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Young People’s Voting Behaviour in Europe pp 7–25Cite as

Palgrave Macmillan

Age, Generations and Voting Behaviour

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Abstract

This chapter provides a detailed overview of theories related to the relationship between age and politics (in particular, the voting choice), along with some historical and empirical examples. Furthermore, it introduces the reader to the so-called life-cycle, generation and period effects in order to outline the proper analytical framework for empirical analysis. In this regard, the author discusses the strategy for empirical analysis, presenting research questions, analytical framework, data and method. First, the author presents the conceptual features and the empirical success of the voting behaviour models and a specific comparative research design aimed at achieving a potential for generalization. Finally, the author translates the conceptual features of voting choice models into empirical variables which are testable through survey and party-related data.

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Notes

  1. 1.

    In the Appendix, the Continuity Guide presents a summary of surveys and datasets used, time in which data was collected, alongside with the list of all the variables included in the analysis.

  2. 2.

    The socio-demographic variables included in the current research are age; gender; socio-economic status (measured on income levels); education; social class according to the Erikson–Goldthorpe–Portocarero class schema (Erikson et al. 1979; Erikson and Goldthorpe 1992); and church attendance.

  3. 3.

    The battery of items measuring the confidence in several institutions (i.e. armed forces, parliament, civil services, justice system, police and so on) includes ‘easy’ and ‘difficult’ items. They are cumulative items which are not recognized by factor analysis. Therefore, I have run an ordinal IRT showing that all items are related to one component. Thus, I have created an additive index showing confidence in institutions. The Appendix presents the results of the IRT analysis.

  4. 4.

    These ten types of values are Achievement, Conformity, Tradition, Self-Direction, Stimulation, Hedonism, Security, Power, Universalism and Benevolence.

  5. 5.

    In particular, I have run a principal components factor (PCF) analysis including variables measuring respondents’ opinions on issues linked to values (for instance, opinion on divorce, on homosexuality, on euthanasia and on abortion) and variables showing attitudes toward issues like ‘cheating on taxes’, ‘accepting a bribe’ and ‘avoiding a fare in a public transport’. The result was two additive indexes linked to two different factor components. The first index is an indicator of libertarian values. The second one is an indicator of civic duties. Results of the PCF are presented in the Appendix.

  6. 6.

    In particular, datasets from ParlGov.org from 1981 to 2000. The files are ‘external_party_castles_mair.csv’, ‘view_party.csv’, ‘view_cabinet.csv’.

  7. 7.

    For a debate on the importance of the left–right dimension, see Downs (1957), Inglehart and Klingemann (1976), Fuchs and Klingemann (1989) and Bobbio (1994).

  8. 8.

    Within each party: same value of independent variable, same predicted value (y-hat). The independent variable has different effect for different parties. ‘Affinity’ is a descriptive association between variables, not a causal relationship. The predicted values (y-hats) are then centered on their means and saved as scores for the empirical analysis as party-respondent-specific predictors (van der Eijk and Franklin 1996; van der Eijk et al. 2006).

  9. 9.

    The models generally used in electoral studies are conditional logit models. Nevertheless, I have tested my general explanatory model for Italy in 1981 using both the conditional logit and the fixed-effects logistic regression and the results are identical. Since the analysis of this research is based on a pooled dataset with different countries and years, fixed-effects logistic regression is more appropriate. Furthermore, since models include dummies for countries and years, random-effects logistic regression is more suitable.

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Authors and Affiliations

  1. University of Florence, Florence, Italy

    Nicola Maggini

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  1. Nicola Maggini
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Maggini, N. (2017). Age, Generations and Voting Behaviour. In: Young People’s Voting Behaviour in Europe. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-59243-9_2

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