Measuring and analysing the factors that affect the quality of life (QoL) in cities and regions has long been the subject of theoretical and empirical work in a wide range of fields. More recently there have been an increasing number of studies involving traditional so-called objective indicators of QoL as well as more subjective measures of well-being, drawing on the emerging new science of happiness. This article presents an overview of studies in this field and highlights the key issues and debates pertaining to measuring, analysing and theorising QoL and happiness in cities and regions. It also highlights the importance of geographical and socio-economic contextual factors pertaining to QoL, well-being and happiness with a particular emphasis on the impact of social and spatial inequalities and social justice

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.

Background In recent years there have been numerous attempts to define and measure happiness in various contexts and pertaining to a wide range of disciplines, ranging from neuroscience and psychology to philosophy, economics and social policy. This article builds on recent work by economists who attempt to estimate happiness regressions using large random samples of individuals in order to calculate monetary ‘compensating amounts’ for different life ‘events’. Methods We estimate happiness regressions using the ‘major life event’ and ‘happiness’ data from the British Household Panel Survey. Results The data and methods used in this article suggest that in contrast to living states such as ‘being married’, it is more events such as ‘starting a new relationship’ that have the highest positive effect on happiness. This is closely followed by ‘employment-related gains’ (in contrast to employment status). Also, women who become pregnant on average report higher than average levels of subjective happiness (in contrast to ‘being a parent’). Other events that appear to be associated with happiness according to our analysis include ‘personal education-related events’ (e.g. starting a new course, graduating from University, passing exams) and ‘finance/house related events’ (e.g. buying a new house). On the other hand, the event that has the highest negative impact upon happiness according to our analysis is ‘the end of my relationship’ closely followed by ‘death of a parent’. Adverse health events pertaining to the parents of the respondents also have a high negative coefficient and so does an employment-related loss. Conclusion 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.