World Population Prospects The 2010 Revision Gerhard K. Heilig United Nations, Department of Economic and Social Affairs (DESA) Population Division - Population

World Population ProspectsThe 2010 Revision

Gerhard K. Heilig

United Nations, Department of Economic and Social Affairs (DESA)Population Division - Population Estimates and Projections Section

www.unpopulation.org

5 May 2011

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Source: United Nations, Department of Economic and Social Affairs, Population Division (2011): World Population Prospects, the 2010 Revision. New York

WPP preparation process: Overview

Estimation of population, fertility, mortality and migration

Probabilistic projection of total fertility

Projection of life expectancy

Projection of migration

Table of content

1

2

3

0

4

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Team

Chief

Gerhard K. Heilig

Population Affairs Officer

Danan Gu


Nan Li


Thomas Spoorenberg


Kirill Andreev


Patrick Gerland

Editorial Assistant

Neena Koshy

Information Systems Assistant

Sarada Ravinuthala

Information Systems Assistant

Chandrasekhar Yamarthy

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Mandate

Prepare the World Population Prospects Official United Nations population projections, used throughout the UN System Recent revisions prepared every second year Latest revision: 2010 (230 countries) 22 revisions since the early 1950s

Prepare the World Urbanization Prospects Estimates and projections of urban population for 230 countries Estimates and projections of major urban agglomerations (about 5000) Latest revision: 2009 Since 1988

Publish results, develop methodology Web sites, wall charts, CD-ROMs/DVDs, databases Model life tables, probabilistic projections (Bayesian Hierarchical Model)

Develop specialized databases and software DemoBase, DemoData, DataArchive Estimation and projection software, probabilistic models, data checking

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1

World Population Prospects: What we do

60% of Workload

Update and development of new databases and software, server

and database maintenance, backup

Start of WPP

EvaluationAdjustments

Data Collection 230 countries / areas

Uploading to DatabaseCalculation of Variants

Aggregation of Regions

Epidem. Modelingfor AIDS Countries

3

CD-ROMs / DVDs

Online DatabaseWeb Sites

Statistical ReportsVol. 1, 2

Early Release Data

Wall Chart

25% of Workload

15% of Workload

Checking of Results

2

Methodological Report(on web site)

Start of WUP

Fixing of Errors

Data Collection, EstimationProjection,

Aggregation, Checking Output Production

0

Consulting / Feedback

Responding to clients

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0

1. Census data + post-enumeration surveys(from United Nations Demographic Yearbook database and directly from National Statistical Offices)

2. All available demographic and health surveys (DHS, DSS, MICS, WHS, etc.) for estimating fertility and mortality

3. Estimates from population and vital registers (from statistical reports of National Statistical Offices or their web sites)

4. Scientific reports and data collections (Human Mortality Database, child mortality estimates, etc.)

5. Data and estimates provided by other international agencies (CELADE, Regional Commissions, EUROSTAT, ESCAP, UNICEF, UNAIDS, WHO)

WPP data sources

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Methodology

The UN approach to estimating population (by age and sex), fertility, mortality and migration

Availability & Data Quality

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Population statistics are sufficient for cohort component projection.Projection benefits significantly from Census data.

Population statistics Insufficient for cohort component projection. Projection can be done only with Census data.

Figure 2: Data Availability from Estimates. Population in five-year age groups by sex: Number of data points 1950-2008 in five-year intervals

Source: UNPD, DemoData. Chart by GKH.

Population statistics usually sufficent for cohort component projection. Estimation often can be improved with Census data.

Register Data: Population by Age and Sex

Sufficient for cohort-component projection

Partially sufficient for cohort-component projection

Not sufficient for projection

≈ 2/3 of countries have insufficient population estimates by age and sexto establish consistent time-seriesfrom 1950 to present

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Data Sources: Child Mortality Estimation (WPP2008) 1

Number of countries according to the most recent data available

Only countries with a population of 100,000 or more in 2009

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Data Sources: Adult Mortality Estimation (WPP2008) 1



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Data Sources: Fertility Estimation (WPP2008) 1



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Estimation of Population from Censuses 1

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Estimation of Population from Censuses 1

Under-reported migrant workers

Under-reported births

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Estimation of Total Population 1

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Estimation of Mortality: 1q0, 5q0 (Senegal) 1

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Estimation of Mortality: 5q0 (Senegal)

1950 1960 1970 1980 1990 2000 2010

0.0

0.1

0.2

0.3

0.4

0.5

A. Birth histories

UNPD Spline 1978 WFS1986 DHS1992-1993 DHS1997 DHS1999 DHS2000 MICS2005 DHS2008-2009 MICS

Saint-Louis (SEN), VR Bamako (MLI), VR

Ouaga. (DSS)

Mauritius, VR

1950 1960 1970 1980 1990 2000 20100.

