Introduction
============

Reducing gender inequalities in health is recognized as a crucial goal of active and healthy ageing research and policy ([@jcz007-B33]). Against the backdrop of a steady growth in life expectancy in Europe, there has been limited improvement in terms of healthy life years at older ages, with women systematically reporting higher rates of morbidity, disability, and healthcare utilization than men, even though they live longer ([@jcz007-B93]; [@jcz007-B19]; [@jcz007-B74]; [@jcz007-B21]).

Health differences between women and men are the result of the combination of both biological and social factors ([@jcz007-B14]; [@jcz007-B74]) and are widely recognized as attributable to differences in socioeconomic status (SES) ([@jcz007-B93]; [@jcz007-B68]; [@jcz007-B74]). The interaction between gender and SES is deeply associated with health ([@jcz007-B68]). Socioeconomic resources---considered as 'fundamental causes' of individual health ([@jcz007-B54])---structure over the life course the likelihood of women's and men's differential exposure and vulnerability to disease, their access to health-related resources, as well as the differential consequences of poor health ([@jcz007-B55]; [@jcz007-B68]). For example, gender-specific socioeconomic disparities in terms of education, labour market participation, financial independence, and family responsibilities may contribute to widening the gender gaps in physical and mental health throughout the life span ([@jcz007-B26]; [@jcz007-B15]; [@jcz007-B78]; [@jcz007-B24]). At the same time, the welfare state can play an important role in redistributing socioeconomic resources which are important to health, and thus contributing to lowering gender and socioeconomic inequalities in health ([@jcz007-B31]; [@jcz007-B47]; [@jcz007-B7]).

Gender inequalities in health are not static across the life span and differ by specific disease outcome ([@jcz007-B64]; [@jcz007-B74]). On the one hand, some studies have found that women's disadvantage in health tend to diminish with advancing age ([@jcz007-B59]; [@jcz007-B18]; [@jcz007-B75]) up until the point at which---among adults in their 60 s and older---women report better self-reported health than men ([@jcz007-B96]). On the other hand, others have found that gender inequalities in mental health and wellbeing tend to increase as individuals age, and are highest among the oldest adults ([@jcz007-B71]; [@jcz007-B60]). Moreover, men may be more likely to engage in more health risk behaviours than women (such as alcohol and drug use, abuse, and dependence) that adversely affect their health and risk of premature mortality ([@jcz007-B19]; [@jcz007-B15]; [@jcz007-B74]; [@jcz007-B78]). Conversely, women may be more likely to suffer from nonfatal and chronic debilitating disorders (e.g. arthritis and disability) that do not necessarily result in their death but do negatively impact their wellbeing later in life ([@jcz007-B19]; [@jcz007-B74]). Moreover, gender inequalities in health vary considerably cross-nationally, suggesting that the gender gaps in health are affected by country-specific characteristics ([@jcz007-B10]; [@jcz007-B21]; [@jcz007-B16]; [@jcz007-B24]; [@jcz007-B40]).

While the extent of gender-based health inequalities, and the social determinants underlying them, are well documented ([@jcz007-B15]; [@jcz007-B78]), there has been little research on the extent to which the differential impact of SES on the health of women and men varies across different macro-level contexts ([@jcz007-B68]; [@jcz007-B10]; [@jcz007-B74]; [@jcz007-B36]). The knowledge gap is even greater when considering older women and men, despite their high use of healthcare services and the importance of health to support independence in later life.

Although some research has examined cross-national differences in the degree and patterning of gender inequalities in health among different socioeconomic groups ([@jcz007-B49]; [@jcz007-B75]; [@jcz007-B50]; [@jcz007-B10]), the large majority of the literature has mostly been cross-sectional and focussed on the adult population as a whole. The intersections and trajectories of SES, gender, and health in later life therefore remain unclear. Furthermore, the association between SES and health by gender shows mixed results depending on the SES indicator considered, the health outcome under examination, as well as other factors (such as political, economic, social, and cultural) ([@jcz007-B55]; [@jcz007-B70]; [@jcz007-B57]).

The associations between SES and health may be confounded by unobserved factors ([@jcz007-B48]). Unobserved permanent personal characteristics (e.g. biological factors, personality traits, intellectual abilities, or childhood conditions) that differ between individuals and that may be associated with both SES and health can be one source of confounding. Fixed-effects and 'hybrid' (between‐within) models have been identified as a specific way of addressing the impact of these unobserved individual factors (i.e. omitted variables) ([@jcz007-B1]; [@jcz007-B86]; [@jcz007-B12]; [@jcz007-B11]). Additionally, the different patterning in the intersections between gender and SES depending on the health outcome analyzed points out the need to understand the complexity and multidimensionality of health in later life with a gender-sensitive approach ([@jcz007-B56]; [@jcz007-B68]).

In middle and old ages, women have more chronic conditions, greater levels of depression, disability, and morbidity than men ([@jcz007-B19]; [@jcz007-B74]; [@jcz007-B21]). The accumulation of these deficits in multidimensional health domains can be measured by a 'Frailty Index' ([@jcz007-B79]). A Frailty Index (FI) is a count of health deficits, reflecting the proportion of potential deficits affecting a given person, and indicating the likelihood that frailty is present. This measure provides a more complete picture of older adults' overall health, and it is consistently found to be a strong predictor of adverse health outcomes, including the subsequent mortality ([@jcz007-B34]; [@jcz007-B81]). Moreover, frailty is an important concept for all those who plan and provide care for older adults, since it is appropriate to identify those who need geriatric interventions ([@jcz007-B87]).

This study addresses the shortcomings of the previous literature by investigating whether the association between three different measures of SES (education, income, and wealth) and frailty after midlife (age 50 years to baseline) vary according to gender across nine European countries with different macro-level characteristics. Thereby, we combine micro and macro determinants of health, showing how multiple dimensions of socioeconomic resources are of different importance for the health of women and men living in different contexts. Most importantly, this article aims at integrating and extending the previous literature overcoming some of its methodological limitations, specifically by applying a longitudinal design, controlling for time-constant unobserved heterogeneity at the individual level, and addressing the problem of selective panel attrition. The comparative approach, the modelling of longitudinal data, and the inclusion of frailty as a health outcome represent the key contributions of this study.

