Demographic change and population projections Dr Nik Lomax School of Geography, University of Leeds [email protected] 

Why do we need demographic estimates and projections? • To understand and plan for demand on services and resources • To allow governments to formulate policy based on evidence • Demography is at the heart of all human systems • Planning, environment, inclusion, health 

Source: https://population.un.org/wpp/ 2050 9.96bn 2020 7.79bn 1950 2.54bn 

Pressure from both growth and decline 

Population growth The 47 least developed countries are amongst the world’s fastest growing populations Puts pressure on already strained resources However, where young populations are growing this provides an opportunity for rapid economic growth known as the demographic dividend 

Sub-Saharan Africa: growth in working age population 

Spain: an ageing (and declining) population 

Potential Support Ratios • PSR is the number of working people for each person aged over 65 • Provides a measure of the ‘burden’ placed on the working age population • Implications for health and social care as well as economic pressures 0 2 4 6 8 10 12 14 1950 1970 1990 2000 2005 2010 2015 2020 2030 2050 2075 2100 number working age to 65+ Potential Support Ratio Sub-Saharan Africa Spain Source: Author’s calculations from https://population.un.org/wpp/ 

The demographic transition the transition from high to low death and birth rates Formal theory Notestein (1945) Source: https://www.buddinggeographers.com/demographic- transition-model-dtm/ 

Other theories of demographic transition Second demographic transition (van der Kaa 1987): fertility falls below replacement (due to the rise in contraception use and women in the workforce) Third demographic transition (Coleman 2006): when smaller birth cohorts reach the labour market, the demand for labour rises, and is filled by international migration Fourth demographic transition (Frey 2015): the spatial distribution of ethnic minorities (of immigrant origin) shifts from their initial places of settlement (mainly large cities) to other parts of the country 

What are the demographic components of population change? Births Deaths International migration Internal migration (important for change at the sub-national level) 

The contribution of the demographic components to population change fluctuate over time • Natural change = births minus deaths • Net migration = in migration minus out migration 

Demographic components can change due to external and internal events Source: https://theconversation.com/whats-happened-to-uk- migration-since-the-eu-referendum-in-four-graphs-127891 

Types of projection • results based on the input data you use • Can create scenarios where you alter the assumptions about the input data Deterministic • Based on running the model many times, sampling from a range of possibilities in the component dataset • Usually report the average and quantify the uncertainty that exists Probabilistic 

Scenarios from a deterministic model • National Population Projection produced by ONS every two years • Latest projections are based on 2018 data • Driven by assumptions about the future trend of the demographic components • Uses a cohort component model (more later) 

UK population in 2043 using different assumptions "high population" variant assumes high fertility, life expectancy and net migration 4.3 million above principal projection "low population" variant assumes low fertility, life expectancy and net migration 5.2 million below principal projection 

Probabilistic projection for the UK • Median = 72.5 million • Upper 95% = 74.7 million • Lower 95% = 70 million • Produced by the United Nations using UK data Source: https://population.un.org/wpp/Graphs/Probabilistic/POP/TOT/826 

Where do the UK data come from? Births are registered with the General Register Office and this is a legal requirement. Age of mother and sex of child is recorded Deaths are registered with the General Register Office and this is a legal requirement. Reported by age, sex and local authority of residence International migration data comes primarily from a survey at the border called the International Passenger Survey Internal migration data comes from re-registrations with a doctor reported in NHS data (and other adjustments, e.g. for university students) 

How are these data used in projection Cohort component model Applies demographic rates to different population sub-groups 

The cohort component projection model •Previous year data and census Resident population in base year •e.g. an 18 year old becomes 19 Age on the resident population •Age specific fertility rate calculated from most recent five-year trend Add births •Age specific mortality rate calculated from most recent five-year trend •In migration assigned using a migration matrix Subtract deaths •Age specific out migration rate calculated from most recent two-year trend Add and subtract internal migrants •Age and sex international migration trend – most recent five-year average Add and subtract international migrants 

How do we decide what assumptions to use in the model? Trends in past data Ask the experts Scenarios Incorporate uncertainty 

UK trends in components 

Source: ONS, https://bit.ly/3dMkEAD 

Component rates vary by demographic group: fertility Source: ONS, https://bit.ly/3dw9NKW 

Component rates vary by demographic group: mortality 0.00 0.10 0.20 0.30 0.40 0.50 4 9 141924293439444954596469747984899499 Probability of dying Age Age Specific Mortality Male Female Source: ONS, https://bit.ly/3dw9NKW Source: author calculations from ONS, https://bit.ly/2UdrW9o 

Component rates vary by demographic group: migration • Wilson, T., 2010. Model migration schedules incorporating student migration peaks. Demographic research, 23, pp.191-222. • Rogers, A. and Castro, L.J., 1981. Model migration schedules. 0.00 50.00 100.00 150.00 200.00 250.00 300.00 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 Migrants per 1,000 population Internal migration rate (2020) Male Female Childhood Student Labour force Retirement Elderly Source: author calculations from ONS https://bit.ly/2UHFfPB 

