Global Population Projections: methods and …...“Lutz and his fellow demographers at Vienna’s International Institute for Applied Systems Analysis believe that advancing education

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Click to edit Master title styleGlobal Population Projections: A critical analysis of key methods and assumptionsJohn Wilmoth, Director Population Division, DESA, United Nations

African Population ConferenceSession 19, “Population projections: Methods, assumptions and implications”Entebbe, Uganda, 18 November 2019

Copyright © 2019 by United Nations, made available under a Creative Commons license (CC BY 3.0 IGO) http://creativecommons.org/licenses/by/3.0/igo/


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Population Division

• 26 sets of population projections, every 2 or 3 years since 1951• Early projections were for the world or large regions only• Projections for individual countries beginning in 1968• 2019 edition includes projections from 2020 to 2100

for 235 countries or areas

Brief history of UN projections

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Population Division

• Core assumptions about future trends grounded in theories of demographic transition• Transition theory reflected in functional form of

fertility and mortality models• Enhancements due to Bayesian hierarchical model

o More reliable results for countries/areas with less reliable data or at earlier stages of transition

o Provides probabilistic assessment of alternative future trends

Methods and assumptions of UN projections

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Population Division

Classic model of demographic transition

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Death rateBirth rate

Population size

Popu

latio

n si

ze

Time

Birt

h/de

ath

rate

s



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Three phases of TFR trend: Pre-decline, decline and post-decline

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Phase II Phase IIIPhase I

TFR

TFR

decr

emen

t

Double logistic model

0—

Phase I Phase II Phase III

TFR

Time

Total fertility rate



Population Division

Model of historical trend in life expectancy at birth

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e0

e 0in

crea

se


0—

Time

e 0

Life expectancy at birth



Population Division

Projected global population 2015-2100UN 2019 medium variant with 80- and 95-percent prediction intervals

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Medium variant80% Lower

80% Upper

95% Lower

95% Upper

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2015 2030 2045 2060 2075 2090

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Projected global population 2015-2100UN 2019 medium with prediction intervals and high/low-fertility variants

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Medium variant

High-fertility variant

Low-fertility variant

80% Lower

80% Upper

95% Lower

95% Upper

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2015 2030 2045 2060 2075 2090

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• What are the key differences?

• How to explain the differences?

• Begin by comparing medium projections

UN and IIASA projections

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Medium variant

80% Lower

80% Upper

95% Lower

95% Upper

7

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2015 2030 2045 2060 2075 2090

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Global population trend 2015-2100UN 2019 medium variant with 80- and 95-percent prediction intervals



Population Division

Global population trend 2015-2100UN 2019 medium with prediction intervals and IIASA medium (SSP2) scenario

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IIASA medium (SSP2)

Medium variant

80% Lower

80% Upper

95% Lower

95% Upper

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2015 2030 2045 2060 2075 2090

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• Statistical modeling versus scientific reasoning

• Educational change: considered versus ignored

• Historical experience versus expert judgement

What accounts for the difference in the two sets of projections?

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Population Division

“This difference is mostly due to different methods of deriving long-term fertility assumptions for the different parts of the world, where the UN relies primarily on statistical extrapolation models andIIASA gives more weight to expert arguments and scientific reasoning.”

Lutz et al. 2018, p. 117 (emphasis added)

What accounts for the difference in the two sets of projections?

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Population Division

• Role of expert arguments and scientific reasoning• Informed by data and theory, including theories of

demographic transition• Three examples:

o Exclusion of observations pre-dating modern contraceptiono Long-term mortality trends informed by trends in record longevityo Post-decline fertility model well justified by data and theory

Statistical extrapolation model of fertility decline

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Population Division

Phase III begins, by definition, after the first of two consecutive increases in TFR over 5-year intervals after reaching its minimum when TFR < 2

Post-transition fertility trend

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2.1

Phase II

Phase IIITF

R

Time



Population Division

• Eastern & South-Eastern Asiao Chinao China - Hong Kong SARo China - Macao SARo China - Taiwan Province of Chinao Japano Singaporeo Viet Nam

• Latin America and the Caribbeano Arubao Barbados

• Northern Americao United States of America

40 countries/areas in Phase III by 2019

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• Europe o Armeniao Austriao Belaruso Belgiumo Bulgariao Channel Islandso Czechiao Denmarko Estoniao Finlando Franceo Germanyo Hungaryo Italyo Latvia

o Lithuaniao Luxembourgo Maltao Netherlandso Norwayo Republic of Moldovao Romaniao Russian Federationo Slovakiao Sloveniao Spaino Sweden, o Switzerlando Ukraineo United Kingdom



Population Division

“Although development continues to promote fertility decline at low and medium HDI levels, our analyses show that at advanced HDI levels, further development can reverse the declining trend in fertility. The previously negative development-fertility relationship has become J-shaped, with the HDI being positively associated with fertility among highly developed countries.”