Gender Inequalities in Health: Possible Underlying Mechanisms
=============================================================

Micro Level: Gender, SES, and Health
------------------------------------

Research has, so far, highlighted several explanations for gender differences in health, typically referring to a set of biological, psychosocial, behavioural, and social factors that can impact the health of women and men in different ways ([@jcz007-B93]; [@jcz007-B74]). Among them, SES is widely recognized as the most important determinant of gender differences in health ([@jcz007-B26]; [@jcz007-B61]; [@jcz007-B50]; [@jcz007-B70]; [@jcz007-B42]). The idea, underlying the fact that individuals with higher SES are more likely than their lower SES counterparts to enjoy better health, is that SES embodies an array of 'flexible resources' ([@jcz007-B70])---such as knowledge, money, power, or prestige---that can be used by individuals to avoid or deal with illnesses, minimizing their negative consequences on health, and to better cope with stressful life events ([@jcz007-B54]). Hence, women's relative lower SES places greater limits on their access to health-related resources, leading to a reduction in their health ([@jcz007-B82]; [@jcz007-B77]; [@jcz007-B61]; [@jcz007-B68]; [@jcz007-B74]). The gender-specific socialization explanations are worth mentioning because the social organization of men's and women's lives and relations may affect their exposure and vulnerability to specific risks and health behaviours (e.g. excessive alcohol consumption) through differences in employment patterns, social roles or role-related activities, or to differences in their social and economic burdens ([@jcz007-B14]; [@jcz007-B75]).

However, it is still unclear to what extent SES has the same differential impact on the health of women and men in later life. On the one hand, the large majority of the existing evidence is from single-country cross-sectional analyses that did not find any interactive association between gender and SES with health at older ages ([@jcz007-B23]; [@jcz007-B46]; [@jcz007-B91]; [@jcz007-B20]). The same results were found in studies based on national longitudinal studies from England ([@jcz007-B65]; [@jcz007-B62]), Spain ([@jcz007-B66]), and Sweden ([@jcz007-B69]). On the other hand, the association between SES and health was found to be stronger in older men than in older women in one study from Spain ([@jcz007-B76]). In contrast, a stronger association between SES and health in older women was reported in one cross-sectional study from Spain ([@jcz007-B52]) and in one follow-up study from the UK ([@jcz007-B37]). Other studies reported mixed results depending on the SES indicator and the health outcome considered ([@jcz007-B61]; [@jcz007-B39]; [@jcz007-B72]; [@jcz007-B85]; [@jcz007-B84]; [@jcz007-B29]; [@jcz007-B91]).

Macro Level: Socioeconomic Context, Gender, and Health
------------------------------------------------------

The related question---and our focus---asks how or in what ways SES affects the health of older women and men differently across countries. A variety of comparative studies analyzing the association between SES and health across European countries showed mixed results in the interaction between SES and gender. One cross-sectional study comparing 17 Western European countries did not find any difference between genders in the association of education with self-reported health ([@jcz007-B9]). Another cross-sectional, cross-national study showed no clear pattern by gender in the relationship between education and self-reported health ([@jcz007-B94]). Similarly, cross-sectional associations between SES and self-reported health varied by gender but in different directions among the countries and European regions studied in other works ([@jcz007-B49]; [@jcz007-B73]; [@jcz007-B50]; [@jcz007-B22]; [@jcz007-B41]; [@jcz007-B83]). The same cross-sectional fluctuations in gender and SES interactions depending on country were also reported in a study of 11 European countries ([@jcz007-B42]) and in one using data from 13 European countries ([@jcz007-B10]).

One of the theories that has been suggested to explain the differential gender gap in health across countries is the 'constrained choice' theory ([@jcz007-B15]). According to it, the differences in health between women and men can be due to macro-level opportunities and constraints that directly and indirectly shape health-related individual priorities and choices. This suggests that the systematic differences in health conditions between women and men across countries may be explained by the interaction between the state, the market, and the family in welfare provision ([@jcz007-B30]). The role of the welfare state is important to population health and gender equality in health in terms of how the state interacts with the family ([@jcz007-B27]), and thereby reducing the specific welfare burden on women ([@jcz007-B31]; [@jcz007-B47]; [@jcz007-B7]). Women's SES is related to the extent to which the welfare state facilitates female autonomy and economic independence from the family ([@jcz007-B67]; [@jcz007-B7]).

Useful here is to combine gender stratification concepts, specifically defamilization, with others like the decommodification of labour and healthcare. Defamilization refers to the extent to which the welfare state permits individual entitlements to a socially acceptable standard of living independent of family relationships ([@jcz007-B31]; [@jcz007-B47]; [@jcz007-B4], [@jcz007-B7]). In contrast, decommodification refers to the degree to which the welfare state frees individuals from market dependence for a socially acceptable standard of living ([@jcz007-B30]; [@jcz007-B5],[@jcz007-B6], [@jcz007-B8]). While high levels of defamilization (and decommodification) are characteristic in Northern European countries, women in Southern European countries are strongly dependent on family. Consequently, we would expect lower gender gap in health in social democratic welfare states and higher gender gap in health in familistic ones ([@jcz007-B16]; [@jcz007-B80]).

Therefore, this study will analyse the association between SES and health after midlife, and the extent to which this varies by gender in different European contexts. This is done in a set of three European welfare clusters, that is Northern Europe (Denmark and Sweden), Western Europe (Austria, Belgium, France, Germany, and Switzerland), and Southern Europe (Italy and Spain). We classified the nine European countries into these three generic welfare clusters because they roughly represent different geographical regions and welfare state regimes, and because this operationalization is also consistent with various social theories. Comparisons of health inequalities are based on the FI ([@jcz007-B81]) and made across three structural variables (educational level, income, and wealth) suitable to investigate the SES of older adults ([@jcz007-B38]; [@jcz007-B51]). Thus, this study will examine whether the varying amount of SES changes within the three welfare clusters correspond in differentiated changes in the magnitude of health inequalities between women and men. The research question is 'does the impact of SES on health after midlife vary among women and men depending on the welfare cluster?'

