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Ageing, Health, Loneliness and Wellbeing

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Abstract

Older people experience high rates of depression and suicide, yet they make a positive net contribution to the economy through activities such as employment, volunteering, and looking after grandchildren. The wellbeing of older people is therefore important not only on moral but also economic grounds. To understand which policies will facilitate the overall wellbeing, we use Australian data to explore the determinants of wellbeing and loneliness of natives and migrants in the 65–85 age group, taking into account the extent to which social networks contribute to the wellbeing and possible reduction in loneliness. Results show that social networks have a strong positive effect on wellbeing and a strong effect in reducing loneliness among both natives and migrants. The positive effect of social networks is stronger for females than males.

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Notes

  1. https://www.lifeinmindaustralia.com.au/about-suicide/suicide-data/suicide-facts-and-stats. Accessed 10 December 2019.

  2. See Steptoe et al. (2015) for detailed explanation.

  3. Most migrants in the elderly group are married only once, and with an individual of the same cultural group. Controls for multiple marriages or partners from other countries of origin yield coefficients that are no different from zero.

  4. These include responses to whether the person surveyed is: currently an active member of a club; attend events that bring people together; chat with your neighbours; have telephone, email or mail contact with friends; see members of my extended family or relative; encourage others to get involved with a group; make time to keep in touch with friends; give money to charity if asked; get in touch with a local politician or councillor; get involved in union or political party; make time to attend services at a place of worship; talk about current affairs with friends and family; and volunteer your spare time to work in NGOs.

  5. See “Appendix” for detailed information on the principal component analysis.

  6. The panel random estimator transforms the data by subtracting from each observation a portion h of its time average, where h depends on the variance of ui and vit and the number of period for which data are observed (Wooldridge 2010). Although h is not known in practice it can always be estimated (various methods are discussed in Wooldridge 2010). An estimated h close to zero results in random effect estimates being close to those obtained by pooled OLS, implying that time-invariant unobserved heterogeneity is relatively unimportant, as the variance of ui is small relative to that of vit. Conversely and more commonly, if the estimated h is close to 1, then the variance of ui is large relative to that of vit, and the bias caused by unobserved time-invariant heterogeneity is large.

  7. These indicators are measured using a comparable metric based on a 100-point scale.

  8. The estimates of a2 under various iterations of model (1) are as follows:

    Variables used

    Lagged

    SWBit, SNit-1

    Differences^

    D.SWBit, D.SNit

    Differences lagged^

    D.SWBit, D.SNit-1

    Differences lead^

    D.SWBit, D.SNit+1

    a2—3 variables

    .077***

    .015*

    .038*

    .010

    1. All variables in model (1) are transformed in differences when the Difference version is estimated
    2. ***p < .01; ** p < .05; * p < .1

    .

  9. The high level of happiness of Italian migrants has been interpreted as the result of the social connectivity between Italians and Australians through religion and the social activities organised by the Catholic Church (Lau and Morse 2008; Angelini et al. 2015; Furlan and Faggion 2016). About 25% of Australians are Catholic according to the 2016 Census.

  10. Recently, a number of papers have analysed the paradox of higher growth rate but lower happiness in China. See, for instance, Knight and Gunatilaka (2010) and Graham et al. (2015). Studies in China also find that happiness is associated with the rank of an individual’s income and consumption within a relevant reference group (see e.g. Cheng et al. 2014, 2020; Wang et al. 2019).

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Acknowledgements

Funding was provided by DP, RC (Grant No. DP190102778). Tani, Cheng and Wang acknowledge financial support from UNSW’s Institute of Ageing Futures (seed grant 2019). Tani and Cheng also acknowledge support from the Australian Research Council (grant no. DP 190102778).

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Authors and Affiliations

  1. School of Business, University of New South Wales, Canberra, Australia

    Massimiliano Tani

  2. Social Policy Research Centre, University of New South Wales, Sydney, Australia

    Zhiming Cheng

  3. Department of Economics, University of Kent, Canterbury, UK

    Matloob Piracha

  4. Department of Economics, Macquarie University, Sydney, Australia

    Ben Zhe Wang

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Appendix

Appendix

Answers to the following 13 questions, reported on a 6-point Likert scale ranging from rarely (= 0) to very often (= 6), with the exception of question 1 for which the answer is a dichotomous Yes/No, have been used to generate the social network indicator via a Principal Component Analysis (CPA):

Do, or are, you:

  • currently an active member of a club? (CPclub)

  • attend events that bring people together? (CPevent)

  • chat with your neighbours? (CPchat)

  • have telephone, email or mail contact with friends? (CPcontact)

  • see members of my extended family or relative? (CPrelative)

  • encourage others to get involved with a group? (CPinvolve)

  • make time to keep in touch with friends? (CPfriend)

  • give money to charity if asked? (CPcharity)

  • get in touch with a local politician or councillor? (CPpolitician)

  • get involved in union or political party? (CPparty)

  • make time to attend services at a place of worship? (CPworship)

  • talk about current affairs with friends and family? (CPaffair)

  • volunteer your spare time to work in NGOs? (CPvolunteer)

The eigenvectors of the PCA are displayed in Table 5. They are the basis on which the social network indicator is obtained.

Table 5 PCA analysis: eigenvectors
Full size table

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Tani, M., Cheng, Z., Piracha, M. et al. Ageing, Health, Loneliness and Wellbeing. Soc Indic Res 160, 791–807 (2022). https://doi.org/10.1007/s11205-020-02450-4

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