the global demographic future: how we got to here—and where we may be in 2035 nicholas eberstadt...

The Global Demographic Future:How We Got To Here—And Where We May Be In 2035

Nicholas Eberstadt

Wendt Chair in Political Economy

American Enterprise Institute

[email protected]

Gaidar Foundation

Moscow

June 2015

mailto:[email protected]

Outline of Presentation

The Population Explosion—Was Malthus Right?

Are Natural Resources Becoming More Scarce?

Human Resources: The Health Explosion/Education Explosion/

Wealth Explosion

Family Planning And The Demographic Future

Global/Regional Outlook For The World To 2035: (With Breakouts for China / Russia / India /Japan /

Western Europe / and USA)

19501954

19581962

19661970

19741978

19821986

19901994

19982002

20062010

20140

1000000000

2000000000

3000000000

4000000000

5000000000

6000000000

7000000000

8000000000

World Population: 1950-2015(Estimated projected, in Billions)

Popu

latio

n (b

illio

ns)

U.S. Census Bureau, International Data Base. http://www.census.gov/population/international/data/idb/informationGateway.php (Date Accessed: April 1, 2015)

http://www.census.gov/population/international/data/idb/informationGateway.php

US Census Bureau, “Total Midyear Population for the World: 1950-2050” http://www.census.gov/population/international/data/worldpop/table_population.php ,“Historical Estimates of World Population,” Summary: Lower Estimatehttp://www.census.gov/population/international/data/worldpop/table_history.php (Date Accessed: April 1, 2015)

1 130 260 390 520 650 780 910 1040 1170 1300 1430 1560 1690 1820 1950100

1000

10000

Historical Estimates of World Population, 1-2010 (In Millions)

Popu

latio

n (m

illio

ns)

http://www.census.gov/population/international/data/worldpop/table_population.php



http://www.census.gov/population/international/data/worldpop/table_history.php



1913191819231928193319381943194819531958196319681973197819831988199319982003200820130.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Rice Prices Deflated by PPI1913-2013

Ln Rice = - 0.011*(year) + 22.686 (-10.99) (11.10)Adj. R-squared: 0.5451 Number of Observation: 101

Note: Before PPI deflation, GYCPI Indexed to the 1977-1979 arithmetic mean at 100; PPI indexed to 1982=100; not seasonally adjustedGYCPI – Grilli and Yang data, provided by Stephan Pfazenfeller, Updated to 2013, (Date Accessed: April 1, 2015) and Federal Reserve Economic Data, http://research.stlouisfed.org/fred2/graph/?&chart_type=line&graph_id=0&category_id=&recession_bars=On&width=630&height=378&bgcolor=%23B3CDE7&graph_bgcolor=%23FFFFFF&txtcolor=%23000000&ts=8&preserve_ratio=true&id=PPIACO&transformation=lin&scale=Left&range=Max&cosd=1913-01-01&coed=2009-11-01&line_color=%230000FF&link_values=&mark_type=NONE&mw=4&line_style=Solid&lw=1&vintage_date=2010-01-11&revision_date=2010-01-11&mma=0&nd=&ost=&oet=&fml=a# (Date Accessed: April 1, 2015).

1913191819231928193319381943194819531958196319681973197819831988199319982003200820130.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Wheat Prices Deflated by PPI1913-2013

Ln Wheat = - 0.009*(year) + 17.871 (-11.30) (11.58)Adj. R-squared: 0.5590 Number of Observation: 101


1913191819231928193319381943194819531958196319681973197819831988199319982003200820130.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Maize Prices Deflated by PPI1913-2013

Ln Maize = - 0.012*(year) + 24.102 (-13.80) (14.04)Adj. R-squared: 0.6545 Number of Observation: 101


19001904

19081912

19161920

19241928

19321936

19401944

19481952

19561960

19641968

19721976

19801984

19881992

19962000

20042008

20120.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

The Relative Price Of 10 Foodstuffs: A Long-Term DeclineGYCPIF/MUV Prices (Indexed): 1900-2013

The long term relative price trend-line here drops by about 50% between 1900 and 2013

Ln GYCPIF/MUV = - 0.006*(year) + 12.349 (-10.04) (10.00)Adj. R-squared: 0.4690 Number of Observation: 114

GYCPIF-MUV – Grilli and Yang data, provided by Stephan Pfazenfeller, Updated to 2013, (Date Accessed: April 1, 2015)

19001904

19081912

19161920

19241928

19321936

19401944

19481952

19561960

19641968

19721976

19801984

19881992

19962000

20042008

20120.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

The Relative Price Of 24 Commodities: Long Term Decline

GYCPI/MUV Prices (Indexed): 1900-2013Please note: this index does not include fuels

Ln GYCPI/MUV = - 0.006*(year) + 12.591 (-12.49) (12.51)Adj. R-squared: 0.5782 Number of Observation: 114

GYCPI-MUV – Grilli and Yang data, provided by Stephan Pfazenfeller, Updated to 2013, (Date Accessed: April 1, 2015)

186918781887189619051914192319321941195019591968197719861995200420130

20

40

60

80

100

120

Nominal Crude Oil prices: 1861-2015Q1 (current US$)

US

dolla

rs p

er b

arre

l

Ln Oil = 0.025*(year) – 47.196 (13.99) (-13.66)Adj. R-squared: 0.5583 Number of Observation: 155

Source: BP, “Crude oil prices historical data,” available at: http://www.bp.com/en/global/corporate/about-bp/energy-economics/statistical-review-of-world-energy/review-by-energy-type/oil/oil-reserves.html; 2015 data: “Trading Conditions update,” available at: http://www.bp.com/en/global/corporate/investors/results-and-reporting/trading-conditions-update.html.

