schmeets & Te Riele (2014) and Van Beuningen & Schmeets (2012) find no evidence for a decline, including among subpopulations, in contacts with relatives, friends and neighbours, providing informal help, volunteering, political participation, social trust and trust in (political) institutions in the period 1989-2010. Other studies (Coumans, 2010; Te Riele, 2012) show that the proportion of people contacting their friends and family less than weekly was only 4 percent in 2009, and 5 percent in 2010. In 2009, only 0.6 percent seldom or never contacted their family and friends. However, this research barely touches on the socially marginalised people in Dutch society, as it focuses on a total of 17 social capital indicators of participation and trust (Van Beuningen & Schmeets, 2012). Consequently, high social capital rates do not necessarily mean that the number of socially excluded people is low. And increasing social capital rates do not necessarily indicate that the socially excluded group does not increase over the years. Higher shares of socially excluded people might increase cleavages in Dutch society, resulting in a decrease of social inclusion: a society for all (World Summit on Social Development, 1995; UNDESA, 2007). Apart from the existing cleavages in social capital – in particular by the level of education, i.e. lower and higher educated, and ethnicity, i.e. non-western immigrants and native Dutch – (Schmeets & Te Riele, 2014), a further divide in society
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might be revealed: between people who are socially excluded and people who are not. The socially excluded are characterised by low economic resources, lack of social contacts and cultural activities, and will refrain from civic participation. Considering social exclusion from the societal perspective, another potential consequence of social exclusion are the increasing costs of public aids, health expenses and unemployment. Ultimately, the socially excluded may become a potential danger for society by demonstrating their grievances by political protest, such as demonstrations, strikes and even violence (Gurr, 1970; Gurr & Moore 1997; Opp 2009; Seybolt & Shafiq 2012). To prevent this undesirable situation for the people involved as well as for society as a whole, it is important to gain insight into the size of this marginalised population. Furthermore, as some subpopulations show a rather high, and others a low social capital rate, e.g. higher versus lower educated, and natives versus ethnic minorities, this begs the question of whether such subpopulations are mirrored in high and low numbers of the socially excluded. This paper uses the concept of social exclusion and the dimensions thereof as developed in the Netherlands by Vrooman & Hoff (2013). Their research focuses on improving the measurement of social exclusion by using focus groups and a fairly limited sample of 650 respondents. We aim to further elaborate on the framework of social exclusion by creating a composite overall indicator which is based on a total of 42, fairly low correlated, indicators. This overall indicator fits nicely into the definition of social exclusion, covering four distinguished dimensions safeguarding that someone shows low scores on at least two of the four dimensions. Furthermore we also provide a reliable estimate of the socially excluded based on a large and rich dataset. The following research question will be addressed. How can we compose an overall social exclusion index, illustrated by calculating the number of socially excluded people in Dutch society? For this purpose some 25 indicators were added to the 2010 survey EU-Statistics on Income and Living Conditions (EU-SILC). The indicators were divided across the following four dimensions: (1) limited social participation; (2) inadequate access to basic social rights and institutions; (3) material deprivation; and (4) lack of normative integration. With this study, we aim to contribute with: 1) the introduction of a framework for social exclusion; (2) the development of a social exclusion index based on a very rich dataset including over 10,000 people aged 15 years or older; (3) providing a transparent tool for other National Statistical Institutes to develop a social exclusion index; and (4) a comparison of various subpopulations in terms of their degree of social exclusion. First, we will elaborate the concept of social exclusion. Next, we will outline our research method on social exclusion: the index. Thereafter, we will detail our findings for various subpopulations and relate the rate of social exclusion to health as an external criterion. The paper ends with some conclusions and a discussion.
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The concept of social exclusion
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2.1
Social inclusion and social exclusion
The concept of social exclusion tends to be defined as the opposite of social inclusion. Social inclusion is, broadly speaking, people’s abilities to exercise their human rights and the set of civil liberties that enable them to participate in society and to reinforce their individual and collective identity. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO, 2012), an inclusive society is defined “as a society for everyone, in which every individual has an active role to play. That society is built on the fundamental values of fairness, equality, social justice, human rights and freedoms, as well as on principles of tolerance and recognition of diversity.” In sum, an inclusive society is ‘a society for all’ (World Summit on Social Development, 1995; UNDESA, 2007). Social inclusion is an overarching concept that is linked to various other concepts, such as human capital, social capital, social cohesion, well-being, and freedom from poverty (ECLAC, 2007; Boarini & Fron, 2013). It is also often connected to social protection and social security in order to relate inclusiveness to specific drivers such as basic needs, adequate income, income security, access to health care, access to services, social justice, and political and social participation (Babajanian & Gagen-Zanker, 2012; Behrendt & Bonnet, 2013). Inclusive policies are often targeted at a reduction of poverty, material deprivation, and low work intensity (Eurostat, 2012), focussing on socially vulnerable groups, such as migrants, the homeless and the most lowly educated (Ramot, 2013; Balbo, 2013; Boarini & Fron, 2013).
2.2
Social exclusion: a multidimensional concept
The description of the concept of social exclusion below is restricted to a short summary of the most relevant literature. For a more extended review, we refer to other publications (Sen, 1985; Room, 1997; Saraceno, 2001; Tsakloglou & Papadopouplus, 2001; Burchard et al., 2002; Muffels, 2004; Poggi, 2004; Devicenti & Poggi, 2011). The concept of social exclusion is applied widely for policy as well as research purposes. However, it often remains vague and is used as an umbrella term. Moreover, the definition of it is frequently imbedded in discussion of its similarities and differences to the concept of poverty. Poverty is also a diffuse concept in that it can be conceived in various ways, i.e. objective versus subjective, relative or absolute, and as a narrow or a broad concept. Therefore, poverty is not necessarily related to financial matters. For example, someone’s level of income may be sufficient objectively, but he or she can still feel poor subjectively. According to the literature (JehoelGijsbers, 2004; Saraceno, 2001; Room, 1995; Berghman, 1995), social exclusion differs from poverty in the sense that it is more dynamic, it refers to relationships instead of the distribution of income or material goods, it relates to social indicators rather than personal resources and it is multidimensional instead of one-dimensional. However, these differences are not as unambiguous as they may seem at first sight. Especially when poverty is conceived in a broader sense or referring to subjective indicators instead of financial matters, the differences are not unequivocal. Differences become particularly obvious when comparing exclusion with financial poverty. However, even in this comparison both concepts remain closely intertwined (Devicienti & Poggi, 2011).
