Investigating the Role of Neighbourhood Characteristics in Determining Life Satisfaction

Melbourne Institute Working Paper No. 24/03

Date: September 2003


Mike Shields
Mark Wooden


This paper reports on an analysis of life satisfaction data collected as part of the first wave of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. More specifically, the clustered nature of the HILDA sample was used to test the role of neighbourhood effects in accounting for inter-personal differences in self-reported life satisfaction scores. A regression model predicting individual differences in life satisfaction was developed and tested for men and women separately. When this model was estimated allowing for fixed neighbourhood effects (based on the Census Collection District in which a sample member resides), strong support for sizeable effects were found. Indeed, observable individual and household characteristics (such as age, sex, employment status and household income) were only found to account for about 12 to 14 per cent of the variation in measured life satisfaction. Of the variance unexplained, close to 10 per cent could be accounted for by unobserved differences across neighbourhoods. While identifying the presence and magnitude of neighbourhood effects proved to be relatively straightforward, determining the source of these neighbourhood differences is a very different matter. Essentially, these neighbourhood effects can arise either because individuals in the same neighbourhood tend to behave similarly because they face similar environments or have similar characteristics, or because the behaviour of individuals is affected by the behaviour of other residents of the neighbourhood. Some evidence was uncovered to suggest that the latter type of effect might be relatively more powerful in explaining differences in life satisfaction. Unfortunately, this conclusion is tentative at best, with measurable neighbourhood characteristics only found to have a relatively small impact on the overall explanatory power of the regression models.

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