As explained above, a core element of our theoretical expectations is that if the welfare state decommodifies labour ([@jcz007-B30]) as well as health ([@jcz007-B5],[@jcz007-B6], [@jcz007-B8]), then there should be a weaker association between SES and health---for both women and men---living in highly decommodifying welfare states (Denmark and Sweden). Since these latter countries are also characterized by higher levels of defamilization ([@jcz007-B4], [@jcz007-B7]), our hypothesis is that *compared with men, SES is expected to be weakly associated with health changes for women living in countries with high defamilisation and decommodification* (Denmark and Sweden). By contrast, always comparing with men, we expect SES to have a stronger impact on health changes for women living in the Southern European countries (Italy and Spain), due to their lowest levels of defamilization and less generous levels of welfare provision as compared to other European countries.

Data and Methods
================

Data and Sample
---------------

We use individual-level panel data from the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a multidisciplinary, cross-national, and longitudinal research project focusing on adults aged 50 or older living in residential households ([@jcz007-B17]). The survey includes detailed information about demographics, family structure, SES, and health. SHARE data collection is based on computer-assisted personal interviewing. Sampling strategies varied by country. Detailed information about the entire SHARE project is available at [www.share-project.org](http://www.share-project.org).

This study uses data from the first (2004--2005), second (2006--2007), fourth (2011--2012), fifth (2013), and sixth (2015) wave of SHARE. The retrospective third wave of SHARE (SHARELIFE), carried out in 2008--2009, was excluded from the analyses as it focuses only on the respondents' life histories and because the questionnaire and variables are very different from the core data. However, we used information from the third wave to identify respondents who exited the panel (i.e. respondent's death year).

The analytical sample includes data from nine European countries (Austria, Belgium, Denmark, France, Germany, Italy, Spain, Sweden, Switzerland) and consisted of 13,955 respondents (50,459 observations) of age 50 and older, who were present in the first wave of SHARE. Since the health outcome of interest was *change* in frailty levels, we restricted the sample to any individual participating in at least two waves. The overall response rate at baseline was 61.8 per cent, ranging from 37.6 per cent (Switzerland) to 73.6 per cent (France) ([@jcz007-B25]). Out of the 21,407 respondents in the first wave of SHARE, 19,078 (89.1 per cent) provided valid information for the variables used in this study, and 13,955 of them (65.2 per cent) participated in at least one follow-up measurement. In total, these respondents provided 50,459 observations across five waves of SHARE (*n~2004/2005~* = 13,955, *n~2006/2007~* = 12,157, *n~2011/2012~* = 8,896, *n~2013~* = 8,137, and *n~2015~* = 7,314), which is an average of 3.6 observations per person. Of the initial respondents, 18.4 per cent (3,939) died within 11 years of follow-up after the first interview. Additional detailed information on survey participation, response rates, panel retention, and sample design of the SHARE survey is available elsewhere ([@jcz007-B25]; [@jcz007-B13]). [Table 1](#jcz007-T1){ref-type="table"} reports the characteristics of the analytical sample.

###### 

Descriptive statistics of variables in the analyses

                                                             Whole Sample   Men       Women
  ---------------------------------------------------------- -------------- --------- ---------
  *Age* [^a^](#tblfn3){ref-type="table-fn"}                  (67.93)        (67.97)   (67.90)
  *Gender*                                                                            
   Male                                                      46.34                    
  * *Female                                                  53.66                    
  *Frailty Index (FI)* [^a^](#tblfn3){ref-type="table-fn"}   (0.12)         (0.11)    (0.14)
  *Education*                                                                         
   Low                                                       47.11          42.53     51.07
   Medium                                                    31.36          33.35     29.64
   High                                                      21.53          24.13     19.29
  *Income*                                                                            
   1st quartile                                              25.05          21.61     28.02
   2nd quartile                                              25.00          24.25     25.65
   3rd quartile                                              25.00          25.92     24.20
   4th quartile                                              24.95          28.21     22.13
  *Wealth*                                                                            
   1st quartile                                              25.05          22.74     27.04
   2nd quartile                                              24.99          25.38     24.66
   3rd quartile                                              25.02          25.60     24.52
   4th quartile                                              24.94          26.29     23.78
  *Marital status*                                                                    
   Married                                                   72.31          81.07     64.75
   Never Married                                             5.47           5.56      5.40
   Divorced                                                  7.33           6.21      8.30
   Widowed                                                   14.88          7.17      21.54
  *Number of children*                                                                
   Childless                                                 9.73           10.04     9.46
   1                                                         17.39          16.62     18.06
   2                                                         37.75          38.43     37.16
   3+                                                        35.13          34.91     35.33
  *Wave*                                                                              
   \[1\] 2004--2005                                          27.66          27.71     27.61
   \[2\] 2006--2007                                          24.09          24.40     23.83
   \[4\] 2011--2012                                          17.63          17.57     17.68
   \[5\] 2013                                                16.13          16.04     16.20
   \[6\] 2015                                                14.49          14.28     14.68
  *Welfare cluster*                                                                   
   Southern Europe                                                                    
   Italy                                                     12.12          11.85     12.35
   Spain                                                     10.08          9.70      10.40
   Western Europe                                                                     
   Austria                                                   6.59           6.21      6.91
   Germany                                                   9.98           10.23     9.76
   France                                                    12.76          12.09     13.34
   Switzerland                                               4.93           4.93      4.93
   Belgium                                                   19.51          20.12     18.98
   Northern Europe                                                                    
   Denmark                                                   8.98           9.32      8.69
   Sweden                                                    15.05          15.55     14.63

*Note:* Unless otherwise indicated, values are reported in percentages. Unweighted pooled dataset (Individual-Year, *N* = 50,459).

*Source:* SHARE data, years 2004--2015 (own estimates).

Continuous variable: mean (in brackets).

###### 

Variance composition for Frailty Index

                                  Mean   SD        Min    Max
  -------------------- --------- ------- ------- -------- -------
  Frailty Index (FI)   Overall    0.124  0.105      0     0.838
                       Between           0.096      0     0.733
                       Within            0.053    −0.259  0.575

*Note:* Individual-Year, *N* = 50,459.

*Source:* SHARE data, years 2004--2015 (own estimates).