http://www.bp.com/en/global/corporate/about-bp/energy-economics/statistical-review-of-world-energy/review-by-energy-type/oil/oil-reserves.html


186918781887189619051914192319321941195019591968197719861995200420130

20

40

60

80

100

120

140

f(x) = 21.8753734947727 exp( 0.00264836147650106 x )R² = 0.0363754121699957

Real Crude Oil prices: 1861-2015Q1 (2013 US $)

US

dolla

rs p

er b

arre

l

Ln Oil = 0.003*(year) – 1.838 (2.40) (-0.86)Adj. R-squared: 0.0301 Number of Observation: 155

Source: BP, “Crude oil prices historical data,” available at: http://www.bp.com/en/global/corporate/about-bp/energy-economics/statistical-review-of-world-energy/review-by-energy-type/oil/oil-reserves.html; 2015 data: “Trading Conditions update,” available at: http://www.bp.com/en/global/corporate/investors/results-and-reporting/trading-conditions-update.html ; and Robert Sahr, “Inflation Conversion Factors,” Oregon State University, available at: http://liberalarts.oregonstate.edu/spp/polisci/research/inflation-conversion-factors-convert-dollars-1774-estimated-2024-dollars-recent-year



http://www.bp.com/en/global/corporate/investors/results-and-reporting/trading-conditions-update.html

http://liberalarts.oregonstate.edu/spp/polisci/research/inflation-conversion-factors-convert-dollars-1774-estimated-2024-dollars-recent-year

http://liberalarts.oregonstate.edu/spp/polisci/research/inflation-conversion-factors-convert-dollars-1774-estimated-2024-dollars-recent-year

CCPI CCPI’ ---- GYCPI

Real Price Trends for Natural Resources: 1900-2008

Source: David Harvey et al., “Long-Run Commodity Prices and Economic Growth: 1650-2010,” (University of Nottingham, 2014), available at: http://www.nottingham.ac.uk/~lezdih/commod.pdf .

Including Oil

Without Oil

Ultra-Longterm Real Price Trends: Natural Resources Indices, 1650-2010

CCPI CCPI’Source: David Harvey et al., “Long-Run Commodity Prices and Economic Growth: 1650-2010,” (University of Nottingham, 2014), available at: http://www.nottingham.ac.uk/~lezdih/commod.pdf .

Including Oil

Without Oil

190019071914192119281935194219491956196319701977198419911998200520120

0.5

1

1.5

2

2.5

3

3.5

4

0

1000

2000

3000

4000

5000

6000

7000

8000

Real Wheat, Rice, and Maize Prices versus World Population: 1910-2013

(Prices Deflated by PPI)

Rice WheatMaize World PopulationMoving average (World Population)

Whe

at, R

ice,

and

Mai

ze P

rice

s

Note: Before PPI deflation, GYCPI Indexed to the 1977-1979 arithmetic mean at 100; PPI indexed to 1982=100; not seasonally adjustedGYCPI – Grilli and Yang data, provided by Stephan Pfazenfeller, Updated to 2013, (Date Accessed: April 1, 2015) and Federal Reserve Economic Data, http://research.stlouisfed.org/fred2/graph/?&chart_type=line&graph_id=0&category_id=&recession_bars=On&width=630&height=378&bgcolor=%23B3CDE7&graph_bgcolor=%23FFFFFF&txtcolor=%23000000&ts=8&preserve_ratio=true&id=PPIACO&transformation=lin&scale=Left&range=Max&cosd=1913-01-01&coed=2009-11-01&line_color=%230000FF&link_values=&mark_type=NONE&mw=4&line_style=Solid&lw=1&vintage_date=2010-01-11&revision_date=2010-01-11&mma=0&nd=&ost=&oet=&fml=a# (Date Accessed: April 1, 2015). World Population Data: US Census Bureau, “Total Midyear Population for the World: 1950-2050” http://www.census.gov/population/international/data/worldpop/table_population.php ,“Historical Estimates of World Population,” Summary: Lower Estimatehttp://www.census.gov/population/international/data/worldpop/table_history.php (Date Accessed: April 1, 2015)



Real Global GDP: 1900-2010(Angus Maddison and Maddison Project estimates, trillions)

19001907191419211928193519421949195619631970197719841991199820050

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

Sources: For 1900-2008: Angus Maddison, “Statistics on World Population, GDP and Per Capita GDP, 1-2008 AD,” Table 2:GDP, available at http://www.ggdc.net/maddison/Maddison.htm (Date Accessed: February 26, 2013);;For 2009 and 2010: derived from per capita GDP estimates for The Maddison-Project, http://www.ggdc.net/maddison/maddison-project/home.htm,2013 version, and annual population estimates from UN Population Division, “World Population Prospects: The 2012 Revision, Excel Tables - Population Data, available at http://esa.un.org/wpp/Excel-Data/population.htm , (Data Accessed: April 6, 2015).

http://www.ggdc.net/maddison/Maddison.htm


http://esa.un.org/wpp/Excel-Data/population.htm

http://esa.un.org/wpp/Excel-Data/population.htm

1900 1908 1916 1924 1932 1940 1948 1956 1964 1972 1980 1988 1996 2004 20120

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Relative Primary Commodity Prices vs. Real Global GDP:1900-2013

Wor

ld G

DP

(Intl

Gea

ry-K

ham

is 1

990$

, tr

illon

s)

Com

mod

ity P

rice

Inde

x

Angus Maddison, “Statistics on World Population, GDP and Per Capita GDP, 1-2008 AD,” Table 2: GDP, available at http://www.ggdc.net/maddison/Maddison.htm (Date Accessed: February 26, 2013) and GYCPI/MUV – Grilli and Yang data, provided by Stephan Pfazenfeller, Updated to 2013, (Date Accessed: April 1, 2015)