00.

10.

20.

30.

40.

5

B. CEB/CS

Fouta Toro (Maures)1960-1961 survey1978 WFS1986 DHS1992-1993 DHS1996 MICS1997 DHS1999 DHS2005 DHS2006 MICS2008-2009 MICS

1950 1960 1970 1980 1990 2000 2010

0.0

0.1

0.2

0.3

0.4

0.5

C. Household deaths

Fouta Toro (Toucouleurs & Peuls)Fouta Toro (Maures)

1950 1960 1970 1980 1990 2000 2010

0.0

0.1

0.2

0.3

0.4

0.5

D. Vital Registration

Mauritius (VR)BamakoDakarSaint-Louis

1950 1960 1970 1980 1990 2000 2010

0.0

0.1

0.2

0.3

0.4

0.5

E. Multiround survey

1970-1971 Multiround Survey1978-1979 Multiround SurveyDakarCentral valleyThienaha

1950 1960 1970 1980 1990 2000 20100.

00.

10.

20.

30.

40.

5

F. Demographic Surveillance Sites

OuagadougouBandafassiFakao-PalmarinFakaoMlompNiakharN'gayokhèmePeul BandePaos-Koto

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Estimation of Mortality: 45q15 (Senegal)

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

A. Derived from 5q0 and relational model

UNPD (CD-N)Timaeus/SahelCD-SouthCD-North

Loess reg.Spline

UNPD

Saint-Louis (SEN), VR

Bamako (MLI), VROuaga. (DSS)

Mauritius, VR

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

B. Household deaths

Fouta Toro (Toucouleurs & Peuls)1988 Census

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

C. Intercensal survival

1960-1971 surveys1972-1982 census estimates1978-1988 census1982-1992 census estimates1992-2002 census estimates

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

D. Maternal orphanhood

1986 DHS (at marriage)1986 DHS1986 DHS (=> marriage)1988 Census1992-1993 DHS2005 DHS

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

E. Siblings

1992-1993 DHS2005 DHSDHS-Siblings (pooled analysis)

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

F. Vital Registration

Mauritius (VR)BamakoDakarSaint-Louis

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

G. Multiround survey

1970-1971 Multiround Survey1978-1979 Multiround SurveyDakar

1950 1960 1970 1980 1990 2000 2010

0.1

0.2

0.3

0.4

0.5

0.6

H. Demographic Surveillance Sites

OuagadougouBandafassiFakaoMlomp

NiakharN'gayokhèmePeul BandePaos-Koto

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Estimation of Fertility: TFR (Senegal)

1950 1960 1970 1980 1990 2000 2010

23

45

67

8

Observed (weighted)

1957 Middle Senegal (D)1957 Middle Senegal (I)1957 Middle Senegal (D-A)1960-61 survey (D)1960-61 survey (D-A)1970-71 EDN (D)1978 WFS (D)1978 WFS (D-A)1986 DHS (D)1986 DHS (D-A)1988 census (D)1992-93 DHS (D)

1992-93 DHS (D-A)1997 DHS (D)1997 DHS (D-A)1999 ESIS (D)1999 ESIS (D-A)MICS-MD (D)2002 census (D)2002 census (D-A)2005 ESIS (D)2005 ESIS (D-A)2006 MIS (D)2008-2009 MIS (D)

WPP08 Loess regression

Cubic Spline

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Estimation of Fertility: ASFR (Bangladesh) 1

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Estimation of Fertility: ASFR (Bangladesh)

Lines are weighted cubic spline and loess regression trends

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Data Quality Assessment (based on WPP2006) 1

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Methodology: The UN approach to projecting total fertility

A Bayesian Hierarchical Model (BHM) based on bi-logistic decline curves of fertility

