1. family and parenting
2. children

Fertility Convergence
Tiloka De-Silvaa
a
Silvana Tenreyroa;b
London School of Economics, CfM; b CEP, CEPR
July 2015
Abstract
A vast literature has sought to explain large cross-country di¤erences in fertility rates. Income,
mortality, urbanization, and female labour force participation, among other socioeconomic variables, have been suggested as explanatory factors for the di¤erences. This paper points out that
cross-country di¤erences in fertility rates have fallen very rapidly over the past four decades, with
most countries converging to a rate just above two children per woman. This absolute convergence
took place despite the limited (or absent) absolute convergence in other economic variables. The
rapid decline in fertility rates taking place in developing economies stands in sharp contrast with
the slow decline experienced earlier by more mature economies. The preferred number of children
has also fallen, suggesting a shift to a small-family norm. The convergence to replacement rates
will lead to a stable world population, reducing environmental concerns over explosive population growth. In this paper we explore existing explanations and bring in an additional factor
in‡uencing fertility rates: the population programs started in the 1960s, which, we argue, have
accelerated the global decline in fertility rates over the past four decades.
Key words: fertility rates, birth rate, convergence, macro-development, Malthusian growth, population.
For helpful conversations we thank Charlie Bean, Robin Burgess, Francesco Caselli, Laura Castillo, Per Krusell,
and Elizabeth Murry. The authors declare that they have no relevant or material …nancial interests that relate to the
research described in this paper.
1
I
Introduction
A vast literature in macro-development has tried to explain the determinants of fertility rates. Most
studies build on the seminal framework of Becker (1960), Becker and Barro (1988), and Barro and
Becker (1989), who illustrate how economic variables can in‡uence fertility choice.1
This paper brings attention to the rapid convergence in total fertility rates (TFR) experienced by
most developing countries in the past few decades.2 The world’s average TFR declined from over 5
children per woman in 1960, to 2.5 in 2013. This trend is not driven by just a few countries: in 1960,
half the countries in the world had a TFR above 5.8 children per woman. By 2013, the median TFR
fell to just 2.2 children per woman, almost equal to the world’s estimated replacement fertility rate of
2.25.
This rapid convergence has taken place in countries at widely di¤erent levels of development (measured as GDP per capita). Indeed, though there is a negative relationship between fertility and
development across countries, suggestive of a substitution towards quality over quantity in the classic Barro-Becker framework, the fertility-development relationship has shifted downward and become
‡atter over time. The downward shift is considerable: today the typical woman has, on average, 2.5
fewer children than the typical woman living in a country at a similar level of development in 1960.
While fertility rates tend to be higher in rural than in urban areas, increased urbanization does not
appear to be the main driver of the recent fertility decline: fertility rates in rural areas have also fallen
sharply. Carrying out a straightforward decomposition of the overall fall in fertility into a within-region
e¤ect and a urbanization (or between-region) e¤ect, we …nd that the within-e¤ect accounts for over 85
percent of the decline in fertility, while urbanization accounts for the other 15 percent. Put di¤erently,
fertility has declined signi…cantly both in rural and urban areas and only a small fraction (15 percent)
of the decline in fertility can be accounted for by urbanization.
Another factor often cited as a determinant of fertility is female labour force participation. The
cross-country correlation between fertility rrates and female labour force participation, however, is very
weak and the share of women in the labour force has not changed much in developing countries over
the past few decades. In contrast, infant and child mortality rates are more positively correlated with
fertility. The relationship is nonmonotonic: it is positive at low levels of mortality rates, becoming
‡atter thereafter— that is, fertility does not change with mortality once mortality exceeds a (fairly
1
Two recent examples in this literature are Manuelli and Seshadri (2009) and Doepke (2004).
TFR is de…ned as the number of children that would be born to a woman if she were to live to the end of her
childbearing years and bear children in accordance with current age-speci…c fertility rates. Throughout the paper, we
use fertility, fertility rate, and TFR interchangeably.
2
2
low) threshold. Mortality rates are argued to be a determinant of TFR: given a desired number of
children, more births are needed to ensure that the right number of children survives to adulthood.
Interestingly, though, surveys reveal that the number of desired children also fell signi…cantly since
the 1970s, suggesting that the higher fertility rate in earlier periods was not solely the result of a
calculated overshooting needed to meet a desired target: there seems to have been a global shift
towards a small-family norm. Lower mortality rates did play a role, we shall argue, in triggering a
number of population policies aimed at reducing fertility.
It is instructive to contrast the fast convergence witnessed by developing countries in recent decades
with the rather slow and secular decline in fertility rates experienced by more mature economies:
the fertility decline began as early as the mid-1700’s in some European countries and only reached
replacement levels in the early twenthieth century. While declines in mortality rates did not precede
the fertility transition in every developed country, it has done so in almost every developing country.