• World GDP • Commodity Price Index (GYCPI/MUV)



1900 1907 1914 1921 1928 1935 1942 1949 1956 1963 1970 1977 1984 1991 1998 20050

1000

2000

3000

4000

5000

6000

7000

8000

9000

Estimated GDP Per Capita, 1900-2010:World and Selected Regions(Angus Maddison estimates)

(199

0 In

tern

ation

al G

eary

-Kha

mis

dol

lars

)

Source: The Maddison-Project, http://www.ggdc.net/maddison/maddison-project/home.htm, 2013 version. (Date Accessed: April 1, 2015)

• Africa• Asia• World • Latin America

The Worldwide Health Explosion:Estimated Life Expectancy at Birth, 1950/55-2005/10(UN Population Division estimates, both sexes, years)

Major area, region, country 1950-1955 2005-2010 Absolute Change (years) % Change

World 46.9 68.7 21.8 46.5%

More Developed Regions 64.7 76.9 12.2 18.9%

Less Developed Regions 41.6 67.0 25.4 61.1%

Least Developed Countries 36.4 58.4 22 60.4%

--Asia 42.2 70.3 28.1 66.6%

--Latin America and the Caribbean 51.4 73.4 22 42.8%

--Sub-Saharan Africa 36.2 52.9 16.7 46.1%

--Russian Federation 58.5 67.2 8.7 14.9%

United Nations Population Division, World Population Prospects 2012 Revision, http://esa.un.org/wpp/Excel-Data/mortality.htm/ (Date Accessed: April 2, 2015).

http://esa.un.org/wpp/Excel-Data/mortality.htm/

The Worldwide Health Explosion--continuedEstimated Infant Mortality Rates, 1950/55-2005/10

(UN Population Division estimates, both sexes, deaths per 1,000 live births)

United Nations Population Division, World Population Prospects 2012 Revision, http://esa.un.org/wpp/Excel-Data/mortality.htm / (Date Accessed: April 2, 2015)

Major area, region, country 1950-1955 2005-2010 Absolute Change (years) % Change

World 135 42 -93 -68.9%

More Developed Regions 60 6 -54 -90.0%

Less Developed Regions 153 46 -107 -69.9%

Least Developed Countries 199 72 -127 -63.8%

--Asia 146 37 -109 -74.7%

--Latin America and the Caribbean 126 21 -105 -83.3%

--Sub-Saharan Africa 183 79 -104 -56.8%

--Russian Federation 101 11 -90 -89.1%

http://esa.un.org/wpp/

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 1080

500100015002000250030003500400045005000

Changes in Lifespan Inequality with Improving Health Total, Sweden 1751 vs. 2011

(Age at Death from every 100,000 persons born)

1751 2011

Gini Index for length of life:1751 = 0.462011 = 0.08

Life expectancy at birth:1751: 38 years2011: 82 years

Notes: The number of deaths per 100,000 infants ages 0-1 was 19,722 in 1751, and 206 in 2011.Source: Human Mortality Database. Sweden, Total (1x1) Life tables, available at http://www.mortality.org/cgi-bin/hmd/country.php?cntr=SWE&level=1 Accessed August 18, 2014.

http://www.mortality.org/cgi-bin/hmd/country.php?cntr=SWE&level=1



10 20 30 40 50 60 70 80 900

0.1

0.2

0.3

0.4

0.5

0.6

0.71776

Gini Index for Lifespan Inequality vs. Life Expectancy at Birth: Sweden, 1751-2011

Life Expectancy at birth (years)

Gin

i Coe

ffici

ent

Source: Calculations based on author’s calculations derived from data available at: Human Mortality Database. Sweden, Total (1x1) Life tables, available at http://www.mortality.org/ Accessed August 29, 2014.

Gini Coefficient= -0.0093*(Life Expectancy) + 0.8049 (-177.08) (275.89)

R-squared: 0.9918 Number of Observation: 261

http://www.mortality.org/


30 35 40 45 50 55 60 65 70 75 800

0.1

0.2

0.3

0.4

0.5

0.6

Gini Coefficient vs. Life Expectancy: Males and Females, 63 selected countries,

Postwar Period

Life Expectancy at birth (years)

Gin

i coe

ffici

ent

Gini Coefficient= -0.0093*(Life Expectancy) + 0.7991 (-65.60) (92.68)

R-squared: 0.9603 Number of Observation: 180

Source: Figure from Anand and Nanthikesan, “A Complication of Length-of-Life: Distribution Measures for Abridged Life Tables,” Harvard Center for Population and Development Studies Working Paper Series, Vol. 11, No. 4. April 2001.

Death-Age Inequality vs. Life Expectancy at Birth : 63 Selected Countries, Postwar Period

And What These Imply For 20th Century Planetary Trends

0

0.1

0.2

0.3

0.4

0.5

0.6

0 10 20 30 40 50 60 70 80

Male Life Expectancy at Birth (years)

Gin

i Coe

ffici

ent

Sources: All estimates except Italy and Sweden from S. Anand and S. Nanthikesan, “A Complication of Length-of-Life: Distribution Measures for Abridged Life Tables,” Harvard Center for Population and Development Studies Working Paper Series, Vol. 11, No. 4. April 2001.Sweden (2011) and Italy (2009) are based on author’s calculations derived from: Human Mortality Database. Italy and Sweden, Total (1x1) Life tables, available at http://www.mortality.org/ Accessed August 18, 2014.

