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Life satisfaction and the economic and social characteristics of neighbourhoods

Abstract

This paper uses data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey to investigate the association between neighbourhood effects and life satisfaction. We find that neighbourhood measures of social support and interaction and the absence of socio-economic deprivation are positively and significantly correlated with individual life satisfaction. Neighbourhood fixed effects, however, explain only an additional 1.5 to 2.5% of the variance in life satisfaction over the 14% explained by individual characteristics.

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Notes

  1. 1.

    These rules stipulated that where a dwelling contained three or fewer households, all such households should be sampled. Where there were four or more households occupying one dwelling, all households had to be enumerated and a random sample of three households obtained (based on a predetermined pattern).

  2. 2.

    We drop 18 individuals for whom no life satisfaction measure is available and a further 48 cases where the other required information is incomplete. We are, thus, able to use over 99.5% of the original sample.

  3. 3.

    For example, is a community defined by residence in a street, group of streets, suburb or town? Further, does it even make sense to restrict membership of a community to the residents of that community? What about people who work or participate in other activities within that locality? The question of how to geographically demarcate a local community remains, and we are unable to explore alternative definitions with the available data.

  4. 4.

    There are two further bands—very remote Australia and migratory areas—which none of the CDs selected in the HILDA sample fall into.

  5. 5.

    The incidence of ‘don’t know’ responses ranged from 1% (for rubbish and litter lying around) to 11% (for neighbours helping each other out) of the sample. Such cases were excluded in the construction of neighbourhood averages. The neighbourhood measures are calculated using the pooled sample of males and females.

  6. 6.

    As with many studies in the life satisfaction literature (e.g. Di Tella et al. 2003), we have also estimated the models using a linear fixed effects model on the assumption that the life satisfaction can be treated as continuous and cardinal. In practice, this assumption makes little difference to the estimates of the determinants of life satisfaction (see, for example, Ferrer-i-Carbonel and Frijters 2004). These results, all of which are available from the corresponding author, confirm this for our data, and crucially, the estimated importance of neighbourhood effects remains of the same magnitude.

  7. 7.

    One potential method to tackle this issue is the approach adopted by Dustmann and Preston (2001) who used broad region of residence dummies as instruments for local area ethnic density in equations explaining feelings of racial hostility in the UK. Their argument was that individuals (given their taste for living in close proximity to ethnic minorities) can self-select into and out of areas that have different ethnic minority densities. However, while whites might decide not to reside in high ethnic minority density areas, they are unlikely to move out of the broader region of residence (i.e. they simply might locate to a neighbourhood a few miles away, but still live in the same broad region). In our context, the use of regional dummies (i.e., states and territory dummies) to instrument neighbourhood characteristics in life satisfaction equations is less appropriate given that there are state-level differences in Australia that are likely to directly impact on life satisfaction (as suggested by our estimates). On a practical level, we also would require a number of instruments as we find that neighbourhood characteristics are multi-dimensional in their association with life satisfaction. Clearly, forming one aggregate neighbourhood index would lose valuable information. Consequently, we would need to find at least three exogenous instruments with enough statistical power to determine the differential effects of the various ‘groups’ of neighbourhood characteristics, which simply are not available in our data.

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Acknowledgements

The paper uses the data in the confidentialized unit record file from the Household, Income and Labour Dynamics in Australia Survey. We would like to thank the Australian Government Department of Family and Community Services and the Australian Research Council for financially supporting this research. The Faculty of Economics and Commerce at the University of Melbourne provided additional financial support. The authors would also like to thank Simon Freidin, participants at the fourth Quality of Life Conference, seminar participants at the Melbourne Institute of Applied Economic and Social Research and the University of New South Wales, and three anonymous referees for constructive comments. The usual disclaimer applies.

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Correspondence to Michael A. Shields.

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Responsible editor: Deborah Cobb-Clark

Appendix

Appendix

Table 5 Sample means and standard deviations

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Shields, M.A., Wheatley Price, S. & Wooden, M. Life satisfaction and the economic and social characteristics of neighbourhoods. J Popul Econ 22, 421–443 (2009). https://doi.org/10.1007/s00148-007-0146-7

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Keywords

  • Life satisfaction
  • Neighbourhood effects
  • HILDA Survey

JEL Classification

  • C25
  • I31
  • Z31