Substantial spatial variation in component rates 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 under 18 under 20 20-24 25-29 30-34 35-39 40-44 45 and over Births per 1,000 women Age Specific Fertility Rates (2019) Islington Camden Bradford Slough Source: https://www.ons.gov.uk/visualisations/dvc417/index.html 

Other demographic variation “Almost universally, women with higher levels of education have fewer children. Better education is associated with lower mortality, better health, and different migration patterns” • Lutz and Samir (2011) It is well established that demographic rates vary substantially by ethnic group • Coleman (2010) Lutz, W. and Samir, K.C., 2011. Global human capital: Integrating education and population. Science, 333(6042), pp.587-592. Coleman, D. (2006). Immigration and ethnic change in low-fertility countries: A third demographic transition. Population and development Review, 401-446. 

Demographic rates by ethnic group 0 20 40 60 80 100 120 140 160 <20 20-24 25-29 30-34 35-39 40+ ASFR, 2001 White Black-Caribbean Black-African Indian Pakistani Bangladeshi Chinese Other 0 20 40 60 80 100 120 140 160 180 200 0to4 5to9 10to14 15to19 20to24 25to29 30to34 35to39 40to44 45to49 50to54 55to59 60to64 65to69 70to74 75+ BAN BLA BLC CHI IND MIX OAS OBL OTH PAK WBI https://www.ethpop.org/ 

https://theconversation.com/what-the-uk-population-will-look- like-by-2061-under-hard-soft-or-no-brexit-scenarios-117475 

Direct and indirect impact of migration assumptions • numbers of people who enter the country through immigration or leave through emigration Direct Impact • Cumulative over time with influence on fertility and mortality Indirect impact 

Direct impact • Cumulative contribution of immigration and emigration to 2061 • Baseline scenario results Immigration Emigration Net White British 6,338,023 7,822,886 -1,484,863 Black Caribbean 177,491 301,973 -124,482 Bangladeshi 266,143 232,433 33,709 Other Black 164,182 116,796 47,386 Mixed 802,122 435,129 366,993 Pakistani 1,077,808 501,435 576,373 Other 1,237,843 419,949 817,894 Black African 1,368,427 417,984 950,444 Other Asian 1,864,125 787,998 1,076,127 Chinese 2,070,552 774,963 1,295,588 Indian 2,345,361 756,360 1,589,001 White Other 10,203,796 3,671,332 6,532,464 Total 27,915,872 16,239,237 11,676,635 

International migration assumptions under each scenario • Combination of models fit to previous migration data, continuation of trends and interpretation of policy Lomax, N., Wohland, P., Rees, P. and Norman, P., 2020. The impacts of international migration on the UK’s ethnic populations. Journal of Ethnic and Migration Studies, 46(1), pp.177-199. 

Results under different scenarios 

Results under different scenarios Group 1 – very reliant on international migration to grow 

Results under different scenarios Group 2 – somewhat reliant on international migration to grow 

Results under different scenarios Group 3 – continue to grow under all migration scenarios 

Results under different scenarios Group 4 – decline under all migration scenarios 

https://theconversation.com/what-the-uk-population-will-look- like-by-2061-under-hard-soft-or-no-brexit-scenarios-117475 

https://theconversation.com/what-the-uk-population-will-look- like-by-2061-under-hard-soft-or-no-brexit-scenarios-117475 

Short term shock – the effect of COVID-19 (2020) mid-year population estimates year to June 2020 have just been released Provides a glimpse of the effects of COVID-19 on population change Deaths 13% higher than the previous year Internal migration down 11% on the previous year International migration very similar to previous year Number of births was lowest since 2003 (but continuation of trend) Lowest growth in two decades 

Future shocks ENVIRONMENTAL PRESSURES CONFLICT & GLOBAL EVENTS ECONOMIC PERFORMANCE PANDEMICS 

The Census as a benchmark Image by PaulSh on Flickr. 

Small area demographic profiles • Demographic data used to understand all kinds of spatial phenomena • Composite indicators provide insight in to the make-up of areas • See https://maps.cdrc.ac.uk 

What next? Take a look at the data, visualisations, methods and assumptions used in population projections • Global data from the United Nations • UK data from the ONS Think about (and challenge) the validity of model outputs • “All models are wrong, but some are useful” (George E. P. Box) 

List of resources and references • UN World Population Prospects: https://population.un.org/wpp • Office for National Statistics National population projection data and method: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/pop ulationprojections/bulletins/nationalpopulationprojections/2018based • Office for National Statistics subnational population projection data and methods: https://www.ons.gov.uk/releases/subnationalpopulationprojectionsforengland2018base d • CDRC maps: https://maps.cdrc.ac.uk • Van de Kaa, D. J. (1987). Europe's second demographic transition. Population bulletin, 42, 1-59. • Coleman, D. (2006). Immigration and ethnic change in low-fertility countries: A third demographic transition. Population and development Review, 401-446. • Frey, W. H. (2015). Diversity explosion: How new racial demographics are remaking America. Brookings Institution Press. 

Demographic change and population projections Dr Nik Lomax School of Geography, University of Leeds