Approximate Global Life Expectancy,

2000

Approximate Global Life Expectancy,

1900

Italy (1872)

Italy (2009)

Sweden (2011)

Sweden (1751)



Region (no. of countries) 1950 1960 1970 1980 1990 2000 2010

World (146) Average Years of Schooling (Female)2.74 3.18 3.92 4.78 5.68 6.56 7.44

Average Years of Schooling (Male)3.50 4.03 4.87 5.91 6.59 7.63 8.35

Gender ratio (female/male %)78.3 79.0 80.5 80.9 86.2 86.0 89.0

All Developing (122) Average Years of Schooling (Female)1.55 2.00 2.77 3.69 4.73 5.70 6.65



Middle East/North Africa (18) Average Years of Schooling (Female)0.44 0.63 1.10 2.10 3.50 5.10 6.45



Sub-Saharan Africa (33) Average Years of Schooling (Female)0.97 1.12 1.49 2.09 3.14 3.97 4.65



Latin America and the Caribbean (25)

Average Years of Schooling (Female)2.36 2.87 3.60 4.43 5.82 7.04 8.13



The Global Education ExplosionEstimated Educational Attainment by Sex, 1950-2010

(Barro-Lee estimates, population age 15 and over, 146 countries)

Barro, Robert J. and Lee, Jong-Wha; “A New Data Set of Educational Attainment in the World, 1950–2010,” Journal of Development Economics 104 (2013) p. 184-198, Table 4 pg. 189.

Estimated World Adult Education Profile, 1950-2010: (Barro-Lee estimates, World Population Aged 15+ ,146 Countries)

1950 1960 1970 1980 1990 2000 20100%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

No schooling Primary Secondary Tertiary

Barro, Robert J. and Lee, Jong-Wha; “A New Data Set of Educational Attainment in the World, 1950–2010,” Journal of Development Economics 104 (2013) p. 184-198, Table 4 pg. 189.

Wail, Benaabdelaali; Said, Hanchane; Abdelhak, Kamal; “A New Data Set of Educational Inequality in the World, 1950–2010: Gini Index of Education by Age Group,” Figure.A.2, pg. 23, 2011, Journal of Economic Literature

Gini Index for 15+ MYS by region, gender and year

0 2 4 6 8 10 120

0.10.20.30.40.50.60.70.80.9

1

Gini Coefficient for Educational Attainment by Mean Years of Schooling: Females, 15 and over, 1950-2010

Advanced Countries Developing Countries East Asia and the Pacific Europe and Central Asia

Latin America and the Carribbean Middle East and North Africa South Asia Sub-Saharan Africa

Mean Years of Schooling (years)

Gin

i Coe

ffici

ent

Gini Coefficient = -0.0754*(Mean Years of Schooling) + 0.9003 (-26.77) (60.55)

R-Squared: 0.9299 Observations: 56

Source: Mean Years of Schooling: Robert Barro and Jong-Wha Lee, “A New Data Set of Educational Attainment in the World, 1950-2010,” (April 2010); Gini: Benaabdelaali Wail, Hanchane Said and Kamal Abdelhak, “A New Data Set of Educational Inequality in the World, 1950-2010: Gini Index of Education by Age Group” (August 2011).

0 2 4 6 8 10 120

0.10.20.30.40.50.60.70.80.9

1

R² = 0.915546001051831

Gini Coefficient for Educational Attainment by Mean Years of Schooling: Males, 15 and over, 1950-2010

Advanced Countries Developing Countries East Asia and the PacificEurope and Central Asia Latin America and the Carribbean Middle East and North AfricaSouth Asia Sub-Saharan Africa

Mean years of schooling

Gin

i Coe

ffici

ent

Gini Coefficient = -0.0686*(Mean Years of Schooling) + 0.8441

(-24.20) (49.35)R-Squared: 0.9155 Observations: 56


0 2 4 6 8 10 120

0.10.20.30.40.50.60.70.80.9

1

Gini Coefficient for Educational Attainment by Mean Years of Schooling: Both Sexes, 15 and over, 1950-2010

Advanced Countries Developing Countries East Asia and the PacificEurope and Central Asia Latin America and the Carribbean Middle East and North AfricaSouth Asia Sub-Saharan Africa

Mean years of schooling

Gin

i Coe

ffici

ent

Gini Coefficient = -0.0730*(Mean Years of Schooling) + 0.8789 (-36.37) (77.22)

R-Squared: 0.9232 Observations: 112


Total global household wealth 2000-2014, by region

(estimated, in current $trillions)

Source: Anthony Shorrocks, James Davies, and Rodrigo Lluberas, Global Wealth Databook 2014, Credit Suisse Research Institute (Zurich, Switzerland: Credit Suisse Group, 2014), available at: https://publications.credit-suisse.com/tasks/render/file/?fileID=5521F296-D460-2B88-081889DB12817E02 .

North America Europe Asia-Pacific China Latin America India Africa

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

1500

1700

1900

2100

2300

2500

2700

2900

3100

Estimated Per Capita Caloric Availability By Region:1961-2011

Least Developed Countries WorldAfrica Asia

Kcal

per

cap

ita p

er d

ay

AfricaWorld

Food and Agriculture Organization of the United Nations, “Food supply – Balance Sheets,” http://faostat3.fao.org/browse/FB/FBS/E (Date Accessed: April 2, 2015).