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The most essential discrepancy between the two concepts boils down to the multidimensional character of social exclusion, unlike the financial or material features that characterize poverty (Sen, 1985; Tsakloglou & Papadopouplus, 2001; Burchard et al., 2002; Muffels, 2004; Poggi, 2004). Even when poverty is broadly interpreted, deprivation in other non-financial domains is still seen to be related to or caused by financial of material deprivation (Nolan & Whelan, 1996). All in all, poverty is conceived as part of exclusion, but not necessarily and not exclusively, i.e. someone may be socially excluded without being poor, and being poor does not automatically mean exclusion. Poverty in itself is not a sufficient condition to be socially excluded. Although the specific conceptualisation of the dimensions of social exclusion varies according to the available data, basically a distinction can be made between (a) economic-structural exclusion, containing material deprivation and inadequate access to social rights; and (b) social and socialcultural exclusion, containing limited social participation and insufficient normative integration. As a consequence of the unclear conceptualisation, Jehoel-Gijsbers et al. (2008) show that the operationalisation of social exclusion is also ambiguous. Often it is indirect and focuses on risk factors, such as low income or poor health. Since this approach is more directed at the potential causes of social exclusion than at the phenomenon itself, it is not to be preferred over an empirical assessment of social exclusion. Therefore, apart from multidimensionality, Jehoel-Gijsbers et al. (2008) present another important characteristic of social exclusion: objectivity, i.e. that the concept can best be operationalised by purely objective social exclusion indicators, instead of risk factors. Furthermore, the third characteristic refers to relativity, i.e. that social exclusion can only be seen as a relative phenomenon within the population. Thus, the meaning of a certain social exclusion score in a population can best be interpreted in relation to the exclusion scores of populations with different characteristics, or to the same population in another year. Hoff & Vrooman (2011, p.17) elaborated further on the three abovementioned characteristics and developed a definition of social exclusion which emphasises especially the multidimensionality of the concept: “Someone is socially excluded if there is a deficiency in at least two of the following domains: material deprivation, social participation, access to social rights/institutions and normative integration.” We adopted this definition and introduce an index in which all four dimensions contribute to the concept of social exclusion. Someone is considered as socially excluded if he or she is deprived on at least two of the four dimensions (see also section 3.1). Moreover, in line with the characteristic of objectivity, we include only social exclusion indicators. Risk factors, such as low income or poor health, are not included in the index. Lastly, we acknowledge the characteristic of relativity by comparing the social exclusion outcome for different subpopulations.
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Method
3.1
Method and analysis
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One of the main challenges is the selection of the appropriate indicators for each dimension of social exclusion. In a former study by Statistics Netherlands, in cooperation with The Netherlands Institute for Social Research (Jehoel-Gijsbers et al., 2008), the choice of indicators was based on a combination of theoretical grounds and the method of analysis. Theoretical considerations are especially relevant when the index is based on fixed weighting factors to be applied within the dimensions, and when no threshold value is used. A combined theoretical-empirical approach often results in the use of scaling techniques, such as Overalls – an optimal scaling method based on alternating least squares. For the Netherlands, Hoff & Vrooman (2011) and Vrooman & Hoff (2013) applied the Overalls method in two different ways: bottom-up and top-down. In the bottom-up approach, first the dimensions are constructed based on the items (using categorical Principal Component Analysis, cat-PCA), and then an Overalls procedure is used to verify whether the selected items measure the concept of social exclusion. The top-down approach first uses the Overalls technique to construct a general index, and thereafter the four dimensions are searched for. Although our study also uses combined theoretical-empirical research to develop a measurement tool, we apply a fairly straight-forward and stepwise approach in which the following steps can be distinguished: Step 1: Choice of dimensions and sub-dimensions and item-selection; Step 2: Construction of sum scores per dimension; Step 3: Construction of an overall sum score and determination of a theoretically useful threshold value for social exclusion; Step 4: Exploring the influence of background characteristics on social exclusion by bivariate and multivariate logistic regression analysis.
Step 1: Dimensions and item selection In line with the aforementioned studies, we distinguish four dimensions: (1) limited social participation; (2) inadequate access to basic social rights and core institutions; (3) material deprivation; and (4) lack of normative integration. Within these dimensions, we also determine several sub-dimensions, such as social relationships, and political and cultural participation within the participation dimension (Figure 1). These dimensions and sub-dimensions have mainly been determined on a theoretical basis, and can be considered as indicators that predict social exclusion. The items are not equally distributed over the four dimensions. An exploratory analysis based on the 46 items and referring to one social exclusion dimension results in a higher contribution of the social participation and material deprivation dimension, due to the relatively high number of items. This is confirmed by a Principal Component Analysis. Based on the Kaiser criterion (Eigen value > 1.0) 12 factors are distinguished. The explained variance for the first factor (12.5%) is substantially higher than the second (5.6%). Although the explained variance is relatively low, a one-factor solution could be found. However, such an empirical solution shows that some theoretical dimensions contribute much more to the index than others. Another problem is that most items are not normally distributed,
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and many are even very skewed. We therefore also employed Overals. If we apply the common practice criteria of a component loading of at least 0.30, a weight of 0.10 or higher, and logical quantifications of the categories, a one-dimension solution results in an overrepresentation of the items belonging to the social participation (8 items) and the material dimensions (8 items). Both the access to basic rights dimension and the dimension of normative integration are based on only one item. In addition, we looked at a solution in which we predetermined four dimension solutions using PCA and Overals. The PCA result mirrors to some extent the theoretical expectations; but some loadings are very low and evidently do not belong to the theoretical dimension. The Overals solution yielded barely interpretable results. Furthermore, the assignment of weights to the specific indicators within each dimension, as suggested by Poggi (2004) and Devicienti and Poggi (2011), yielded ambiguous results, and for three of the four dimensions rather weak correlations with the sum scores. For the sake of straightforwardness and interpretability of the results, we chose to apply a stepwise method. In addition to the theoretically based predetermination of sub-dimensions in our model, we grouped the social exclusion indicators into eight sub-dimensions as a result of explorative analyses, using PCA and Reliability (Figure 1). See Annex 2 for component loadings within the four dimensions. We did so to check which indicators, out of a total of 46, should be removed from the distinguished theoretical dimensions. This revealed that only four extremely skewed items with hardly any positive responses and/or very low factor loadings had to be removed. Also the option was explored to test a measurement model with first and second order dimensions by a Structural Equation Model (SEM). However, as shown, the variables are rather skewed, which violates the assumption of a normal distribution. Furthermore, the correlations between the sub-dimensions are not high. Specifically, traditional analyses methods such as confirmatory factor analyses are based on maximising correlations. Therefore we specified the sub-dimensions as causes of the four dimensions rather than effects. This distinction is known as formative versus reflective modelling (Edwards & Bagozzi 2000; Jarvis, Mackenzie & Podakoff 2003). In SEM the standard is reflective modelling. A relationship should be modelled formatively as opposed to reflectively if (see Jarvis et al., 2003): (1) there is no reason to expect the indicators will be correlated, and (2) excluding an indicator may alter the meaning of the dimension. We continued the analyses by treating the model formatively. So, we accepted that the sub-dimensions within the dimensions are not necessarily highly correlated. We also did not want to alter the meaning of the dimensions. However, we did not test the model with partial least square path modelling (PLS) (rather than the maximum likelihood (ML) method in reflective modelling), as this formative method would assign weights to sub-dimensions. We did not want to do this for three reasons. First, a variation in weights implies that some indicators are more important than others. This would not only have an impact on the variation of the indicators within each of the four dimensions, but also an impact on the weights of the four dimensions on social exclusion. Again, following the social exclusion definition, our belief is that we should keep the impact of the four dimensions equal. Second, we developed the index also for monitoring social exclusion. Weights in year t, will very probably change in year t + x. Consequently, the models change, which makes it problematic to define whether it is a real change or a change due to the model. And third, using equal weights (the sum
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scores) is more transparent than dealing with unequal weights. If other National Statistical Institutes were to follow this approach, it would be recommendable to use equal weights for the selected items in order to achieve harmonised input across countries, and improve international comparability.
