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).
The elderly population is the population aged 65 years and over.
Elderly dependency rate is defined as the ratio between the elderly population and the working-age population (15-64 years), multiplied by 100.
Access to metropolitan areas typology distinguishes TL3 regions based on the level of access to metropolitan areas (Fadic et al., 2019). At a first level, regions, where at least half of the regional population live in a metropolitan area of at least 250 000 inhabitants, are considered as “metropolitan regions”, and as “non-metropolitan” otherwise. Metropolitan regions are further distinguished in “large metropolitan regions” regions if they include or are part of a metropolitan area of at least 1.5 million inhabitants. Non-metropolitan regions are sub-classified in regions “with access to a metropolitan region” if half of its population can reach a metropolitan area within a 60-minute drive. When half of the regional population can reach only a smaller-sized city (between 50 000 and 250 000 inhabitants), the region is classified as “with access to a small/medium city”. In all other cases, the region is classified as “remote”. The classification relies on the concept of FUAs (Dijkstra et al., 2019; OECD, 2012) to delineate metropolitan areas of at least 250 000 inhabitants or smaller-sized cities.
Source
OECD (2020), OECD Regional Statistics (database), OECD, Paris, https://doi.org/10.1787/region-data-en.
Reference years and territorial level
2002-19, TL3 regions or TL3 regions classified according to metropolitan access classification (see definition).
Further information
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, Paris, https://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, https://doi.org/10.1787/9789264174108-en.
Figure notes
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.