The increase in life expectancy, together with the high fertility rates in developing countries, is why
population growth rates rose so fast in the postwar period. The fear of a population explosion lent
impetus to what e¤ectively became a global family planning program, which we believe was the most
likely driver of the acceleration in the global fertility decline. The initiative, propelled in its beginnings
by intellectual elites in the United States, Sweden, and some developing countries, most notably India,
mobilized international private foundations as well as national governmental and nongovernmental
organizations to advocate and enact policies aimed at reducing TFR.
By 1976, following the preparation of the World Population Plan of Action at the World Population
Conference in Bucharest in 1974, 40 countries, accounting for 58 percent of the world’s population and
virtually all of the larger developing countries, had explicit policies to reduce fertility rates. Between
1976 and 2013, the number of countries with direct government support for family planning rose to
160.
We argue that while socioeconomic factors do play an important role in the worldwide fertility
decline, the timing and speed of the decline over the past four decade suggests that the global family
planning program played a signi…cat role in accelerating the process. In line with this hypothesis, the
data establish a strong positive association between per capita spending on family planning programs
and the percent reduction in TFR. Collectively, the global family planning programs provided a policy
template for fertility reduction, though there were signi…cant di¤erences in the actual implementation,
as the policies had to be tailored to the speci…c context of highly diverse countries. There were two main
3
elements common to all programs:3 1) promoting an increase in contraceptive supply and information
(preferences and take up rates for di¤erent contraceptives varied signi…cantly across countries and over
time); and 2) creating public campaigns aimed at reversing pro-natalist attitudes and establishing a
new small-family norm. Indeed, mdedia campaigns appeared to have been critical in complementing
contraceptive provision, as the initial phase of the program, focused mostly on contraception methods,
did not appear to be su¢ cient to change fertility rates. During the 1970s, slogans proliferated in
di¤erent media outlets (TV, radio, magazines), street posters, brochures, and billboards, all conveying
a similar message regarding the bene…ts of small families.
While urban areas were easier to serve through the existing transport and communication infrastructure, most countries formed mobile teams to reach residents living in remote rural areas—
indeed, some countries, like South Korea, focused their e¤orts particularly on rural areas. Not surprisingly, then, fertility rates fell also outside urban centers. Though religious groups were generally
opposed to birth control policies, the family planning programs expanded in Buddhist, Christian, and
Muslim countries alike. Remarkably, fertility reduction programs took place under both democratic
and autocratic regimes, whether oriented to the political left or right (e.g. Chile under both Allende and Pinochet), and with or without strong government support (in some countries, like Brazil,
family planning programs were initiated and almost exclusively run by nonpro…t, nongovernmental
organizations, while in others, like Singapore or India, the government was fully involved).
The absolute convergence to a global fertility rate close to replacement rates will lead to a constant
population level, reducing environmental concerns over explosive population growth. To the extent
that lower fertility rates are associated with higher levels of capital per capita (through lower capital
dilution) and higher investment in human capital, particularly for women (Goldin and Katz 2002), the
trends bode well for development and living standards in the poorest regions of the world.4
The rest of the paper is organized as follows. Section II studies the time-series and cross-sectional
evidence on fertility rates since 1960. Section III revisits the evidence relating fertility to key covariates.
Section IV discusses in detail the global population program and its e¤ects on fertility reduction.
Section V presents concluding remarks.
3
Other measures put in place, although not uniformly in all countries, were increases in the legal age of marriage
(e.g., Egypt and Tunisia), tax incentives (e.g., tax exceptions for families of up to three children in Korea), promotion of
domestic contraceptive production, establishment of family planning clinics, post-partum follow-up programs, legalization of abortion, maternity leave and allocation of public apartments and school choice for families of up to two or three
children (e.g., Singapore), etc. Di¤erent countries opted for di¤erent speci…c policies, adjusted to the domestic context.
4
Insofar as the U.S. experience can be of guidance, the di¤usion of contraception and the decline of fertility and
postponement of childbearing could increase female empowerment in developing countries through higher levels of
investment in human capital (Goldin and Katz 2002).
4
II
Fertility across Time and Space
Since the 1960s, the world’s TFR has steadily declined, more than halving over the …ve decades that
we analyze. This decline has been experienced by most countries in the world and is not skewed
by the experience of a few countries, particularly China’s one-child policy. Using the World Bank’s
World Development Indicators (WDI), Figure 1 illustrates these developments by plotting the TFR
histograms for the start of each decade; the bars show the fraction of countries for each TFR interval.
(The …gure shows 2013 rather than 2010 to report the latest information.) As the …gure illustrates,
there is a clear change in the shape of the distribution of fertility over time. In 1960, nearly half the
countries in the world had a fertility rate between 6 and 8, with the median rate in the distribution
equal to 5.8. In 2013, the largest mass of countries is concentrated around 2, with the median TFR
equal to 2.2. The skewness changed from highly negative to highly positive over the period.
