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
eberstadt@aei.org
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)
World Population: 1950-2015
(Estimated projected, in Billions)
8
7
Population (billions)
6
5
4
3
2
1
U.S. Census Bureau, International Data Base. http://www.census.gov/population/international/data/idb/informationGateway.php (Date Accessed: April 1, 2015)
2013
2010
2007
2004
2001
1998
1995
1992
1989
1986
1983
1980
1977
1974
1971
1968
1965
1962
1959
1956
1953
1950
0
Historical Estimates of World Population, 1-2010
(In Millions)
Population (millions)
10000
1000
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 Estimate
http://www.census.gov/population/international/data/worldpop/table_history.php (Date Accessed: April 1, 2015)
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
100
1
100
Rice Prices Deflated by PPI
1913-2013
3.5
3.0
2.5
2.0
1.5
1.0
0.5
1913
1916
1919
1922
1925
1928
1931
1934
1937
1940
1943
1946
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
0.0
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 adjusted
GYCPI – 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_bgcol
or=%23FFFFFF&txtcolor=%23000000&ts=8&preserve_ratio=true&id=PPIACO&transformation=lin&scale=Left&range=Max&cosd=1913-01-01&coed=2009-1101&line_color=%230000FF&link_values=&mark_type=NONE&mw=4&line_style=Solid&lw=1&vintage_date=2010-01-11&revision_date=2010-0111&mma=0&nd=&ost=&oet=&fml=a# (Date Accessed: April 1, 2015).
Wheat Prices Deflated by PPI
1913-2013
3.5
3.0
2.5
2.0
1.5
1.0
0.5
1913
1916
1919
1922
1925
1928
1931
1934
1937
1940
1943
1946
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
0.0
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 adjusted
GYCPI – 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_bgcol
or=%23FFFFFF&txtcolor=%23000000&ts=8&preserve_ratio=true&id=PPIACO&transformation=lin&scale=Left&range=Max&cosd=1913-01-01&coed=2009-1101&line_color=%230000FF&link_values=&mark_type=NONE&mw=4&line_style=Solid&lw=1&vintage_date=2010-01-11&revision_date=2010-0111&mma=0&nd=&ost=&oet=&fml=a# (Date Accessed: April 1, 2015).
Maize Prices Deflated by PPI
1913-2013
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
1913
1916
1919
1922
1925
1928
1931
1934
1937
1940
1943
1946
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
0.0
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 adjusted
GYCPI – 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_bgcol
or=%23FFFFFF&txtcolor=%23000000&ts=8&preserve_ratio=true&id=PPIACO&transformation=lin&scale=Left&range=Max&cosd=1913-01-01&coed=2009-1101&line_color=%230000FF&link_values=&mark_type=NONE&mw=4&line_style=Solid&lw=1&vintage_date=2010-01-11&revision_date=2010-0111&mma=0&nd=&ost=&oet=&fml=a# (Date Accessed: April 1, 2015).
The Relative Price Of 10 Foodstuffs: A Long-Term Decline
GYCPIF/MUV Prices (Indexed): 1900-2013
2.0
1.8
The long term relative price trend-line here
drops by about 50% between 1900 and 2013
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
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)
The Relative Price Of 24 Commodities: Long Term Decline
GYCPI/MUV Prices (Indexed): 1900-2013
2.0
Please note: this index does not include fuels
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
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)
Nominal Crude Oil prices: 1861-2015Q1
(current US$)
120
US dollars per barrel
100
80
60
40
20
1865
1870
1875
1880
1885
1890
1895
1900
1905
1910
1915
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015 Q1
0
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/reviewby-energy-type/oil/oil-reserves.html; 2015 data: “Trading Conditions update,” available at: http://www.bp.com/en/global/corporate/investors/results-and-reporting/tradingconditions-update.html.
Real Crude Oil prices: 1861-2015Q1
(2013 US $)
140
US dollars per barrel
120
100
80
60
40
20
1865
1870
1875
1880
1885
1890
1895
1900
1905
1910
1915
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015 Q1
0
(no statistically meaningful trend)
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/reviewby-energy-type/oil/oil-reserves.html; 2015 data: “Trading Conditions update,” available at: http://www.bp.com/en/global/corporate/investors/results-and-reporting/tradingconditions-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
Real Price Trends for Natural Resources:
1900-2008
Including Oil
Without Oil
CCPI
CCPI’
---- GYCPI
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 .