###### 

Variance composition for level of education, income, and wealth

  Variables                 Overall   Between   Within           
  ------------------------ --------- --------- -------- -------- --------
  **Level of Education**                                         
   Low                      23,771     47.11     6740    48.30    100.00
   Medium                   15,823     31.36     4365    31.28    100.00
   High                     10,865     21.53     2850    20.42    100.00
  *Total*                   50,459    100.00    13,955    100     100.00
  **Income**                                                     
   First quartile           12,640     25.05    6,930    49.66    52.07
   Second quartile          12,617     25.00    7,626    54.65    46.06
   Third quartile           12,613     25.00    7,613    54.55    44.97
   Fourth quartile          12,589     24.95    6,556    46.98    52.02
  *Total*                   50,459    100.00    28,725   205.84   48.58
  **Wealth**                                                     
   First quartile           12,639     25.05    5,806    41.61    63.17
   Second quartile          12,611     24.99    6,980    50.02    49.96
   Third quartile           12,625     25.02    7,086    50.78    48.44
   Fourth quartile          12,584     24.94    5,764    41.30    58.43
  *Total*                   50,459    100.00    25,636   183.7    54.44

*Note:* Individual-Year, *N* = 50, 459.

*Source:* SHARE data, years 2004--2015 (own estimates).

###### 

Linear hybrid models predicting frailty, by welfare cluster. Beta coefficient (first column) and 95 per cent confidence intervals (second column)

                                                                                  Southern                                       Western          Northern                                                                                                         
  ------------------------------------------------------------------------------- ---------------------------------------------- ---------------- ----------------------------------------------- ---------------- ----------------------------------------------- ----------------
  **Within variance**                                                                                                                                                                                                                                              
  *Income (ref.: 1st quartile)*                                                                                                                                                                                                                                    
   2nd quartile                                                                   0.002                                          −0.006, 0.009    0.003                                           −0.002, 0.008    −0.003                                          −0.010, 0.004
   3rd quartile                                                                   0.004                                          −0.004, 0.012    0.003                                           −0.002, 0.008    −0.003                                          −0.011, 0.004
   4th quartile                                                                   0.006                                          −0.003, 0.015    0.002                                           −0.002, 0.007    −0.003                                          −0.010, 0.004
  *Wealth (ref.: 1st quartile)*                                                                                                                                                                                                                                    
   2nd quartile                                                                   0.003                                          −0.006, 0.012    0.000                                           −0.006, 0.005    −0.007                                          −0.016, 0.001
   3rd quartile                                                                   0.004                                          −0.006, 0.015    −0.001                                          −0.007, 0.004    −0.003                                          −0.011, 0.005
   4th quartile                                                                   0.010[\*](#tblfn9){ref-type="table-fn"}        0.000, 0.019     −0.001                                          −0.007, 0.006    −0.003                                          −0.012, 0.006
  *Interaction: Gender* [\*](#tblfn9){ref-type="table-fn"} *Income*                                                                                                                                                                                                
   Women [\*](#tblfn9){ref-type="table-fn"} 2nd quartile                          −0.007                                         −0.018, 0.004    −0.001                                          −0.008, 0.005    0.004                                           −0.005, 0.013
   Women [\*](#tblfn9){ref-type="table-fn"} 3rd quartile                          −0.009                                         −0.021, 0.003    −0.002                                          −0.009, 0.004    0.002                                           −0.007, 0.011
  Women [\*](#tblfn9){ref-type="table-fn"} 4th quartile                           0.003                                          −0.010, 0.016    −0.001                                          −0.007, 0.006    0.004                                           −0.005, 0.013
  *Interaction: Gender* [\*](#tblfn9){ref-type="table-fn"} *Wealth*                                                                                                                                                                                                
   Women [\*](#tblfn9){ref-type="table-fn"} 2nd quartile                          0.000                                          −0.012, 0.012    −0.002                                          −0.009, 0.006    0.005                                           −0.006, 0.015
   Women [\*](#tblfn9){ref-type="table-fn"} 3rd quartile                          −0.005                                         −0.019, 0.009    −0.006                                          −0.014, 0.002    0.001                                           −0.010, 0.012
   Women [\*](#tblfn9){ref-type="table-fn"} 4th quartile                          −0.011                                         −0.025, 0.003    −0.002                                          −0.011, 0.007    0.000                                           −0.013, 0.012
  **Between variance**                                                                                                                                                                                                                                             
  *Level of Education (ref.: Low)*                                                                                                                                                                                                                                 
   Medium                                                                         −0.005                                         −0.017, 0.008    −0.006                                          −0.013, 0.001    −0.003                                          −0.013, 0.007
   High                                                                           −0.015                                         −0.032, 0.001    −0.012[\*\*](#tblfn10){ref-type="table-fn"}     −0.020, −0.004   −0.011[\*](#tblfn9){ref-type="table-fn"}        −0.020, −0.001
  *Income (ref.: 1st quartile)*                                                                                                                                                                                                                                    
   2nd quartile                                                                   0.000                                          −0.026, 0.025    −0.015[\*](#tblfn9){ref-type="table-fn"}        −0.027, −0.002   −0.025[\*](#tblfn9){ref-type="table-fn"}        −0.044, −0.006
   3rd quartile                                                                   −0.014                                         −0.036, 0.009    −0.022[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.034, −0.009   −0.026[\*\*](#tblfn10){ref-type="table-fn"}     −0.045, −0.008
   4th quartile                                                                   −0.012                                         −0.034, 0.010    −0.027[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.039, −0.016   −0.040[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.059, −0.022
  *Wealth (ref.: 1st quartile)*                                                                                                                                                                                                                                    
   2nd quartile                                                                   −0.011                                         −0.034, 0.011    −0.018[\*\*](#tblfn10){ref-type="table-fn"}     −0.030, −0.007   −0.037[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.053, −0.021
   3rd quartile                                                                   −0.018                                         −0.040, 0.003    −0.037[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.047, −0.027   −0.043[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.057, −0.029
   4th quartile                                                                   −0.020                                         −0.040, 0.000    −0.041[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.051, −0.031   −0.037[\*\*\*](#tblfn11){ref-type="table-fn"}   −0.053, −0.021
  *Interaction: Gender* [\*](#tblfn9){ref-type="table-fn"} *Level of education*                                                                                                                                                                                    
   Women [\*](#tblfn9){ref-type="table-fn"} Medium                                −0.021[\*](#tblfn9){ref-type="table-fn"}       −0.038, −0.005   −0.011[\*](#tblfn9){ref-type="table-fn"}        −0.020, −0.002   −0.002                                          −0.015, 0.011
   Women [\*](#tblfn9){ref-type="table-fn"} High                                  −0.021                                         −0.044, 0.002    −0.008                                          −0.018, 0.003    −0.003                                          −0.016, 0.010
  *Interaction: Gender* [\*](#tblfn9){ref-type="table-fn"} *Income*                                                                                                                                                                                                
   Women [\*](#tblfn9){ref-type="table-fn"} 2nd quartile                          −0.005                                         −0.040, 0.030    0.001                                           −0.017, 0.019    0.007                                           −0.019, 0.033
   Women [\*](#tblfn9){ref-type="table-fn"} 3rd quartile                          0.006                                          −0.025, 0.036    0.006                                           −0.011, 0.023    0.002                                           −0.022, 0.025
   Women [\*](#tblfn9){ref-type="table-fn"} 4th quartile                          −0.010                                         −0.040, 0.020    0.001                                           −0.015, 0.017    0.002                                           −0.022, 0.025
  *Interaction: Gender* [\*](#tblfn9){ref-type="table-fn"} *Wealth*                                                                                                                                                                                                
   Women [\*](#tblfn9){ref-type="table-fn"} 2nd quartile                          −0.014                                         −0.046, 0.017    −0.004                                          −0.020, 0.012    0.004                                           −0.018, 0.027
   Women [\*](#tblfn9){ref-type="table-fn"} 3rd quartile                          −0.025                                         −0.055, 0.005    0.002                                           −0.013, 0.016    0.002                                           −0.018, 0.023
   Women [\*](#tblfn9){ref-type="table-fn"} 4th quartile                          −0.037[\*\*](#tblfn10){ref-type="table-fn"}    −0.066, −0.009   −0.011                                          −0.025, 0.003    −0.007                                          −0.028, 0.015
  *Gender (ref.: Men)*                                                                                                                                                                                                                                             
   Women                                                                          0.068[\*\*\*](#tblfn11){ref-type="table-fn"}   0.044, 0.092     0.021[\*\*](#tblfn10){ref-type="table-fn"}      0.006, 0.035     0.010                                           −0.011, 0.030
  AIC                                                                             −29642.4                                                        −89741.9                                                         −41275.4                                        
  BIC                                                                             −29217.6                                                        −89233.0                                                         −40838.6                                        
  No. of observations                                                             11200                                                           27132                                                            12127                                           
  No. of groups (individuals)                                                     3036                                                            7615                                                             3304                                            