Least Developed

Asia

http://faostat3.fao.org/browse/FB/FBS/E

http://faostat3.fao.org/browse/FB/FBS/E

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2010 20110%

1000%

2000%

3000%

4000%

5000%

6000%

7000%

8000%

9000%

Percent Living Under $1.25/Day by Region, 1981-2011:World Bank Estimates

East Asia and Pacific Latin America and the Caribbean Middle East and North Africa

South Asia Sub-Saharan Africa Total

Total

Sub-Saharan Africa

East Asia

South Asia

Latin America

MENA

World Bank, PovcalNet, “Regional Aggregation using 2005 PPP,” http://iresearch.worldbank.org/PovcalNet/index.htm?1 (Date Accessed: April 1, 2015)

http://iresearch.worldbank.org/PovcalNet/index.htm?1



Source: Literacy Rates: UNESCO Institute for Statistics - UNESCO UIS, http://www.uis.unesco.org/Pages/default.aspx, November 21, 2011; TFR: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2010 Revision, http://esa.un.org/unpd/wpp/unpp/panel_population.htm, November 21, 2011.

0 20 40 60 80 100 1200

1

2

3

4

5

6

7

8

6.62

1.6

2.38

5.79

2.25

1.741.74

2.16

2.632.38

1.39

2.94

5.49

3.5

1.18

2.9

1.92.11

1.46

5.95

4.66

2.8

4.67

2.6

4.85

6.2

1.91.64

2.45

4.64

6.07

1.92

4.65

1.421.51.51

2.672.582.85

2.35

5.36

1.64

4.6

3.35

4.34

1.46

4.15

5.45

3.31

2.73

2.19

1.77

4.86

1.38

2.4

3.27

2.54

4.8

2.322.7

3.02

1.41

3.37

5.42

1.411.02

1.46

4.83

6

2.72

1.9

6.46

1.33

4.71

1.67

2.41 2.52.38

5.11

2.08

3.4

2.95

1.98

2.76

7.19

5.61

2.52

3.65

2.56

4.1

3.08

2.6

3.27

1.36

2.4

1.51.331.44

5.43

3.993.85

3.03

5.03

1.621.62

5.22

1.25 1.39

2.55

1.41

2.36

4.6

2.42

3.57

3.13.45

5.58

1.63

4.34.03

1.64

2.04 2.152.5

6.38

1.39

1.862.12

2.55

1.89

4.65

5.48

6.2

3.47

f(x) = − 0.0483766707571961 x + 6.74124289125388R² = 0.600650309884712

What Determines Family Size?Female Literacy Rates c. 2000 vs. Total Fertility

Rates, 2005-2010

Literacy Rate, Female 15+, most recent year

Tota

l Fer

tilit

y Ra

te, 2

005-

2010

What Determines Family Size?Contraceptive Prevalence, 2006-2010 vs. Total Fertility Rates,2005-2010

Source: Contraceptive prevalence, 2006-2010: UNICEF "The State of the World's Children 2009.” http://www.unicef.org/sowc09/statistics/tables.php, November 21, 2011; TFR: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, The 2010 Revision, http://esa.un.org/unpd/wpp/unpp/panel_population.htm, November 21, 2011.

0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6

7

8

6.62

1.6

2.38

5.79

2.25

1.741.93

2.161.91

2.38

1.39

1.79

2.94

5.49

2.61

3.5

1.18

2.9

1.9

5.95

4.66

2.8

4.67

1.65

2.6

4.85

6.2

1.91.64

2.45

5.08

4.64

6.07

1.92

4.65

1.5

2.05

3.95

2.672.582.85

2.35

4.68 4.6

2.75

1.97

3.35

5.1

1.58

4.34

1.46

2.3

4.15

5.455.27

2.33

3.553.31

2.73

2.19

1.77

4.86

2.12.4

1.32

3.27

2.54

4.8

2.73.02

1.86

3.37

5.42

1.46

4.83

6

1.9

6.46

4.71

1.67

2.412.5 2.38

5.11

2.08

3.4

2.95

1.75

2.76

7.19

5.61

1.92

2.52

3.65

2.56

4.1

3.08

2.6

3.27

1.36 1.291.5

1.33 1.44

5.43

2.13

3.99 3.85

3.03

5.03

1.621.62

5.22

4.4

6.4

2.55

1.41

2.36

4.6

2.42

3.57

3.13.45

5.58

1.63

6.53

4.34.03

1.64

2.04 2.152.5

6.38

1.39

1.832.072.12

2.46

4

1.89

4.65

5.48

6.2

3.47

f(x) = − 0.0502458693241407 x + 5.6540899793737R² = 0.570606981536351

Contraceptive Prevalence (%), most recent year

Tota

l Fer

tilit

y Ra

te, 2

005-

2010

What Determines Family Size?Per Capita GDP 2005 vs. Total Fertility Rates, 2005-2010

Source: Angus Maddison, “Per Capita GDP PPP (in 1990 Geary-Khamis dollars),” Historical Statistics for the World Economy: 1-2008 AD, table 3, http://www.ggdc.net/maddison/ (accessed November 21, 2011); Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2010 Revision, http://esa.un.org/unpd/wpp/unpp/panel_population.htm, accessed November 21, 2011.

