a Care Regime Typology of Elder, Long-Term Care Institutions

a Care Regime Typology of Elder, Long-Term Care Institutions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article a Care Regime Typology of Elder, Long-Term Care Institutions Maike van Damme, Jeroen Spijker, Dimitris Pavlopoulos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3981497/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study generates a classification of 26 European OECD countries with respect to care regimes. We build upon the literature on the dimensions of defamilialization and familialism and empirically test how these two dimensions indicate different types of care regimes. Using Latent Profile Analysis, we group the 26 countries based on five indicators of institutional elderly care. These indicators cover formal elder care support in care in kind as well as in financial support. The results reveal three care regime types: ‘defamilialized’; ‘medium familialism’; and ‘familialism-by-default’. This classification contributes to developing a theoretical framework of care institutions. Care regimes Elder Care Institutions Latent Profile Analysis Typology Figures Figure 1 1. INTRODUCTION In this article, we build upon previous research on the search for European care regime typologies. Care regimes are ‘social joins’ that secure adequate interactions between demographic and economic institutions (for an extensive discussion, see Bettio & Plantenga, 2004 ). In contrast to general welfare regimes, that focus on social transfers in general, care regimes have as a core dimension the relationship between the state and the family. Although several scholars have attempted to derive such a clustering of countries for the late 1990s and early 2000s, they were either not theoretically satisfying because they were covering many different dimensions (e.g. Kraus et al., 2010 ), they didn’t get a clear empirical clustering of countries (e.g. Saraceno & Keck, 2010 ), or they were based on ranking countries based on ‘eye-balling’ the data rather than rigorous empirical cluster analysis (Bettio & Plantenga, 2004 ; Pacolet et al., 2000 ). A few more recent studies, such as Ariaans et al. ( 2021 ) and Povolini (2021) using data from around 2018, used cluster analyses, but also still here the connection between theory and the conceptualization and operationalization of indicators measuring care provision present some shortcomings. None of these studies uses an extensive theoretical framework, describing the underlying dimensions on which the empirical clustering of countries could be based. In this article, we build upon the theoretical frameworks of amongst others Pfau-Effinger ( 2005 ) and Saraceno and Keck ( 2010 ), and empirically check our expected classification using latent profile analyses (LPA). The article loosely builds upon a study by Van Damme and Spijker ( 2023 ) that classified countries into care regimes based on intergenerational upward and downward support. Since this article is guiding other articles in this collection on caregiving and their consequences, here we focus squarely on elder care. 2. A CONCEPTUAL FRAMEWORK OF REGIME DIFFERENCES IN (IN)FORMAL CARE INSTITUTIONS 2.1 Defamilialization and Familialism: The ‘Logic’ of Care Regimes Which is the main logic that separates certain care regimes from others? Corresponding to Esping-Andersen’s two dimensions (‘logics’) of stratification and decommodification, from which he derived three ideal types of general welfare states, what are the key dimensions in care arrangements? We distinguish two dimensions from the literature (for an extensive discussion, see Van Damme & Spijker, 2023 ): A) defamilialization entails unburdening families from care responsibilities and family dependencies (Lohmann & Zagel, 2016 ); B) familialism covers the extent to which dependencies among family members and their negative social and economic consequences can be actively reduced by state support. We refer to dependencies such as women’s economic dependence on a breadwinner, children’s dependence on their parent’s care, and elderly people’s dependence on their adult children (Lohmann & Zagel, 2016 : p. 53). Familialism thus entails strengthening the role of the family by alleviating negative consequences of family dependencies. Saraceno and Keck ( 2010 ) build upon these ideas. In their theoretical framework, the authors propose the three-fold conceptualization of familialism-by-default (when there is little or no state support for family care, meaning that the responsibility lies with the family), supported familialism (where there is state support for caregiving, but only financial (and little care in kind)), and de-familialisation (provision of both care in kind and financial aid to reduce family care responsibilities as much as possible). They also have a rare fourth cluster which focuses on ‘optional individualism’ (in the words of Lohmann and Zagel ( 2016 )) or ‘optional familialism’ (here both practical services and financial support policies are offered, but by allowing families the choice to share the caregiving responsibilities between the state and the family according to their preferences) (Knijn & Kremer, 1997 ; Leitner, 2003 ). Provision of services, through time or money, may take place directly by the state or indirectly via the market (which is reimbursed at a later stage). Moreover, note that we do not make a distinction regarding whether this ‘state’ support is targeted at the caregiver or at the care user. After all, cash for care use provided by the state in one way or another (and either targeted at the caregiver or user) alleviates the financial care burden for all. Note that care solely provided by the market implies within-country inequalities in care access that we do not consider here (see our reflection in the discussion). 2.3 Expectations on ‘Ideal’ Types Combining Defamilialization and Familialism Based on the policies supporting defamilialization or familialism, we expect that each of the ‘regimes’ of countries we observe, has elements of these dimensions to certain degrees. More specifically, we expect to find at least three ‘ideal’ types of elder care clusters: A defamilialized class, a supported familialism one, and a familialism-by-default group. Research on earlier periods have identified similar distinctions between groups of countries. For instance, building upon Anttonen and Sipilä ( 1996 ), Bettio and Plantenga ( 2004 ) based their ranking of countries on eye-balling scores on a variety of indicators, using data from the late 1990s. Their point of departure are the two extreme models of social care services, a Scandinavian model of public services and a southern European family care model. Countries with this last type of family care model rank high on informal caregiving, but formal care arrangements are underdeveloped. Scandinavian countries are located at the opposite extreme with high levels of formal care support. This care regime has universalist public policies, covering a large part of the population, where the state would substitute rather than support the family in caregiving (Bettio & Plantenga, 2004 ). These two extremes are confirmed by the data of Saraceno and Keck ( 2010 ) on a continuum of defamilialism towards familialism-by-default. Saraceno and Keck ( 2010 ) performed a cluster analysis, but did not achieve a satisfying solution of this analysis with respect to both child and elder care. Bettio and Platenga (2004) and Saraceno and Keck ( 2010 ) have less consensus about the placement of all other European countries in groups, as they are more ambiguous and less coherent. Yet, we may expect a third ‘ideal’ type that is based on the logic of supported familialism. Bettio and Plantenga ( 2004 ) mention that Austria and Germany are characterized by mostly a private care strategy, based on the subsidiarity principle (Esping-Andersen, 1990). While both countries rank medium on institutional care for elderly, there is a large reliance on the family (women) for care provision. In these countries, the emphasis is not so much on care in kind, but rather on alleviating the financial burden of care. We may thus expect that Austria and Germany will be classified in a third ‘ideal’ type, that of supported familialism. Saraceno and Keck ( 2010 ) highlight that, next to Germany, the Czech Republic and Hungary have supported familialism characteristics. We now describe our selection of indicators, with which we do not only update the classifications of Bettio and Plantenga ( 2004 ) and Saraceno and Keck ( 2010 ), but also perform a more rigorous empirical country classification into care regimes. 