Composite Indicators for the Family Change: ‘Familism' vs ‘Individualism' in the International Context
Ayuso-Sánchez, Luis Manuel,De-Miguel-Luken, Verónica
- Published
- 2016-07-01
- Language
- en
Abstract
The aim of this paper is to propose a composite indicator to measure ‘familism’, conformed by two main dimensions: values on one hand (duty to take care of the family, importance of the family, sacrifices for the family...) and behaviours, on the other (predominance of married couples instead of cohabitant couples, high frequency of contact among members, family support…). In contrast to this idea of ‘familism’ we find that of individualism, that defends the independence of family members, tolerance to new family models, cohabitation instead of marriage,… , that implies less frequency of interaction among relatives and more governmental intervention towards children and elderly care. We observe that a higher degree of ‘familism’ does not always match with a lower degree of individualism when both dimensions, attitudes and behaviours, are considered. For instance, we find countries which are individualist in values but not in behaviours (such as Spain), whilst others, such as Japan, are ‘familist’ both in values and behaviours and finally, others, such as Sweden, are individualist with regards to both perspectives. We propose two different methodological approaches to the question. First, we use microdata from the Family, Work and Gender Roles module of the International Social Survey Programme-ISSP (years 1994, 2002 and 2012), in which 45 countries have participated. Information for the three rounds is collected for 17 countries with very different family values and welfare systems (for instance, Sweden, Japan, Russia, Spain, United Kingdom or the United States). From this data source, we create a first index on familism that can be related to individual sociodemographic characteristics. Second, we complete it through the inclusion of macro data (such as the divorce rate per country), in order to refine comparison at a country level by adding new variables to the previous index.
Full text
LUIS AYUSO-SÁNCHEZ VERÓNICA DE MIGUEL-LUKEN UNIVERSITY OF MALAGA (SPAIN) Composite Indicators for the Family Change: ‘Familism' vs ‘Individualism' in the International Context Third ISA Forum of Sociology Vienna, July 10-14, 2016 Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech Research project: Family challenges in the beginning of the XXI century. The impact of family individualization on culture, fertility and social welfare (CSO2013-46440-P).
Objectives To build up some indexes on family issues from survey data at a micro level that allow us to study individual behaviours in explanatory models and compare across countries. To contrast these indexes with macro data at a country level, that allows us to classify the countries according this double perspective.
Data sources ‘Soft’ data: International Social Survey Programme (ISSP) – 2012 Family and Changing Gender Roles IV (previous waves will be included, when possible). ‘Hard’ data: OECD Family Database / Social Spending Statistics. UN Statistical Databases. World Bank Databases. Eurostat. Still important gaps in the available data, even for OECD countries (e.g. maternal employment rates for Iceland, Japan, Norway…)
Methodology Composite indicators from the ISSP microdata. People’s opinions, behaviours and attitudes towards different family issues COMPOSITE INDICATOR(S) AT THE INDIVIDUAL LEVEL Objective macro data from official statistics. Indicators at a country level – what is ‘really’ happening COMPOSITE INDICATOR(S) AT THE COUNTRY LEVEL
Composite indicators from ISSP data Index for ‘familism’ (care and costs) Index for ‘tolerance towards new family forms’ Index for ‘domestic tasks and family care’ Index for ‘decisions at home (children’s education, leisure time and income administration)’ Index ‘equity in the couple’
ISSP’s composite indicators for ‘familism’ Who should provide childcare / help to elderly people. Q33. People have different views on childcare for children under school age. Who do you think should primarily provide childcare? Q35. Thinking about elderly people who need some help in their everyday lives, such as help with grocery shopping, cleaning the house, doing the laundry, etc. Who do you think should primarily provide this help? Who should cover the costs of childcare / help to elderly people. Q34. Who do you think should primarily cover the costs for children under school age? Q36. And who do you think should primarily cover the costs of this help to these elderly people?
Who should take care of the children/elderly? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Philippines Argentina China Poland Mexico Croatia Bulgaria Venezuela South Africa Switzerland Latvia Taiwan India Russia Chile Japan Czech Rep. Turkey Austria United States Spain Slovakia Israel Ireland Germany-West Great Britain Lithuania Canada South Korea Australia Slovenia France Germany-East Norway Finland Sweden Denmark Islandia family government family -children+government-elderly government-children+family-elderly family+other government+other other na Source: own elaboration from the ISSP 2012 microdata.
Who should cover the costs? Source: own elaboration from the ISSP 2012 microdata. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% China Philippines Bulgaria Argentina Switzerland United States Croatia Canada India Mexico South Africa Venezuela Poland Japan Australia Ireland Latvia Great Britain Taiwan Czech Rep. Russia Turkey France Chile Lithuania Slovakia Slovenia Norway Germany Spain Austria South Korea Finland Denmark Israel Sweden Iceland family government family -children+government-elderly government-children+family-elderly employers-children+family-elderly employers-children+government-elderly
Missing data… Some alternative sources have been searched in order to fill the gaps in the data. However, still many missing data… so imputation is risky (even cluster analysis is not useful since gaps sometimes affect a whole dimension). Three approaches are tried: Factor analysis with the variables with no missing data (5 var., 29 countries). Factor analysis with the countries with values in all considered variables (10 var., 18 countries). Factor analysis with variables for each dimension with less missing values ( 7 var., 25 countries).
Thanks for your attention!