FIGURE 1
Fertility histograms over time
0
2
4
6
8
10
.2
0
.05
Fraction
.1
.15
.2
Fraction
.1
.15
.05
0
0
.05
Fraction
.1
.15
.2
.25
1980
.25
1970
.25
1960
0
2
6
8
10
4
6
TFR
8
10
4
6
8
10
8
10
.25
0
.05
Fraction
.1 .15
.2
.25
.2
.05
0
2
2
2013
Fraction
.1 .15
.2
Fraction
.1 .15
.05
0
0
0
2000
.25
1990
4
0
2
4
6
TFR
8
10
0
2
4
6
TFR
Notes: The …gure shows fertility histograms at the beginning of each decade. (2013 is used
rather than 2010 to report the latest information). The data comes from the World Bank’s WDI
database.
5
Key summary measures are reported in Table 1, showing the evolution of the world’s TFR, together
with the median and the range. As the Table shows, the median TFR has fallen dramatically, with
the median woman now giving birth to 2.2 children, down from a 5.8 median in 1960.
TABLE 1
Fertility summary statistics
Year
Mean
Median
Min
Max
1960
5
5.8
1.9
8.2
1970
4.7
5.5
1.8
8.2
1980
3.7
3.4
1.4
9
1990
3.3
2.8
1.3
8.7
2000
2.6
2.4
0.9
7.7
2013
2.5
2.2
1.1
7.6
Notes: The table reports summary statistics of the total fertility rate at the start
of each decade. The mean fertility rate is the "World" fertility rate available from
the WDI, while the median, minimum and maximum are calculated using crosscountry fertility rates for each year.
This decline in fertility rates has taken place across most regions in the world, as shown in Figure
2, which depicts the average TFR across broadly de…ned regions over time. As the …gure illustrates,
between 1960 and 2015, large declines in TFR took place in Latin America and the Caribbean, South
Asia, and the Middle East and North Africa. Interestingly, while the global average continues to
decline, fertility rates have been increasing slightly in North America, which reached its lowest TFR in
the 1980s, and Europe and Central Asia, which bottomed up in the 1990s. This also suggests a slight
convergence to 2 taking place in regions where the TFR was below 2.
6
FIGURE 2
5
4
3
2
Total Fertility Rate
6
7
Fertility trends across regions
1960
1970
1980
1990
2000
2010
Year
North Americ a
Europe and Central As ia
Eas t As ia and Pac ific
Latin Americ a and Caribbean
Middle East and North Afric a
South As ia
Sub Saharan Afric a
Notes: This …gure plots the trends in fertility trends by region, as de…ned by the World Bank,
between 1960 and 2013. The data comes from the WDI database.
As shown in Table 2, fertility rates in East Asia and the Paci…c fell from 5.4 to 1.81 over the period
from 1960 to 2013 (a 66 percent reduction), while Latin America and the Caribbean went from an
average TFR of 5.98 in 1960 to 2.16 in 2013 (a 64 percent decline). The Middle East and North Africa’s
TFR fell from 6.87 to 2.83, the largest absolute decline in fertility from among all world regions, while
South Asia’s TFR fell from 6.02 in 1960 to 2.56 in 2013. While absolute declines in fertility were not as
large in North America or Europe and Central Asia, the percentage declines in both regions have been
signi…cant— nearly 50 percent in North America and close to 40 percent in Europe and Central Asia.
Convergence in Sub-Saharan Africa has been slower, as this region recorded the lowest percentage
decline in fertility over all 53 years. However, since the 1980s, TFR fell from 6.7 to 5, whic represents
a sizeable decline.5 Within this region, South Africa has already reached a TFR of 2.4, and Mauritius
in 2013 reported the lowest African TFR, 1.4.
5
The replacement fertility rate for Sub-Saharan Africa is also much higher than for the rest of the world: it is 2.52
as opposed to the world average of 2.25. (See Table A4 in the Appendix for replacement fertility rates by country.)
7
TABLE 2
Fertility decline from 1960-2013
Region
Absolute decline in TFR Percentage decline in TFR
North America
1.8
49.8
Europe and Central Asia
1.1
39.0
East Asia and the Paci…c
3.6
66.4
Latin America and Caribbean
3.8
63.8
Middle East and North Africa
4.2
60.3
South Asia
3.5
57.5
Sub-Saharan Africa
1.6
23.6
Notes: This table reports the absolute and percentage decline in TFR over the 1960–2013 period by region.
Fertility declines are calculated using TFR data from the WDI database and the regions are as de…ned by
the World Bank.
III
Determinants of Fertility Rates
In this section, we study the covariation of fertility rates with the main variables emphasized in the
literature. The data are taken from the World Bank’s WDI database unless otherwise noted.
A
Fertility and Income
Several empirical studies have documented a negative relationship between fertility rates and income.
While the relationship between fertility rates and income is indeed negative in the cross-section of
countries, the main fact that this paper wishes to emphasize is that the relationship has shifted
downward and become ‡atter over time. This development is illustrated in Figure 3, which shows the
relationship between TFR and real GDP per capita both in 1960 and in 2013. The …gure also shows
a …tted polynomial line. The downward shift has been, on average, around 2.5 children per woman,
meaning that today a woman has 2.5 children less than a woman living in a country at the same level
of development had in 1960.
