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More Pensioners, Less Income Inequality? The Impact of Changing Age Composition on Inequality in Big Cities and Elsewhere

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Modelling Aging and Migration Effects on Spatial Labor Markets

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

As is the case in most developed countries, the population of New Zealand is ageing numerically and structurally. Population ageing can have important effects on the distribution of personal income within and between urban areas. The age structure of the population may affect the distribution of income through the life-cycle profile of earnings but also through the spatial-temporal distribution of income within the various age groups. By decomposing New Zealand census data from 1986 to 2013 by age and urban area, this chapter examines the effects of population ageing on spatial-temporal changes in the distribution of personal income to better understand urban area-level income inequality (measured by the Mean Log Deviation index). We focus explicitly on differences between metropolitan and non-metropolitan urban areas. New Zealand has experienced a significant increase in income inequality over the last few decades, but population ageing has slightly dampened this trend. Because metropolitan areas are ageing slower, the inequality-reducing effect of ageing has been less in these areas. However, this urban-size differential-ageing effect on inequality growth has been relatively small compared with the faster growth in intra-age group inequality in the metropolitan areas.

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Notes

  1. 1.

    See, for example, Hyslop and Maré (2005) and Ball and Creedy (2015).

  2. 2.

    For example, studies like Deaton and Paxson (1994) and Cameron (2000) found that population ageing increases inequality.

  3. 3.

    See for example Castells-Quintana et al. (2015) for a review of the literature of the trends and determinants of income inequality in Europe.

  4. 4.

    See e.g. Cameron (2000), Zhong (2011) and Peichl et al. (2012).

  5. 5.

    See Evans et al. (1996) for a description of these reforms.

  6. 6.

    See Perry (2014, 2015), Karagedikli et al. (2000, 2003), and Alimi et al. (2016).

  7. 7.

    See OECD (2016).

  8. 8.

    See Hyslop and Maré (2005) and Ball and Creedy (2015).

  9. 9.

    A 2016 OECD report, which examines 153 metropolitan areas in 11 countries, finds that inequality in metropolitan areas is higher than the national average in all countries apart from Canada (OECD 2016 , p. 33).

  10. 10.

    Given that migrants are predominantly young, net inward migration contributes to the relative youthfulness of the big cities. However, a study of the effects of migration on the income distribution would need to take into account the differential effects of net permanent & long term migration (which is on average more skilled than the local labour force and, like student migration, disproportionally towards the metropolitan areas) and temporary migration (which is less skilled and more attracted to non-metropolitan areas). The explicit analysis of the effects of migration on income inequality is beyond the scope of the present chapter.

  11. 11.

    See Lam (1997) for a review of the literature that examines the role of demographic variables (including changes in age structure) on income inequality.

  12. 12.

    See for example Mookherjee and Shorrocks (1982), Cameron (2000), Zhong (2011), Peichl et al. (2012) and Lin et al. (2015).

  13. 13.

    Metropolitan areas defined as urban areas that make up the six largest New Zealand cities (in order of size) of Auckland, Wellington, Christchurch, Hamilton, Tauranga and Dunedin. All other urban areas are considered non-metropolitan areas.

  14. 14.

    Mookherjee and Shorrocks (1982) note that this approximation appears sufficient for computational purposes (p. 897). However, experimentation with a range of changing income distributions shows that the sign of C3 can be sometimes different from that of C3′ and, similarly, the sign of C4 can be different from that of C4′. This may lead to slightly different interpretations. In this chapter we follow Mookherjee and Shorrocks (1982) and use the approximate decomposition. Results for the exact decomposition are available upon request.

  15. 15.

    Hence people not in paid employment and business owners reporting a loss have been excluded.

  16. 16.

    See OECD (2016).

  17. 17.

    The labour force participation rate for those aged 15–24 declined from 76% in 1986 to 61% in 2013, with full-time employment falling by even more at 40 percentage points.

  18. 18.

    New Zealand Superannuation is the public pension paid to all residents over the age of 65 (immigrants must have resided in the country for 10 years or longer). Any eligible New Zealander receives NZ Super regardless of how much they earn through paid work, savings and investments, what other assets they own or what taxes they have paid. NZ Super is indexed to the average wage. The after-tax NZ Super rate for couples (who both qualify) is based on 66% of the ‘average ordinary time wage’ after tax. For single people, the after-tax NZ superannuation rate is around 40% of that average wage. See https://www.workandincome.govt.nz/eligibility/seniors/superannuation/payment-rates.html

  19. 19.

    The graphs in panel F are scaled. To calculate the scaled age group contribution to total difference, the density of each age group in each year is scaled by their respective income share.

  20. 20.

    This hollowing out of the income distribution is not necessarily evidence of a ‘vanishing middle class’ phenomenon that has been reported for the USA and other developed countries (e.g., Foster and Wolfson 2010). To investigate a ‘vanishing middle class’ phenomenon would require a comparison of lifetime income across population groups rather than a comparison of age-specific income. This is beyond the scope of the present chapter.

  21. 21.

    This is due to the approximation method. For this age group, (\( \overline{\pi_a{r}_a}-\overline{\pi_a} \)) < 0. See Eq. (8.4).

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Disclaimer

Access to the data used in this study was provided by Statistics New Zealand (SNZ) under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act 1975. All frequency counts using Census data were subject to base three rounding in accordance with SNZ’s release policy for census data.

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Correspondence to Omoniyi B. Alimi .

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Alimi, O.B., Maré, D.C., Poot, J. (2018). More Pensioners, Less Income Inequality? The Impact of Changing Age Composition on Inequality in Big Cities and Elsewhere. In: R. Stough, R., Kourtit, K., Nijkamp, P., Blien, U. (eds) Modelling Aging and Migration Effects on Spatial Labor Markets. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-68563-2_8

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