Academic, vocational or general? An analysis of public opinion towards education policies with evidence from a new comparative survey

Abstract

Education policy is a salient topic both in political debates and in the scholarly literature. Still, the study of individual policy preferences on education policy has received little scholarly attention, mostly because existing comparative surveys provide only very crude measures on education policy. To address this research gap, we conducted a representative survey of public opinion on the details of education policy in eight Western European countries. This article, first, presents analyses of these data, focusing on people’s preferences for public expenditure on education relative to other social policies as well as its distribution across different education sectors (early childhood and pre-primary education, general schools, vocational education and training, and higher education). In contrast to existing surveys, our survey forces citizens to prioritize between different policy areas and education sectors. We investigate determinants of individual preferences, focusing particularly on self-interest, ideological norms and institutional feedback effects. We find that individual educational background, partisan ideology and having children are significantly associated with variation in preferences. Furthermore, we find tentative evidence for self-undermining institutional feedback effects. © 2017, © The Author(s) 2017.

Keywords

Author Keywords

Education policy public opinion INVEDUC survey policy preferences welfare state research

Index Keywords

female human male Vocational education education policy human experiment comparative study Europe child ideology childhood infant policy welfare public opinion welfare provision Western European

Author Affiliations

Universität Konstanz, Konstanz, Baden-Wurttemberg, Germany

Universität Konstanz, Konstanz, Baden-Wurttemberg, Germany, Universität Zürich, Zurich, ZH, Switzerland

Funding & Acknowledgements

European Commission, EC

financed by a Starting Grant from the European Research Council (ERC), Grant No. 311769. To avoid ambiguity, we also made the following definition available to respondents on request: \u2018Early childhood education refers to the early childhood educational development for children between 0\u20132 years and pre-school education from the age of 3 to the start of primary education\u2019. As Tables C1\u2013C3 in Online Appendix show, running multilevel models instead of the single-level equations with country fixed-effects and country-clustered errors produces highly similar results. In additional models, we used several other opera-tionalizations of the variables (e.g. educational years, individual income and non-linear effects), which produced highly similar results. We discuss these findings below. In calculating the descriptive statistics, we employ survey weights in order to correct for selection probabilities and population sizes (see Busemeyer et al., 2017 for more details). When we use education years instead, this also becomes visible, as support for higher education (in both Q3 and Q4) increases with years of education but decreases for vocational education and training (VET; in the constrained scenario, Q4). http://ec.europa.eu/education/policy/strategic-framework_en

European Research Council, ERC

Grant: 311769

financed by a Starting Grant from the European Research Council (ERC), Grant No. 311769. To avoid ambiguity, we also made the following definition available to respondents on request: \u2018Early childhood education refers to the early childhood educational development for children between 0\u20132 years and pre-school education from the age of 3 to the start of primary education\u2019. As Tables C1\u2013C3 in Online Appendix show, running multilevel models instead of the single-level equations with country fixed-effects and country-clustered errors produces highly similar results. In additional models, we used several other opera-tionalizations of the variables (e.g. educational years, individual income and non-linear effects), which produced highly similar results. We discuss these findings below. In calculating the descriptive statistics, we employ survey weights in order to correct for selection probabilities and population sizes (see Busemeyer et al., 2017 for more details). When we use education years instead, this also becomes visible, as support for higher education (in both Q3 and Q4) increases with years of education but decreases for vocational education and training (VET; in the constrained scenario, Q4). http://ec.europa.eu/education/policy/strategic-framework_en

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Academic, vocational or general? An analysis of public opinion towards education policies with evidence from a new comparative survey

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