8
FIGURE 3
The Fertility-Income Relation in 1960 and 2013
0
2
4
6
8
RWA
KEN
NER SYRHND
DOM DZA
CRI
MDG
OMNPHLCIVNIC
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SGPMAC
KORHKG
4
6
8
Log(GDP per capita)
TFR 1960
TFR 2013
10
12
lowess TFR ln_gdp 1960
lowess TFR ln_gdp 2013
Notes: The …gure shows the scatter plots and lowess smoothed relationship between
fertility and log of per capita GDP (in constant 2005 US$) in 1960 and 2013. The data is
from the WDI database.
A signi…cant amount of theoretical work has been devoted to generate a negative relationship
between fertility rates and income. See for example Jones, Schoonbroodt, and Tertilt (2011), who
study the theoretical conditions under which economic models can yield the negative relation observed
in the data, and Manuelli and Sheshadri (2009), who seek to explain di¤erences in fertility rates across
countries with productivity and tax di¤erences. But given the recent declines in fertility rates, the
real challenge seems to be how to explain why countries with markedly di¤erent income levels are
converging to very similar TFR. In Section IV we come back to this challenge and argue that the
population programs started in the 1960s provide an explanation for the decline.
B
Fertility and Urbanization
We now investigate whether increased urbanization can account for the decline in fertility rates. Rural
areas have historically had much higher fertility rates than urban ones. Arguably, in rural areas,
children can be a signi…cant input in agricultural production. Moreover, despite the fact that parents
can earn higher average wages in urban areas, it can cost more to raise children there, as the costs of
9
housing and (typically compulsory) education are higher.6 The negative relationship is illustrated in
Figure 4, which plots the proportion of population living in urban areas against TFR for all countries
(again, using data from the WDI).
FIGURE 4
Fertility and Urbanization
0
20
40
60
80
100
2
4
TFR
6
8
YEM
RWA
OMN
LBY
NER
AFG
MWI
CIV
BDI
KEN
SEN
ETH
TGO
SAU
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COM
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1980
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100
10
2013
10
2000
10
1990
20
20
40
60
80
% urban population
100
8
TFR
6
4
0
0
0
NER
MLI
SOM
TCD
BDI
NGAGMB
ZAR
UGABFAZMB
AGO
MWI TZA
TMP
MOZ
SSD
SEN
GNB
AFG
GIN
CIV
GNQ
BEN
LBR
CMRCOG
ERI
COM
SLE
TGO
ETH
RWA
MDG
SDN
KEN
CAF MRT
W
SM
YEM
SLB
IRQ GAB
GHASTP
TJK
PNG
TON
GTM
ZWE
VUT
SWZ
FSM
BOLDJJIOR
KGZ
PAK
HTI
NAM
LSO
LAO
PHL
ISR
HND
SYR
KIR
KHM EGY
PRY
OMN
DZA
MAR
BLZFJI
KAZ
SAU
BWA
KWT
GUY
ECU
NIC
DOM
IND
PAN
MNG
PER
GUM
ZAF
VEN
LBY
LKA
TKM
IDN
NPL
CPV
COL
MDV
JPYF
AM
SUR
NCL
BTN
TUN
UZB
BGD
GRD
SLV
MEX
ARG
ATG
BHR
TUR
ISL
URY
AZE
IRL
FRA
QAT
VCT
PRK
BRN
MYS
NZL
MMR
IRN
GBR
AUS
BHS
SWE
USA
BRB
NOR
GEO
ARE
BRA
CHL
TTOLCA
CRI
FIN
BEL
ALB
VIR
VNM
ARM
DNK
NLD
RUS
ABW
CHN
MNE
PRI
BLR
CAN
SVN
LTU
EST
ROM
CHE
HRV
UKR
BGR
LBN
CYP
CHI
MUS
MDA
AUT
LVA
CZE
CUB
MKD
ITA
PN
THA
DEU
HUN
ESP
POL
BIH SVK
PRT
GRC
KORJMLT
HKG
MAC
2
8
TFR
6
4
TFR
6
4
2
0
NER
AFGSOM
TCD
TMP
ZAR
BDI
UGA
AGO
MLI
BFAYEM
ETH
MWI
SSD
NGA
ZMB
BEN
ERI
GIN
SLE
GMB
RWA
LBR
GNB
MOZ
GNQ
TZA
CMR
MDG
SEN
SDN
CAF
CIV
MRT
COM
TGO
COG
KEN
GTM
SLB
GHA
STP IRQ GAB
PNG
W
SM
PAK
I
VUT
FSM
HTIHND BOL DJ
TON
SWZ
LAO
NPL
LSO
ZWE
J OR
NAM
SAU
TJK
SYR
KIR
PHL
KHM
CPV
OMN
PRY
BTN EGY
BLZ
BWA
MDV
NIC
QAT
IND TKM
BGD
FJI
ECU
LBY
ISR
SLV
PER
DOM
ZAF
KWT
MYS
PAN
VEN
GUM
BHR
SUR
MEX
COL
ARE
JMAR
AM
GUY
GRD
UZB
NCL
DZA
IDN
PYF
TUR
ARG
MMR
KGZ
CRI
BRN
ALB
VCT
BRA
LCA
ATG