3. OPERATIONALIZATION, DATA, AND METHOD 3.1 Care Institutions Indicators In Table 1 we present the indicators used to measure the extent of care support by the state for the elderly, taken from the article by Van Damme and Spijker ( 2023 ). We consider five indicators to measure the degree of unburdening and strengthening of family obligations. Data stem from the year 2009 where available. We use this year as we will use this typology in further articles of this collection to explain country differences in caregiving and its consequences. As in these chapters SHARE 2004–2017 and ELSA are used, we needed to gather information for this less recent period. After all, when we want to explain the consequences of caregiving by contextual care institutions, these care institutions should go first in the time order. Indicators on care institutions for the elderly. We use five indicators for institutional support for elderly care. (1) Long-Term Care Services . Our analysis begins by drawing upon Verbakel’s (2018) work by constructing an index to measure long-term care services in kind for the elderly. Our index deviates from the one by Verbakel as we only want to focus on LTC services available and not on the care needs, nor on financial support. The LTC service index is based on the mean standardized value of two components: (a) the number of LTC beds in institutions and hospitals per 1000 population aged 65+; and (b) the number of LTC workers per 100 people aged 65+. These data are taken from the report ‘Health at a Glance’ (OECD, 2011 ). We thus did not include the proportion of the population receiving LTC, nor LTC public expenditure as percentage of GDP (although we will consider this last component separately, see below). (2) Care leave . Time provided by the state to support care for an ill relative is considered by the indicator care leave arrangements by the state, either paid or unpaid, whereby paid leave is given a weight of two. This indicator therefore varies between 0 (no leave) to 3 (both paid leave and unpaid leave is possible). The required information comes from (info from Colombo et al., 2011: Annex 4.A1, p.139). (3) Financial allowances . We separate the familialism dimension from the defamilialization dimension by including financial support by the state to the caregiver or care user (we do not differentiate between the two but take the sum of both). A functional equivalent of cash for care benefits are tax credits. Hence, we sum up these three measures and get an indicator that varies between 0 (no cash for care) and 3 (financial benefits for caregivers, users, and tax credits) – a hypothetical situation, since states chose either one or the other arrangement (info from Colombo et al., 2011: Annex 4.A1, p.139). (4) Respite care . The final state service indicator considers respite care, providing the possibility for the caregiver to alleviate the care burden (be it either in the short- or in the long-term). Respite care includes very different types of interventions providing temporary ease from the burden of care. Often, the objective of such breaks is to restore the caregiver’s ability to bear the care load. Day-care services, in-home respite, and institutional respite are all included in the definition of this binary indicator. (5) Long-Term Care expenditure . The final indicator is a separate financial indicator that represents a state’s general financial support to alleviate LTC for the elderly. This indicator includes LTC public expenditure (based on the health component only) as a share of GDP (OECD, 2011 ), correcting for the total number of years spent living in an unhealthy condition after reaching the age of 65 (EHLEIS, 2022). We explored the possibility of using an indicator on the share of private expenditure from total LTC spending as well. Yet, we could not find such comparable information on LTC spending, only for spending on general health care. Moreover, it is difficult to determine where private expenditure should be placed in the multidimensional spectrum of familialism-by-default, supported familialism, and defamilialisation. Private spending may indicate within-country heterogeneities (Verbakel et al., 2023), but we refrain from such heterogeneities in this paper (see discussion). Table 1 Elderly Care Institutions and Associated Indicators for 26 European OECD Countries. Data Period 2009. Public policy with respect to care for the old country LTC services a Care leave (paid (*2) and unpaid) b Financial allowances c Respite care d LTC exp as % of GDP/unhealthy person years e Austria -0.60 0.33 0.33 1 0.24 Belgium 1.09 1.00 0.67 1 0.63 Czech Republic -0.45 0.00 0.33 1 0.09 Denmark 0.54 0.67 0.33 1 1.10 Estonia 0.14 0.67 0.00 0 0.06 Finland 1.11 0.67 0.33 0 0.41 France 0.14 1.00 0.67 1 0.23 Germany -0.19 0.33 0.67 1 0.23 Greece -1.12 0.00 0.00 0 0.01 Hungary -0.23 0.33 0.33 0 0.06 Iceland 0.94 . . . 0.45 Ireland 0.22 0.33 0.67 1 0.70 Italy -1.25 0.00 0.33 0 0.16 Latvia -1.37 0.00 0.33 0 0.07 Lithuania -0.90 0.00 0.00 0 0.19 Luxembourg 0.24 0.33 0.67 0 0.38 Netherlands 0.84 1.00 0.67 1 1.01 Norway 1.37 0.67 0.67 0 0.98 Poland -1.44 0.67 0.33 0 0.09 Portugal -1.14 0.00 0.00 0 0.03 Slovak Republic -0.36 0.00 0.67 1 0.00 Slovenia -0.17 0.67 0.00 1 0.18 Spain -0.65 1.00 0.33 1 0.19 Sweden 1.92 0.67 0.67 1 1.05 Switzerland 0.76 0.00 0.33 1 0.58 United Kingdom 0.00 0.00 0.67 1 0.29 a. Index of LTC facilities, 2009: two items: Long-term care beds in institutions and hospitals, per 1 000 population aged 65 and over, Long-term care workers as share of population aged 65 and over (1); b. Care leave, paid counted twice, unpaid leave counted once (2); c. Sum of carer allowance, cared allowance, care tax credit (2); d. Respite care (yes/no) (for definition, see text) (2); e. Long-term care public expenditure (health component), as share of GDP corrected for number of poor health years at 65+, 2009 (3). Sources : 1. Health at a Glance: OECD indicators, OECD ( 2011 ): Specific tables: 8.7 on Beds, 8.6 on LTC workers; 2. Colombo et al. (2011: p. 139); 3. Health at a Glance: OECD indicators, OECD ( 2011 ), corrected for poor health years (65+) (from European Health and Life Expectancy Information System ( 2022 ): Specific Table: Activity Limitation (SILC, Limited But Not Severely, Not Limited and Severely Limited)*, in 28 European Countries, by Sex, at Age 65, from 2004 to 2016, accessed 18th of October 2022. 3.2 Method Latent profile analysis We perform a latent profile analysis (LPA, which differs from traditional cluster analyses (such as k-means) as it is a model-based probabilistic clustering method. Using the associations between the five indicators, we estimate the posterior probabilities per country, given their latent class (or ‘ideal’ type). In line with the theoretical concept of hybrid countries that possess elements of more than one class, we may identify such cases where countries are equally likely to belong to one or another class (i.e. their posterior probabilities are much lower than 1 for various latent classes). Another advantage of LPA over (hierarchical) cluster methods is that there is no initial partitioning where cases cannot be reassigned to a better fitting cluster in subsequent stages of the process (Gore Jr 2000 ). LPA assumes a normal distribution of the indicator values within each latent class, although other distributions are possible (Vermunt and Magidson 2002 ). Each latent class (1, …, K) has a density \({{f}_{k}\left({x}_{i}\right|{\mu }_{k},\sigma }_{k}^{2}\) ), where 𝑥 i is an observed value for country i on a continuous indicator m, and 𝜇 k and 𝜎 2 k are its mean and variance in class k. The joint mixture model is given by \(f\left({x}_{i}\right)=\sum _{k=1}^{k}{\pi }_{k}{f}_{k}\left({x}_{i}\right|{\mu }_{k},{\sigma }_{k}^{2})\) (1) where 𝜋 k is the relative class size, the class probability, or the ‘mixture weight’. That is, each country’s density in the mixture distribution is a sum of the class-specific normal densities weighted by the class probabilities (Bauer 2022 ). We estimated various models with a different number of classes and compared their fit. Of the 2-class, 3-class, and 4-class solutions, the 3-class solution performed the best. We did not run analyses of more than 4-classes, since the 4-class solution did not have a straightforward interpretation anymore with the fourth class covering only Germany and Ireland. The 2-class solution has BIC 297, AIC 275, and classification errors 0.0362. The 3-class solution has BIC 302, AIC 269, and 0.0242 classification errors. The 4-class solution has BIC 314, AIC 272, and 0.0095 classification errors. Although the fit of less or more than three classes has also is good, we decided for the 3-class solution because of a good separation of the countries into latent classes, and sufficiently large groups. Note that we did a sensitivity check leaving Iceland out (it has many missing values) and found similar results (results available upon request). 