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In this paper we present an account of a 3-year research project that is aimed at dynamically simulating urban and regional populations in Britain. In the context of this project we are using data from the 1991 UK Census Small Area Statistics (SAS) and the British Household Panel Survey (BHPS), in order to dynamically simulate the entire population of Britain into 2021 at the small area level. This paper discusses the structure, aims and objectives of SimBritain and presents some preliminary results. Firstly, alternative spatial microsimulation strategies are discussed and their advantages and drawbacks are outlined. Next, the difficulties in calibrating and validating dynamic microsimulation models such as SimBritain are highlighted and ways to tackle these difficulties are explored. The paper then presents some model outputs that highlight the geographical variation of a wide range of socio-economic variables through the 1990s. Moreover, in light of these outputs, the paper discusses the potential of SimBritain for policy analysis.

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.

This paper analyses the effect accessibility has on General Practitioner (GP) utilisation rates at the sub‐national level for Ireland. Specifically, the aim of this paper is to estimate whether there is an urban–rural differential in GP utilisation rates. We do this by simulating micro‐level healthcare data. Using this synthetic data, simple logit models are employed to estimate the likelihood that individuals in different jurisdictions will attend a GP surgery. These individual logit estimates are then inputted into a spatial interaction model to highlight areas with low GP accessibility given their health status. The policy implications of these results are discussed in relation to both the healthcare literature and current Irish healthcare policy.

Microsimulation describes economic and social events by modelling the behaviour of individual agents. These models have proved useful in evaluating the impact of policy changes at the micro‐level. Spatial microsimulation models contain geographical information and allow for a regional or local approach to policy analysis. This paper builds on previous work on urban systems by employing similar modelling techniques for the analysis of rural areas. It describes the development of the SMILE (Simulation Model for the Irish Local Economy) model. SMILE is a dynamic spatial microsimulation model designed to analyse the impact of policy change and economic development on rural areas in Ireland. At its core, SMILE is a model of population. It simulates the basic components of population change, fertility, mortality and internal migration, at a small area level. This paper describes the method for projecting population change at the sub‐county level. Results from the 1991 and 1996 dynamic model at county level are discussed, and a brief comparison is made with other methods. Finally, the features that distinguish microsimulation models from other population projection models are discussed. Copyright © 2005 John Wiley & Sons, Ltd.

In this article, we use a dynamic spatial microsimulation model of Britain for the analysis of the geographical impact of policies that have been implemented in Britain in the last 10 years. In particular, we show how spatial microsimulation can be used to estimate the geographical and socio-economic impact of the following policy developments: introduction of the minimum wage, winter fuel payments, working families tax credits, and new child and working credits. This analysis is carried out with the use of the SimBritain model, which is a product of a 3-year research project aimed at dynamically simulating urban and regional populations in Britain. SimBritain projections are based on a method that uses small area data from past Censuses of the British population in order to estimate small-area data for 2001, 2011, and 2021.

The analysis presented in this article suggests that what matters the most in people's lives in Britain is to have good dynamic interpersonal relationships and to be respected at work with that respect being constantly renewed. These 'goods' are as much reflected through dynamic events as static situations. Relationships at work appear to be of a similar order of importance to those at home. Other factors that contribute to higher than average levels of subjective happiness, at least at a superficial level, include delaying death and keeping illness at bay, having babies, buying homes and cars and passing exams. The analysis presented here also suggests that people should not expect too much from their holidays and wider families. The findings presented in this article may help us to understand a little better the propensity for groups to be more or less happy and may help us to begin to better understand the importance of the dynamics of social context-the context in which we come to terms with reward and loss.

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