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This article aims to add a regional science perspective and a geographical dimension to our understanding of substantive questions regarding self-reported happiness and well-being through the specification and use of multilevel models. Multilevel models are used with data from the British Household Panel Survey and the Census of UK population to assess the nature and extent of variations in happiness and well-being to determine the relative importance of the area (district, region), household, and individual characteristics on these outcomes. Having taken into account the characteristics at these different levels, we are able to determine whether any areas are associated with especially positive or negative feelings of happiness and well-being. Whilst we find that most of the variation in happiness and well-being is attributable to the individual level, some variation in these measures is also found at the household and area levels, especially for the measure of well-being, before we control for the full set of individual, household, and area characteristics. However, once we control for these characteristics, the variation in happiness and well-being is not found to be statistically significant between areas.

Abstract.  Turnout at general elections across Europe is in decline as it is in other established democracies. A particular cause for concern is that young people are less likely to participate than older voters. Evidence presented in this article, based on national election results and the 2002–2003 European Social Survey, shows the overall turnout rate for 22 European countries in elections between 1999 and 2002 was 70 per cent compared to 51 per cent for electors aged less than 25. The authors examine national variations in turnout for young people across Europe, and use multilevel logistic regression models to understand these variations, and to test the extent to which they are attributable to the characteristics of young people and the electoral context in each country. Variations in turnout among young people are partially accounted for by the level of turnout of older voters in the country and partly by the characteristics of young voters, including the level of political interest and civic duty. The authors conclude that both individual‐level and election‐specific information are important in understanding the turnout of young electors.

This paper reviews the automated zone-design procedures adopted for the creation of 2001 Census output geography in the United Kingdom. A microsimulation approach is used for the creation of household records to populate actual postcode and enumeration district boundaries, and a series of output area design scenarios are applied to these data, allowing the effects of the new design constraints to be evaluated. The authors identify the advantages of using an intra-area correlation measure for the maximization of social homogeneity within output areas, and explore the differences between the 1991 and 2001 approaches to output geography.

The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications.