Abstract
In this paper panel data is used to estimate the relationship between geographic reference income and subjective wellbeing in Australia. Recent cross-sectional US-based studies suggest that the income of other people in a neighbourhood—geographic reference income—impacts on individual wellbeing but is mediated by geographic scale. On controlling for a household’s own income, subjective wellbeing is raised by neighbourhood income and lowered by region-wide income. However, these findings could be driven by the self-selection of innately happy or unhappy individuals into higher-income areas. This study’s methodology takes advantage of panel-data modelling to show that unobserved individual heterogeneity is in fact correlated with reference income, but on curbing its impacts through the inclusion of fixed-effects we find that there is still a positive relationship between reference income and subjective wellbeing at the neighbourhood level. However, we detect no relationship at the region-wide level. Additionally, the subjective wellbeing relationship is the same no matter an individual’s rank in the distribution of incomes within an area. The neighbourhood wellbeing relationship has implications for policies addressing residential segregation and social mixing.
Similar content being viewed by others
Data Availability
This empirical paper uses general-purpose survey data from the restricted version of the Household, Income and Labour Dynamics in Australia (HILDA) survey. To access this data, eligible researchers are required to submit an application to the surveys managers, The Melbourne Institute at the University of Melbourne. We also use Australian equivalised household income data for small areas that is not freely available and must be licensed from the Australian Bureau of Statistics.
Notes
See https://ministers.treasury.gov.au/ministers/jim-chalmers-2022/media-releases/release-national-wellbeing-framework. The report itself is available at: https://treasury.gov.au/publication/p2023-mwm
See Broxterman et al. (2019) for a series of papers discussing endogenous amenities and the consumer city.
For information on the design of the HILDA Survey see Watson & Wooden (2012).
New members of a household that were part of the original sample frame participate in surveys from the year that they join the household. A top-up sample of individuals and households was also added to the survey in wave 11 (2011).
Authors own calculation using the HILDA Survey waves 1, 6, 11, and 16.
We use this method of equivalence as it is the same method used for the equivalised income data reported in the Australian Census, which is our source of reference group income. This method was performed by first calculating a household’s equivalence factor that allocates points to each person in a household (1 point to the first adult, 0.5 points to each additional person who is 15 years and over, and 0.3 to each child under the age of 15). Household income was then divided by the sum of the points allocated to each person (the equivalence factor).
CPI data was obtained from Australian Bureau of Statistics catalogue 6401.0 – Consumer Price Index, Australia.
Access to the Restricted Release of the HILDA Survey was required for this research, as it contains the location of responding households at the neighbourhood level. The confidentialised release of the HILDA Survey, referred to as the general release, does not contain detailed geography—location is limited to city-wide or regional measures, such as the household’s greater capital city, section of state, and remoteness area.
The borders of a small number of SA2s changed over the study period. We ensure concordance of the SA2 data by employing consistent 2011 ASGS border definitions across all census years.
The population of SA2s generally ranges between 3000 and 25,000 persons, with an average population of 10,000 (ABS, 2020).
As based on the individual’s Section of State classification in the Australian Statistical Geography Standard (ASGS).
For example, popular econometrics and statistical software package Stata provides only a random-effects specification for its ordered logit or ordered probit panel-data models (StataCorp, 2021).
The author estimated an ordered probit model as a robustness check, but only on the cross-sectional data.
All prior studies of reference income and wellbeing log transform their income variables, and so we also follow this convention.
Senik (2008) does not offer a rationale for the 41 years of age threshold, but it likely represents the early career stage of LFP, characterised by the strongest opportunities for positive earnings growth. This is evidenced in the 2016 Australian census, which documents a peak in the median earnings of full-time workers at 41 years (ABS, 2016).
We considered the inclusion of measures of social capital, including participation in i’s local community. However, these measures are contained in the voluntary self-completion questionnaire of the HILDA Survey, which have lower levels of response compared to our other predictors, significantly reducing model sample size.
The maximum household income observed is $4,594,092. Removing the bottom and top five percent of observations, as ranked by household income, reduces the average to $60,134 and the standard deviation to a less extreme $31,512.
The application of Wald tests to REWB models is demonstrated in Schunck (2013).
A less positive interpretation could be applied to social mixing. An influx of lower-income residents into higher-income neighbourhoods would raise the wellbeing of new residents, as reference incomes are higher relative to their previous neighbourhood. However, if this influx of lower-income residents lowers reference income, the wellbeing of existing residents will be reduced.
References
ABS. (2016). Australian Bureau of Statistics (ABS) 2016 Census. https://www.abs.gov.au/websitedbs/censushome.nsf/home/2016
ABS. (2020). Australian Statistical Geography Standard (ASGS). Australian Bureau of Statistics. http://www.abs.gov.au/websitedbs/D3310114.nsf/home/Australian+Statistical+Geography+Standard+(ASGS)
Baetschmann, G., Ballantyne, A., Staub, K. E., & Winkelmann, R. (2020). feologit: a new command for fitting fixed-effects ordered logit models. The Stata Journal, 20(2), 253–275. https://doi.org/10.1177/1536867X20930984
Bell, A., Fairbrother, M., & Jones, K. (2019). Fixed and random effects models: making an informed choice. Quality & Quantity, 53(2), 1051–1074. https://doi.org/10.1007/s11135-018-0802-x
Blanchflower, D. G., & Oswald, A. J. (2004). Well-being over time in Britain and the USA. Journal of Public Economics, 88(7), 1359–1386. https://doi.org/10.1016/S0047-2727(02)00168-8
Brodeur, A., & Flèche, S. (2019). Neighbors’ income, public goods, and well-being. Review of Income and Wealth, 65(2), 217–238. https://doi.org/10.1111/roiw.12367
Brown, S., Durand, R. B., Harris, M. N., & Weterings, T. (2014). Modelling financial satisfaction across life stages: a latent class approach. Journal of Economic Psychology, 45, 117–127. https://doi.org/10.1016/j.joep.2014.09.001
Brown, S., Gray, D., & Roberts, J. (2015). The relative income hypothesis: a comparison of methods. Economics Letters, 130, 47–50. https://doi.org/10.1016/j.econlet.2015.02.031
Broxterman, D., Coulson, E., Ihlanfeldt, K., Letdin, M., & Zabel, J. (2019). Endogenous amenities and cities. Journal of Regional Science, 59(3), 365–368. https://doi.org/10.1111/jors.12449
Cameron, A. C., Trivedi, P. K. (2010). Microeconometrics Using Stata: Revised Edition. Stata Press
Cheshire, P. C., Nathan, M., & Overman, H. G. (2014). Urban Economics and urban policy: challenging conventional policy wisdom. Edward Elgar Publishing.
Clark, A. E., Westergård-Nielsen, N., & Kristensen, N. (2009). Economic satisfaction and income rank in small neighbourhoods. Journal of the European Economic Association, 7(2/3), 519–527. https://doi.org/10.1162/JEEA.2009.7.2-3.519
Clark, W. A. V., Ong ViforJ, R., & Truong, N. T. K. (2022). Neighbourhood selection and neighbourhood matching: choices, outcomes and social distance. Urban Studies, 59(5), 937–955. https://doi.org/10.1177/00420980211044029
Department of Social Services & Melbourne Institute of Applied Economic and Social Research. (2021). The Household, Income and Labour Dynamics in Australia (HILDA) Survey, Restricted Release 20 (Waves 1–20). ADA Dataverse, V3. https://doi.org/10.26193/PI5LPJ
Distante, R. (2013). Subjective well-being, income and relative concerns in the UK. Social Indicators Research, 113(1), 81–105. https://doi.org/10.1007/s11205-012-0083-z
Duesenberry, J. S. (1949). Income, saving, and the theory of consumer behaviour. Harvard University Press.
Ferrer-i-Carbonell, A. (2005). Income and well-being: an empirical analysis of the comparison income effect. Journal of Public Economics, 89(5), 997–1019. https://doi.org/10.1016/j.jpubeco.2004.06.003
Ferrer-i-Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimates of the determinants of happiness? The Economic Journal, 114(497), 641–659. https://doi.org/10.1111/j.1468-0297.2004.00235.x
Frijters, P., Haisken-DeNew, J. P., & Shields, M. A. (2004). Money does matter! evidence from increasing real income and life satisfaction in east Germany following reunification. American Economic Review, 94(3), 730–740. https://doi.org/10.1257/0002828041464551
Frijters, P., Haisken-DeNew, J. P., & Shields, M. A. (2005). The causal effect of income on health: evidence from German reunification. Journal of Health Economics, 24(5), 997–1017. https://doi.org/10.1016/j.jhealeco.2005.01.004
Glaeser, E. L., Kolko, J., & Saiz, A. (2001). Consumer city. Journal of Economic Geography, 1(1), 27–50. https://doi.org/10.1093/jeg/1.1.27
Guerrieri, V., Hartley, D., & Hurst, E. (2013). Endogenous gentrification and housing price dynamics. Journal of Public Economics, 100, 45–60. https://doi.org/10.1016/j.jpubeco.2013.02.001
Hirschman, A. O., & Rothschild, M. (1973). The changing tolerance for income inequality in the course of economic development. Quarterly Journal of Economics, 87(4), 544–566. https://doi.org/10.2307/1882024