*Note*: ref.: reference category. Models include all the control variables. The estimates of the control variables (age, age^2^, age^3^, marital status, number of children, SHARE waves, and country of residence) are found in [Supplementary Table A4](#sup1){ref-type="supplementary-material"}.

*P* \< 0.05.

*P* \< 0.01.

*P* \< 0.001.

*Source*: SHARE data, years 2004--2015 (own estimates).

Dependent Variable: Frailty Index
---------------------------------

For a dependent variable, we use a 40-item FI of accumulated deficits, constructed in accordance with standard procedures ([@jcz007-B90]; [@jcz007-B81]). Frailty is considered a comprehensive concept and measure of health at older ages and it is highly predictive of subsequent adverse health outcomes ([@jcz007-B34]; [@jcz007-B81]). Current deficits used to construct the dependent variable are measured at each wave of SHARE and include objective health markers (grip strength), weight loss (body mass index deficit), functional impairments in personal and instrumental activities of daily living, self-reported health and comorbidities, mood (sadness or depression, lack of enjoyment, etc.), limitations in cognition (impaired orientation to date: day, month, year, and day of the week, etc.), and other measures (see [Supplementary Table A1](#sup1){ref-type="supplementary-material"}). Each individual's deficit points were summed and divided by the total number of deficits evaluated (in our case 40) to obtain a FI with a theoretical range from 0 (no deficits present) to 1 (all deficits present). For example, a respondent with five deficits would have a FI value of 0.125 (5/40). Higher values indicated a greater number of health problems and hence greater frailty. The reliability coefficient, Cronbach's alpha, for the 40 items, is 0.861, which is commonly considered adequate to sum the items to a scale. The distribution of the FI approximately showed a gamma distribution. Missing values for each item were negligible: except for grip strength (8.58 per cent of missing), all items showed less than 4 per cent missing values. Full information on the FI deficit variables and cut-off points, are reported in [Supplementary Table A1](#sup1){ref-type="supplementary-material"}.

Independent Variables
---------------------

Gender and SES are the key independent variables. SES is operationalized using three indicators, namely education, income, and wealth. Education is based on the international classification ISCED-97 and refers to the respondent's highest level of education. We classified education as low (ISCED 0, 1, and 2), medium (ISCED 3 and 4), and high (ISCED 5 and 6). This variable is collected only in the baseline interview and contained 1.83 per cent missing cases. Country and wave-specific quartiles of income and wealth were estimated at the household level and adjusted for family size (by dividing the variables by the square root of household size). Income and wealth were calculated based on an average of the five imputations provided in SHARE, which compensate for nonresponse. These two measures were assessed in each wave of the survey and refer, by survey design, to the year preceding the measurement of the dependent variable (i.e. the reference period ranges from time $t - 1$ and $t$).

Control variables include age, age-squared, age-cubed (to allow for nonlinear relations), current marital status (four categories: married[^1^](#jcz007-EN1){ref-type="fn"}; never married; divorced; widowed), current number of children (childless, 1, 2, 3+), SHARE waves, and country of residence. SHARE collected information on marital status and number of children in each wave of the study. We added these two control variables into the models due to their associations with SES and health ([@jcz007-B82]; [@jcz007-B37]; [@jcz007-B50]; [@jcz007-B39]; [@jcz007-B53]; [@jcz007-B24]). For all the control variables, missing values were below 2 per cent.

