john bongaarts population council, new york süssmilch
TRANSCRIPT
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Africa’s slow fertility transition
John Bongaarts Population Council, New York
Süssmilch Lecture
Max Planck Institute, Rostock 3 Sep 2015
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0
1
2
3
4
1900 1950 2000 2050 2100 2150
Billi
ons
Population projections for sub-Saharan Africa
2015 projection 2002/4 projection
2015 population
Source, United Nations
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0
10
20
30
40
2000 2020 2040 2060
Crude birth and death rates sub-Saharan Africa
Crude birth rate Crude death rate
2015 2002/4 2002/4 2015
Source: United Nations
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0
1
2
3
4
5
6
7
8
1980 1990 2000 2010 2020
Birt
hs p
er w
oman
TFR trends in sub-Saharan Africa
BeninBotswanaBurkina FasoBurundiCameroonCape VerdeCentral African RepublicChadComorosCongo (Brazzaville)Congo Democratic RepublicCote d'IvoireEthiopiaGabonGambiaGhanaGuineaKenyaLesothoLiberiaMadagascarMalawiMaliMauritaniaMozambiqueNamibiaNigerNigeriaRwandaSao Tome and PrincipeSenegalSierra LeoneSouth AfricaSudanSwazilandTanzaniaTogoUganda
Source: DHS
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Socioeconomic development
Mortality decline
Fertility
Family planning Program
Diffusion processes
Determinants of fertility
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Hypotheses
1. Africa’s development is slow 2. Africa is exceptional 3. Family planning programs are lacking
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Outline 1. Fertility and development trends
• Levels • Pace
2. African exceptionalism 3. Impact of family planning programs 4. Conclusions
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0
1
2
3
4
5
6
7
8
1960 1980 2000 2020
Birt
hs p
er w
oman
Total fertility rate
Africa Other LDCs
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Development indicators 1970-2010 - GDP per capita (at PPP) from the PWT - Education, % with primary + (Wittgenstein) - Life expectancy at birth (UN 2013) - Percent urban (UN 2014).
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500
1000
2000
4000
8000
1960 1980 2000 2020
$ pe
r cap
ita
GDP per capita(PPP) Other LDCs Africa
0
20
40
60
80
100
1960 1980 2000 2020
%. w
ith P
rimar
y+
Education Other LDCs Africa
30
35
40
45
50
55
60
65
70
75
1960 1980 2000 2020
Year
s
Life expectancy Other LDCs Africa
0
10
20
30
40
50
60
70
1960 1980 2000 2020
%
Percent urban Other LDCs Africa
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Average at the time of transition onset
Sub-Saharan Africa
Other LDCs
TFR decline % 10 10
Year of transition onset 1994 1975
GDP/cap(log) 6.9 7.7
Education (% primary+) 29 42
Life expectancy 51 59
Percent urban 29 40
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Conclusions 1) African transitions later in time
Consistent with conventional theory 2) But early relative to level of development Consistent with diffusion theories
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Outline 1. Fertility and development trends
• Levels • Pace
2. African exceptionalism 3. Impact of family planning programs 4. Conclusions
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-0.05
0
0.05
0.1
0.15
1960 1980 2000 2020Birt
hs p
er w
oman
/yea
r Total fertility rate, pace
Other LDCs Africa
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-2-10123456
1960 1980 2000 2020
% y
ear
GDP per capita Pace
Other LDCs Africa
0
1
2
1960 1980 2000 2020
%/Y
ear
Education pace
Africa Other LDCs
-0.2
0
0.2
0.4
0.6
0.8
1960 1980 2000 2020
Year
s
Life expectancy Pace
Africa Other LDCs
00.10.20.30.40.50.60.7
1960 1980 2000 2020
%
Percent urban Pace Other
LDCs Africa
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Average pace at the time of transition onset
Sub-Saharan Africa
Other LDCs
TFR 0.09 0.15 Year 1994 1975 GDP/cap(log) 0.008 0.034 Education (% primary+) 1.2 2.0 Life expectancy 0.12 0.47 Percent urban 0.29 0.57
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Conclusions 3) African transitions are slow because the pace of development is slow
Consistent with conventional theory
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Outline 1. Fertility and development trends
• Levels • Pace
2. African exceptionalism 3. Impact of family planning programs 4. Conclusions
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012345678
100 1000 10000 100000
Birt
hs p
er w
oman
$ per year
TFR by GDP/capita, 2010
Africa Other LDCs
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012345678
100 1000 10000 100000
Birt
hs p
er w
oman
$ per year