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Model of fertility decline

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

12345678910

Children per woman

Fer

tility

dec

line

Fast/Fast Fast/Slow Slow/Slow

Model: Bi-logistic function to estimate the rate of fertility decline

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1

2

3

4

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Year

Chi

ldre

n pe

r w

oman

Fast/Fast Fast/Slow Slow/Slow Floor

1

2

3

4

5

6

7

8

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

YearC

hild

ren

per

wom

anFast/Fast Fast/Slow Slow/Slow Floor

Projections: Fertility

A. From high to low fertility

B. From medium to 1.85

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Model of fertility decline

Model: Bi-logistic function to estimate the rate of fertility decline

1 2

1 21 2

( )(81) (81)

1 exp[ ( )] 1 exp[ ( )]m m

k kP t

Ln Lnt t t t

t t

Parameter Slow/Slow Fast/Slow Fast/Fast

k1 ............... -0.11 -0.16 -0.25

Δt1.............. 5.03 4.34 4.01

tm1 .............. 5.77 5.06 5.17

k2 ............... 0.15 0.22 0.31

Δt2.............. 2.75 3.02 4.32

tm2 .............. 3.21 3.52 3.94 In probabilistic fertility projection: Parameters values are replaced

by distributions. Some 100,000 trajectories of fertility decline are calculated by sampling from these parameter distributions.

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Fertility models and empirical data

-0.3

-0.2

-0.1

0

0.1

0.2

012345678910

Average annual decline in TFR

Total Fertility

Average annual TFR decline

Fast/Fast

Fast/Slow

Slow/Slow

China

Iran

Mexico

Poland

2

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BHM: A fertility transition model

Phase I: Not modeled.

Phase II: Fertility transition, modeled by Bayesian Hierarchical Model (BHM)

Phase III: Modeled with a first order autoregressive time series model [AR1], with its mean fixed at the approximate replacement-level fertility of 2.1

2

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2BHM: A fertility transition model

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2BHM: A fertility transition model

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2BHM: Bi-logistic functions and TF projections

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2Probabilistic Projection of Population

Based on estimates from WPP2010; probabilistic fertility, deterministic mortality

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Total Population Population age 15-64

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Total Population Population age 15-64

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Methodology: The UN approach to projecting life expectancy

3

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Models for projecting life expectancy at birth

Illustration of the double-logistic function (based on a curve from Japan). The left plot illustrates the double-logistic function of 5-year gains in life expectancy. The right plot is a time-series of life expectancy, e(0), with gains modeled according to the double-logistic function

3

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Models for projecting life expectancy at birth 3

Illustration of the double-logistic function (based on a curve from Japan). The left plot illustrates the double-logistic function of 5-year gains in life expectancy. The right plot is a time-series of life expectancy, e(0), with gains modeled according to the double-logistic function

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Models for projecting life expectancy at birthUN Model: Five-year gains in life expectancy at birth (in years): Male

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5 92.5

e(0)

5-y

ea

r g

ain

s

Very fast (p90)

Fast pace (p75)

Medium Pace (m)

Slow pace (p25)

Very slow (p10)

3

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Models for projecting life expectancy at birthUN Model: Five-year gains in life expectancy at birth (in years): Female

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5 92.5

e(0)

5-y

ea

r g

ain

s

Very fast (p90)

Fast pace (p75)

Medium Pace (m)

Slow pace (p25)

Very slow (p10)

3

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Projections: Mortality models and empirical data

Male: average annual gain in e0 at a given e0

-0.3

-0.1

0.2

0.4

0.6

0.8

1.0

35 40 45 50 55 60 65 70 75 80 85

year

Average annual gain

Oman

Norway

Japan

Very fast (p90)

Fast pace (p75)

Medium Pace (m)

Slow pace (p25)

Very slow (p10)

3

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Probabilistic mortality projection

ctct

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ctc

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tcl

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offunction smoothed ,0Normal~

,Normal Truncated~

Fcn Logistic Double param 6)|(

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period time countries; ,...,1;expectancylife,

Data:• Male life expectancy at birth from 1950 through 2005;• Estimates from UN World Population Prospects (WPP2006)

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Data Checking 5

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DemoData: Empirical Database 5

Different Data Sources

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Thank You !

www.unpopulation.org

Documents

World Population Prospects The 2010 Revision Gerhard K. Heilig United Nations, Department of Economic and Social Affairs (DESA) Population Division - Population