Regions and cities facing ageing

While population ageing challenges all regions, large metropolitan regions have fewer elderly residents relative to the working-age population.

While demographic change is often less prominent in the public debate than other global megatrends, the effects of population decline and ageing within OECD countries will be significant (OECD, 2019). Although increases in life expectancy are one of the greatest human achievements, the transition to an ageing society will create challenges in ensuring high-quality public services. Continuous ageing of the population of OECD regions and cities will put social security systems under pressure, as shrinking workforces will have to cover the benefits for an increasing number of retirees. Moreover, healthcare and other public services will have to be adapted while tax revenues might decline due to a shrinking workforce.

Population ageing has been asymmetric across regions, affecting specific places more strongly than others. The differences within countries are particularly significant in Australia, Canada, France and the United Kingdom, where the elderly dependency rate (the share of individuals aged 65 or older over the economically-active population 15-64 years old), ranges from more than 50% in some regions to less than 10% in others (Figure 4.16).

Not all types of regions face the same level of pressure from ageing. In most countries, dependency rates remain significantly lower in metropolitan regions compared to other regions (Figure 4.17). This is particularly the case in countries where all non-metropolitan regions have particularly high elderly dependency rates such as Denmark, France, Japan and Korea. In these countries, all non-metropolitan regions have elderly dependency rates above 40% (reaching 62% in Japan). Elderly dependency rates in metropolitan regions remain below 30% in all OECD countries, with the exception of Japan where the rate is 46%. Between 2002 and 2019, the elderly dependency rate increased from 7.6 percentage points in remote regions near a small/medium city to 10 percentage points in regions near a metropolitan area across OECD countries (Figure 4.18).

OECD (2020), OECD Regional Statistics (database), OECD, Paris, http://dx.doi.org/10.1787/region-data-en.

2002-19, TL3 regions or TL3 regions classified according to metropolitan access classification (see definition).

Dijkstra, L., H. Poelman and P. Veneri (2019), “The EU-OECD definition of a functional urban area”, OECD Regional Development Working Papers, No. 2019/11, OECD Publishing, Paris, https://doi.org/10.1787/d58cb34d-en.

Fadic, M. et al. (2019), “Classifying small (TL3) regions based on metropolitan population, low density and remoteness”, OECD Regional Development Working Papers, No. 2019/06, OECD Publishing, Parishttps://doi.org/10.1787/b902cc00-en.

OECD (2019), OECD Regional Outlook 2019: Leveraging Megatrends for Cities and Rural Areas, OECD Publishing, Paris, https://doi.org/10.1787/9789264312838-en.

OECD (2012), Redefining “Urban”: A New Way to Measure Metropolitan Areas, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264174108-en.

Figure 4.16: 2019 data, except USA (2018).

Figure 4.17: 2019 Population weighted average elderly ratios, except USA (2018).

Figure 4.18: Population weighted average elderly ratios by metropolitan access typology covering the following countries: AUS, AUT, BEL, CAN, CHL, CZE, DNK, EST, FIN, FRA, DEU, GRC, HUN, IRL, ISL, ITA, KOR, LVA, LTU, LUX, NLD, NOR, POL, PRT, ESP, SVK, SVN, SWE, CHE, GBR. Figure excludes JPN, MEX and USA.

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