Figure 1 Theoretical framework of social exclusion
11 items
Reflective
12 items
Reflective
14 items Reflective
5 items
- Social participation - Cultural participation - Civic participation
- Satisfaction with services - Living accommodation - Availability of help or problem solving
- Financial issues - Possession of certain goods
Formativ e
Formative
Participation
Access to social rights /institutions
Formative
Formative
Social exclusion Formative
Reflective
Material deprivation
Formative
Normative integration
Formative
Step 2: Sum scores per dimension In a second step, we construct sum scores for each dimension whereby a low score refers to a low level of exclusion and a high score to a high level of exclusion. After a threshold value for exclusion is determined for each item, we check the distribution of the category above this value for several background characteristics. The purpose is to check whether the relationship between the items and the characteristics is consistent with our expectations. Next, the sum scores are redistributed into quartile scores (0-3). Again, a higher quartile score refers to a higher level of exclusion on the dimension concerned. Step 3: Construction of overall sum score for social exclusion As a third step, the quartile scores are summed up resulting in one sum score, ranging from 0 to 12. A score of 0 indicates no exclusion at all, while a score of 12 indicates maximal exclusion on all four
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dimensions. All other scores in between can be interpreted as a combination of quartile scores. In total, 208 combinations are possible, leading to sum scores ranging from 0 (0-0-0-0) to 12 (3-3-3-3). For example, the score 1 can be reached in four ways: 0-0-0-1; 0-0-1-0; 0-1-0-0; and 1-0-0-0. Likewise, four combinations are also possible for the score 11: 2-3-3-3; 3-2-3-3; 3-3-2-3; and 3-3-3-2. The sum scores of 2 and 10 already yield in a total of 8 combinations. Interpretation of the scores is based on the equal importance of the separate items, so no weights are used. In accordance with the definition provided by Hoff & Vrooman (2011), someone should be maximally excluded, which means a quartile score of 3 on at least two of the four dimensions, to be considered socially excluded. This requirement is only met for the sum scores 10, 11 and 12. Along these lines, the data from the 2010 EU-SILC ad hoc module are analysed (for a more detailed overview, see Coumans, 2012). Next, we detail the excluded population for various background characteristics, such as gender, age, ethnicity, level of education and marital status. For this purpose, we use cross-tabulations and Chi-squared tests. In separate analyses the results per dimension and overall are also adjusted for other background characteristics by a logistic regression analysis. Finally, as a check for external concordance validity, we relate the sum score for social exclusion to the selfreported state of health and in a separate logistic regression analysis adjust this relation for age, sex, level of education, ethnicity, marital status and income. Step 4: The influence of background characteristics As a last step we detail the excluded population for various background characteristics, such as gender, age, ethnicity, level of education and marital status. For this purpose, we applied bivariate analysis, using cross-tabulations and Chi-square tests. In separate analyses, the results per dimension and the overall results are also adjusted for other background characteristics by a logistic regression analysis. In this analysis the independent variables were included simultaneously. Lastly, as a check for external concordance validity, we related the sum score for social exclusion to the self-reported state of health and in a separate logistic regression analysis, and adjusted this relation for age, sex, level of education, ethnicity, marital status and income.
3.2
Data source
In 2010 an ad hoc module on social exclusion in the survey European Union - Statistics on Income and Living conditions was introduced (EU-SILC). EU-SILC focussed on compiling actual and internationally comparable data on income and the level and structure of poverty and social exclusion. However, this only covered the material deprivation dimension. In order to include the three other dimensions of social exclusion, some 20 questions were added to the Dutch EU-SILC (see Annex). This survey is conducted as a rotated panel drawn from respondents, aged 16 years or older, who participated in the Labour Force Survey (LFS). Within the LFS, the respondents are interviewed five times at quarterly intervals: a Computer Assisted Personal Interview (CAPI) was used for the first interview, and for the successive four interviews the mode changed into Computer Assisted Telephone Interview (CATI). Within each household that took part in the fifth wave one person was selected for
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the EU-SILC survey; the response rate was about 80 percent (n=10,124), and some 30 percent of the original LFS sample participated. A relatively short interview of some 15 minutes was sufficient to collect the EU-SILC data. Table 1 provides background information about the participants of EUSILC.
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Table 1 Background characteristics of the participants of EU-SILC, 2010 Gender Male Female
n
%
4,865 5,259
48.1 51.9
1,053 1,476 1,895 2,013 1,702 1,141 844
10.4 14.6 18.7 19.9 16.8 11.3 8.3
8,232 952 929 11
81.3 9.4 9.2 0.1
5,712 745 553 3,114
56.4 7.4 5.5 30.8
872 2,133 4,058 2,943
8.6 21.1 40.1 29.1
118 10,124
1.2 100.0
Age 16- 24 years 25-34 years 35-44 years 45-54 years 55-64 years 65-74 years 75 years or older Ethnicity Native Dutch Western immigrant Non-western immigrant Missing Marital status Official marriages and cohabitation Divorced Widowed Never been married Level of education Primary education Junior gen sec education Senior gen sec education Higher vocational education/university Missing Total
The EU-SILC is provided with register data from the Social Statistical Database (SSD) annually. The SSD contains micro-data on people, households, jobs, profits, social security benefits, pensions, addresses and dwellings (Van Rooijen, 2010). The SSD also includes information on disposable household income and wealth.
