Abstract
Intergenerational relationships in ageing societies differ between countries: the shapes they take are difficult to explain, and it is tempting to refer to the vague concept of ‘culture’. This chapter contends that norms can be associated with the frequency of contacts between adult children and their elderly parents. The empirical analysis applies multilevel ordered logistic regression models to cross-country data derived from the Survey of Health, Ageing and Retirement in Europe (SHARE). The three-level structure considers the adult child, his or her parent (aged 50 or more), and the country (ten European countries). The normatively accepted division of responsibility for elderly care between family and state (a variable based on Eurobarometer data) is considered at the macro-level. The results suggest a strong association between social norms and frequency of intergenerational contacts.
Notes
- 1.
Following the classification of welfare states, three regimes or clusters of family policy are identified (e.g. Esping-Andersen 1990; Ferrera 1996): (1) the Scandinavian social democratic countries (DK, SE), (2) the conservative countries (AU, BE, DE, FR), and (3) the familistic regimes in the Mediterranean (ES, GR, IT). The Netherlands is considered a hybrid between liberal, conservative, and social democratic, and its classification depends on the focus of study. SHARE collected data also in Ireland and Israel. However, these countries are not considered in this chapter in order to keep the focus on the three clusters mentioned above.
- 2.
I have also controlled for the presence of at least one sibling living with the parents, and results (not shown here) do not change appreciably from those presented below.
- 3.
The gllamm command in STATA with a minimum of eight integration points allows three-level models with an ordered logistic framework (Rabe-Hesketh and Skrondal 2005).
- 4.
Both the AIC (Akaike 1971, 1974) and the BIC (Schwarz 1978) are measures of the goodness of fit of an estimated model used for model selection between parametric models with different numbers of parameters. AIC and BIC are calculated as follows: \( {\hbox{AIC}} \equiv - 2\log (L) + 2(k) \); \( {\hbox{BIC}} \equiv - 2\log (L) + k\log (N) \), where L is the maximum likelihood for the estimated model, k is the number of independently adjusted parameters within the model, and N is the number of data-points used in the fit. Given several competing models, AIC and BIC allow a ranking. Models with smaller deviance, AIC, and BIC should be preferred over other models. Whereas the deviance which is −2 times the log-likelihood can simply be reduced by adding a new predictor to the model, the AIC and the BIC penalise for adding new predictors to the model, with BIC being more conservative than the AIC (Gelman and Hill 2007: 524–525).
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Acknowledgements
This chapter uses data from SHARE release 2.3.0, as of November 13th 2009. SHARE data collection in 2004–2007 was primarily funded by the European Commission through its 5th and 6th framework programmes (project numbers QLK6-CT-2001-00360; RII-CT-2006-062193; CIT5-CT-2005-028857). Additional funding by the US National Institute on Aging (grant numbers U01 AG09740-13S2; P01 AG005842; P01 AG08291; P30 AG12815; Y1-AG-4553-01; OGHA 04-064; R21 AG025169) as well as by various national sources is gratefully acknowledged (see http://www.share-project.org for a full list of funding institutions).
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Bordone, V. (2012). Social Norms and Intergenerational Relationships. In: De Santis, G. (eds) The Family, the Market or the State?. International Studies in Population, vol 100. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4339-7_8
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