LKA
LBN
URY
IRN
MNG
CHL
TUN
BHS
ISL
VIR
USA
PRI
AZE
MUS
PRK
NZL
IRL
FRA
ABW
NOR
MNE
KAZ
TTO VNM
BRB
DNK
AUS
NLD
FIN
CYP
MLT
THA
MKD
ARM
BEL
GBR
GEO
CUB
PRT
SWE
CHN
CHE
CAN
KOR
CHI
BIHMDA
HRV
LTU
DEU
POL
AUT
EST
PN
HUN
ROM
SVK
BLR
GRC
SVN
ITA
LVA
BGR
RUS
EJSP
CZE
UKR
HKG
MAC
2
8
YEM
NER
AFGSOM
BDI
TCD
RWA
ETH
AGO
OMN
ZAR
UGA
MLI
MWI
BFA
SSD
BEN
GNB
SEN
GIN
SLE
ERI
NGA
ZMB
CMR LBR
CIV
TGO
MDG
MOZ
TZA
LAO
SDN
GMB
MDV
DJ
I
KEN
PAK
MRT
GNQ
IRQ
SLB
SAU
CAF
SWZ
BTN
KHM
GHA
COM
GTM
J OR
HTI
STP
COGGAB
TMP
CPV
SYR
NAM
ZWE
TJKHND
NPL
WFSM
SM
LSO
VUT
BOL
IRN LBY
PNG
DZA
NIC
TONKIRBWA
BGD
PRY
BLZ
ARE
EGY
TKM
PHL
UZB
MAR
MNG
QAT
SLV
INDGRD
PER BHR
ECU
KGZ
ZAF
VNM
BRN
DOM
MMR
LCA
FJIMYS
PYF
TUN
MEXVEN
NCL
IDN
COL
TUR
PAN
LBN
GUM
ALB
ARG
VCT
JCRI
AM
VIR
ISR
BRA
AZE
KAZSUR
BHS
CHL
ARM
URY
CHN
TTOLKA
GUYMUS
MDA
CYP SWE
KWT
PRK
ISL
ABW
MKD
PRI
GEO
NZL
THA
SVK
IRL
ATGMNE
POL
USA
LTU
EST
MLT
LVA
NOR
BLR
RUS
CZE
AUS
HUN
ROM
UKR
CAN
BGR
FIN
CUB
FRA
BRB
BIHPRT
MAC
DNK
HRVAUT
NLD
BEL
CHE
KOR
JGBR
PN
CHI
SVN
DEU
GRC
ESP
ITA
HKG
0
20
40
60
80
% urban population
100
0
20
40
60
80
% urban population
100
Notes: The …gure shows the scatter plots and smoothed polynomial relationship between
fertility and urbanization at the start of each decade. Urbanization is measured as the
proportion of the population living in urban areas. Data comes from the WDI database.
Interestingly, though on average rural areas have higher TFR, the urbanization process alone cannot
account for the sharp decline in fertility rates observed over the past …ve decades. Rather, it appears
that fertility rates fell rapidly both in urban and rural areas. We are able to quantitatively explore
this issue and assess the contribution of urbanization using TFR data from rural and urban areas
obtained from the Demographic and Health Surveys (DHS). We decompose the fall in fertility rates
into a within-region e¤ect (corresponding to the decline in fertility within rural areas or urban areas)
and a between-region e¤ect (urbanization), corresponding to the decline in fertility rates due to the
change in a country’s urban population share.
6
This idea is presented in Becker (1960) as farmers having a comparative advantage in producing both children and
food, though this advantage is smaller for higher “quality”of childrearing. Caldwell’s net wealth ‡ow theory (1976) also
supports the view that wealth ‡ows from children to parents in primitive agricultural societies, whereas the direction of
‡ows reverses as society modernises and costs of raising children go up.
10
In formulas, the overall fertility rate equals the weighted average of urban and rural fertility rates:
Ft =
where
R;t
R;t FR;t
+
U;t FU;t
is the proportion of the country’s population living in rural areas in period t,
U;t
=1
R;t ,
and FR;t and FU;t are the rural and urban fertility rates at time t, respectively.
With some algebra, the change in overall fertility between time 0 and time t can be exactly decomposed as:
Ft = Ft
F0 = (
|
R;t FR
U;t FU )
{z
}
U rbanization (between ef f ect)
+(
|
R
FR;t +
{z
U
W ithin-ef f ect
FU;t )
}
where 0 and t correspond to the start and end of the period, respectively; and the terms denoted with
a bar are the time averages:
xj =
xj;t + xj;o
; j = R; U ;
2
x = ; F:
We perform this decomposition for 63 developing countries in which the DHS was carried out.7 Surprisingly, wit an average contribution of about 15 percent, the results indicate that the urbanization
process has not contributed very much to the overall fall in fertility rates (see Figure 5).