Myrskylä et al. 2009, Nature, p. 741 (emphasis added)

Reversal of fertility decline

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Human Development Index

Tota

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Population Division

Education as a predictor

18photo credit: UN Photo/Harandane Dicko, Mark Garten, Eskinder Debebe



Population Division

“The assessment of these recent trends in Africa is one of the main reasons why the UN projections – based on an extrapolative model of the total fertility rates that does not consider the changing educational structure – results in higher assumptions of future fertility than the IIASA (Wittgenstein Centre) projections. The medium (SSP2) scenario from IIASA is based on the assumption that improvements in female education will continue, and result in a more rapid fertility decline.”


Assertion that treatment of education explains the difference

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Population Division

“For the definition and substantive reasoning of the specific assumptions made, the reader is referred to the chapters of [Lutz et al. 2014] which provide comprehensive reviews of the scientific literature on the drivers of future fertility mortality, migration and education trends and the results of the largest ever expert surveyfor assessing the validity of alternative arguments drawing from over 550 international experts who either participated in a series of five substantive meetings or took part in an extensive online survey.”


IIASA mid- and long-term assumptions

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Population Division

• UN medium projection, based on historical experience, accounts for educational change implicitly• IIASA medium projection, based on expert judgement,

accounts for educational change implicitly• Neither uses an explicit model of educational change in

setting assumptions for the medium projection

Accounting for educational change: Does it explain the difference?

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Population Division

“Lutz and his fellow demographers at Vienna’s International Institute for Applied Systems Analysis believe that advancing education in developing countries, brought about by increasing urbanization, should be factored into future population projections, which the UN doesn’t do. The IIASA, using those factors predicts a stabilizing population by mid-century, followed by a decline. Lutz believes that the human population will be shrinking as early as 2060.”

Bricker and Ibbitson 2019, Empty Planet: The Shock of Global Population Decline, chapter 2 (emphasis added)

Widespread misunderstanding about role of education in projection models

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Population Division

Total fertility rate (TFR), Nigeria, 1950-2100UN 2019 medium variant with 80-percent prediction intervals

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0.0

0.1

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1234567

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UN medium80% Upper

80% Lower

0

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1950 1980 2010 2040 2070 2100Year


Birt

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Population Division

Total fertility rate (TFR), Nigeria, 1950-2100UN 2019 medium with prediction intervals and IIASA medium (SSP2) scenario

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0.0

0.1

0.2

0.3

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1234567

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IIASA mediumUN medium80% Upper

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1950 1980 2010 2040 2070 2100Year


Birt

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Population Division

For African countries, projected medium TFR values are on average 18% lower for IIASA compared to UN (up to 40% lower for some countries), with smaller relative differences by 2100

TFRs for African countries, IIASA vs. UN medium

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12345678

1 2 3 4 5 6 7 8

IIASA

UN

2010-2015

IIASA < UN

IIASA > UN +20%

-20%

1

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1 2 3

IIASA

UN

2095-2100

IIASA < UN

IIASA > UN +20%

-20%

-40%

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1 2 3 4 5

IIASA

UN

2045-2050

IIASA < UN

IIASA > UN +20%

-20%

-40%



Population Division

• Track record of UN projections• Validation of probabilistic intervals• Reliability of expert predictions• Coherence of IIASA alternative scenarios• Accelerated fertility decline for Africa• Aggregation of alternative scenarios

Critical assessment of two methodologies

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Population Division

Population in 2015:• +3.8% (280 mio)

to -3.1% (-225 mio)

UN population projections: Past and Present1980 to 2019 revisions of the World Population Prospects (WPP)

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-4 -2 0 2 4 6

198019821984198819901992199419961998200020022004200620082010201220152017

Error (percent)

UN re

visi

ons

4.4

4.8

5.2

5.6

6.0

6.4

6.8

7.2

7.6

1980 1985 1990 1995 2000 2005 2010 2015

Tota

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(bill

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)

Time

UN 1980UN 1982UN 1984UN 1988UN 1990UN 1992UN 1994UN 1996UN 1998UN 2000UN 2002UN 2004UN 2006UN 2008UN 2010UN 2012UN 2015UN 2017UN 2019



Population Division

UN 2010 out-of-sample validation: 1990-2010

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Coverage (percent)

Quantity MARE (%)80% prediction

interval95% prediction

intervalTotal fertility rate 12.3 72 87Female life expectancy 2.0 83 94Male life expectancy 2.5 83 91Total population 2.7 73 85

* MARE is the mean absolute relative error. Coverage is the proportion of the 1990-2010 observations that fell within their prediction interval, in percent.