4. FINDINGS 4.1 Labelling the Latent Classes In this section, we aim to theorize the specific characteristics of each care regime. Table 2 gives the latent class profile on which we base our labelling of classes. We find three classes, of which the first class occurs in 23% of all the cases (countries), the second almost for 40%, and the last class covers about 37%. Looking at the membership probabilities for the indicators given membership of the first class (the smallest one in size), we see that the probabilities for services in kind, as well as the responsibilities to care do not lie at the state. The differences with the third class are striking and mainly opposite. The first class has the lowest conditional probabilities on all five indicators, whereas the third class has the highest scores on all indicators apart from respite care. This justifies labels of familialism-by-default versus defamilialism for the first and the third class, respectively. Finally, there is some kind of middle class that has elements of both defamilialism and familialism . We would be tempted to label this class supported familialism , were it not that against our expectations, it is not LTC expenditure, nor financial allowances (care allowances and/or tax credits) more specifically, that are high for this regime. We thus refer to this regime as ‘ medium ’ care support. Table 2 Latent Class Profile for Elderly Care Institutions for 26 Countries, Standardized Indicators. Data Period 2009. FbD medium DF Overall Size 0.234 0.397 0.369 LTC facilities (continuous) Mean -1.320 -0.2226 1.061 -0.0065 Care leave -1.669 0.5312 0.145 0.0318 0.1937 -0.3986 0.3992 0.4824 0.3021 0.3964 0.8718 0.0697 0.3726 0.6661 0.4099 Mean -0.985 -0.1094 0.407 -0.124 Care financial support -1.153 0.7256 0.3189 0.151 0.3523 -0.2883 0.1795 0.2309 0.1775 0.1991 0.5766 0.0821 0.3088 0.3853 0.2839 1.442 0.0128 0.1413 0.2862 0.1646 Mean -0.823 -0.0526 0.409 -0.0627 LTC respite care -1.4 0.9698 0.2467 0.297 0.4347 0.7001 0.0302 0.7533 0.703 0.5653 Mean -1.337 0.182 0.0763 -0.2129 LTC expenditure (continuous) Mean -0.755 -0.566 0.881 -0.0768 facilities = LTC availability index; leave = care leave (2*paid, 1*unpaid), unstandardized values for four categories 0-1-2-3; financial support = care allowances for caregiver/-user, tax credits, unstandardized values for three categories 0-1-2; respite care = alleviation of care, unstandardized values for two categories 0–1; expenditure = LTC expenditure/GDP/unhealthy life year. All indicators are standardized. In Table 3 , we present the posterior-predicted probabilities for the countries. We immediately notice some expected regional differences, but we also see within-region differences. The last class (the ‘Nordic’ countries and the Netherlands) is the most defamilialized and the first class (with the Southern and Eastern European countries) the least, thus justifying label ‘ familialism-by-default ’ for class 1 and ‘ defamilialism ’ for class 3. Inconsistent, however, with our expectations based on the theoretical framework of Saraceno and Keck ( 2010 ), we identify a class characterized by a medium amount of formal elder care support. We refrain from labelling this class ‘ supported familialism ’, since this class does not show relatively high financial support for families, while having little in-kind care. Moreover, unexpected is that these are the ‘North-West European’ countries and not the ‘Eastern European’ ones. Table 3 Latent Class Posterior Predicted Probabilities for Elder Care Institutions for 26 Countries. Data Period 2009. country FbD medium DF Austria 0.000 1.000 0.000 Belgium 0.000 1.000 0.000 Czech Republic 0.989 0.011 0.000 Denmark 0.000 0.000 1.000 Estonia 0.994 0.006 0.000 Finland 0.000 0.002 0.998 France 0.000 1.000 0.000 Germany 0.010 0.990 0.000 Greece 1.000 0.000 0.000 Hungary 0.999 0.001 0.000 Iceland 0.000 0.290 0.710 Ireland 0.000 1.000 0.000 Italy 1.000 0.000 0.000 Latvia 1.000 0.000 0.000 Lithuania 1.000 0.000 0.000 Luxembourg 0.000 1.000 0.000 Netherlands 0.000 0.000 1.000 Norway 0.000 0.000 1.000 Poland 1.000 0.000 0.000 Portugal 1.000 0.000 0.000 Slovak Republic 0.918 0.082 0.000 Slovenia 0.216 0.784 0.000 Spain 0.002 0.998 0.000 Sweden 0.000 0.000 1.000 Switzerland 0.000 0.999 0.001 United Kingdom 0.000 0.997 0.003 In Table 4 , however, we see that the average score of this medium familialism care regime is indeed ‘medium’ on all of the indicators. Only Ireland and Switzerland have a high ranking on financial family expenditure, which makes them hybrid cases in this care regime. The medium class also includes Belgium (here showing more defamilializing elements), France, Luxembourg, and the UK, but also Spain. While the UK and Ireland in the general welfare state literature are often classified separately due to their liberal market welfare state regime, Ireland, sometimes also has been observed to have elements of countries of the ‘Mediterranean’ region due to it being traditionally a highly Catholic country (Arts & Gelissen, 2002 ). In our LPA results this is not demonstrated as it is still performing better in state care compared to the Southern and Eastern European countries. Surprisingly, Spain emerges also as a country in this regime. Its classification within this group appears to be highly influenced by its extensive financial support for elderly care leave. Table 4 Latent Class Averages of Three Latent Classes based on Care Institutions Standardized Indicators for 26 OECD countries. Data Period 2009. care institutions for elderly Type Country facilities leave fin sup respite care expenditure Total standardized score 1 Denmark 0.61 -0.26 0.67 0.87 2.12 0.80 1 Finland 1.25 -0.26 0.67 -1.11 0.13 0.14 1 Iceland 1.06 . . . 0.24 0.65 1 Norway 1.53 1.05 0.67 -1.11 1.79 0.79 1 Sweden 2.13 1.05 0.67 0.87 1.97 1.34 2 Netherlands 0.94 1.05 1.56 0.87 1.86 1.26 Class average DF 1.25 0.52 0.85 0.08 1.35 0.81 2 Austria -0.64 -0.26 -0.21 0.87 -0.36 -0.12 2 Belgium 1.22 1.05 1.56 0.87 0.76 1.09 2 France 0.17 1.05 1.56 0.87 -0.39 0.65 2 Germany -0.19 1.05 -0.21 0.87 -0.38 0.23 2 Ireland 0.27 1.05 -0.21 0.87 0.97 0.59 2 Luxembourg 0.29 1.05 -0.21 -1.11 0.05 0.01 2 Slovenia -0.16 -1.57 0.67 0.87 -0.54 -0.15 2 Spain -0.70 -0.26 1.56 0.87 -0.48 0.20 2 Switzerland 0.86 -0.26 -1.10 0.87 0.64 0.20 2 United Kingdom 0.03 1.05 -1.10 0.87 -0.20 0.13 Class average medium 0.12 0.39 0.23 0.67 0.01 0.28 3 Estonia 0.18 -1.57 0.67 -1.11 -0.88 -0.54 3 Hungary -0.23 -0.26 -0.21 -1.11 -0.85 -0.53 3 Latvia -1.48 -0.26 -1.10 -1.11 -0.85 -0.96 3 Lithuania -0.96 -1.57 -1.10 -1.11 -0.48 -1.04 3 Portugal -1.23 -1.57 -1.10 -1.11 -0.94 -1.19 3 Czech Republic -0.47 -0.26 -1.10 0.87 -0.78 -0.35 3 Greece -1.21 -1.57 -1.10 -1.11 -1.02 -1.20 3 Italy -1.35 -0.26 -1.10 -1.11 -0.58 -0.88 3 Poland -1.56 -0.26 0.67 -1.11 -0.77 -0.61 3 Slovak Republic -0.38 1.05 -1.10 0.87 -1.03 -0.12 Class average FbD -0.87 -0.65 -0.66 -0.71 -0.82 -0.74 a. Index of LTC facilities, 2009: two items: Long-term care beds in institutions and hospitals, per 1 000 population aged 65 and over, Long-term care workers as share of population aged 65 and over (1); b. Care leave, paid counted twice, unpaid leave counted once (2); c. Sum of carer allowance, cared allowance, care tax credit (3); d. Respite care (yes/no) (for definition, see text) (4); e. Long-term care public expenditure (health component), as share of GDP corrected for number of poor health years at 65+, 2009 (5). Sources : 1. Health at a Glance: OECD indicators, OECD ( 2011 ): Specific tables: 8.7 on Beds, 8.6 on LTC workers; 2. Colombo et al. (2011: p. 139); 3. Colombo et al. (2011: p. 139); 4. Colombo et al. (2011: p.139); 5. Health at a Glance: OECD indicators, OECD ( 2011 ), corrected for poor health years (65+) (from European Health and Life Expectancy Information System ( 2022 ): Specific Table: Activity Limitation (SILC, Limited But Not Severely, Not Limited and Severely Limited)*, in 28 European Countries, by Sex, at Age 65, from 2004 to 2016, accessed 18th of October 2022; All indicators are standardized. To give more insight into the ranking of countries on the indicators and their care regime membership, we portray the scores of each country on each of the five indicators on the one hand and a general average index score on the other hand. This index is the average of the standardized values of all five indicators and as such is a general measure of formal elder care support. The markers of the countries show care regime membership of each country. We clearly see that for most indicators, the defamilialized countries are on the right side of the figures (indicating high formal support) and the familialism-by-default countries are on the left side of the figures. 5. DISCUSSION AND CONCLUSION: TOWARDS A LONG-TERM ELDERLY CARE REGIME TYPOLOGY In this article, we have empirically constructed three classes of countries based on how care institutions shape intergenerational responsibilities between the state and the family. We built upon the three-fold distinction of defamilialism, supported familialism, and familialism-by-default by Saraceno and Keck ( 2010 ), as well as the literature on defamilialization and familialism, initiated by Leitner ( 2003 ) and clarified and summarized by Lohmann and Zagel ( 2016 ). Although Saraceno and Keck ( 2010 ) suggested to cluster countries in these three major groups, their cluster solution did not show an interpretable solution in line with their theoretical framework. In contrast, we indeed found three ‘ideal’ types of care regimes, even though the supported familialism care regime could not be identified. We will use our regimes to explore regional variation in outcomes related to caregiving across Europe in this collection. Such an analysis will examine the extent to which ‘similar causes have similar consequences’, thereby indicating underlying theoretical mechanisms (Arts & Gelissen, 2002 ). Given the absence of a comprehensive theoretical framework for institutional care (as well as limited data across numerous country contexts), a typology helps us to gain insight into general contextual care institutions effects. A solid theoretical background facilitates hypothesis testing and consequently enables us to draw reliable conclusions about the impact of the institutional context on various micro-level outcomes (Arts & Gelissen, 2002 ), with most importantly the outcome of people’s caregiving. Through this empirically tested typology, we aim to contribute to the development of an extensive theoretical framework for institutional care and its potential impact in Europe. 