100 1000 10000 1000000

1

2

3

4

5

6

7

8

6.62

1.6

2.38

5.79

2.25

1.74

2.16

2.632.38

1.39

5.49

3.5

1.18

2.9

1.9

1.46

5.95

4.66

2.8

4.67

2.6

4.85

6.2

1.91.64

2.45

4.64

1.92

4.65

1.421.5

2.672.582.85

2.35

5.36

1.64

4.6

3.35

4.34

1.46

4.15

5.45

3.31

2.73

2.19

1.77

4.86

1.38

2.4

3.27

2.54

4.8

2.322.7

3.02

1.41

3.37

5.42

1.411.46

4.83

6

2.72

6.46

4.71

1.67

2.412.5 2.38

5.11

2.08

3.4

2.95

1.98

2.76

7.19

5.61

2.52

3.65

2.56

3.08

2.6

3.27

1.36

2.4

1.51.33 1.44

5.43

3.85

3.03

5.03

1.621.62

5.22

1.251.39

2.55

1.41

2.36

4.6

3.57

3.13.45

5.58

1.63

4.3

1.64

2.04 2.152.5

6.38

1.39

1.862.12

2.55

1.89

4.65

5.48

6.2

3.47

f(x) = − 1.13977033774869 ln(x) + 12.3497207957073R² = 0.572635874747536

GDP per capita, 2005 (1990 Geary-Khamis International $)

Tota

l Fer

tilit

y Ra

te, 2

005-

2010

Source: Macro International Inc, 2011. MEASURE DHS STATcompiler. http://www.measuredhs.com, February 24, 2012. Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2010 Revision, http://esa.un.org/unpd/wpp/unpp/panel_population.htm, November 21, 2011.

What Determines Family Size?Total Fertility Rates 2005-2010 vs. Wanted Total Fertility Rates, c. 2005

0 1 2 3 4 5 6 7 80

1

2

3

4

5

6

7

8

1.61.74

2.162.38

5.49

3.5

1.9

5.95

2.8

4.67

2.6

6.2

2.45

5.08

6.07

4.65

2.67 2.582.85

2.35

4.68 4.6

3.35

1.58

4.344.15

5.45

2.33

3.553.31

2.73

2.192.4

3.27

2.54

4.8

2.7

3.37

5.42

4.83

6

1.9

6.46

4.71

2.38

5.11

3.4

2.952.76

7.19

5.61

3.65

3.08

2.6

3.27

1.51.33

5.43

3.85

5.035.22

2.55

3.57

5.58

6.53

4.3

2.152.5

6.38

1.39

2.46

1.89

5.48

6.2

3.47

f(x) = 0.974229449388729 x − 0.0669697384892274R² = 0.931420408400794

Wanted Fertility Rate, most recent year

Tota

l Fer

tilit

y Ra

te, 2

005-

2010

http://www.measuredhs.com/

0 10 20 30 40 50 60 70 80 90 100-3

-2

-1

0

1

2

3

-0.2-0.1

-0.2

-0.8000001-0.899999600000002

-1.5

-0.700000000000001-0.8000002

-0.4000001-0.5

-2.6

-0.2000003

-0.499999900000001

-0.899999900000001-0.7000003-0.6999998

-0.5000001-0.700000000000001

-0.5999999-0.5

-0.4000001

-1.4

-0.6999998

0.9999999

-0.5-0.6999998

-0.5999999-0.700000000000001

-1.5

-1-0.8000001

-0.399999900000001

2.7

-0.8

-0.1

-1.2

-0.3000002

-1

-0.5999999 -0.6000004

-1.2

-0.3

-0.5999999-0.4000001

-0.700000000000001-0.5999999

-0.899999900000001-1.1

-0.4000001-0.1999998

-0.399999600000001

-1

-0.3

-1-0.899999900000001

1.6

0.9000001

-1.8-1.6

-0.8000002-0.5999999 -0.6000001

-1.8

-0.7000003-0.5999999

-1

-0.699999900000002

-0.2

-1.6

-0.1-0.2

-0.3

-1.9

-1

-0.5f(x) = 0.00658324202260879 x − 0.918540411931135R² = 0.042140371527814

Contraceptive Prevalence, 2006-2010

Exce

ss F

ertil

ity (T

FR-W

ante

d TF

R)No Clear Relationship

Contraceptive Prevalence and “Excess Fertility”, 2000/10

Source: Contraceptive prevalence, 2006-2010: UNICEF "The State of the World's Children 2012.“; Wanted TFR and TFR: Macro International Inc, 2012. MEASURE DHS STATcompiler. http://www.measuredhs.com

10 100 1000 10000 1000000

1

2

3

4

5

6

7

8

9

R² = 0.525438419377251

Total Fertility Rate versus GDP per Capita (exchange rate):Global Relationship As Of 1960

GDP per capita (constant 2000 US$)

Tota

l Fer

tility

Rat

e

TFR = -0.890*(LN GDP) + 11.807 (-10.09) (18.75)R-Squared: 0.5203 Observations: 94

World Development Indicators, World Bank, 2013, http://data.worldbank.org/indicator/all (Date Accessed: February 14, 2013)

http://data.worldbank.org/indicator/all

10 100 1000 10000 1000000

1

2

3

4

5

6

7

8

Total Fertility Rate versus GDP per Capita (Exchange Rate):Global Relationship As Of 2010


Tota

l Fer

tility

Rat

e

TFR = -0.643*(LN GDP) + 7.888 (-13.83) (21.12)R-Squared: 0.5222 Observations: 175



Total Fertility Rates versus GDP per Capita (exchange rate):1960 vs. 2010 Correlations

100 1000 10000 1000000

1

2

3

4

5

6

7

8

9

Tot

al F

erti

lity

Rat

e (b

irth

s p

er w

oman

)



2010

1960


DRAFT ONLY

100 1000 10000 100000100

1000

10000

100000

1000000

Modern Economic Growth In One ChartPredicting Global Per Capita GDP (PPP) With Life Expectancy, Urbanization, Education, and Index of Economic Freedom (Fraser Institute): 1970-2010

Lagged Variables (five year lag)

Predicted GDP per capita, PPP (constant 2011 international $)