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Results
4.1
The four dimensions
The dimension of participation was measured with 11 items, covering three different forms of participation, i.e. social participation, cultural participation and civic/political participation (see Annex). The reliability (Cronbach’s α) was 0.58, indicating a low to moderate level of scalability. Since, according to the PCA, all the items in their subcategory contributed to the dimension of participation, they were put together in one sum score. This score ranged from 0 (minimal participation) to 26 (maximally excluded). Almost no respondents scored 26, which would indicate that they were maximally excluded in all aspects of participation, and only 3.1 percent scored 19 or higher. The threshold value for the highest quartile score lies at 14.
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The dimension of access to basic rights and institutions contained 15 items, of which five referred to satisfaction with certain services, three items asked about the availability of help or a solution when needed, and 7 items were included about living accommodation and living environment. Since the use of two of the services was almost nil, these services were left out of further analyses. Although the internal consistency of the scale was fairly low (α = 0.49), it was robust for deleting certain items. The PCA showed four sub-dimensions of access to basic rights and institutions, i.e. one about satisfaction with official help regarding employee insurance and social work, one about the possibility of being able to get help when necessary, one about accommodation and one about the living environment. The item ‘dwelling too dark’ was removed from the analyses because of its extremely low loadings in the PCA. If we consider these sub-dimensions as a layer of separate constructs, then the α per dimension varies from 0.19 for the items about help to 0.57 for the items about satisfaction with help from institutions. Since the distribution of the sum score was very skewed, the quartiles are not evenly filled. About half of the population had no problems with accessing basic rights (score 0), well over one quarter fell in the second quartile (score 1), 12 percent in the third (score 2) and almost 8 percent were included in the quartile with the most problems in accessing basic rights (score 3 or higher). The dimension of material deprivation included 15 items. These items were measured at the household level, and contained 11 items on financial issues (such as whether respondents can afford an annual one-week holiday or how difficult it is to pay for living expenses). Four items asked about possession of certain goods, such as a colour TV, a washing machine and a car. The α of this dimension is 0.76, which is fairly high. The sum score ranged from 0 (no material deprivation) to 19 (deprived in all aspects of this dimension). The PCA shows two possible solutions. One solution shows one single factor with the items about the possession of certain goods loading very low. The other solution showed two factors, for the first factor the items about possession loaded very low and for the second relatively high. We decided to maintain the items about possession except for one (i.e. “possession of a colour TV”) and to construct one sum score for material deprivation. Only 0.1 percent were deprived in all aspects, and the frequencies of the scores 14 to 18 were also very low. The threshold values for the quartile scores of this dimension were 1, 2, 3 and 6, which means that the distribution of this dimension was again very skewed in the direction of deprivation. Normative integration was measured with five items about how acceptable certain behaviour is perceived to be on a scale from 1 to 10. The following kinds of behaviour were included: ‘avoiding paying the fare on public transport’, ‘throwing away litter in a public place’, ‘paying cash for services to avoid taxes’, ‘smoking in public buildings’, and ‘failing to report damage you have accidentally done to a parked vehicle’. The results show that most of these kinds of behaviour are perceived as unacceptable. ‘Paying cash for services to avoid taxes’ is perceived as the most acceptable kind of behaviour, while ‘failing to report damage you have accidentally done to a parked vehicle’ is seen as the most unacceptable. Cronbach’s α of this dimension is 0.48 and thus fairly low. The PCA showed one factor for which all items loaded from fairly well to well. The sum score ranged from 0 to 41, and is highly skewed. Almost a quarter of the Dutch population perceives the above forms of behaviour as
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totally unacceptable (score 0), the highest score is 41 out of 50, and only 2.5 percent score 18 or higher. The threshold value for the least integrated quartile is 9.
4.2
The social exclusion scale
Based on the quartile scores of each dimension, we finally construct a sum score, which ranges from 0 to 12. Less than 1 percent scored 12 and thus were maximally excluded on all four dimensions. The same counts for score 11. 2.7 percent had a score of 10 (and were excluded on at least two dimensions). As noted before, someone can be conceptually considered socially excluded when the score on the exclusion scale is at least 10 and the person is maximally deprived on two or more dimensions. This is the case for 4.2 percent of the population, which compares to the 4.8 percent found by Vrooman & Hoff (2012). The sum score for social exclusion is normally distributed (Figure 2).
Figure 2 Distribution of the sum score for social exclusion % 18 16 14 12 10 8 6 4 2 0 0
1
2
3
4
5
6
Sum score on social exclusion
7
8
9
10
11
12
4,2%
Source: EU-SILC, 2010
In Table 2 bivariate results on social exclusion are presented for the various subpopulations as to gender, age, ethnicity, marital status, level of education, and income. 1 In Table 3 the findings are adjusted for all other background characteristics. Unadjusted as well as adjusted for background variables, there are no significant differences between men and women on social exclusion. However, there are some differences at the level of sub-dimensions. Adjusted for all background characteristics,
1
Note that no interactions between background variables were included in the models. First of all, there were no a priori and theoretically based hypotheses about any interactions. Second, out of all 15 potential interactions only one appeared to be significant. This may be coincidence. Third, the effect of most significant interactions per dimension, as expressed by the odds ratios, varied from 0.9 to 1.1 and is almost nil. Finally, the few interactions that had some effect showed little consistency in their direction within and between the dimensions, and were therefore hard to interpret.
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men and women differ on all four dimensions. For men, exclusion is particularly associated with less normative integration and for women exclusion is related to material deprivation.
