Tolerance, social capital, and life satisfaction: a multilevel model from transition countries in the European Union

Abstract

Tolerance of others on grounds of race, ethnicity, nationality, religion and sexuality is an important component of social capital but has received scant attention in the social capital well-being literature. We examine the components of social capital and their relationship with life satisfaction using data from the Life in Transition Survey in European Union transition countries. A principal component factor analysis identifies three distinct and independent social capital components: tolerance, ties, and trust. Using a multilevel modelling approach, we estimate the relation between these components and life satisfaction, whilst controlling for individual and area effects. Tolerance, ties (networks) and trust are positively associated with life satisfaction.

KEYWORDS:

• Life satisfaction
• social capital
• tolerance
• transition countries
• multilevel model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The replication data developed from the Life in Transition Survey (LiTS) III described in the paper, is available online at https://doi.org/10.7910/DVN/SOTRDJ, Harvard Dataverse.

Notes

1 STATA’s factor, pcf command is used.

2 We use the user-written command polychoric in STATA 15.

3 Alternatively, if we apply the Kaiser criterion and retain all factors with eigenvalue over 1, seven factors will be retained explaining 74 per cent of the overall variance. The factor loading breakdown for these is provided in Appendix Table A1. Here, it can be identified that factor 1 relates to measures of formal ties, factor 2 relates to measures of trust particularly for political and economic institutions, factor 3 relates to measures of tolerance and factors 4–7 relate to other various combinations of trust indicators. If these factor scores are generated instead, the general results that are presented later in the paper are robust to these alternative factors, i.e. the factors exhibiting all these measures are significant and positive (except for factor 7). These results are presented in the supplemental documentation Table A5 in Appendix. In summary, the number of factors either three or seven does not matter for the overall general conclusions. It should also be noted that the Kaiser criterion is known for over specifying the number of factors (Mooi et al., 2018) and this is why we rely on both the screeplot and theoretical reasoning for deciding to constrain the factor analysis to three factors as factors 1, 2 and 3 are loaded heavily by all the components included and in the order of ties, trust and tolerance indicators respectively.

4 A sensitivity analysis was completed with a different continuous Log of income indicator. It is not reported as there were missing values with this variable. The results of income were robust. A correlation matrix of our variables indicates that there are no multicollinearity concerns present in our data. This table is not reported but can be obtained from the authors.

5 Previous literature have also estimated linear versions of ordinal dependent variables (Ferrer-I-Carbonell & Frijters, 2004). We also report the multilevel linear version of this estimation which can be viewed by interested readers in Appendix A3. 

Additional information

Notes on contributors

Frank Crowley

Frank Crowley is a lecturer in Economics and Director of the Spatial and Regional Economics Research Centre at Cork University Business School, University College Cork. His primary research interests are in innovation, enterprise development and regional and urban development.

Edel Walsh

Edel Walsh is a lecturer in the Department of Economics, Cork University Business School, University College Cork. Her research area is health economics, specifically well-being determinants, health inequalities and ageing populations.