Act

ual G

DP

per

capi

ta, P

PP (c

onst

ant

2011

inte

rnati

onal

$)

ln(GDP per capita) = 0.044 (Life Expectancy) + 0.018 (Percent Urban) + 0.107 (Mean years of schooling) + 0.109 (Fraser EFI) + 3.631 (20.32) (21.64) (14.70) (7.33) (33.55)R-squared: 0.8585 Number of Observation: 1317

Over five-sixths of the difference in per capita output between countriesAnd within countries over time [1970-2010]can be explained by just four factors;Health; Education; Urbanization; and “Business Climate”

Source: GDP and Life Expectancy: World Bank, World Development Indicators, available at http://data.worldbank.org/data-catalog/world-development-indicators, accessed September 15, 2014. Urbanization: United Nations, Department of Economic and Social Affairs, Population Division (2014). World Urbanization Prospects: The 2014 Revision, available at: http://esa.un.org/unpd/wup/CD-ROM/Default.aspx, accessed August 15, 2014. Education: Author’s calculations derived from Robert Barro and Jong-Wha Lee, "A New Data Set of Educational Attainment in the World, 1950-2010," Journal of Development Economics, vol 104, (April 2010): 184-198. Available at: http://www.barrolee.com/ Accessed August 15, 2014. North Korea data: Author’s calculations derived from Central Bureau of Statistics, 2008 DPRK National Census (Pyongyang, DPRK: 2009). available at: https://unstats.un.org/unsd/demographic/sources/census/2010_PHC/North_Korea/Final%20national%20census%20report.pdfEconomic Freedom Index: Fraser Institute, Economic Freedom Network, available at: http://www.freetheworld.com/, accessed September 15, 2014.

http://www.barrolee.com/

https://unstats.un.org/unsd/demographic/sources/census/2010_PHC/North_Korea/Final%20national%20census%20report.pdf

http://www.freetheworld.com/

42

Copyright Nicholas Eberstadt

Sub-Sa

haran Afri

caIndia

Bangladesh and Pakis

tan

Latin Americ

a and the Carib

bean

Near East

Northern

Africa

Northern

America

Oceania

Baltics and CIS

Eastern

Europe

Japan

Weste

rn Euro

peChina

-200000000-100000000

0100000000200000000300000000400000000500000000

Not Your Father’s World Labor ForceTotal Projected Growth of Working

Age Population (15-64) By Region or Country: 2015 – 2035

(millions)

Mill

ions

Note: Total global manpower change for 1995-2015 was approximately 1.3 billion.Source: US Census Bureau International Data Base, available at http://www.census.gov/ipc/www/idb/informationGateway.php, accessed April 16, 2015.

Total projected global change, 2015/35: approx. 800 million

http://www.census.gov/ipc/www/idb/informationGateway.php

43

Source: United States Census Bureau, International Data Base, “Mid-year population by single year age groups,” available at: http://www.census.gov/population/international/data/idb/informationGateway.php, accessed on April 15, 2015.


08

162432404856647280

100+

1500000010000000 5000000 0 5000000 1000000015000000

New AbnormalPopulation Structure: China, 2015 vs. 2035

(projected)

Female 2035 Male 2035 Female 2015 Male 2015

Population (millions)


44

Two Hundred Fifty Million Shades of Gray

Projected percentage population 65+: Urban and rural China, 2000-2040

0

5

10

15

20

25

30

35

2000 2010 2020 2030 2040

% o

f P

op

ula

tio

n a

ged

65+

Urban Rural

Source: Zeng et al. 2008.

45

10000000

5000000 0

5000000

10000000

Column3 Column2Column4 Column1


Source: Department of Population and Employment Statistics National Bureau of Statistics, “China Population Census: Tabulation of the 2010 Population Census of the People’s Republic of China” (Beijing: China Statistics Press, 2012).

5132129374553616977

85+


China’s Rural “Labor Reserve”: Already Cherry-PickedAge/Sex/Education Structures for Urban China vs. Rural China, 2010

Urban Rural


46

15202530354045505560

4000000 3000000 2000000 1000000 0 1000000 2000000 3000000 4000000

Soweto With Chinese Characteristics?Structure of Working-Age (15-64) PopulationChinese Cities, 2010 (legal residents vs. illegal

migrants)

Resident Female Resident Male Migrant Female Migrant Male


Source: Department of Population and Employment Statistics National Bureau of Statistics, “China Population Census: Tabulation of the 2010 Population Census of the People’s Republic of China” (Beijing: China Statistics Press, 2012).


47

1960-1965

1965-1970

1970-1975

1975-1980

1980-1985

1985-1990

1990-1995

1995-2000

2000-2005

2005-2010

2010-2015

2015-2020

2020-2025

2025-2030

2030-203540.00

80.00

Russia: Not-So-Great Expecta-tions

Expectation of Life at Birth, Males plus Females:

Russia v. Less Developed Regions, 1960-2035

(UNPD Projections)Russia

Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, “Life expectancy at birth – both sexes,” World Population Prospects: The 2012 Revision, http://esa.un.org/unpd/wpp/index.htm (accessed on April 30, 2015).


http://esa.un.org/unpd/wpp/index.htm

48

Coldest Country In Africa?Male Probability at Age 20 of Living until a Given Age:

Russia vs. Africa, 2012 (WHO Estimates)

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 1000

102030405060708090

100

Russia Africa

Prob

abili

ty

Age

Source: World Health Organization, Health Statistics and Health Information Systems, http://apps.who.int/gho/data/node.main.687?lang=en/ .(Date Accessed: April 11, 2014)


http://apps.who.int/gho/data/node.main.687?lang=en/

49

Annual USPTO patents awarded 2000-2013: Select US States and Russia

Source: Patents By Country, State, and Year - Utility Patents(December 2013). http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cst_utl.htm (Date Accessed: April 11, 2014)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 201310

100

1000

10000

100000

1000000

Total US

Cali-fornia

New York

Texas

Ken-tucky

Russia

Al-abama

Missis-sippiWest

Vir-ginia

Neck and Neck with Alabama

Arkansas


http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cst_utl.htm

50



08

162432404856647280

100+

1500000010000000 5000000 0 5000000 1000000015000000

Demographic Dividend?Population Structure: India, 2015 vs.