Table 2 Percentages for the most deprived quartile (quartile 4) per dimension and score 10-12 of the sum score by background characteristics
Gender Male Female Age 16- 24 years 25-34 years 35-44 years 45-54 years 55-64 years 65-74 years 75 years or older Ethnicity Native Dutch Western immigrant Non-western immigrant Marital status Official marriages and cohabitation Divorced Widowed Never been married Level of education Primary education Junior gen sec education Senior gen sec education Higher vocational education/university Income First quintile Second quintile Third quintile Fourth quintile Fifth quintile Total
Sum score exclusion
Participation
% score 10-12
% Quartile 4 n.s
4.6 3.9
n.s
***
7.7 7.9 ***
***
2.7 10.1 1.7 6.2
***
***
***
13.6 8.2 7.3 5.0 4.9 7.8
*** 18.7 22.7 26.2
11.8 ***
34.6 31.7 22.7 16.4 11.6 23.4
*** 17.4 25.5 11.0 35.8
39.2 26.1 22.4
8.1 ***
* 22.9 23.7 27.1
***
***
10.0 ***
***
15.2 45.9 27.9 26.0
4.7 9.3 7.6
*** 48.8 29.0 25.7 20.1 19.1 11.3 8.4
18.1 24.4 48.1
5.3 11.6 2.5 12.4
*** 26.8 20.2
***
***
55.6 33.8 20.9
1.5
***
***
***
***
% Quartile 4
25.4 21.3 23.6 23.7 19.3 13.9 21.0
7.0 12.7 9.7
23.5 38.8 44.2 15.9
7.5 7.4 4.0
% Quartile 4 19.5 23.3
11.4 12.2 9.4 7.3 5.7 4.2 2.3
22.6 22.6 31.2 ***
Normative integration
***
13.5 13.5 19.1 24.2 26.6 26.8 50.5
3.7 4.6 9.3
Material deprivation
n.s
23.9 22.8
7.9 3.8 4.8 5.0 3.7 1.7 1.5
11.1 6.0 2.4 2.1 0.3 4.2
Access to basic rights % Quartile 4
21.2 ***
46.8 29.4 18.0 10.3 3.5 21.4
*** 28.0 23.6 23.8 20.7 20.8 23.4
n.s. = not significant (p > 0.05) * = p < 0.05 ** = p < 0.01 *** = p < 0.001
The youngest age category is the most excluded of all the age categories. However, after adjustment for other background characteristics, this is no longer the case and only the age categories over 65 are less excluded than the youngest. The category aged 65 or older turns out to be the least excluded; less than 1 percent of them are socially excluded. Social exclusion of the youngest category only seems to be linked with less normative integration and material deprivation, while lack of participation is of
13
hardly any importance. Social exclusion of the oldest age category is exclusively associated with lack of participation. Divorced people are relatively more often socially excluded than people who are married (including cohabitation), widowed or never married. However, after adjustment the odds ratio for divorced people hardly differs from that of people who have never been married. For both categories, being excluded is especially linked to material deprivation. Lack of participation seems slightly more important for divorced people than for people who have never been married. Non-western migrants appear to be relatively more often socially excluded than western migrants and the native Dutch population. After adjustment their risk of being socially excluded is almost twice as high as that of western immigrants or native Dutch people. Social deprivation of non-western immigrants seems to be associated with less normative integration and material deprivation. However, it should be noted that after adjustment the difference for normative integration vanishes. Furthermore, the results show that more lowly educated people are more often socially excluded. Before adjustment for the other characteristics, the category with primary education was about as socially excluded as the category with junior general secondary education. After adjustment the difference between these categories remains very small, while the risk of being socially excluded is much decreased for those with senior secondary education (OR = 0.4) and those who have attended a higher vocational college or university (OR = 0.3). The social exclusion of the least educated was determined only by a lack of participation and material deprivation. Social exclusion is highly differentiated by income. In the lowest income quintile over 10 percent are socially excluded; this percentage consistently decreases over the second, third and fourth income quintiles and in the highest quintile only 0.3 percent are socially excluded. After adjustment this pattern remains the same. Social exclusion of the lowest income quintile is only related to material deprivation and to a lesser extent to a lack of participation.
14
Table 3 Logistic regression models for social exclusion and the fourth quartile of each (dependent) dimension of background characteristics (n = 10.124) Sum score exclusion (score 10-12)
B
S.E.
Gender (Male =ref) Age (16- 24 years=ref) 25-34 years 35-44 years 45-54 years 55-64 years 65-74 years 75 years or older Ethnicity (Nat. Dutch =ref) Western immigrant Non-western immigrant Marital status (Official marriages and cohabitation = ref) Divorced Widowed Never been married Education (Primary =ref) Junior gen sec educ Senior gen sec educ Higher voc./university Income (First quintile =ref) Second quintile Third quintile Fourth quintile Fifth quintile
-0.2
0.13
0.4 0.4 0.7 0.4 -0.6 -1.0
0.29 0.28 0.29 0.30 0.38 0.46
0.1 0.7
0.23 0.26
Participation (fourth quartile)
Wald
Sig.
3.2 34.0 2.1 2.4 6.8 1.7 2.6 4.3 6.4 0.2 6.4
n.s. *** n.s. n.s. ** n.s. ** * * n.s. *
23.7
*** *** n.s. *** *** n.s. *** *** *** ** *** *** ***
2.07 1.10 1.94
0.2 -0.1 0.0
0.09 0.10 0.08
0.76 0.44 0.26
-0.7 -1.2 -1.8
0.10 0.10 0.11
0.63 0.29 0.19 0.05
-0.2 -0.6 -0.8 -1.1
***
0.09
-0.7
0.7 0.1 0.7
0.19 0.33 0.17
-0.3 -0.8 -1.3
0.23 0.23 0.27
-0.5 -1.3 -1.7 -3.0
0.16 0.19 0.23 0.43
15.1 0.1 15.6 36.5 1.5 13.1 25.1 109.1 8.8 44.1 55.2 50.3
Constant
-2.4
0.36
44.3
Nagelkerke R-square
0.15
Exp. (B) 0.79
B
S.E.
-0,4
0.06
1.51 1.55 2.10 1.47 0.54 0.38
0.6 0.8 1.2 1.3 1.2 1.9
0.17 0.16 0.16 0.16 0.17 0.18
1.10 1.92
0.1 0.9
0.10 0.14
Wald 46.2 155.0 12.3 24.6 57.7 61.9 53.3 109.5 38.0 0.7 37.8 8.9
0.19
Sig . *** *** *** *** *** *** *** *** *** n.s. ***
Exp. (B) 0.68
Access to basic rights (fourth quartile)
Material deprivation (fourth quartile)
Normative integration (fourth quartile)
B
B
B
S.E.
Wald
Sig
0.2
0.09
1.81 2.25 3.43 3.64 3.48 6.61
0.5 0.3 0.3 0.1 -0.2 -0.9
0.18 0.18 0.19 0.20 0.23 0.31
1.09 2.40
0.6 0.1
0.13 0.22
4.1 33.0 7.3 2.7 3.5 0.6 0.7 8.0 24.0 24.0 0.2
* *** ** n.s. n.s. n.s. n.s. ** *** *** n.s.