Classification of Countries
---------------------------

Assuming relative homogeneity of the key features of their socioeconomic institutions and policies ([@jcz007-B58]), we classified the nine European countries into three generic welfare clusters, which roughly represent different welfare state regimes and geographical regions ([@jcz007-B3]): *Northern Europe* (Denmark and Sweden). In accordance with the [@jcz007-B30], [@jcz007-B31]) and other typologies (e.g. [@jcz007-B32]), these two countries are classified as social democratic welfare states. The welfare policies of Denmark and Sweden, characterized by a universalistic approach to social rights, show high levels of defamilization ([@jcz007-B4], [@jcz007-B7]). In addition, they promote gender equality both on the labour market and in the care responsibilities, actively supporting dual-earner household arrangements ([@jcz007-B47]), in particular in families with young children ([@jcz007-B35]).*Southern Europe* (Italy and Spain). These countries have been classified as a distinct welfare state regime ([@jcz007-B32]; [@jcz007-B28]) because of their specificities: they are characterized by a sub-protective and more fragmented system of welfare provision with a higher reliance on family support as a form of welfare provision compared to other European countries ([@jcz007-B8]). The state support to families is extremely limited and women are encouraged to take up the family and care responsibilities ([@jcz007-B7],[@jcz007-B8]).*Western Europe* (Austria, Belgium, France, Germany, and Switzerland). These countries are classified in a different way according to the typology applied. They belong to the Bismarckian cluster in the [@jcz007-B32] typology, but some of them are recognized as conservative by others ([@jcz007-B2]). Generally, these countries represent a different regime than the Southern or Northern ([@jcz007-B30], [@jcz007-B31]), although there is not yet full agreement and some of them may share common characteristics with countries belonging to other welfare state regimes.

Analytic Strategy
-----------------

Statistical analysis is conducted using linear hybrid models ([@jcz007-B1]; [@jcz007-B12]; [@jcz007-B11]) and it aims at evaluating the associations between SES and frailty separately for each welfare cluster and gender. In doing so, we follow the procedure described by [@jcz007-B86]. Hybrid models are random-effects models that allow for separate within-cluster effects (i.e. fixed-effects estimates) and between-cluster effects ([@jcz007-B11]). Hence, like fixed-effects methods, hybrid models can control for time-constant unobserved individual heterogeneity ([@jcz007-B1]; [@jcz007-B12]; [@jcz007-B86]; [@jcz007-B11]). The great advantage of this approach is that it permits the inclusion of time-invariant variables (e.g. gender) in a fixed-effects framework. Before clustering the countries, we fitted separate hybrid models by country to check the similarity between the single country estimates (for details, see [Supplementary Table A2](#sup1){ref-type="supplementary-material"})[^2^](#jcz007-EN2){ref-type="fn"}.

Since a low level of education can lead to a low income and, consequently, to a low wealth, which in turn affect negatively health status ([@jcz007-B51]), we estimated three models for each country and gender: the first contains only education and all the basic control variables, the second adds dummies for each income quartile, and the third adds dummies for each wealth quartile. The first model allows us to estimate the total effect of education on frailty (Model 1), while the second and third models estimate respectively the total effect of income (Model 2, net of education) and wealth (Model 3, net of education and income). Moreover, the modifying effect of gender on the SES-frailty association was evaluated by including a product term between gender and each SES measure in separate regression models for all older adults combined[^3^](#jcz007-EN3){ref-type="fn"}.

In epidemiological literature, researchers have stressed that measuring effects on the additive scale is most appropriate for assessing the public health relevance of an exposure ([@jcz007-B45]). Contrary to multiplicative models (e.g. Poisson or log-linear models), modelling the FI in linear hybrid models allow us to measure effect modification on the additive scale.

Changes in the FI can be related to different types of attrition, including gender-specific health-related non-response, or selective mortality by gender. To adjust for sample loss due to attrition we estimated the regression models using inverse probability weighting (IPW). To calculate the weights, we have estimated a series of logistic regression models for response versus non-response in wave $t$ as a function of independent variables ($X_{t - 1}$) in a previous wave $t~–~1$, conditional on having participated in wave $t~–~1$ ([@jcz007-B95]; [@jcz007-B90]). The variables included in the models to calculate the inverse probability weights were the FI, gender, age, education, income, wealth, marital status, number of children, and country of residence. For each observation, we computed the inverse of the predicted probabilities from these models (${1/\hat{p}}_{t}$) and then used them to weight each observation in the multivariate analysis. The use of IPW as method to adjust for attrition gives more weight to those individuals with key demographic, socioeconomic, and health factors leading to a high probability of dropping out of the panel.

Since the violation of homoscedasticity assumption may be present when the dependent variable of linear regression models is not symmetric, we computed robust standard errors to relax the assumption of absence of heteroscedasticity. All analyses are performed using Stata 15.1.

Results
=======

Figures from 1 to 3 present the estimates from multivariate hybrid models which investigated---separately for welfare clusters and gender---associations between SES and frailty, controlling for time-constant unobserved heterogeneity at the individual level (full model estimates in tabular form are shown in [Supplementary Table A3](#sup1){ref-type="supplementary-material"}). The upper panel of the figures reports the within-effects (i.e. longitudinal) estimates, thus only considering variance within individuals. The lower panel presents the between-individual (i.e. cross-sectional) estimates. Filled circles represent the estimates obtained when controlling for socio-demographic variables and level of education (Model 1), while hollow rhombuses refer to estimates from models that also include quartiles of income (Model 2) and filled squares refer to estimates from models that add quartiles of wealth (Model 3). When interpreting the results from the regression analyses, it is important to note that the variation for the FI and SES measures is mainly driven by between-individual variance. However, there is also enough within variance to justify a fixed-effect approach ([Table 2](#jcz007-T2){ref-type="table"} and [3](#jcz007-T3){ref-type="table"}).

The main results are as follows. In all the three welfare clusters there is a statistically significant and clear educational gradient in frailty for both genders ([Figures 1--3](#jcz007-F1){ref-type="fig"}, Model 1). In line with our expectations, the educational gradient appears to be strongest for women living in Southern European countries, less strong in Western European countries, and smallest in Northern European countries. In the case of Southern Europe ([Figure 1](#jcz007-F1){ref-type="fig"}, Model 1), for example, a woman's FI is lower by 0.056 points if she belongs to the highest level of education instead of the lowest one (95 per cent CIs: −0.071, −0.040; *P* \< 0.001). This means that lower-educated Southern European women report at least two more deficits than higher-educated women in the 40-item FI. Including additional controls for quartiles of income ([Figures 1--3](#jcz007-F1){ref-type="fig"}, Model 2) reduces the magnitude of the educational coefficients, but does not alter the overall pattern. However, in this case, the total effect of income appears to be smallest for both men and women living in Southern European countries. Moreover, once relying solely on within-individual variance, the longitudinal association between income and frailty is not statistically significant (*P* \> 0.05).