5.3 Strengths and limitations In this article, we chose to take a bird’s eye view of the ‘essential’ features of regimes or ‘ideal’ types (Arts & Gelissen, 2002 based on Max Weber (1949)) and not delve into within-country heterogeneities. After all, we attempted here to distil the ‘essential’ elements of care regime types by performing latent profile analyses and consequently identify hybrid countries based on predicted probabilities that point to countries having elements from more than one class (i.e. by predicted probabilities that were not either 0 or 1). However, because the classification of countries into classes was so rigid, we hardly identified hybrid cases based on posterior probabilities; the separation of classes was very high. It is important to also note the limitations and reliability of our care regime typology. Like any regime typology, it is easy to arrive at (slightly) different classifications when one emphasizes certain characteristics more than others or incorporates additional characteristics. Furthermore, like any regime typology, there is also the issue of within-country heterogeneity. Our regime typology does not take differences within countries in intergenerational family solidarity (Dykstra & Fokkema, 2011 ), gender, education, income, and wealth (Quashie et al., 2022 ; Verbakel et al., 2017 ) and urban versus rural (Glasgow, 2000 ) differences into account. We believe this aspect warrants a separate paper to be left for future research. After all, all of these heterogeneities seem important, and focusing on one heterogeneity overlooks other important inequalities. Finally, we acknowledge that our country classification is only based on five indicators. The inclusion of additional and different indicators may alter the classification. Declarations Author Contribution Maike van Damme and Jeroen Spijker collected the data and wrote and reviewed the text, Dimitris Pavlopoulos performed the latent profile analysis. References Anttonen A, Sipilä J (1996) European Social Care Services: Is It Possible To Identify Models ? J Eur Social Policy 6(2):87–100. https://doi.org/10.1177/095892879600600201 Ariaans M, Linden P, Wendt C (2021) Worlds of long-term care: A typology of OECD countries. Health Policy 125(5):609–617 Arts W, Gelissen J (2002) Three worlds of welfare capitalism or more? A state-of-the-art report. J Eur Social Policy 12:137–158 Bauer J (2022) A Primer to Latent Profile and Latent Class Analysis. In M. Goller, E. Kyndt, S. Paloniemi, & C. Damşa (Eds.), Methods for Researching Professional Learning and Development: Challenges, Applications and Empirical Illustrations (pp. 243–268). Springer International Publishing. https://doi.org/10.1007/978-3-031-08518-5_11 Bettio F, Plantenga J (2004) Comparing Care Regimes in Europe. Fem Econ 10(1):85–113 Colombo F, Llena-Nozal A (2011) Jérôme Mercier, & Tjadens, F. Help Wanted? Providing and Paying for Long-Term Care . http://dx.doi.org/10.1787/9789264097759-en Dykstra PA, Fokkema T (2011) Relationships between parents and their adult children: a West European typology of late-life families. Ageing Soc 31(4):545–569. https://doi.org/10.1017/S0144686X10001108 European Health and Life Expectancy Information System (2022) Specific Table: Activity Limitation (SILC, Limited But Not Severely, Not Limited and Severely Limited)*, in 28 European Countries, by Sex, at Age 65, from 2004 to 2016 . http://www.eurohex.eu/IS/web/app.php/Ehleis/HealthLifeGeographic/SILC/SILCAL Glasgow N (2000) Rural/Urban Patterns of Aging and Caregiving in the United States. J Fam Issues 21(5):611–631. https://doi.org/10.1177/019251300021005005 Gore Jr PA (2000) Cluster analysis. In H. E. A. Tinsley & S. D. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling. (pp. 297–321). Academic Press. https://doi.org/10.1016/B978-012691360-6/50012-4 Knijn T, Kremer M (1997) Gender and the Caring Dimension of Welfare States: Toward Inclusive Citizenship. Social Politics: Int Stud Gend State Soc 4(3):328–361. https://doi.org/10.1093/oxfordjournals.sp.a034270 Kraus M, Riedel M, Mot E, Willeme P, Rohrling G, Czypionka T (2010) A Typology of Long-Term Care Systems in Europe. ENEPRI Research Report, Issue, p 91 Leitner S (2003) Varieties of familialism: The caring function of the family in comparative perspective. Eur Soc 5(4):353–375. https://doi.org/10.1080/1461669032000127642 Lohmann H, Zagel H (2016) Family policy in comparative perspective: The concepts and measurement of familization and defamilization. J Eur Social Policy 26(1):48–65. https://doi.org/10.1177/0958928715621712 OECD (2011) Health at a glance 2011: OECD indicators. OECD Publishing. Issue. https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2011_health_glance-2011-en Pacolet J, Bouten R, Lanoye H, Versieck K (2000) Social Protection for Dependency in Old Age: A Study of the Fifteen EU Member States and Norway. Comparative Report commissioned by the European Commission and the Belgian Minister of Social Affairs, Issue Pavolini E (2021) Long-term care social protection models in the EU. European Social Policy Network (ESPN), Issue Pfau-Effinger B (2005) WELFARE STATE POLICIES AND THE DEVELOPMENT OF CARE ARRANGEMENTS. Eur Soc 7:321–347 Quashie NT, Wagner M, Verbakel E, Deindl C (2022) Socioeconomic differences in informal caregiving in Europe. Eur J Ageing 19(3):621–632. https://doi.org/10.1007/s10433-021-00666-y Saraceno C, Keck W (2010) Can we identify intergenerational policy regimes in Europe? Eur Soc 12(5):675–696. https://doi.org/10.1080/14616696.2010.483006 Van Damme M, Spijker J (2023) Country Differences in Long-term Care Institututions: Towards a Care Regime Typology. SocArXiv Verbakel E, Tamlagsrønning S, Winstone L, Fjær EL, Eikemo TA (2017) Informal care in Europe: findings from the European Social Survey (2014) special module on the social determinants of health. (1464-360X (Electronic)) Vermunt JK, Magidson J (2002) Latent Class Cluster Analysis. In: McCutcheon AL, Hagenaars JA (eds) Applied Latent Class Analysis. Cambridge University Press, pp 89–106. https://doi.org/10.1017/CBO9780511499531.004 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3981497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274477971,"identity":"c8138f09-dcaf-4761-9bec-5a1269872254","order_by":0,"name":"Maike van Damme","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYLACHhDB3gBhGBCvhecAyVokEojUojsj+dmDNzXbovklHz988IbhsLw5A/PhD/i0mN1IMzecc+x27szZacaGcxgOG+5sYEuTwKvlzAEzaR6227kbbuewSfMwHGbccIDHDK/DzM4c/ybN8+927v6bZ9h/A7XYbzjA/xm/w473mEnztgFtkeBhYwZqSQTawoDfYcd7yiTn9t3OnXEmzVhyjkF68s5mNjP8Wg6zb5N48+12bn/74Ycf3lRY225nb36M12FoABQpzCSoHwWjYBSMglGAHQAASHlMTroSRkQAAAAASUVORK5CYII=","orcid":"","institution":"Centre for Demographic Studies","correspondingAuthor":true,"prefix":"","firstName":"Maike","middleName":"van","lastName":"Damme","suffix":""},{"id":274477972,"identity":"333660ae-9177-4139-944f-8e91037cbcf8","order_by":1,"name":"Jeroen Spijker","email":"","orcid":"","institution":"Centre for Demographic Studies","correspondingAuthor":false,"prefix":"","firstName":"Jeroen","middleName":"","lastName":"Spijker","suffix":""},{"id":274477973,"identity":"282162b2-7877-4114-bd91-64195ab401a1","order_by":2,"name":"Dimitris Pavlopoulos","email":"","orcid":"","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Dimitris","middleName":"","lastName":"Pavlopoulos","suffix":""}],"badges":[],"createdAt":"2024-02-23 10:36:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3981497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3981497/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51624637,"identity":"eaf0a3d4-b4bf-4fea-b0de-6302f728178d","added_by":"auto","created_at":"2024-02-26 07:01:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46236,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized Scores on five Indicators used for Classifying 26 Countries. Data Period 2009.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3981497/v1/bef1259fcc8f718df1208b42.png"},{"id":66954355,"identity":"f5db8cd8-80d0-4e5b-974b-0192205cdf6c","added_by":"auto","created_at":"2024-10-18 10:53:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198823,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3981497/v1/30f34746-a1d8-4989-a6fd-34853440bedb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"a Care Regime Typology of Elder, Long-Term Care Institutions","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn this article, we build upon previous research on the search for European care regime typologies. Care regimes are \u0026lsquo;social joins\u0026rsquo; that secure adequate interactions between demographic and economic institutions (for an extensive discussion, see Bettio \u0026amp; Plantenga, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In contrast to general welfare regimes, that focus on social transfers in general, care regimes have as a core dimension the relationship between the state and the family. Although several scholars have attempted to derive such a clustering of countries for the late 1990s and early 2000s, they were either not theoretically satisfying because they were covering many different dimensions (e.g. Kraus et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), they didn\u0026rsquo;t get a clear empirical clustering of countries (e.g. Saraceno \u0026amp; Keck, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), or they were based on ranking countries based on \u0026lsquo;eye-balling\u0026rsquo; the data rather than rigorous empirical cluster analysis (Bettio \u0026amp; Plantenga, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pacolet et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA few more recent studies, such as Ariaans et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Povolini (2021) using data from around 2018, used cluster analyses, but also still here the connection between theory and the conceptualization and operationalization of indicators measuring care provision present some shortcomings. None of these studies uses an extensive theoretical framework, describing the underlying dimensions on which the empirical clustering of countries could be based.