4.1
*** *** n.s. *** n.s. n.s. n.s. n.s. *** ** *** *** ***
1.61 0.83 2.00
1.1 0.6 0.6
0.09 0.12 0.08
1.16 0.99 1.19
-0.6 -0.8 -1.2
0.11 0.11 0.13
0.66 0.59 0.34 0.37
-0.8 -1.5 -2.3 -3.0
***
0.06
-0.2
* * n.s. n.s. *** *** *** *** *** ** *** *** ***
1.25 0.88 1.04
0.5 -0.2 0.7
0.13 0.24 0.10
0.52 0.31 0.16
0.1 0.0 0.2
0.20 0.20 0.20
0.08 0.09 0.09 0.10
6.3 1.5 0.2 327.5 48.0 156.5 283.7 155.2 8.4 46.7 80.9 114.5
0.79 0.56 0.44 0.34
-0.4 -0.5 -1.1 -1.0
0.13 0.12 0.14 0.14
0.5 0.0 0.7 21.4 4.0 0.07 0.08 76.7 11.0 18.7 59.6 48.6
0.19
14.6
***
0.49
-2.8
0.27
107.3
0.07
Exp. (B) 1.19
S.E. 0.2
0.06
1.62 1.34 1.42 1.16 0.83 0.41
0.1 0.3 0.5 0.2 -0.4 -0.5
0.14 0.14 0.14 0.15 0.16 0.18
1.86 1.10
0.4 1.4
0.10 0.15
0.29
n.s. = not significant (p > 0.05) * = p < 0.05 ** = p < 0.01 *** = p < 0.001
15
Wald
Sig
Exp. (B) 1.21
S.E.
-0.4
0.05
1.08 1.32 1.70 1.28 0.68 0.63
-0.5 -0.7 -0.9 -1.1 -1.6 -2.0
0.11 0.11 0.11 0.12 0.13 0.17
1.51 4.03
0.0 -0.2
0.09 0.15
Wal d 58.1 196.0 22.9 47.8 69.7 96.4 134.1 132.3 2.4 0.0 2.4
Sig.
49.6
*** *** ** *** n.s. n.s. n.s. n.s. *** n.s. * * *
1.50 1.41 1.47
n.s.
0.91
9.0 79.9 0.3 4.1 14.3 2.8 5.4 6.4 103.5 15.7 92.6
** *** n.s. * *** n.s. * * *** *** ***
163.7
*** *** *** *** *** *** *** *** *** *** *** *** ***
2.88 1.91 1.89
0.4 0.3 0.4
0.09 0.13 0.07
0.52 0.46 0.31
-0.1 -0.1 -0.2
0.12 0.12 0.12
0.08 0.09 0.10 0.14
132.4 27.8 61.4 89.7 32.9 48.5 88.8 802.3 102.3 310.0 473.6 454.9
0.45 0.22 0.11 0.05
0.0 -0.2 -0.2 -0.2
0.09 0.09 0.09 0.09
21.7 7.4 34.2 2.2 1.0 1.6 2.1 14.9 0.1 4.0 6.2 5.6
0.18
1.5
n.s.
0.81
-0.1
0.16
0.4
0.08
*** *** *** *** *** *** *** *** n.s. n.s. n.s.
Exp. (B) 0.68 0.60 0.48 0.40 0.32 0.21 0.14 1.01 0.80
0.89 0.86 0.84 1.03 0.84 0.81 0.81
4.3
Social exclusion and health
As a check for external convergence validity we relate the sum score for social exclusion to respondents’ self-reported state of health, ranging from 1 to 5. The results show that the higher the score on social exclusion (the more someone is excluded), the less often good or very good health (score 1 or 2) is reported (Figure 3). The percentage of people with good or very good health status is higher than average for a sum score in the range from 0-4. Furthermore, the percentage of healthy people decreases remarkably above a score of 10 for social exclusion. Even after adjustment of the percentages by gender, age, ethnicity and level of education in an MCA, this pattern remains stable. A logistic regression analysis also validates the conclusion that a score of at least 10 for exclusion is related to a significantly worse self-reported health status (score 3, 4 or 5). The odds ratio is 4.2, which indicates that the chance of self-reported health being from good to very good is more than 4 times higher for the non-socially excluded population, who score 0 to 9, in comparison to the socially excluded, who score 10-12. When the covariates are included in the model, the odds ratio for social exclusion is still 3.8, indicating that the gap in self-reported health still exists after controlling for these background characteristics. Figure 3 Self-reported health by social exclusion sum score (0-12) Sum score social exclusion 12 11 10 9 8 7 6 5 4 3 2 1 0
Total population 0 Source: EU-SILC, 2010
20
40
60
80
100
% with good or very good self-reported health
16
5
Conclusions and discussion
In this paper we have elaborated on social exclusion as the counterpart of social inclusion; the concept refers to people who lack resources. We have distinguished between (lack of) economic-structural resources, including material deprivation and inadequate access to social rights, and social and socialcultural resources, including limited social participation and insufficient normative integration. The PCAs per dimension show that the four theoretically predetermined dimensions and sub-dimensions of social exclusion were clearly expressed by the indicators in our study. The internal consistency of each dimension, as expressed by Cronbach’s α, ranges from 0.48 to 0.76. As mentioned before, this α was used not as a criterion to assess the quality of the construct of social exclusion, but merely as a means of selecting the items for each dimension. It was therefore not problematic that, except for the material dimension, the α was fairly low. To cover the concept in all its aspects, we chose to keep nearly all 46 items in the final index. Only very skewed items with hardly any positive responses and very low factor loadings were eliminated. The result was that 42 items were included in the social exclusion index. For theoretical and transparency reasons we did not choose to weight the items within the dimensions, and decided to use sum scores for each dimension. In the next step the four dimensions were each divided into four quartiles, and subsequently combined into one sum score ranging from 0 to 12. Having determined that conceptually a score of 10 to 12 implies social exclusion, 4.2 percent belong to this deprived category. The merits of an index compared to a single indicators approach are obvious. A summarization of the 42 indicators by one overall index makes the interpretation for comparing subpopulations easier. Also monitoring the trend in social exclusion is far easier based on one figure than on 42. However, an index evidently has also some drawbacks. It may hide underlying differences going in various up- and downward directions. Some subpopulations (hardly) differ in their overall scores, while they do differ on some of the four dimensions. For that reason we included the results based on the four single dimensions as well. This shows that, e.g., there is no difference in the overall score on social exclusion between men and women, however, they differ in all four distinguished dimensions. Comparing several sub-groups, and taking into account their interrelationships, the following categories seem especially vulnerable to social exclusion: non-western immigrants, divorced people and people who have never been married, the least educated and the lowest income quartile. This mirrors largely the positions of those groups in the scores on the social capital index (Van Beuningen & Schmeets, 2012). Moreover, social exclusion appears to be closely related to (self-reported) health status. These results are in line with some of the drivers of social exclusion as elaborated by Babajanian & Hagen-Zanker (2012). Our findings are also completely consistent with those of Vrooman & Hoff (2012). Congruent with their findings, and contrary to what one may expect, older people tend to be not more excluded than the younger age categories. In our study, people aged 65 or older are even less excluded than the younger age categories. Also in line with the study of Vrooman & Hoff, we found that although the oldest people (75+) have a much higher risk on limited social participation (OR = 6.6, Table 2), their lower risk of social exclusion is due to a lower risks of a lack
17