![Linear hybrid models predicting frailty, by gender (Southern Europe). Estimates and 95 per cent confidence intervals.\
*Note*: Filled circles indicate estimates from models with level of education and sociodemographic controls only (Model 1); hollow rhombuses refer to models with additional controls for quartiles of income (Model 2); filled squares indicate estimates from models that add quartiles of wealth (Model 3). Models include all the control variables. Complete models are displayed in Supplementary Table A3.\
*Source*: SHARE data, years 2004--2015 (own estimates).](jcz007f1){#jcz007-F1}

![Linear hybrid models predicting frailty, by gender (Western Europe). Estimates and 95 per cent confidence intervals.\
*Note*: Filled circles indicate estimates from models with level of education and sociodemographic controls only (Model 1); hollow rhombuses refer to models with additional controls for quartiles of income (Model 2); filled squares indicate estimates from models that add quartiles of wealth (Model 3). Models include all the control variables. Complete models are displayed in Supplementary Table A3.\
*Source*: SHARE data, years 2004--2015 (own estimates).](jcz007f2){#jcz007-F2}

![Linear hybrid models predicting frailty, by gender (Northern Europe). Estimates and 95 per cent confidence intervals.\
*Note:* Filled circles indicate estimates from models with level of education and sociodemographic controls only (Model 1); hollow rhombuses refer to models with additional controls for quartiles of income (Model 2); filled squares indicate estimates from models that add quartiles of wealth (Model 3). Models include all the control variables. Complete models are displayed in Supplementary Table A3.\
*Source:* SHARE data, years 2004--2015 (own estimates).](jcz007f3){#jcz007-F3}

Model 3 ([Figures 1--3](#jcz007-F1){ref-type="fig"}) adds quartiles of wealth to Model 2. Considering the between variance, the results show a clear wealth gradient in frailty, which appears to be less steep for men living in Southern European countries and for both women and men living in the Scandinavian countries. Similarly to income (Model 2), when relying exclusively on within-individual variance, the longitudinal association between wealth and frailty is not statistically significant (*P* \> 0.05). The exception being women living in Western European countries ([Figure 2](#jcz007-F2){ref-type="fig"}, Model 3), where we find that a woman's FI rises by 0.007 points if she drops from the 3rd quartile of wealth to the 1st quartile (95 per cent CIs: −0.013, −0.002; *P* \< 0.01). Despite this effect size is negligible, a Wald test confirms this result (*P* \< 0.01), indicating that wealth has an overall longitudinal impact on the frailty levels of Western European women. It is interesting to note that the level of education has a statistically significant indirect effect, even after controlling for both income and wealth ([Figure 1--3](#jcz007-F1){ref-type="fig"}, Model 3).

To substantiate these findings, we evaluated potential effect modification of gender on the relationships between SES and frailty including gender and SES interaction terms in separate regression models for all older adults combined. [Table 4](#jcz007-T4){ref-type="table"} shows the results from these linear hybrid models, estimated separately for each welfare cluster (see [Supplementary Table A4](#sup1){ref-type="supplementary-material"} for full model estimates). Turning to our research question, [Table 4](#jcz007-T4){ref-type="table"} shows that the association between SES and frailty is stronger for women than for men in Southern (education and wealth) and in Western European countries (only for education), as indicated by the statistically significant effect modification of gender in those contexts (between-individual estimates). For example, we find that Southern European women are more vulnerable than men to the influence of wealth in terms of frailty: a woman's FI drops by 0.037 points if she belongs to the 4^th^ quartile of wealth instead of the 1st quartile (95 per cent CIs: −0.066, −0.009; *P* \< 0.01). The results of Wald tests confirm that the interaction terms are jointly different from zero (*P* \< 0.05).

As a robustness check, we estimated a fully interacted hybrid model to examine whether SES-related changes in the FI differed significantly by gender and welfare cluster (results available on request), and then a Wald test on the joint significance of all the interaction terms between welfare cluster, gender and the three measures of SES. The test rejects the null hypothesis of equality of the coefficient for education only (*P* \< 0.001). Since the time frame (i.e. the sequencing of the independent, control, and dependent variable) may be relevant for the analyses using fixed-effects models ([@jcz007-B65]), we additionally adopted a more restrictive 'time-adjusted' analysis: to overcome possible endogeneity issues, we lagged independent and control variables by one period relative to the dependent variable, which reduced the final sample to 36,504 observations from 13,955 individuals. The results hardly changed after allowing for lagged relationships (results available upon request).

Discussion and Conclusion
=========================

In this study, we have analyzed how the longitudinal associations between SES and health after midlife differs by gender and macro-level context in a sample of individuals aged 50 and above living in 9 European countries. Previous literature suggests that some of the complex relationships found between gender and health may be driven by individual socioeconomic factors as well as by the macro-level contexts in which individuals live. Our study makes a significant contribution to the literature on gender inequalities in health in later life by investigating the longitudinal associations between three measures of SES (education, income, and wealth) and frailty, a multi-dimensional comprehensive concept and measure of health. We tested these associations using comparative cross-national data and estimating 'hybrid' (between-within) regression models in different European welfare state clusters (Southern, Western, and Northern).

Considering only the between-individual variance in the hybrid models, our results support the cross-sectional findings that SES, as predictor of health in later life, does not have the same impact across gender within different socioeconomic contexts. What our results clearly show is that only in Southern (Italy and Spain) and Western European countries (Austria, Belgium, France, Germany, and Switzerland) the impact of education and wealth on health is stronger for women. Conversely, in Northern Europe (Denmark and Sweden) we did not observe any gender difference according to SES. The fixed-effects estimates from the hybrid models show that the intra-individual change in income and wealth does not cause a substantive change in health after midlife. Hence, our results partially corroborate the hypothesis that the longitudinal influence of SES---and, most importantly, the effect modification of gender---on health after age 50 is weaker in countries with high defamilization and decommodification. This is in line with the previous literature, since frailty-free life expectancy is lower for women than men, but these differences are less marked in Sweden and Denmark ([@jcz007-B80]). However, the fixed-effects estimates suggest that income and wealth might have only limited impact on health after midlife, while models with between-variation components might overestimate the influence of SES on health because they do not control for unobserved (time-constant) heterogeneity at the individual level. Moreover, while statistically significant, the effect sizes of the three measures of SES found in this study are not large.