The contribution of urbanization does not vary signi…cantly with a country’s fertility or urbanization rates. This result suggests that while urbanization is indeed negatively correlated with fertility
rates, there are other forces at work driving down fertility in both rural and urban areas around the
worl.
7
It should be noted that since the DHS are carried out in di¤erent years and at di¤erent intervals in di¤erent countries,
the period over which the changes are computed is not the same for every country. More details of the data are available
in Table A3 in the Appendix.
11
FIGURE 5
Decomposition of the Decline in Fertility Rates
Urbanization effect
Within effect
Peru
Paraguay
Urbanization effect
Within effect
Zimbabw e
Zambi a
U ganda
Togo
Tanzania
Sierra Leone
N i geri a
Senegal
R w anda
N i ger
N amibi a
Mal i
Mal aw i
Mozambique
Liberia
Madagascar
Kenya
Within effect
Lesotho
Ghana
Gui nea
Ethi opia
Eri trea
C ote d' Ivoi re
C ongo D emocratic R epubl ic
C had
C omoros
C ameroon
Burundi
Benin
Urbanization effect
Burki na Faso
Vietnam
Turkey
Philippines
Pakistan
Nepal
Kyrgyzstan
Kazakhstan
Indonesia
India
Cambodia
Bangladesh
Azerbaijan
-.5 0 .5 11.5
Sub Saharan Africa
-.5 0 .5 1 1.5
Asia
Nicaragua
Honduras
Haiti
Guatemala
El Salvador
Ecuador
Colombia
Dominican Rep.
Yemen
Brazil
Bolivia
Latin America and Caribbean
-.5 0 .5 11.5
Within effect
Ukraine
Morocco
Moldova
Jordan
Egypt
Armenia
Albania
-.5 0 .5 1 1.5
Europe, Middle East, North Africa
Urbanization effect
Notes: The …gure plots the decomposition of the overall fall in fertility by the urbanization e¤ect and the within-region
e¤ect. The data on urban and rural fertility is taken from the Demographic and Health Survey database and covers
63 developing countries over di¤erent time periods. The data on proportion of population living in urban areas for
the corresponding years is taken from the World Development Indicators database.
(See Table A3 in the Appendix for more details.)
C
Fertility and Female Labour Force Participation
We now explore the relationship between fertility and female labour force participation, the latter is
often viewed as a key covariate in fertility choice.8 The relationship is plotted in Figure 6; which shows
the cross-country data in di¤erent decades, together with a …tted line. The data on female labour force
8
A key premise underlying economic models of fertility since Becker (1960) is that childbearing is a time-consuming
activity, an assumption that mediates the theoretical relation between fertility rates and income. Some models explicitly
or implicitly assume that mothers have a comparative advantage in childbearing (e.g., Mincer 1963; Becker 1965). In
these models, as the value of female time in the market increases, the opportunity cost of having children also increases;
this tends to reduce the demand for children (substitution e¤ect) and can indeed o¤set increases in the demand for
children stemming from higher income (income e¤ect).
12
participation (the proportion of women aged 15+ years who are participating in the labour force) com
from estimates by the International Labour Organization (ILO), published in the World Development
Indicators and are available from 1980 through to 2013. As the plots in Figure 6 show, the relationship
is U-shaped rather than negative and seems to be ‡attening out over time.
This result should not be too surprising: in 2013 female labour force participation was highest in
Sub-Saharan African countries (at an average rate above 63 percent), higher indeed than in North
America, Europe, and Central Asia (regions for which participation rates were between 50 and 60
percent). In contrast, participation rates for women are vert low in South Asia (35 percent), the
Middle East, and North Africa (just over 20 percent in 2013), regions with remarkably low TFR. For
a given country over time (that is, controlling for country-…xed e¤ects), the relationship appears to be
negative, meaning that increases in labour force participation are associated with decreases in fertility
rates, though the statistical association is very low (the R-square coe¢ cients are below 5 percent.)