Ultra-Longterm Real Price Trends:
Natural Resources Indices, 1650-2010
Including Oil
Without Oil
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 .
Real Wheat, Rice, and Maize Prices versus World Population:
1910-2013
Rice
Wheat
Maize
World Population (Billions)
2010
2005
2000
1995
1990
0
1985
0
1980
1
1975
0.5
1970
2
1965
1
1960
3
1955
1.5
1950
4
1945
2
1940
5
1935
2.5
1930
6
1925
3
1920
7
1915
3.5
1910
8
1905
4
1900
Wheat, Rice, and Maize Prices
(Prices Deflated by PPI)
World Population
Note: Before PPI deflation, GYCPI Indexed to the 1977-1979 arithmetic mean at 100; PPI indexed to 1982=100; not seasonally adjusted
GYCPI – 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_bgcol
or=%23FFFFFF&txtcolor=%23000000&ts=8&preserve_ratio=true&id=PPIACO&transformation=lin&scale=Left&range=Max&cosd=1913-01-01&coed=2009-1101&line_color=%230000FF&link_values=&mark_type=NONE&mw=4&line_style=Solid&lw=1&vintage_date=2010-01-11&revision_date=2010-0111&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 Estimate
http://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)
50
40
30
20
10
0
1900
1904
1908
1912
1916
1920
1924
1928
1932
1936
1940
1944
1948
1952
1956
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
2008
(1990 International Geary-Khamis dollars) Trillions
60
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).
60
2.0
1.8
50
1.4
40
1.2
30
1.0
0.8
20
0.6
Commodity Price Index
1.6
0.4
10
0.2
0
0.0
1900
1905
1910
1915
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
World GDP (Intl Geary-Khamis 1990$, trillons)
Relative Primary Commodity Prices vs. Real Global GDP:
1900-2013
• World GDP
• Commodity Price Index (GYCPI/MUV)
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)
Estimated GDP Per Capita, 1900-2010:
World and Selected Regions
9000
8000
7000
6000
5000
4000
3000
2000
1000
• World
• Latin America
• Asia
Source: The Maddison-Project, http://www.ggdc.net/maddison/maddison-project/home.htm, 2013 version. (Date Accessed: April 1, 2015)
• Africa
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
1950
1945
1940
1935
1930
1925
1920
1915
1910
1905
0
1900
(1990 International Geary-Khamis dollars)
(Angus Maddison estimates)
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--continued
Estimated Infant Mortality Rates, 1950/55-2005/10
(UN Population Division estimates, both sexes, deaths per 1,000 live births)
Major area, region, country
1950-1955
2005-2010
Absolute Change
(years)
% Change
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%
World
United Nations Population Division, World Population Prospects 2012 Revision, http://esa.un.org/wpp/Excel-Data/mortality.htm / (Date Accessed: April 2, 2015)
Changes in Lifespan Inequality with Improving Health
1751
Life expectancy at birth:
1751: 38 years
2011: 82 years
2011
Gini Index for length of life:
1751 = 0.46
2011 = 0.08
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.
110+
105
100
95
90
85
80
75
70
65
60
55
50
45
40
35
30
25
20
10
5
0
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
15
Total, Sweden 1751 vs. 2011
(Age at Death from every 100,000 persons born)
Gini Index for Lifespan Inequality vs. Life Expectancy at Birth:
Sweden, 1751-2011
0.7
1776
Gini Coefficient
0.6
0.5
0.4
0.3
0.2
0.1
0
10
20
30
40
50
60
70
80
Life Expectancy at birth (years)
Gini Coefficient= -0.0093*(Life Expectancy) + 0.8049
(-177.08)
(275.89)
R-squared: 0.9918 Number of Observation: 261
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.
90
Gini Coefficient vs. Life Expectancy:
Males and Females, 63 selected countries, Postwar Period
0.6
Gini coefficient
0.5
0.4
0.3
0.2
0.1
0
30
40
50
60
Life Expectancy at birth (years)
70
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.
80
Death-Age Inequality vs. Life Expectancy at Birth :
63 Selected Countries, Postwar Period
And What These Imply For 20th Century Planetary Trends
0.6
Italy (1872)
Approximate
Global Life
Expectancy,
2000
Sweden (1751)
Gini Coefficient
0.5
0.4
0.3
Approximate
Global Life
Expectancy,
1900
0.2
Italy (2009)
0.1
Sweden (2011)
0
0
10
20
30
40
50
60
70
80
Male Life Expectancy at Birth (years)
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.