TFR by GDP/capita, 2010
Africa Other LDCs
012345678
0 50 100
Birt
hs p
er w
oman
% primary+
TFR by education, 2010
Africa Other LDCs
012345678
40 60 80 100
Birt
hs p
er w
oman
Years
TFR by life expectancy, 2010
Africa Other LDCs
012345678
0 50 100
Birt
hs p
er w
oman
Percent urban
TFR by percent urban, 2010
Africa Other LDCs
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TFR 2010
Africa effect 1.18** GDP/cap -0.36* Education -0.019*** Life expectancy -0.02 Percent urban 0.00 R2 0.84 N 71 Year 2010
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Socioeconomic dev. Mortality decline
Cost and benefits of children
Fertility preferences
Demand for contraception
Use of contraception
Fertility
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TFR Contraceptive prevalence
Desired Family size
Africa effect 1.18** -10.9* 1.24* GDP/cap (log) -0.36* 0.83 0.3 Education -0.019*** 0.51*** -2.7*** Life expectancy -0.02 0.65 -0.04 Percent urban 0.00 -0.15 0.01 R2 0.84 0.86 0.72 N 71 71 39 Year 2010 2010 Latest DHS
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Conclusions: African fertility is high relative to development Consistent with theories about African exceptionalism (e.g. Caldwell)
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Caldwell (1992) : ”(1) African traditional society stressed the importance of ancestry and descent. …younger generations assisted the older generations .. for males at least, high fertility ultimately brought substantial economic returns… (2) Polygyny led in West and Middle Africa to separate spousal budgets. The father was spared much of the cost of rearing children. (3) There was strength and safety in numbers. Communal land tenure meant that large families could demand a greater share of the land… (4) Family planning programs were nonexistent or weak ..regarded as foreign or as incompatible with African culture
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Outline 1. Fertility and development trends
• Levels • Pace
2. African exceptionalism 3. Impact of family planning programs 4. Conclusions
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Socioeconomic dev. Mortality decline
Cost and benefits of children
Fertility preferences
Demand for contraception
Use of contraception
Fertility
Family planning program
Cost of contraception
Unmet need for contraception
Contraceptive access /information
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0
20
40
60
80
1960 1980 2000
Perc
ent o
f cou
ples
Met and unmet need for contraception,developing world
Potential demand forcontraception
Unmetneed
Currentuse
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0
10
20
30
40
50
60
70
# pr
egna
ncie
s Planned and unplanned pregnancies
Africa
Planning status Unintended Intended
Pregnancy outcome Abortion Unintended birth Intended birth
Source: Guttmacher
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0
10
20
30
40
50
1974 1976 1978 1980 1982 1984 1986
Con
trace
ptiv
e P
reva
lenc
e (%
) Successful FP experiment in Matlab,
Bangladesh
Experimental area
Control area
Source: Cleland et al. 1994
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0 2 4 6
IndonesiaPhilippines
IranJordan
BangladeshPakistan
KenyaUganda
RwandaBurundi
Births per woman
Fertility impact of weak vs strong FP programs
Weak Strong
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0
1
2
3
4
5
6
7
0
10
20
30
40
50
60
1990 1995 2000 2005 2010 2015
Birt
hs p
er w
oman
% m
arrie
d w
omen
Rwanda reproductive trends
TFR Contraceptive use
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Conclusion: 1) High unmet need for contraception and
large numbers of unplanned pregnancies 2) Family planning programs can reduce
fertility by about 1.5 births per woman
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0
1
2
3
4
5
6
2000 2020 2040 2060 2080 2100 2120
Billi
ons
Population projection variants sub-Saharan Africa Variant
High Medium Low
Source: United Nations
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Causes of slow fertility decline in Africa 1) Slow pace of development 2) African pro-natalism 3) Weak or non-existent FP programs
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Sources • Bongaarts John, “Africa’s unique fertility transition”
Paper prepared for the US National Academies of Science Workshop on Recent Trends in Fertility in Sub-Saharan Africa, June 14-15, 2015, Washington DC.
• J. Bongaarts, J. Cleland, J. Townsend, J. Bertrand, and M. Das Gupta, Family Planning Programs for the 21st Century: Rationale and Design, New York Population Council (2012).
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