Several explanations may account for the international variations observed between individuals. On the one hand, at least part of the variation can be ascribed to the more generous, decommodifying welfare state policies of the Scandinavian countries ([@jcz007-B30], [@jcz007-B31]), since they can protect better against the health effects of low SES ([@jcz007-B5]). Evidence of this is that there are weaker associations between education and factors subject to welfare state policy interventions (e.g. employment, income, wealth) in the Northern than in Southern or Western European countries ([@jcz007-B3]). Moreover, the more equal distribution of these resources in the Northern European countries, combined with the highest levels of defamilization ([@jcz007-B4], [@jcz007-B7]), may have contributed to smaller gender inequalities in health than in the less redistributive and less protective Southern and Western European countries.

On the other hand, we recognize the possibility that other factors, unobserved in our study, can account for these macro-level variations. Cross-national differences in the quality and stratification of the use of healthcare ([@jcz007-B92]) -- combined with the fact that women have a higher frequency of healthcare utilization than men ([@jcz007-B14]; [@jcz007-B96])---may also account for some of these differences. This study recommends that future studies should more carefully investigate these and other potential pathways.

The study has three noteworthy limitations that should be highlighted for future studies. First, all dimensions of frailty, except for maximum grip strength, are self-reported and may be sensitive to potential bias caused by cross-cultural ([@jcz007-B43]) and gender differences in reporting styles ([@jcz007-B96]). A possible solution could have been the use of additional information on reporting heterogeneity, examining variation in the evaluation of given health states represented by anchoring vignettes ([@jcz007-B44]). This would have resulted in a more robust analysis, purged from the individual's own health assessment. Unfortunately, the self-administered paper questionnaire containing vignettes has been administered only to a small sample and only in the first two waves of SHARE. Second, results may be affected by cross-national differences in the proportion of institutionalized older adults which are not surveyed in the first wave of SHARE. These two limitations could likely downward-bias the estimates of frailty in Northern European countries and upward-bias them in Southern European countries. Third, the analyses are based on five panel waves and this could limit the within-unit variation in the estimation of the parameters of the fixed-effect (hybrid) models. This may explain why the within-effects estimates were not statistically significant.

Despite the limitations outlined above, this study is, to our knowledge, the first longitudinal cross-national investigation of the magnitude of the relationship between SES and health in relation to gender in a sample of older adults over a 11-year period. This work stresses the important role of SES for maintaining good health at older ages, highlighting how education and wealth have a more powerful impact on health for older women living in the Southern and Western European countries than those living in the Northern European societies. This suggests that decommodifying and defamilializing welfare arrangements can reduce gender inequalities in health at later ages, especially amongst those from the lowest SES groups.

Supplementary Material
======================

###### 

Click here for additional data file.

This study was carried out as part of the multi-country project 'Care, Retirement & Wellbeing of Older People Across Different Welfare Regimes' (CREW). The CREW project is funded by the Joint Programming Initiative "More Years Better Lives" 2017-2020 (<https://crew-more-years-better-lives.org>). This paper uses data from SHARE Waves 1, 2, 3, 4, 5 and 6 (DOIs: 10.6103/SHARE.w1.611, 10.6103/SHARE.w2.611, 10.6103/SHARE.w3.611, 10.6103/SHARE.w4.611, 10.6103/SHARE.w5.611, 10.6103/SHARE.w6.611), see [@jcz007-B17] for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see [www.share-project.org](http://www.share-project.org)). The authors would like to thank the editors and the three anonymous reviewers for their helpful criticisms and suggestions, which have greatly improved the content of the paper. The usual disclaimers apply.

Funding
=======

The authors acknowledge funding from ZonMw (The Netherlands Organization for Health Research and Development). Project Number: 633500001.

Respondents are considered "married" if they reported: (a) being married and living with the spouse; (b) being married but living separated from the spouse; (c) having a registered partnership.

To substantiate our findings, we also applied linear random-effects models (results available upon request).

Following the indications provided by [@jcz007-B86], we estimated the interactions separately for the within and between-effects.

**Damiano Uccheddu** is a PhD Researcher at the Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW) and affiliated with University of Groningen (Faculty of Behavioural and Social Sciences). Before joining the NIDI-KNAW in June 2017, he received a Master's Degree in Sociology and Social Research from the University of Trento (Italy). His PhD project investigates the social and economic inequalities in health and well-being among older adults, from a comparative and longitudinal perspective. His main research interests lie in ageing, social determinants of health, welfare systems, and quantitative methods for social research.

**Anne H. Gauthier** is Senior Researcher at the Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW), Honorary Professor of Comparative Family Studies at the University of Groningen (RUG), and Director of the Generations and Gender Programme (GGP). She obtained her PhD from the University of Oxford and has since held teaching and research positions in Canada, the UK, the US and the Netherlands. Her expertise is on comparative family demography, transition to adulthood, fertility, and family policies.

**Nardi Steverink** is Professor of Sociology (Sociology of Health and Well-being) in the social and behavioural sciences at the University of Groningen (RUG) and the University Medical Center Groningen (UMCG), in the Netherlands. She is also teaching master courses (master sociology of health, well-being, and care) at the Department of Sociology of the University of Groningen. She is member of the Interuniversity Center for Social Science Theory and Methodology (ICS). Her main research interests lie in the social context of development and ageing, and the role social relations and self-management ability play in health and well-being over the life course.

**Tom Emery** is the manager of the Generations and Gender Programme (GGP) at the Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW) in The Hague. The GGP is a comparative, longitudinal survey which is fielded in 19 countries and provides data on family dynamics and demographic change to over 4,000 researchers worldwide. Tom gained a PhD in Social Policy from the University of Edinburgh in 2014 and his thesis examined the interaction between financial support between older parents and their adult children in a number of European countries. His research also covers questions of comparative survey methodology and policy measurements in multilevel contexts.