The Global Education Explosion
Estimated Educational Attainment by Sex, 1950-2010
(Barro-Lee estimates, population age 15 and over, 146 countries)
Region (no. of countries)
World (146)
1950
Average Years of Schooling (Female)
Average Years of Schooling (Male)
Gender ratio (female/male %)
All Developing (122)
Average Years of Schooling (Female)
Average Years of Schooling (Male)
Gender ratio (female/male %)
Middle East/North Africa (18)
Average Years of Schooling (Female)
Average Years of Schooling (Male)
Gender ratio (female/male %)
Sub-Saharan Africa (33)
Average Years of Schooling (Female)
Average Years of Schooling (Male)
Gender ratio (female/male %)
Latin America and the Caribbean
(25)
1960
1970
1980
1990
2000
2010
2.74
3.18
3.92
4.78
5.68
6.56
7.44
3.50
4.03
4.87
5.91
6.59
7.63
8.35
78.3
79.0
80.5
80.9
86.2
86.0
89.0
1.55
2.00
2.77
3.69
4.73
5.70
6.65
2.48
3.01
3.92
5.04
5.83
6.95
7.74
62.5
66.5
70.8
73.3
81.2
82.0
85.9
0.44
0.63
1.10
2.10
3.50
5.10
6.45
1.08
1.51
2.53
4.02
5.72
7.06
8.02
40.6
41.8
43.4
52.2
61.3
72.2
80.4
0.97
1.12
1.49
2.09
3.14
3.97
4.65
1.65
1.97
2.62
3.58
4.67
5.34
5.82
58.8
56.9
57.0
58.4
67.2
74.4
80.0
2.36
2.87
3.60
4.43
5.82
7.04
8.13
2.79
3.31
4.09
4.84
5.99
7.22
8.27
84.4
86.8
88.1
91.6
97.2
97.5
98.4
Average Years of Schooling (Female)
Average Years of Schooling (Male)
Gender ratio (female/male %)
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)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1950
1960
1970
No schooling
1980
Primary
Secondary
1990
2000
2010
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.
Gini Index for 15+ MYS by region, gender and year
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 Coefficient
Gini Coefficient for Educational Attainment by Mean Years of
Schooling: Females, 15 and over, 1950-2010
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
2
Advanced Countries
Europe and Central Asia
South Asia
4
6
8
Mean Years of Schooling (years)
10
Developing Countries
Latin America and the Carribbean
Sub-Saharan Africa
East Asia and the Pacific
Middle East and North Africa
12
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).
Gini Coefficient
Gini Coefficient for Educational Attainment by Mean Years of
Schooling: Males, 15 and over, 1950-2010
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
R² = 0.9155
0
2
Advanced Countries
Europe and Central Asia
South Asia
4
6
8
Mean years of schooling
Developing Countries
Latin America and the Carribbean
Sub-Saharan Africa
10
12
East Asia and the Pacific
Middle East and North Africa
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).
Gini Coefficient
Gini Coefficient for Educational Attainment by Mean Years of Schooling:
Both Sexes, 15 and over, 1950-2010
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
2
4
Advanced Countries
Europe and Central Asia
South Asia
6
8
Mean years of schooling
Developing Countries
Latin America and the Carribbean
Sub-Saharan Africa
10
East Asia and the Pacific
Middle East and North Africa
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).
12
Total global household wealth
2000-2014, by region
(estimated, in current $trillions)
North America
Europe
Asia-Pacific
China
Latin America
India
Africa
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-D460-2B88081889DB12817E02 .
Estimated Per Capita Caloric Availability By Region:
1961-2011
3100
Kcal per capita per day
2900
2700
2500
Asia
World
Africa
2300
2100
Least Developed
1900
1700
Least Developed Countries
World
Africa
Asia
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).
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
1500
Percent Living Under $1.25/Day by Region, 1981-2011:
World Bank Estimates
Percent of people living under $1.25/day
90%
80%
East Asia
70%
60%
Sub-Saharan Africa
50%
Total
40%
South Asia
30%
20%
Latin America
10%
MENA
0%
1981
1984
1987
1990
1993
East Asia and Pacific
Middle East and North Africa
Sub-Saharan Africa
1996
1999
2002
2005
2008
2010
Latin America and the Caribbean
South Asia
Total
World Bank, PovcalNet, “Regional Aggregation using 2005 PPP,” http://iresearch.worldbank.org/PovcalNet/index.htm?1 (Date Accessed: April 1, 2015)
2011
What Determines Family Size?
