Assessing Health Endowment, Access and Choice Determinants: Impact on Retired Europeans’ (In)activity and Quality of Life

Abstract

A future for the E.U., dominated by an ever-increasing population of retired citizens represents a major challenge to social and health policy in European countries. Under Rowe and Kahn's (Gerontol 37(4):433-440, 1997) perspective on positive aging, this paper is interested in exploring the role of health on citizens' active participation after retirement and social engagement to life and quality of life. This paper also aims at finding whether Sen's (Public health, ethics, and equity. Oxford University Press, Oxford, 2004) capability approach or cumulative disadvantage or advantage theory relative to the access to health also verifies in a context of multi-national developed economies. The first part of this study is therefore concerned with generating a health indicator that enables this, whilst controlling for individual heterogeneity in self-rated health responses from 10,859 retired individuals from the SHARE survey. Socioeconomic determinants of health are found not to be critical in determining health in such a developed context whilst cumulative advantage is found relevant for the positive aging of Europeans. Evidence is found that active engagement in activities and quality of life are most certainly a prerogative for the more educated and the healthier retirees, hinting a strategy for European policymakers: cumulative advantage, leveraged by education and health policy, might just be the longThis paper uses data from the early release 1 of SHARE 2004. This release is preliminary and may contain errors that will be corrected in later releases. The SHARE data collection has been primarily funded by the European Commission through the 5th framework programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life). Additional funding came from the US National Institute on Ageing (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064). Data collection in Austria (through the Austrian Science Foundation, FWF), Belgium (through the Belgian Science Policy Office) and Switzerland (through BBW/OFES/UFES) was nationally funded. The SHARE data set is introduced in Borsch-Supan et al. (2005); methodological details are contained in Borsch-Supan and Jurges (2005

Activity Models in Late Adulthood. Educational Context

“…In Europe, extensive research has been carried out regarding the connection of an active lifestyle with longevity, satisfaction with life, life quality, the so-called successful ageing or social capital. The example is the project SHARE Survey of Health, Ageing and Retirement in Europe (Rebelo & Pereira, 2014;Vozikaki et al, 2016;Lee, 2018;Kalbarczyk-Stęclik & Mackiewicz-Łyziak, 2019).…”

Analysis of reporting behavior using the hopit R-package (v0.11.5)

“…Similarly, if interviewees are asked to evaluate their general health (e.g., "Would you say your health is … ? "), they may choose among several categories, such as "very good", "good", "fair", "bad", and "very bad" (King et al 2004;Jürges 2007;Rebelo and Pereira 2014;Oksuzyan et al 2019). In political science, respondents may be asked for their opinions about recent legislation (e.g., "Rate your feelings about the proposed legislation.…”

hopit: An R Package for Analysis of Reporting Behavior Using Generalized Ordered Probit Models

“…Similarly, if interviewees are asked to evaluate their health in general (e.g., "Would you say your health is … ? "), they may choose among several categories, such as "very good", "good", "fair", "bad", and "very bad" (Jürges, 2007;King, Murray, Salomon, & Tandon, 2004;Oksuzyan, Dańko, Caputo, Jasilionis, & Shkolnikov, 2019;Rebelo & Pereira, 2014). In political science, a respondent may be asked for an opinion about recent legislation (e.g.…”

“…When the dependent reported ordered variable is self-rated health status, then the first set of variables -i.e., health variables -assess specific aspects of individuals' health, such as measures of chronic conditions, mobility, difficulties with a range of daily activities, grip strength, anthropometric characteristics, and lifestyle behaviors. Using the second set of independent variables (threshold variables), the model also adjusts for differences across socio-demographic and cultural groups, such as differences in cultural background, gender, age, and education (Jürges, 2007;King et al, 2004;Oksuzyan et al, 2019; but see Rebelo & Pereira, 2014).…”

“…poor Reporting behavior analysis is based on the reclassification of individuals into new response categories. There are two methods of reclassification: (1) Jürges (2007) percentile method (see also Rebelo & Pereira, 2014;Oksuzyan et al, 2019) and (2) reclassification directly based on model-estimated thresholds. In the first method, the classification is based on the calculated latent index which is adjusted for inter-individual differences in reporting behavior.…”