\u003c/p\u003e \u003cp\u003eIn this article, we build upon the theoretical frameworks of amongst others Pfau-Effinger (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and empirically check our expected classification using latent profile analyses (LPA). The article loosely builds upon a study by Van Damme and Spijker (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) that classified countries into care regimes based on intergenerational upward and downward support. Since this article is guiding other articles in this collection on caregiving and their consequences, here we focus squarely on elder care.\u003c/p\u003e"},{"header":"2. A CONCEPTUAL FRAMEWORK OF REGIME DIFFERENCES IN (IN)FORMAL CARE INSTITUTIONS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Defamilialization and Familialism: The \u0026lsquo;Logic\u0026rsquo; of Care Regimes\u003c/h2\u003e \u003cp\u003eWhich is the main logic that separates certain care regimes from others? Corresponding to Esping-Andersen\u0026rsquo;s two dimensions (\u0026lsquo;logics\u0026rsquo;) of stratification and decommodification, from which he derived three ideal types of general welfare states, what are the key dimensions in care arrangements? We distinguish two dimensions from the literature (for an extensive discussion, see Van Damme \u0026amp; Spijker, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e): A) \u003cb\u003edefamilialization\u003c/b\u003e entails \u003cem\u003eunburdening families from care responsibilities and family dependencies\u003c/em\u003e (Lohmann \u0026amp; Zagel, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); B) \u003cb\u003efamilialism\u003c/b\u003e covers the extent to which dependencies among family members and their negative social and economic consequences can be actively reduced by state support. We refer to dependencies such as women\u0026rsquo;s economic dependence on a breadwinner, children\u0026rsquo;s dependence on their parent\u0026rsquo;s care, and elderly people\u0026rsquo;s dependence on their adult children (Lohmann \u0026amp; Zagel, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e: p. 53). Familialism thus entails \u003cem\u003estrengthening the role of the family\u003c/em\u003e by alleviating negative consequences of family dependencies. Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) build upon these ideas. In their theoretical framework, the authors propose the three-fold conceptualization of familialism-by-default (when there is little or no state support for family care, meaning that the responsibility lies with the family), supported familialism (where there is state support for caregiving, but only financial (and little care in kind)), and de-familialisation (provision of both care in kind and financial aid to reduce family care responsibilities as much as possible). They also have a rare fourth cluster which focuses on \u0026lsquo;optional individualism\u0026rsquo; (in the words of Lohmann and Zagel (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)) or \u0026lsquo;optional familialism\u0026rsquo; (here both practical services and financial support policies are offered, but by allowing families the choice to share the caregiving responsibilities between the state and the family according to their preferences) (Knijn \u0026amp; Kremer, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Leitner, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProvision of services, through time or money, may take place directly by the state or indirectly via the market (which is reimbursed at a later stage). Moreover, note that we do not make a distinction regarding whether this \u0026lsquo;state\u0026rsquo; support is targeted at the caregiver or at the care user. After all, cash for care use provided by the state in one way or another (and either targeted at the caregiver or user) alleviates the financial care burden for all. Note that care solely provided by the market implies within-country inequalities in care access that we do not consider here (see our reflection in the discussion).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Expectations on \u0026lsquo;Ideal\u0026rsquo; Types Combining Defamilialization and Familialism\u003c/h2\u003e \u003cp\u003eBased on the policies supporting defamilialization or familialism, we expect that each of the \u0026lsquo;regimes\u0026rsquo; of countries we observe, has elements of these dimensions to certain degrees. More specifically, we expect to find at least three \u0026lsquo;ideal\u0026rsquo; types of elder care clusters: A defamilialized class, a supported familialism one, and a familialism-by-default group. Research on earlier periods have identified similar distinctions between groups of countries. For instance, building upon Anttonen and Sipil\u0026auml; (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), Bettio and Plantenga (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) based their ranking of countries on eye-balling scores on a variety of indicators, using data from the late 1990s. Their point of departure are the two extreme models of social care services, a Scandinavian model of public services and a southern European family care model. Countries with this last type of family care model rank high on informal caregiving, but formal care arrangements are underdeveloped. Scandinavian countries are located at the opposite extreme with high levels of formal care support. This care regime has universalist public policies, covering a large part of the population, where the state would substitute rather than support the family in caregiving (Bettio \u0026amp; Plantenga, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These two extremes are confirmed by the data of Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) on a continuum of defamilialism towards familialism-by-default. Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) performed a cluster analysis, but did not achieve a satisfying solution of this analysis with respect to both child and elder care. Bettio and Platenga (2004) and Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) have less consensus about the placement of all other European countries in groups, as they are more ambiguous and less coherent. Yet, we may expect a third \u0026lsquo;ideal\u0026rsquo; type that is based on the logic of supported familialism. Bettio and Plantenga (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) mention that Austria and Germany are characterized by mostly a private care strategy, based on the subsidiarity principle (Esping-Andersen, 1990). While both countries rank medium on institutional care for elderly, there is a large reliance on the family (women) for care provision. In these countries, the emphasis is not so much on care in kind, but rather on alleviating the financial burden of care. We may thus expect that Austria and Germany will be classified in a third \u0026lsquo;ideal\u0026rsquo; type, that of supported familialism. Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) highlight that, next to Germany, the Czech Republic and Hungary have supported familialism characteristics. We now describe our selection of indicators, with which we do not only update the classifications of Bettio and Plantenga (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), but also perform a more rigorous empirical country classification into care regimes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. OPERATIONALIZATION, DATA, AND METHOD","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Care Institutions Indicators\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e we present the indicators used to measure the extent of care support by the state for the elderly, taken from the article by Van Damme and Spijker (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We consider five indicators to measure the degree of unburdening and strengthening of family obligations. Data stem from the year 2009 where available. We use this year as we will use this typology in further articles of this collection to explain country differences in caregiving and its consequences. As in these chapters SHARE 2004\u0026ndash;2017 and ELSA are used, we needed to gather information for this less recent period. After all, when we want to explain the consequences of caregiving by contextual care institutions, these care institutions should go first in the time order.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIndicators on care institutions for the elderly.