2035 (projected)




51


Source: Derived from Wittgenstein Centre for Demography and Global Human Capital (2015). Wittgenstein Centre Data Explorer Version 1.2. available at http://witt.null2.net/shiny/wittgensteincentredataexplorer/, accessed on April 30, 2015.

Half A Century Behind ChinaEducational Profile of Working Age (15-64) Populations:

China vs. India, 1980-2035 (estimated and projected)

1980198519901995200020052010201520202025203020350%

10%20%30%40%50%60%70%80%90%

100%

China, 1980-2035 (projected)

No Education Incomplete PrimaryPrimary Lower SecondaryUpper Secondary Post Secondary

1980198519901995200020052010201520202025203020350%

10%20%30%40%50%60%70%80%90%

100%

India, 1980-2035 (projected)

No Education Incomplete PrimaryPrimary Lower SecondaryUpper Secondary Post Secondary

http://witt.null2.net/shiny/wittgensteincentredataexplorer/

52



09

1827364554637281

3000000 2000000 1000000 0 1000000 2000000 3000000

Old StoryPopulation Structure: Japan, 2015

vs. 2035 (projected)




53

SayonaraJapan: Childless and Non-grandchild Ratio among Women

Medium Projections, Cohorts born 1935-1990

“Work Session on Demographic Projections.” Figure 7. Pg. 188. Eurostat. Methodologies and Working Papers. 2007. epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-07-021/EN/KS-RA-07-021-EN.PDF (Accessed: Jan 15, 2013)

54

19601964

19681972

19761980

19841988

19921996

20002004

20082012

5,000,000

6,000,000

7,000,000

8,000,000

Where She Stops, Nobody KnowsTotal Live Births: EU-28 countries, 1960-

2013

Tota

l Liv

e bi

rths

Source: European Commission, Eurostat, “Demographic balance and crude rates”. Available at http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do; accessed on November 10, 2014.


55

Japan

United St

ates

Australi

aCan

ada

Polan

d

Slova

k Rep

ublicIta

lyFin

land

Czech

Republic

Hungary

Spain

Portu

gal

Denmark

Austria

OECD EU

ROPEGre

ece

Norway

Nether

lands

France

Belgium

United Ki

ngdom

Swed

enIre

land

Switz

erlan

d

0%

5%

10%

15%

Not All OECD Countries Are Talent MagnetsForeign born with tertiary education as a per-

centage of total 25-64 population: Selected OECD countries, c. 2010

Source: OECD Stat Extracts, “Demography and Population: DIOC – Immigrants by citizenship and age,” available at: http://stats.oecd.org/Index.aspx?DataSetCode=DIOC_CITIZEN_AGE accessed on October 27, 2014.


http://stats.oecd.org/Index.aspx?DataSetCode=DIOC_CITIZEN_AGE

56

Japa

n

Cana

da Italy

Austr

iaPo

land

Slove

niaGer

man

yNor

wayFin

land

Swed

enNet

herla

nds

Greec

eIce

land

Irelan

d

0

0.5

1

1.5

2

Under-Universitied EuropeAverage years of tertiary schooling,

age 15+: OECD countries by re-gion, 2010

Ave

rage

terti

ary

scho

olin

g (y

ears

)

Source: Barro, Robert and Jong-Wha Lee, April 2010, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Available at: http://www.barrolee.com/


http://www.barrolee.com/

57

19601963

19661969

19721975

19781981

19841987

19901993

19961999

20022005

20082011

1200

1400

1600

1800

2000

2200

2400

Continental DivideAnnual Hours Worked: United States vs. Major Continental Economies, 1960-2013

France Germany United States

An

nu

al H

ou

rs W

ork

ed

Note: Germany data from 1976-1990 are OECD estimates for West Germany. 1991-2013 are for all Germany.Source: Organization for Economic Cooperation and Development, OECD StatExtracts, “Average annual hours actually worked per worker,” available at http://stats.oecd.org/Index.aspx?DataSetCode=ANHRS# accessed August 13, 2014.


http://stats.oecd.org/Index.aspx?DataSetCode=ANHRS

58

How Does An Aging Society Get Richer?

Illustrative Patterns of Consumption and Labor Earnings by Age

0

10,000

20,000

30,000

40,000

50,000

60,000

0 10 20 30 40 50 60 70 80 90 100Years of age

Labor earningsTotal consumption

Public

Private

Source: Ronald D. Lee, Global Population Aging and Its Economic Consequences (Washington, D.C.: AEI Press, 2007).

59



011223344556677

100+

5000000

4000000

3000000

2000000

1000000 0

1000000

2000000

3000000

4000000

5000000

American ExceptionalismPopulation Structure: United

States, 2015 vs. 2035 (projected)




60

Neither working nor seeking work

Working

Unemployed /Seeking work


Checked Out In The Prime Of Life

61

Source: Sarah Shannon et al., Growth in the U.S. Felon and Ex-Prisoner Population, 1948-2010, (Paper presented at the Annual Meeting of the Population Association of America, Washington, DC, 2011).

Ex-Con ExplosionEstimated Population of Felons and Ex-felons: USA, 1948-2010

the global demographic future: how we got to here—and where we may be in 2035 nicholas eberstadt...

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