Among the many factors that influence the voting behaviour of individual voters, various spatial characteristics have repeatedly been cited as important factors. The diverse impacts of spatial characteristics are collectively referred to as contextual effects. Contextual effects impact voters in different ways: through differences in the local geographical and socio-economic conditions, the varying influence of local communication interactions, observational influences, differences in local political socialisation and local campaigning, or through the effect of candidate residency. This article presents an overview of the most frequently discussed contextual effects and formulates a general typology of them.
This article studies the links between a country’s labour force participation rate and attitudes towards income redistribution. The article also demonstrates how to specify a multilevel model when analysing contextual effects and it presents several types of random effects structures and options for centering explanatory variables in comparative longitudinal survey data. The contextual effect is decomposed into longitudinal and cross-sectional components for time-varying contextual variables, such as the labour force participation rate. The analysis of redistribution support based on ESS data from 27 countries and nine rounds shows how fundamentally the mentioned properties can influence substantive conclusions. The analyses presented in this article do not provide any evidence for a link between redistribution support and the labour force participation rate. However, the hypothetical configurations of multilevel models presented here cover all possible substantive effects of the labour force participation rate. Contextual effects analysis may thus lead to highly unreliable results when a multilevel model fails to control for the compositional effects of individual-level predictors, when it does not specify random effects at the level to which a substantial variation of the outcome variable may be attributed, and when it does not distinguish between the longitudinal and cross-sectional effects of time-varying variables.
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