Female Literacy Rates c. 2000 vs. Total Fertility Rates, 2005-2010
8
Niger
7
Afghanistan
Mali
Chad
Burkina Faso
Total Fertility Rate, 2005-2010
6
Nigeria
Yemen
Guinea
Sierra Leone
Senegal
Iraq
Mauritania Sudan
5
4
West Bank and Gaza
Pakistan
Egypt
3
Morocco
Bangladesh
Algeria
Tunisia
2
Syria Arabia
Saudi
Jordan
Tajikistan
Malaysia
Kyrgyzstan
Bahrain
Kazakhstan
Turkmenistan
Qatar
Kuwait
Indonesia
Azerbaijan
Turkey Brunei
Darussalam
Maldives
United
Arab
Emirates
Iran
Albania
Oman
y = -0.0484x + 6.7412
R² = 0.6007
1
0
0
20
40
60
80
100
Literacy Rate, Female 15+, most recent year
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.
120
What Determines Family Size?
Contraceptive Prevalence, 2006-2010 vs. Total Fertility Rates,2005-2010
8
Niger
Total Fertility Rate, 2005-2010
7
Mali
Chad
Somalia
Afghanistan
Timor-Leste
Burkina Faso
Nigeria
Yemen
Guinea
Guinea-Bissau
Sierra Leone
Gambia
Comoros
Senegal
Mauritania
Eritrea
Sudan
Solomon Islands
6
5
y = -0.0502x + 5.6541
R² = 0.5706
Iraq
West Bank and Gaza
Vanuatu
Djibouti
Pakistan Haiti
Tajikistan
4
Syria
Saudi Arabia
3
Fiji
Oman
Maldives
2
Jordan
Egypt
Kyrgyzstan
Bhutan
Kazakhstan
Turkmenistan
Uzbekistan
Saint VincentBangladesh
and the
Algeria
Morocco
Dem.
People's Republic of
Guyana
Grenada Indonesia
Azerbaijan
Turkey States of America
Grenadines
Ireland
United
Korea
Tunisia
France
Australia
Norway
Bahamas
Lebanon
United Kingdom
BelgiumIran
Netherlands
AlbaniaCanada
Georgia
Japan
Republic of Korea
1
0
0
10
20
30
40
50
60
70
80
90
100
Contraceptive Prevalence (%), most recent year
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.
What Determines Family Size?
Per Capita GDP 2005 vs. Total Fertility Rates, 2005-2010
8
Niger
Total Fertility Rate, 2005-2010
7
Afghanistan
Mali
Chad
Burkina Faso
Nigeria
Yemen
Guinea
Sierra Leone
Senegal
Iraq
Mauritania
Sudan West Bank and Gaza
6
5
4
Tajikistan
3
Bangladesh
2
y = -1.14ln(x) + 12.35
R² = 0.5726
Pakistan
Jordan
Syria
Saudi
Arabia
Egypt
Malaysia
Kyrgyzstan
Bahrain
Kazakhstan
Oman
Turkmenistan
Qatar
Morocco
Algeria
Kuwait
Indonesia
Azerbaijan
Tunisia Turkey
United Arab Emirates
Iran
Albania
1
0
100
1000
10000
100000
GDP per capita, 2005 (1990 Geary-Khamis International $)
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.
What Determines Family Size?
Total Fertility Rates 2005-2010 vs. Wanted Total Fertility Rates, c. 2005
8
Niger
Total Fertility Rate, 2005-2010
7
Timor-Leste
Mali
Chad
Burkina Faso
Nigeria
Yemen
Guinea
y = 0.9742x - 0.067
R² = 0.9314
6
Sierra Leone
Comoros
Senegal
Mauritania
Eritrea
5
4
Pakistan
Haiti
Jordan
Egypt
Kyrgyzstan
Kazakhstan Turkmenistan
Uzbekistan
Morocco
Bangladesh
Guyana
Azerbaijan Indonesia
Turkey
Maldives
Albania
Georgia
3
2
1
0
0
1
2
3
4
5
6
7
8
Wanted Fertility Rate, most recent year
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.
