The Global Demographic Future: How We Got To Here—And Where We May Be In 2035 Nicholas Eberstadt...
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Transcript of 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
Gaidar Foundation
Moscow
June 2015
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)
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)
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
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
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
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).
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.
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
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).
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).
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%
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.
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
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
Average Years of Schooling (Male)2.48 3.01 3.92 5.04 5.83 6.95 7.74
Gender ratio (female/male %)62.5 66.5 70.8 73.3 81.2 82.0 85.9
Middle East/North Africa (18) Average Years of Schooling (Female)0.44 0.63 1.10 2.10 3.50 5.10 6.45
Average Years of Schooling (Male)1.08 1.51 2.53 4.02 5.72 7.06 8.02
Gender ratio (female/male %)40.6 41.8 43.4 52.2 61.3 72.2 80.4
Sub-Saharan Africa (33) Average Years of Schooling (Female)0.97 1.12 1.49 2.09 3.14 3.97 4.65
Average Years of Schooling (Male)1.65 1.97 2.62 3.58 4.67 5.34 5.82
Gender ratio (female/male %)58.8 56.9 57.0 58.4 67.2 74.4 80.0
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
Average Years of Schooling (Male)2.79 3.31 4.09 4.84 5.99 7.22 8.27
Gender ratio (female/male %)84.4 86.8 88.1 91.6 97.2 97.5 98.4
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
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
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
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).
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
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)
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
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)
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
GDP per capita (constant 2000 US$)
Tota
l Fer
tility
Rat
e
TFR = -0.643*(LN GDP) + 7.888 (-13.83) (21.12)R-Squared: 0.5222 Observations: 175
World Development Indicators, World Bank, 2013, http://data.worldbank.org/indicator/all (Date Accessed: February 14, 2013)
Total Fertility Rates versus GDP per Capita (exchange rate):1960 vs. 2010 Correlations
100 1000 10000 1000000
1
2
3
4
5
6
7
8
9
Tot
al F
erti
lity
Rat
e (b
irth
s p
er w
oman
)
GDP per capita (constant 2000 US$)
World Development Indicators, World Bank, 2013, http://data.worldbank.org/indicator/all (Date Accessed: February 14, 2013)
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.
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
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.
Copyright Nicholas Eberstadt
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
Population (millions)
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+
Population (millions)
China’s Rural “Labor Reserve”: Already Cherry-PickedAge/Sex/Education Structures for Urban China vs. Rural China, 2010
Urban Rural
Copyright Nicholas Eberstadt
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
Population (millions)
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).
Copyright Nicholas Eberstadt
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).
Copyright Nicholas Eberstadt
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)
Copyright Nicholas Eberstadt
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
Copyright Nicholas Eberstadt
50
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 17, 2015.
Copyright Nicholas Eberstadt
08
162432404856647280
100+
1500000010000000 5000000 0 5000000 1000000015000000
Demographic Dividend?Population Structure: India, 2015 vs.
2035 (projected)
Female 2035 Male 2035 Female 2015 Male 2015
Population (millions)
51
Copyright Nicholas Eberstadt
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
52
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 17, 2015.
Copyright Nicholas Eberstadt
09
1827364554637281
3000000 2000000 1000000 0 1000000 2000000 3000000
Old StoryPopulation Structure: Japan, 2015
vs. 2035 (projected)
Female 2035 Male 2035 Female 2015 Male 2015
Population (millions)
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.
Copyright Nicholas Eberstadt
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.
Copyright Nicholas Eberstadt
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/
Copyright Nicholas Eberstadt
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.
Copyright Nicholas Eberstadt
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
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 17, 2015.
Copyright Nicholas Eberstadt
011223344556677
100+
5000000
4000000
3000000
2000000
1000000 0
1000000
2000000
3000000
4000000
5000000
American ExceptionalismPopulation Structure: United
States, 2015 vs. 2035 (projected)
Female 2035 Male 2035 Female 2015 Male 2015
Population (millions)
60
Neither working nor seeking work
Working
Unemployed /Seeking work
Copyright Nicholas Eberstadt
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