FIGURE 6
Fertility and Female Labour Force Participation
10
1990
10
1980
2
TFR
4
6
8
YEM
AFG NER SOM
BDI
TCD
RWA
ETH
OMN
AGO
ZAR
MLI
MWI
BFAUGA
BEN
GNB
SEN
GIN
WBG
SLE
LBR
ERI
ZMB
CIV CMR
TGO
MDG
MOZ
TZA
SDN NGA
LAO
MDVDJI
GMB
KEN
PAK
MRT
GNQ
IRQ
SAU
SLB
CAF
SWZBTNGAB
GHAKHM
GTM
JOR SYR COMSTP
HTI
TMP
COGZWE
CPVNAM
HND
NPL
WSM
LSO VUT
BOL TJK BWA
IRN
DZALBY
NIC
TON
PRYBGD PNG
BLZ TKM
ARE
EGY
PHL
MAR
UZB
MNG
QAT
SLV
IND
PER
ECU
BHR
KGZ MMR
ZAF
VNM
MYS
BRN
DOM
VEN
TUNFJI
MEX
PYF
LCA
NCL
CRI
COL
IDN
TUR
PAN
GUM
LBN
ARG
VCT
VIR
ALB
JAM
ISR
BRA
SUR
AZEARM
KAZ
CHL
BHS
URYNZL
LKA
GUY
TTO
CYP
MDA
KWT
MUS
ISLCHN
PRK
MKD
PRI
GEO
SWE
THA
IRL
SVK
POL
USA
MLT
LTU
EST
LVA
NOR
CZE
AUS
RUS
BLR
MNE
HUN
SGP
UKR
ROM
GBR
BGR
CAN
FIN
CUB
FRA
BRB
BIH
MAC
DNK
BELNLD
HRV
LUX
KOR
PRT
CHE
JPN
AUT
DEU
SVN
GRC
ESP
ITA
HKG
0
0
2
TFR
4
6
8
YEM
RWA
OMN
LBY
AFG NER
MWI
CIV
KEN
BDI
SEN
ETH
JOR
SAU
TGO
AGO
ZMBSOM
COM
BFA
SYRDZA
ZWE
UGA
MDVTCD
MLI
SLE
BEN
LBR
NGA NAM
SLB
SWZ
ERI
TZA
CMR
ZAR
IRQ IRN SDN
BTN
PAK
GHA
GIN
MDG
DJI
STP
MRT
CPV
BGD
GMB
HND
LAOMOZ
BWAGNB
MNG
GTM
WSM
COG
NIC
HTI
CAF
BLZ
QAT
NPL
GNQ
MAR
GAB
PNG
KHM
TJK
LSO
VUT
TON
KWT
BOL
ARE
EGY
TUN
PRY TKMMMR
PHL
SLV
UZB
VNM
PER
BHR ECU
ZAF
TMP
LCA
MEXIDN
IND
DOM
TUR
BRN
VEN
BRA
LBN
COL
VCT
PYF KGZ ALB
FJIGUY
SUR
PAN
MYS
JAM
CRI
LKA
NCL
ARG
TTO
AZE THA
GUM BHS
IRL ISR
VIR
KAZ CHN
KOR
URY
CHL
PRK MKD
PRI MUS
MDA
ISL
ROM
ARM
CYP
GEO
SVK
POL
GRC
PRT
ESP
CZE
NZL
HKG
SVN
BLR
BGR
BRB
HRV
EST
MLT CUB
LTU
UKR
AUS
GBR
HUN
RUS
FRA
USA
LVA BIH
JPN
SGP
CAN
NOR
BEL
MAC
SWE
ITA
AUT
FIN
NLD
CHE
DNK
LUX
DEU
0
20
40
60
80
100
0
20
60
80
100
8
8
10
2013
10
2000
40
2
0
0
2
NER
SOM MLI
TCDZAR BDI
NGA AGO
UGA
GMB
ZMB
BFAMWI
TZA
MOZ
COG ERI
SEN
GNB
AFG TMP
GIN
CIVLBR
BEN
GNQ
CMR
COM
SLE
MRT
TGO
ETH
RWA
MDG
SDN
KEN
CAF
YEM
STP
GAB
IRQ
WBGWSM
SLB
TJK GHA
GTM
TON
PNG
ZWE
DJI SWZPHL
VUT
JOR
BOL
PAK
KGZ
HTI
NAM
ISR
LSO
LAO
HND
SYR
OMNFJIGUY
PRY
DZA
EGY
MAR
BLZ
SAU
KAZ
KWT
BWAKHM
ECU
NIC
IND
PAN
DOM
MNG
GUM
PER
ZAF
VEN
LBY
LKA
TKM
IDN
CPV
COL
NPL
SUR
NCL
JAM
MDV
BTN
UZB
MEX
ARG
SLV
BGD
BHR
TUR
PYF
URY
ISL
FRA
QAT
IRL
BRN
VCT
AZE
PRK
MYS
NZL
IRN TUN
GBR
AUS
MMR
SWE
LCA
BHS
USA
NOR
BRB
CRI
ARE
CHL
TTO
FIN
GEO
BRA
ALB
BEL
VIR
ARM
DNK
VNM
RUS
NLD
CHN
MNE
PRI
BLR
CAN
SVN
LTU
LUX
EST
ROM
CHE
LBN BIH
HRV
BGR
UKR
MDA
CYP
MLT
ITA
MKD
CUB
MUS
SRB
JPN
CZE
AUT
LVA
THA
DEU
HUN
SVK
POL
ESP
GRC
PRT
KOR
SGP
HKG
MAC
TFR
4
6
NER
SOM
TCDZAR BDI
TMP
UGA
MLI
AGO
BFA
ETH
YEM
MWI RWA
NGA LBR
ZMB
BEN
GIN
ERI
SLE
GMB
GNB
GNQ
MOZ
TZA
CMR
SEN
CAF
SDN
WBG MRT
CIV
COM
TGOMDG
COG
KEN
IRQ
GTM SLB
STP
GHA
GAB
WSM
PNG
PAK
DJI
VUT
HTI
TON
SWZ
LAO
BOL LSO
ZWE
NPL
JOR
NAM
SAU
HND
SYR
TJK
PHL
KHM
OMN
CPV
BTN
BLZ PRY
BWA
EGYLBYIND
MDV
QAT
NIC
BGD
FJIKWT
ECU
SLV
ISR
PER
DOM
ZAF
TKM
MYS
PAN
VEN
GUM
BHR
SUR
MAR
MEX
ARE
COL
NCL
JAM
GUY
UZB
DZA
ARG
IDN
TUR
PYF
CRI
MMR
VCT
ALB
BRN
KGZ
BRA
LCA
LKA
URY
IRNLBN
MNG
TUN
CHL
BHS
ISL
PRI
VIR
USA
MUS
NZL
AZE
VNM
PRK
IRL
FRA
NOR
MNE
KAZ
DNK
BRB
LUX
TTO
AUS
FIN
MLT
CYP
NLD
MKD
BEL
ARM
THA
GBR
CUB
GEO
PRT
MDA
SWE
CHE
CAN
SRB
KOR
BIH
HRV
AUT
DEU
POL
EST
LTU
JPN
HUN
BLR
SVK
ROM CHN
ITAGRC
BGR
LVA
SVN
ESP
RUS
CZE
UKR
HKG
MAC
TFR
4
6
AFG
0
20
40
60
Female LFPR
80