No Clear Relationship
Contraceptive Prevalence and “Excess Fertility”, 2000/10
3
Jamaica
Excess Fertility (TFR-Wanted TFR)
2
y = 0.0066x - 0.9185
R² = 0.0421
t-statistic: 1.79
Republic of Moldova
Georgia
Romania
1
Kazakhstan
Armenia
Ukraine
Turkmenistan
Azerbaijan
Uzbekistan
Albania
Kyrgyzstan
Paraguay
Vietnam
Mauritania
Eritrea
Nigeria
Cambodia
Indonesia
Nicaragua
Ghana Cameroon
Zimbabwe
Dominican
El Salvador
Republic
Colombia
Sierra
Mali
Guinea
Leone
LiberiaMozambique
Timor-Leste
Madagascar
South
Egypt
Africa
Cote Congo
d'IvoireDem. Rep.
Gabon
Tanzania
Guyana
Guatemala
Morocco
Ecuador
Turkey
Brazil
SenegalBurkina Faso
Bangladesh
India Jordan
Benin
Comoros
Philippines
Namibia
Togo
Pakistan
Zambia Lesotho
Honduras
Peru
Nepal
Malawi Kenya
0
Chad
-1
Niger
Maldives
Ethiopia
Haiti
Sao Tome and Principe
Uganda
Bolivia
Swaziland
Rwanda
Yemen
-2
Cape Verde
-3
0
10
20
30
40
50
60
70
80
90
100
Contraceptive Prevalence, 2006-2010
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
Total Fertility Rate versus GDP per Capita (exchange rate):
Global Relationship As Of 1960
9
8
Total Fertility Rate
7
6
5
4
3
R² = 0.5254
2
1
0
10
100
1000
10000
GDP per capita (constant 2000 US$)
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)
100000
Total Fertility Rate versus GDP per Capita (Exchange Rate):
Global Relationship As Of 2010
8
Total Fertility Rate
7
6
5
4
3
2
1
0
10
100
1000
10000
GDP per capita (constant 2000 US$)
TFR = -0.643*(LN GDP) + 7.888
(-13.83)
(21.12)
R-Squared: 0.5222
Observations: 175
World Development Indicators, World Bank, 2013, http://data.worldbank.org/indicator/all (Date Accessed: February 14, 2013)
100000
Total Fertility Rates versus GDP per Capita (exchange rate):
1960 vs. 2010 Correlations
9
Total Fertility Rate (births per woman)
8
7
6
5
1960
4
3
2010
2
1
0
100
1000
10000
GDP per capita (constant 2000 US$)
World Development Indicators, World Bank, 2013, http://data.worldbank.org/indicator/all (Date Accessed: February 14, 2013)
100000
Modern Economic Growth In One Chart
Actual GDP per capita, PPP
(constant 2011 international $)
Predicting Global Per Capita GDP (PPP) With Life Expectancy, Urbanization,
Education, and Index of Economic Freedom (Fraser Institute): 1970-2010
1000000
Lagged Variables (five year lag)
Over five-sixths of the difference in per capita output between countries
And within countries over time [1970-2010]can be explained by just four factors;
Health; Education; Urbanization; and “Business Climate”
100000
10000
1000
100
100
1000
10000
100000
DRAFT ONLY
Predicted GDP per capita, PPP (constant 2011 international $)
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)
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.pdf
Economic Freedom Index: Fraser Institute, Economic Freedom Network, available at: http://www.freetheworld.com/, accessed September 15, 2014.
Not Your Father’s World Labor Force
Total Projected Growth of Working Age Population (15-64)
By Region or Country: 2015 – 2035 (millions)
400
Total projected global change, 2015/35: approx. 800 million
300
Millions
200
100
0
-100
-200
Copyright Nicholas Eberstadt
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.
42
New Abnormal
Population Structure: China, 2015 vs. 2035 (projected)
85-89
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
15
5
5
Population (millions)
Female 2035
Male 2035
Female 2015
15
Male 2015
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
43
Two Hundred Fifty Million Shades of Gray
Projected percentage population 65+: Urban and rural China, 2000-2040
35
Urban
Rural
% of Population aged 65+
30
25
20
15
10
5
0
2000
2010
2020
2030
2040
Source: Zeng et al. 2008.
44
China’s Rural “Labor Reserve”: Already Cherry-Picked
Age/Sex/Education Structures for Urban China vs. Rural China, 2010
Urban
10
Rural
85+
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
5
0
5
Population (millions)
No Schooling
Primary
Secondary
Tertiary
10
10
0
Population (millions)
10
Copyright Nicholas Eberstadt
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).