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe use five indicators for institutional support for elderly care. (1) \u003cb\u003eLong-Term Care Services\u003c/b\u003e. Our analysis begins by drawing upon Verbakel\u0026rsquo;s (2018) work by constructing an index to measure long-term care services in kind for the elderly. Our index deviates from the one by Verbakel as we only want to focus on LTC services available and not on the care needs, nor on financial support. The LTC service index is based on the mean standardized value of two components: (a) the number of LTC beds in institutions and hospitals per 1000 population aged 65+; and (b) the number of LTC workers per 100 people aged 65+. These data are taken from the report \u0026lsquo;Health at a Glance\u0026rsquo; (OECD, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We thus did not include the proportion of the population receiving LTC, nor LTC public expenditure as percentage of GDP (although we will consider this last component separately, see below).\u003c/p\u003e \u003cp\u003e(2) \u003cb\u003eCare leave\u003c/b\u003e. Time provided by the state to support care for an ill relative is considered by the indicator care leave arrangements by the state, either paid or unpaid, whereby paid leave is given a weight of two. This indicator therefore varies between 0 (no leave) to 3 (both paid leave and unpaid leave is possible). The required information comes from (info from Colombo et al., 2011: Annex 4.A1, p.139).\u003c/p\u003e \u003cp\u003e(3) \u003cb\u003eFinancial allowances\u003c/b\u003e. We separate the familialism dimension from the defamilialization dimension by including financial support by the state to the caregiver or care user (we do not differentiate between the two but take the sum of both). A functional equivalent of cash for care benefits are tax credits. Hence, we sum up these three measures and get an indicator that varies between 0 (no cash for care) and 3 (financial benefits for caregivers, users, and tax credits) \u0026ndash; a hypothetical situation, since states chose either one or the other arrangement (info from Colombo et al., 2011: Annex 4.A1, p.139).\u003c/p\u003e \u003cp\u003e(4) \u003cb\u003eRespite care\u003c/b\u003e. The final state service indicator considers respite care, providing the possibility for the caregiver to alleviate the care burden (be it either in the short- or in the long-term). Respite care includes very different types of interventions providing temporary ease from the burden of care. Often, the objective of such breaks is to restore the caregiver\u0026rsquo;s ability to bear the care load. Day-care services, in-home respite, and institutional respite are all included in the definition of this binary indicator.\u003c/p\u003e \u003cp\u003e(5) \u003cb\u003eLong-Term Care expenditure\u003c/b\u003e. The final indicator is a separate financial indicator that represents a state\u0026rsquo;s general financial support to alleviate LTC for the elderly. This indicator includes LTC public expenditure (based on the health component only) as a share of GDP (OECD, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), correcting for the total number of years spent living in an unhealthy condition after reaching the age of 65 (EHLEIS, 2022). We explored the possibility of using an indicator on the share of private expenditure from total LTC spending as well. Yet, we could not find such comparable information on LTC spending, only for spending on general health care. Moreover, it is difficult to determine where private expenditure should be placed in the multidimensional spectrum of familialism-by-default, supported familialism, and defamilialisation. Private spending may indicate within-country heterogeneities (Verbakel et al., 2023), but we refrain from such heterogeneities in this paper (see discussion).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eElderly Care Institutions and Associated Indicators for 26 European OECD Countries. Data Period 2009.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePublic policy with respect to care for the old\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLTC services \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCare leave (paid (*2) and unpaid) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinancial allowances \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRespite care \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLTC exp as % of GDP/unhealthy person years \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHungary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIceland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatvia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLithuania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuxembourg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlovak Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlovenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea. Index of LTC facilities, 2009: two items: Long-term care beds in institutions and hospitals, per 1 000 population aged 65 and over, Long-term care workers as share of population aged 65 and over (1); b. Care leave, paid counted twice, unpaid leave counted once (2); c. Sum of carer allowance, cared allowance, care tax credit (2); d. Respite care (yes/no) (for definition, see text) (2); e. Long-term care public expenditure (health component), as share of GDP corrected for number of poor health years at 65+, 2009 (3).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSources\u003c/em\u003e: 1. Health at a Glance: OECD indicators, OECD (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e): Specific tables: 8.7 on Beds, 8.6 on LTC workers; 2. Colombo et al. (2011: p. 139); 3. Health at a Glance: OECD indicators, OECD (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), corrected for poor health years (65+) (from European Health and Life Expectancy Information System (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e): Specific Table: Activity Limitation (SILC, Limited But Not Severely, Not Limited and Severely Limited)*, in 28 European Countries, by Sex, at Age 65, from 2004 to 2016, accessed 18th of October 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Method\u003c/h2\u003e \u003cp\u003e \u003cem\u003eLatent profile analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe perform a latent profile analysis (LPA, which differs from traditional cluster analyses (such as k-means) as it is a model-based probabilistic clustering method. Using the associations between the five indicators, we estimate the posterior probabilities per country, given their latent class (or \u0026lsquo;ideal\u0026rsquo; type). In line with the theoretical concept of hybrid countries that possess elements of more than one class, we may identify such cases where countries are equally likely to belong to one or another class (i.e. their posterior probabilities are much lower than 1 for various latent classes). Another advantage of LPA over (hierarchical) cluster methods is that there is no initial partitioning where cases cannot be reassigned to a better fitting cluster in subsequent stages of the process (Gore Jr \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). LPA assumes a normal distribution of the indicator values within each latent class, although other distributions are possible (Vermunt and Magidson \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEach latent class (1, \u0026hellip;, K) has a density \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{f}_{k}\\left({x}_{i}\\right|{\\mu }_{k},\\sigma }_{k}^{2}\\)\u003c/span\u003e\u003c/span\u003e), where \u0026#119909;\u003csub\u003ei\u003c/sub\u003e is an observed value for country i on a continuous indicator m, and \u0026#120583;\u003csub\u003ek\u003c/sub\u003e and \u0026#120590;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ek\u003c/sub\u003e are its mean and variance in class k. The joint mixture model is given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f\\left({x}_{i}\\right)=\\sum _{k=1}^{k}{\\pi }_{k}{f}_{k}\\left({x}_{i}\\right|{\\mu }_{k},{\\sigma }_{k}^{2})\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003ewhere \u0026#120587;\u003csub\u003ek\u003c/sub\u003e is the relative class size, the class probability, or the \u0026lsquo;mixture weight\u0026rsquo;. That is, each country\u0026rsquo;s density in the mixture distribution is a sum of the class-specific normal densities weighted by the class probabilities (Bauer \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We estimated various models with a different number of classes and compared their fit. Of the 2-class, 3-class, and 4-class solutions, the 3-class solution performed the best. We did not run analyses of more than 4-classes, since the 4-class solution did not have a straightforward interpretation anymore with the fourth class covering only Germany and Ireland. The 2-class solution has BIC 297, AIC 275, and classification errors 0.0362. The 3-class solution has BIC 302, AIC 269, and 0.0242 classification errors. The 4-class solution has BIC 314, AIC 272, and 0.0095 classification errors. Although the fit of less or more than three classes has also is good, we decided for the 3-class solution because of a good separation of the countries into latent classes, and sufficiently large groups. Note that we did a sensitivity check leaving Iceland out (it has many missing values) and found similar results (results available upon request).