of normative integration and limited access to basic rights and institutions. Our study adds to these protective factors the lower risk on material deprivation for the elderly (65+). There are two other factors that increase the credibility of our assessment. First, the social exclusion index is based on a very reliable data source that includes over 10,000 participants. Second, the finding that the percentage of healthy people decreases remarkably above a score of 10 for social exclusion increases our confidence in the quality of our measurement. This result not only supports the choice of score 10 as the threshold value for social exclusion; taking health as an external criterion, it also confirms the expectation that poor health is related to social exclusion and thus confirms its external validity. We also explored the homeless population, using a capture/re-capture method on registers (Coumans, et al., 2010). Based on the findings for 2009, we conclude that 0.2 percent of the Dutch adult population is homeless. It is our belief that we should add this marginalised group – which is not included in the regular sample frames – to the 4.2 percent who are socially excluded, assuming that the homeless will all have low positions on at least two out of the four dimensions, and consequently would score 10-12 on the sum score. This adds up to a percentage of 4.4 socially excluded. This is comparable with the 4.8 percent found by Vrooman & Hoff (2012), who’s study was performed almost simultaneously with ours and who used the same conceptual framework. However, it is based on a rather small sample, a limited number of indicators, and a different approach in calculating the share of socially excluded. The 4.4 percent is also slightly lower than the 6.6 percent people below the poverty line, as defined by Statistics Netherlands (Bos, Otten, Huynen & Ament, 2013). Is the 4.4 percent low or high? Unfortunately, no data for an across country comparison are available. One concept that is at least linked to some extent to our social exclusion index, is Eurostats’ indicator ‘at risk of poverty or social exclusion’, which is based on 9 indicators included in EU-SILC 2010, covering (a) at risk-of-poverty after social transfers, (b) persons severely materially deprived; and (c) persons living in households with very low work intensity. According to this index, 23.1 percent of the EU population were ‘at risk of poverty or social exclusion’, ranging from 14.3 percent in Iceland to 41.6 percent in Bulgaria (Eurostat, 2012). The Netherlands takes the fourth position, after Norway and Sweden, and is doing very well in the European perspective. So, we may assume that the 4.4 percent found in our study will also be a low figure. However, for some sub-populations, divorcees as
well as people with a lower income, the figure of socially excluded goes even beyond one in ten. Such figures might be food for thought for politicians and policymakers in reducing the shares of socially excluded among specific groups in Dutch society. Finally, what do these results mean for other National Statistical Institutes (NSI’s) in Europe? The question arises of whether this study can be replicated by other NSI’s. It is our belief that it should not be a problem for other ‘register’ countries, such as Denmark, Sweden, Iceland, Finland and Slovenia, to add the 25 items to the existing 20 items in the EU-SILC questionnaire. These countries retrieve many variables from registries (e.g. income) and consequently it is feasible to collect the other information in a reasonably short interview – often by telephone. However, most other countries have to collect all the requested EU-SILC information through a survey – often personal interviewing –
18
which already takes some 60 minutes. Inclusion of the 25 questions would imply a further extension of the interview by about 8 minutes, which would jeopardise the quality in terms of, e.g., lower response rates and higher non-response bias. Therefore, probably an amendment to the EU-SILC regulations is the only option to produce an EU-wide comparison of the socially excluded (sub-) populations.
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Annex 1. Descriptive statistics of the social exclusion items. Distribution of the items in the dimension of participation DIMENSIONS
ITEMS
CATEGORIES
Cultural participation
Went to the cinema in the past 12 months
%
1. more than 12 times 4.3 2. 7-12 times 6.5 3. 4-6 times 13.0 4. 1-3 times 29.4 5. Never 46.8 Went to a concert or a show in the past 12 months 1. more than 12 times 2.7 2. 7-12 times 5.1 3. 4-6 times 11.2 4. 1-3 times 28.1 5. Never 52.9 Paid a visit to cultural sights in the past 12 months 1. more than 12 times 2.6 2. 7-12 times 4.7 3. 4-6 times 11.7 4. 1-3 times 32.6 5. Never 48.4 Social participation How often had contact with family 1. weekly 84.3 2. 2 times a month 7.6 3. once a month 4.5 4. less than once a month 1.9 5. hardly ever or never 1.7 How often had contact with friends 1. weekly 81.4 2. 2 times a month 9.7 3. once a month 5.6 4. less than once a month 1.7 5. hardly ever or never 1.5 Getting help from friends with personal problems 1. Yes, always 92.1 2. Sometimes 2.6 3. No, never 5.2 Getting help from friend with financial problems 1. Yes, always 78.3 2. Sometimes 4.7 3. No, never 16.3 Civic/Political Volunteer work in the past 12 months 1. Yes 41.0 participation 2. No 59.0 Offered informal help in the past 12 weeks 1. Yes 34.3 2. No 65.7 Membership of club, society of organisation 1. Yes 56.9 2. No 43.1 Voted at the last elections 1. Yes 86.8 2. No 13.2 * Missing values for the items “getting help from friends with financial problems” and “voted” were recoded to “yes”. This was done because the number of missing values of these two items was very high, i.e. 20 % and 9 % respectively. Since the middle category of the items “getting help from friends with financial and personal problems” appeared to be very small and we were merely interested in the category that never got help, in these items the categories “yes” and “sometimes I do/sometimes I don’t” were combined.
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Distribution of the items in the dimension of access to basic social rights and institutions ITEMS Satisfied with help getting a social benefit from the executive institute for employee insurance
ORIGINAL CATEGORIES NEW CATEGORIES % 1. very satisfied 1-3 Other 98.6 2. satisfied 4/5 (very) dissatisfied 1.4 3. not satisfied/not dissatisfied 4. dissatisfied 5. very dissatisfied Satisfied with help in finding a job by the executive 1. very satisfied 1-3 Other 98.9 institute for employee insurance 2. satisfied 4/5 (very) dissatisfied 1.1 3. not satisfied/not dissatisfied 4. dissatisfied 5. very dissatisfied Satisfied with help from the social insurance company 1. very satisfied 1-3 Other 100 2. satisfied 4/5 (very) dissatisfied 0 3. not satisfied/not dissatisfied 4. dissatisfied 5. very dissatisfied Satisfied with integration by the local authority, aliens 1. very satisfied 1-3 Other 100 department or executive institute for reception of asylum 2. satisfied 4/5 (very) dissatisfied 0 seekers 3. not satisfied/not dissatisfied 4. dissatisfied 5. very dissatisfied Satisfied with social work 1. very satisfied 1-3 Other 99.4 2. satisfied 4/5 (very) dissatisfied 0.6 3. not satisfied/not dissatisfied 4. dissatisfied 5. very dissatisfied Unmet need for dental treatment 1. no 97.7 2. yes 2.3 Unmet need for medical treatment 1. no 98.9 2. yes 1.1 Not able to move to another house when there is a problem 1. no 98.1 and wanting to 2. yes 1.9 Problems with the dwelling: Leaking roof 1. no 96.0 2. yes 4.0 Problems with the dwelling: Damp walls 1. no 91.7 2. yes 8.3 Problems with the dwelling: Rotting window frames 1. no 94.2 2. yes 5.8 Problems with the dwelling: Too dark, not enough light 1. no 96.0 2. yes 4.0 Noise pollution 1. no 75.9 2. yes 24.1 Pollution, grime or other environmental problems 1. no 85.9 2. yes 14.1 Crime violence or vandalism in the area 1. no 83.6 2. yes 16.4 * The original items about satisfaction with various institutes were dichotomized; the categories indicating dissatisfied or very dissatisfied were combined and compared with the other categories (including the ones that did not get this help at all). The item “not able to move when wanting to”, is also recoded into two categories: those who were not able to move despite wanting to versus the rest.