45
Soweto With Chinese Characteristics?
Structure of Working-Age (15-64) Population
Chinese Cities, 2010 (legal residents vs. illegal migrants)
60
55
50
45
40
35
30
25
20
15
4
3
2
Resident Female
1
0
1
Population
(millions)
Copyright Nicholas Eberstadt
Resident Male
2
Migrant Female
3
4
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).
46
Russia: Not-So-Great Expectations
Expectation of Life at Birth, Males plus Females:
Russia v. Less Developed Regions, 1960-2035
(UNPD Projections)
80.00
70.00
60.00
50.00
40.00
2030-2035
2025-2030
2020-2025
2015-2020
2010-2015
2005-2010
2000-2005
1995-2000
1990-1995
1985-1990
1980-1985
1975-1980
1970-1975
1965-1970
1960-1965
Russia
Copyright Nicholas Eberstadt
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).
47
Coldest Country In Africa?
Male Probability at Age 20 of Living until a Given Age:
Russia vs. Africa, 2012 (WHO Estimates)
100
Probability
80
60
40
20
0
20
25
30
35
40
Copyright Nicholas Eberstadt
45
50 55
Russia
60
65
Africa
70
75
80
85
90
95
100
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)
48
Neck and Neck with Alabama
Annual USPTO patents awarded 2000-2013:
Select US States and Russia
1000000
100000
Total
Califor
10000
Texas
New
York
1000
Kentu
cky
100
Alaba
ma
Russia
Arkansas
Mississip
West pi
Virgini
a
10
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Copyright Nicholas Eberstadt
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)
49
Demographic Dividend?
Population Structure: India, 2015 vs. 2035 (projected)
85-89
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
20
10
Female 2035
0
Population (millions)
Male 2035
10
20
Female 2015
Male 2015
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
50
Half A Century Behind China
Educational Profile of Working Age (15-64) Populations:
China vs. India, 1980-2035 (estimated and projected)
China, 1980-2035 (projected)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
India, 1980-2035 (projected)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
No Education
Incomplete Primary
No Education
Incomplete Primary
Primary
Lower Secondary
Primary
Lower Secondary
Upper Secondary
Post Secondary
Upper Secondary
Post Secondary
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.
Copyright Nicholas Eberstadt
51
Old Story
Population Structure: Japan, 2015 vs. 2035 (projected)
85-89
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
3
2
Female 2035
1
0
1
Population (millions)
Male 2035
2
Female 2015
3
Male 2015
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
52
Sayonara
Japan: 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-RA07-021/EN/KS-RA-07-021-EN.PDF (Accessed: Jan 15, 2013)
53
Where She Stops, Nobody Knows
Total Live births
Millions
Total Live Births: EU-28 countries, 1960-2013
8
7
6
Copyright Nicholas Eberstadt
5
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.
54
Not All OECD Countries Are Talent Magnets
Foreign born with tertiary education as a percentage of total 25-64 population: Selected OECD
countries, c. 2010
12%
10%
8%
6%
4%
2%
0%
Copyright Nicholas Eberstadt
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.
55
Under-Universitied Europe
Average tertiary schooling (years)
Average years of tertiary schooling, age 15+: OECD countries
by region, 2010
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Copyright Nicholas Eberstadt
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/
56
Continental Divide
2400
Annual Hours Worked: United States vs. Major Continental
Economies, 1960-2013
Annual Hours Worked
2200
2000
1800
1600
1400
1200
Copyright Nicholas Eberstadt
France
Germany
United States
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.
57
How Does An Aging Society Get Richer?
Illustrative Patterns of Consumption and Labor Earnings by Age
Labor earnings
Total consumption
Public
Private
Source: Ronald D. Lee, Global Population Aging and Its Economic Consequences (Washington, D.C.: AEI
Press, 2007).
58
American Exceptionalism
Population Structure: United States, 2015 vs. 2035
(projected)
85-89
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
5
4
3
Female 2035
2
1
0
1
2
Population (millions)
Male 2035
3
Female 2015
4
5
Male 2015
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
59
Checked Out In The Prime Of Life
Neither working nor seeking work
Unemployed /
Seeking work
Copyright Nicholas Eberstadt Working
60
Ex-Con Explosion
Estimated Population of Felons and Ex-felons: USA, 1948-2010
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).
61