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. FINDINGS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Labelling the Latent Classes\u003c/h2\u003e \u003cp\u003eIn this section, we aim to theorize the specific characteristics of each care regime. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e gives the latent class profile on which we base our labelling of classes. We find three classes, of which the first class occurs in 23% of all the cases (countries), the second almost for 40%, and the last class covers about 37%. Looking at the membership probabilities for the indicators given membership of the first class (the smallest one in size), we see that the probabilities for services in kind, as well as the responsibilities to care do not lie at the state. The differences with the third class are striking and mainly opposite. The first class has the lowest conditional probabilities on all five indicators, whereas the third class has the highest scores on all indicators apart from respite care. This justifies labels of \u003cb\u003efamilialism-by-default\u003c/b\u003e versus \u003cb\u003edefamilialism\u003c/b\u003e for the first and the third class, respectively. Finally, there is some kind of middle class that has elements of both \u003cem\u003edefamilialism\u003c/em\u003e and \u003cem\u003efamilialism\u003c/em\u003e. We would be tempted to label this class \u003cb\u003esupported familialism\u003c/b\u003e, were it not that against our expectations, it is not LTC expenditure, nor financial allowances (care allowances and/or tax credits) more specifically, that are high for this regime. We thus refer to this regime as \u0026lsquo;\u003cb\u003emedium\u003c/b\u003e\u0026rsquo; care support.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLatent Class Profile for Elderly Care Institutions for 26 Countries, Standardized Indicators. Data Period 2009.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFbD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLTC facilities (continuous)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.320\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.2226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.061\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCare leave\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e-1.669\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e-0.3986\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.8718\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.985\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.407\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCare financial support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e-1.153\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e-0.2883\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.5766\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1.442\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.823\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.409\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLTC respite care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e-1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.7001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.337\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.182\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLTC expenditure (continuous)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.755\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.881\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003efacilities\u0026thinsp;=\u0026thinsp;LTC availability index; leave\u0026thinsp;=\u0026thinsp;care leave (2*paid, 1*unpaid), unstandardized values for four categories 0-1-2-3; financial support\u0026thinsp;=\u0026thinsp;care allowances for caregiver/-user, tax credits, unstandardized values for three categories 0-1-2; respite care\u0026thinsp;=\u0026thinsp;alleviation of care, unstandardized values for two categories 0\u0026ndash;1; expenditure\u0026thinsp;=\u0026thinsp;LTC expenditure/GDP/unhealthy life year.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAll indicators are standardized.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we present the posterior-predicted probabilities for the countries. We immediately notice some expected regional differences, but we also see within-region differences. The last class (the \u0026lsquo;Nordic\u0026rsquo; countries and the Netherlands) is the most defamilialized and the first class (with the Southern and Eastern European countries) the least, thus justifying label \u0026lsquo;\u003cb\u003efamilialism-by-default\u003c/b\u003e\u0026rsquo; for class 1 and \u0026lsquo;\u003cb\u003edefamilialism\u003c/b\u003e\u0026rsquo; for class 3. Inconsistent, however, with our expectations based on the theoretical framework of Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), we identify a class characterized by a medium amount of formal elder care support. We refrain from labelling this class \u0026lsquo;\u003cem\u003esupported familialism\u003c/em\u003e\u0026rsquo;, since this class does not show relatively high financial support for families, while having little in-kind care. Moreover, unexpected is that these are the \u0026lsquo;North-West European\u0026rsquo; countries and not the \u0026lsquo;Eastern European\u0026rsquo; ones.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLatent Class Posterior Predicted Probabilities for Elder Care Institutions for 26 Countries. Data Period 2009.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFbD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.989\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.994\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.998\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHungary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIceland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.710\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatvia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLithuania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuxembourg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlovak Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.918\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlovenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.784\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.998\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.997\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, however, we see that the average score of this \u003cb\u003emedium\u003c/b\u003e familialism care regime is indeed \u0026lsquo;medium\u0026rsquo; on all of the indicators. Only Ireland and Switzerland have a high ranking on financial family expenditure, which makes them \u003cem\u003ehybrid\u003c/em\u003e cases in this care regime. The \u003cb\u003emedium\u003c/b\u003e class also includes Belgium (here showing more defamilializing elements), France, Luxembourg, and the UK, but also Spain. While the UK and Ireland in the general welfare state literature are often classified separately due to their liberal market welfare state regime, Ireland, sometimes also has been observed to have elements of countries of the \u0026lsquo;Mediterranean\u0026rsquo; region due to it being traditionally a highly Catholic country (Arts \u0026amp; Gelissen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In our LPA results this is not demonstrated as it is still performing better in state care compared to the Southern and Eastern European countries. Surprisingly, Spain emerges also as a country in this regime. Its classification within this group appears to be highly influenced by its extensive financial support for elderly care leave.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLatent Class Averages of Three Latent Classes based on Care Institutions Standardized Indicators for 26 OECD countries. Data Period 2009.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003ecare institutions for elderly\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003efacilities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eleave\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003efin sup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003erespite care\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eexpenditure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal standardized score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIceland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNorway\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass average DF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBelgium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIreland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLuxembourg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSlovenia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSpain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSwitzerland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnited Kingdom\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass average medium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEstonia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHungary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLatvia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLithuania\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-1.