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Distribution of the items on material deprivation ITEMS Ability to make ends meet with net income
CATEGORIES % 1. very easy 15.4 2. easy 46.7 3. rather easy 14.3 4. rather difficult 11.7 5. difficult 8.4 6. very difficult 3.5 Financial burden of total housing cost 1. no burden at all 45.5 2. some burden 43.4 3. heavy burden 11.1 Financial burden of the repayment of debts from hire purchases or loans 1. no arrears 87.2 2. no burden 5.0 3. some burden 5.4 4. heavy burden 2.3 Arrears on mortgage or rent payments, utility bills, hire purchase instalments or other loan 1. no 94 payments* 2. yes 5.9 Capacity to afford to pay for one week annual holiday away from home 1. yes 84.2 2. no 15.8 Capacity to afford a meal with meat, chicken, fish (or vegetarian equivalent) every second day 1. yes 97.4 2. no 2.6 Capacity to afford changing old furniture for new 1. yes 80.6 2. no 19.4 Capacity to afford buying new clothes on a regular basis 1. yes 88.5 2. no 11.5 Capacity to afford inviting friends or family for dinner once a month 1. yes 93.2 2. no 5.8 Capacity to face unexpected financial expenses of 850 euro, without borrowing money 1. yes 78.2 2. no 21.0 Ability to keep home adequately warm 1. yes 97.7 2. no 2.3 Do you have a colour TV? 1. yes 98.1 2. no 1.9 Do you have a computer? 1. yes 91.4 2. no 8.6 Do you have a washing machine? 1. yes 98.3 2. no 1.7 Do you have a car? 1. yes 84.0 2. no 16.0 * Since there were very few respondents with these arrears, the original four items on mortgage or rent payments, utility bills and hire purchase instalments or other loan payments were combined into one dichotomous item.
Distribution of items on normative integration AVERAGE SCORE
% SCORE 6 -10
Avoiding a fare on public transport
1.90
4.10
Throwing away litter in a public place
1.40
0.93
Paying cash for services to avoid taxes
3.19
14.47
Smoking in public buildings
2.64
11.67
Failing to report damage you’ve accidentally done to a parked vehicle
1.23
0.63
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Annex 2. Solutions PCA per dimension PCA dimension of participation Pattern Matrix(a) Component Went to the cinema in the past 12 months
1 0.71
2 -0.04
3 0.09
4 0.13
Went to a concert or a show in the past 12 months
0.72
0.12
-0.12
-0.12
Paid a visit to cultural sights in the past 12 months
0.77
0.00
0.05
0.01
How often had contact with family
-0.09
0.25
-0.20
0.78
How often had contact with friends
0.04
0.62
-0.05
0.23
Getting help from friends with personal problems
0.04
0.76
0.06
-0.03
Getting help from friend with financial problems
0.03
0.69
0.13
-0.12
Volunteer work in the past 12 months
-0.07
0.07
0.82
0.02
Offered informal help in the past 12 weeks
-0.03
-0.05
0.28
0.46
Membership of club, society of organisation
0.09
0.08
0.70
-0.04
Voted at the last elections
0.19
-0.16
0.08
0.45
Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.
PCA dimension of access to basic social rights and institutions Pattern Matrix(a) Component 1 -0.02
2 -0.87
3 -0.06
4 -0.03
-0.01
-0.87
-0.07
0.01
0.01
-0.35
0.07
0.05
Unmet need for dental treatment
0.09
-0.07
0.02
0.64
Unmet need for medical treatment
0.00
-0.01
0.00
0.74
Not able to move to another house when there is a problem and wanting to Problems with the dwelling: Leaking roof
0.62
-0.03
-0.02
-0.15
-0.14
0.02
0.66
0.18
Satisfied with help getting a social benefit from the executive institute for employee insurance Satisfied with help in finding a job by the executive institute for employee insurance Satisfied with social work
Problems with the dwelling: Damp walls
0.10
0.01
0.70
0.04
Problems with the dwelling: Rotting window frames
0.08
-0.06
0.64
-0.25
Noise pollution
0.64
-0.03
0.04
0.00
Pollution, grime or other environmental problems
0.59
0.04
-0.01
0.15
Crime violence or vandalism in the area
0.59
0.03
0.01
0.07
Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.
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PCA Dimension of material deprivation Pattern Matrix(a) Component 1
2
Ability to make ends meet with net income
0.80
-0.05
Financial burden of total housing cost
0.66
-0.18
Arrears on mortgage or rent payments, utility bills, hire purchase instalments or other loan payments
0.45
-0.26
Financial burden of the repayment of debts from hire purchases or loans
0.43
-0.40
Capacity to afford to pay for one week annual holiday away from home
0.75
0.13
Capacity to afford a meal with meat, chicken, fish (or vegetarian equivalent) every second day
0.43
0.21
Capacity to afford changing old furniture for new
0.78
0.07
Capacity to afford buying new clothes on a regular basis
0.76
0.10
Capacity to afford inviting friends or family for dinner once a month
0.62
0.20
Capacity to face unexpected financial expenses of 850 euro, without borrowing money
0.74
0.02
Ability to keep home adequately warm
0.41
0.09
Do you have a computer?
0.00
0.71
Do you have a washing machine?
0.06
0.30
Do you have a car?
0.20
0.66
Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.
PCA Dimension of normative integration Pattern Matrix(a) Component 1 Avoiding a fare on public transport
0.65
Throwing away litter in a public place
0.70
Paying cash for services to avoid taxes
0.52
Smoking in public buildings
0.49
Failing to report damage you’ve accidentally done to a parked vehicle
0.65
Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.
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