Population Division

Issues with expert-based approaches

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20 J U N E 2 0 1 9 T H E A T L A N T I C

D I S P A T C H E S

T!" #"$ %&' (), and it was over the fate of humanity. On one side was the Stanford biologist Paul R. Ehrlich. In his 1968 best seller, The Population

Bomb, Ehrlich insisted that it was too late to prevent a doomsday apocalypse result-ing from overpopulation. Resource short-ages would cause hundreds of millions of starvation deaths within a decade. It was cold, hard math: The human population was growing exponentially; the food sup-ply was not. Ehrlich was an accomplished butter*y specialist. He knew that nature did not regulate animal populations deli-cately. Populations exploded, blowing past the available resources, and then crashed.

In his book, Ehrlich played out hypo-thetical scenarios that represented “the

kinds of disasters that will occur.” In the worst-case scenario, famine rages across the planet. Russia, China, and the United States are dragged into nuclear war, and the resulting environmental degrada-tion soon extinguishes the human race. In the “cheerful” scenario, population controls begin. Famine spreads, and countries teeter, but the major death wave ends in the mid-1980s. Only half a billion or so people die of starvation. “I challenge you to create one more opti-mistic,” Ehrlich wrote, adding that he would not count scenarios involving benevolent aliens bearing care packages.

The economist Julian Simon took up Ehrlich’s challenge. Technology— water-control techniques, hybridized seeds, management strategies— had revolu-tionized agriculture, and global crop yields were increasing. To Simon, more people meant more good ideas about how to achieve a sustainable future. So he proposed a wager. Ehrlich could choose +ve metals that he expected to become more expensive as resources were depleted and chaos ensued over the next decade. Both men agreed that commodity prices were a +ne proxy for the effects of population growth, and they set the stakes at $1,000 worth of Ehrlich’s +ve metals. If, 10 years hence,

• B U S I N E S S

THE PECULIAR BLINDNESS OF EXPERTS

Credentialed authorities are comically bad at predicting the future. But reliable forecasting is possible.

B Y D AV I D E P S T E I N

I l l u s t r a t i o n b y N A K I M

0619_DIS_Epstein_BizForecasts [Print]_11625884.indd 20 4/22/2019 11:53:43 AM

20

THE PECULIAR BLINDNESS OF EXPERTS

B Y D AV I D E P S T E I N



Population Division

Coherence of IIASA alternative scenarios (SSP1 and SSP3)

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Country groupings Fertility Mortality Migration Education

SSP1: SUSTAINABILITY / RAPID SOCIAL DEVELOPMENT

HiFert Low Low Medium High (SDG)

LoFert Low-Med Low Medium High (SDG)

SSP2: CONTINUATION / MEDIUM POPULATION SCENARIO

HiFert Medium Medium Medium Medium (GET)

LoFert Medium Medium Medium Medium (GET)

SSP3: FRAGMENTATION / STALLED SOCIAL DEVELOPMENT

HiFert High High Low Low (CER)

LoFert High High Low Low (CER)

Source: Lutz et al. 2018, p. 27

?

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Population Division

Shapiro and Hinde, Demographic Research, 2017, p. 1334

Plausibility of accelerated fertility decline in Africa

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Population Division

Global population trend 2015-2100UN 2019 medium variant with 80- and 95-percent prediction intervals

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UN medium variant80% Lower

80% Upper

95% Lower

95% Upper

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Population Division

Global population trend 2015-2100UN 2019 medium with prediction intervals and IIASA medium (SSP2) scenario

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IIASA medium (SSP2)

UN medium variant80% Lower

80% Upper

95% Lower

95% Upper

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Global population trend 2015-2100UN 2019 low/medium/high with prediction intervals and IIASA low/medium/high

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IIASA medium (SSP2)

IIASA low (SSP1)

IIASA high (SSP3)

UN medium variant

UN high-fertility

UN low-fertility

80% Lower

80% Upper

95% Lower

95% Upper

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Population Division

• Relatively small differences between UN and IIASA projections in the short to medium term• Larger differences in the long run are consequential for climate

change and other environmental issues• UN and IIASA teams should work together to understand better

the sources of difference in their projections, to provide accurate explanations and to promote frank discussions of implications

Concluding remarks

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For further information about the work of the Population Division, please visit population.un.org

https://population.un.org/

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Global Population Projections: methods and …...“Lutz and his fellow demographers at Vienna’s International Institute for Applied Systems Analysis believe that advancing education