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-1.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCzech Republic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGreece\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eItaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSlovak Republic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass average FbD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea. Index of LTC facilities, 2009: two items: Long-term care beds in institutions and hospitals, per 1 000 population aged 65 and over, Long-term care workers as share of population aged 65 and over (1); b. Care leave, paid counted twice, unpaid leave counted once (2); c. Sum of carer allowance, cared allowance, care tax credit (3); d. Respite care (yes/no) (for definition, see text) (4); e. Long-term care public expenditure (health component), as share of GDP corrected for number of poor health years at 65+, 2009 (5).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSources\u003c/em\u003e: 1. Health at a Glance: OECD indicators, OECD (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e): Specific tables: 8.7 on Beds, 8.6 on LTC workers; 2. Colombo et al. (2011: p. 139); 3. Colombo et al. (2011: p. 139); 4. Colombo et al. (2011: p.139); 5. Health at a Glance: OECD indicators, OECD (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), corrected for poor health years (65+) (from European Health and Life Expectancy Information System (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e): Specific Table: Activity Limitation (SILC, Limited But Not Severely, Not Limited and Severely Limited)*, in 28 European Countries, by Sex, at Age 65, from 2004 to 2016, accessed 18th of October 2022;\u003c/p\u003e \u003cp\u003e \u003cem\u003eAll indicators are standardized.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo give more insight into the ranking of countries on the indicators and their care regime membership, we portray the scores of each country on each of the five indicators on the one hand and a general average index score on the other hand. This index is the average of the standardized values of all five indicators and as such is a general measure of formal elder care support. The markers of the countries show care regime membership of each country. We clearly see that for most indicators, the defamilialized countries are on the right side of the figures (indicating high formal support) and the familialism-by-default countries are on the left side of the figures.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. DISCUSSION AND CONCLUSION: TOWARDS A LONG-TERM ELDERLY CARE REGIME TYPOLOGY","content":"\u003cp\u003eIn this article, we have empirically constructed three classes of countries based on how care institutions shape intergenerational responsibilities between the state and the family. We built upon the three-fold distinction of defamilialism, supported familialism, and familialism-by-default by Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as well as the literature on defamilialization and familialism, initiated by Leitner (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and clarified and summarized by Lohmann and Zagel (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although Saraceno and Keck (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) suggested to cluster countries in these three major groups, their cluster solution did not show an interpretable solution in line with their theoretical framework. In contrast, we indeed found three \u0026lsquo;ideal\u0026rsquo; types of care regimes, even though the supported familialism care regime could not be identified. We will use our regimes to explore regional variation in outcomes related to caregiving across Europe in this collection. Such an analysis will examine the extent to which \u0026lsquo;similar causes have similar consequences\u0026rsquo;, thereby indicating underlying theoretical mechanisms (Arts \u0026amp; Gelissen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the absence of a comprehensive theoretical framework for institutional care (as well as limited data across numerous country contexts), a typology helps us to gain insight into general contextual care institutions effects. A solid theoretical background facilitates hypothesis testing and consequently enables us to draw reliable conclusions about the impact of the institutional context on various micro-level outcomes (Arts \u0026amp; Gelissen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), with most importantly the outcome of people\u0026rsquo;s caregiving. Through this empirically tested typology, we aim to contribute to the development of an extensive theoretical framework for institutional care and its potential impact in Europe.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Strengths and limitations\u003c/h2\u003e \u003cp\u003eIn this article, we chose to take a bird\u0026rsquo;s eye view of the \u0026lsquo;essential\u0026rsquo; features of regimes or \u0026lsquo;ideal\u0026rsquo; types (Arts \u0026amp; Gelissen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e based on Max Weber (1949)) and not delve into within-country heterogeneities. After all, we attempted here to distil the \u0026lsquo;essential\u0026rsquo; elements of care regime types by performing latent profile analyses and consequently identify hybrid countries based on predicted probabilities that point to countries having elements from more than one class (i.e. by predicted probabilities that were not either 0 or 1). However, because the classification of countries into classes was so rigid, we hardly identified hybrid cases based on posterior probabilities; the separation of classes was very high. It is important to also note the limitations and reliability of our care regime typology. Like any regime typology, it is easy to arrive at (slightly) different classifications when one emphasizes certain characteristics more than others or incorporates additional characteristics. Furthermore, like any regime typology, there is also the issue of within-country heterogeneity. Our regime typology does not take differences within countries in intergenerational family solidarity (Dykstra \u0026amp; Fokkema, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), gender, education, income, and wealth (Quashie et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Verbakel et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and urban versus rural (Glasgow, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) differences into account. We believe this aspect warrants a separate paper to be left for future research. After all, all of these heterogeneities seem important, and focusing on one heterogeneity overlooks other important inequalities. Finally, we acknowledge that our country classification is only based on five indicators. The inclusion of additional and different indicators may alter the classification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMaike van Damme and Jeroen Spijker collected the data and wrote and reviewed the text, Dimitris Pavlopoulos performed the latent profile analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnttonen A, Sipil\u0026auml; J (1996) European Social Care Services: Is It Possible To Identify Models ? 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In: McCutcheon AL, Hagenaars JA (eds) Applied Latent Class Analysis. Cambridge University Press, pp 89\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/CBO9780511499531.004\u003c/span\u003e\u003cspan address=\"10.1017/CBO9780511499531.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Care regimes, Elder Care, Institutions, Latent Profile Analysis, Typology","lastPublishedDoi":"10.21203/rs.3.rs-3981497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3981497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study generates a classification of 26 European OECD countries with respect to care regimes. We build upon the literature on the dimensions of defamilialization and familialism and empirically test how these two dimensions indicate different types of care regimes. Using Latent Profile Analysis, we group the 26 countries based on five indicators of institutional elderly care. These indicators cover formal elder care support in care in kind as well as in financial support. The results reveal three care regime types: \u0026lsquo;defamilialized\u0026rsquo;; \u0026lsquo;medium familialism\u0026rsquo;; and \u0026lsquo;familialism-by-default\u0026rsquo;. This classification contributes to developing a theoretical framework of care institutions.\u003c/p\u003e","manuscriptTitle":"a Care Regime Typology of Elder, Long-Term Care Institutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-26 07:01:31","doi":"10.21203/rs.3.rs-3981497/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a3cd996-542d-4899-ab20-efc85947f677","owner":[],"postedDate":"February 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-08T07:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-26 07:01:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3981497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3981497","identity":"rs-3981497","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}