Ifcher, J., Zarghamee, H., & Graham, C. (2018). Local neighbors as positives, regional neighbors as negatives: competing channels in the relationship between others’ income, health, and happiness. Journal of Health Economics, 57, 263–276. https://doi.org/10.1016/j.jhealeco.2017.08.003
Johnston, D. W., & Stavrunova, O. (2021). Subjective wellbeing dynamics. Australian Economic Review, 54(4), 518–529. https://doi.org/10.1111/1467-8462.12442
Kingdon, G. G., & Knight, J. (2007). Community, comparisons and subjective well-being in a divided society. Journal of Economic Behavior & Organization, 64(1), 69–90. https://doi.org/10.1016/j.jebo.2007.03.004
Luttmer, E. F. P. (2005). Neighbors as negatives: relative earnings and well-being. Quarterly Journal of Economics, 120(3), 963–1002. https://doi.org/10.1162/003355305774268255
Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46(1), 69–85. https://doi.org/10.2307/1913646
Neyman, J., & Scott, E. L. (1948). Consistent estimates based on partially consistent observations. Econometrica, 16(1), 1–32. https://doi.org/10.2307/1914288
OECD. (2023). Poverty rate (indicator). https://doi.org/10.1787/0fe1315d-en
Parkinson, S., Ong, R., Cigdem, M., Tayloy, E. (2014). Wellbeing outcomes of lower income renters: A multilevel analysis of area effects (AHURI Final Report 226). Australian Housing and Urban Research Institute Limited. https://www.ahuri.edu.au/research/final-reports/226
Schunck, R. (2013). Within and between estimates in random-effects models: advantages and drawbacks of correlated random effects and hybrid models. The Stata Journal, 13(1), 65–76. https://doi.org/10.1177/1536867X1301300105
Schunck, R., & Perales, F. (2017). Within- and between-cluster effects in generalized linear mixed models: a discussion of approaches and the Xthybrid command. The Stata Journal, 17(1), 89–115. https://doi.org/10.1177/1536867X1701700106
Senik, C. (2004). When information dominates comparison: Learning from Russian subjective panel data. Journal of Public Economics, 88(9), 2099–2123. https://doi.org/10.1016/S0047-2727(03)00066-5
Senik, C. (2008). Ambition and jealousy: income interactions in the ‘old’ Europe versus the ‘New’ Europe and the United States. Economica, 75(299), 495–513. https://doi.org/10.1111/j.1468-0335.2007.00629.x
Shields, M., Wooden, M. (2003). Investigating the role of neighbourhood characteristics in determining life satisfaction. Melbourne Institute Working Papers Series
Shields, M., Wheatley Price, S., & Wooden, M. (2009). Life satisfaction and the economic and social characteristics of neighbourhoods. Journal of Population Economics, 22(2), 421–443. https://doi.org/10.1007/s00148-007-0146-7
StataCorp. (2021). Stata: Release 17 [Computer software]. StataCorp
Watson, N., & Wooden, M. (2012). The HILDA survey: a case study in the design and development of a successful household panel survey. Longitudinal and Life Course Studies, 3(3), 369–381. https://doi.org/10.14301/llcs.v3i3.208
Watson, N., & Wooden, M. (2021). The household, income and labour dynamics in Australia (HILDA) survey. Journal of Economics and Statistics, 241(1), 131–141. https://doi.org/10.1515/jbnst-2020-0029
Wilkins, R., Botha, F., Vera-Toscano, E., Wooden, M. (2020). The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 18. Melbourne Institute: Applied Economic & Social Research
Wood, G. A., Clark, W. A. V., Ong ViforJ, R., Smith, S. J., & Truong, N. T. K. (2023). Residential mobility and mental health. SSM—Population Health, 21, 101321. https://doi.org/10.1016/j.ssmph.2022.101321
Zumbro, T. (2014). The relationship between homeownership and life satisfaction in Germany. Housing Studies, 29(3), 319–338. https://doi.org/10.1080/02673037.2013.773583
Acknowledgements
This paper uses unit record data from HILDA. HILDA was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to the Australian Government, DSS or the Melbourne Institute. http://dx.doi.org/10.26193/0LPD4U
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Research Involving Human Participants and/or Animals
Not applicable.
Informed Consent
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Phelps, C., Harris, M.N., Rowley, S. et al. Geographic Reference Income and the Subjective Wellbeing of Australians. J Happiness Stud 24, 2855–2880 (2023). https://doi.org/10.1007/s10902-023-00707-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10902-023-00707-6