100
0
20
40
60
Female LFPR
80
100
Notes: The …gure shows the scatter plots and smoothed polynomial relationship between
fertility rates and female labour force participation at the start of each decade (data is only
available from 1980 onwards) for all countries. Female labour force participation is measured as the
proportion of women aged 15+ years in the labour force. Data comes from the WDI database.
13
D
Fertility, Mortality, and Replacement Rates
Infant and child mortality rates are often proposed as key determinants of fertility rates. The premise is
that in countries with high mortality rates, the number of births needed to produce the desired number
of children is higher, leading to a positive relation between TFR and infant and child mortality rates.
This interpretation, based on an individual family’s rational calculation, proves to be problematic
when confronted with two additional pieces of evidence. The …rst is that TFR is also positively
associated with the risk of maternal death (de…ned as the probability that a 15-year-old female will
die eventually from a childbirth-related cause assuming that current levels of fertility and mortality—
including maternal mortality— do not change in the future, taking into account competing causes of
death). In a rational setting, a higher risk of maternal death should decrease rather than increase TFR.
The strong positive correlation in the data between TFR and the risk of maternal death casts some
doubt on the survival probability interpretation o¤ered to explain the positive correlation between
TFR and infant and child mortality rates.9 Rather, it would seem that health or broader economic
factors that increase all types of mortality rates are positively correlated with TFR. The mortality
rate for infants are plotted against TFR in Figure 7, while the mortality rates for children are plotted
against TFR in Figure 8: Figure 9 plots the risk of maternal death against TFR.
In the next section, we argue that the decline in overall mortality rates was important in triggering
the global population-control program, which originated from a concern about explosive population
growth.
9
There is also a positive correlation between TFR and the maternal mortality rate (de…ned as the number of maternal
deaths per 100,000 live births) though the number of observations available is much smaller.
14
FIGURE 7
Total Fertility Rate and Infant Mortality Rate
0
50
100
150
200
250
4
TFR
6
8
YEM
OMN R W A
LBY
N ER
MW
I
C IV
KEN
BD
I AFG
SEN
ETH
JSYR
OR
SAU
TGO
AGO
BFA
C
OM
U
GA
ZW
EZMB
MD
V
MLI
SLE
BEN
TC
DLBR
ZA
SSD
SDD
N
NZAR
SLB
SW
Z
TZA
ER
IIGA GIN
C
MR
IR
QPV
BTN
GH
MD
IR
N
MOZ
NH
AM
DPAK
JG
MR
TA
STP
C
GMB
BGD
NGTM
D
LAO
BW
A
MN
G
C
OG
N
IC
H
TI
C
AF
BLZGAB
QAT
N
MAR
GBOL
KH
MPL
TJ
K
TPNNLSO
TON
KWVU
AR
ET TU
EGY
PR
YLSLV
UMMR
ZB
KIR
M
BHVN
RPH
ZAF
ECPER
UTKM
LC
A
MEX
IN D
D OM
ID
NR
TU
VEN
A
KGZ
LBN
VC
C
TBR
FJ
IOL
PAN
JR
AM
ALB
GU
Y
CMYS
ITH
LKA
AR
GA
ISR
IR
LTTO
BH
S
KOR
UMD
RLKAZ
YAN
H
CMU
S
ISL
RHTC
OM
AR
M
C
YP
GEO
POL
PR
GR
C
ESP
N
BLR
BGR
ZL
EST
BR
MLT
LTU
U
GBR
H
UU
AU
C
R
UE
SKR
BBNS
FR
LVA
A
U
SA
J
C
PN
AN
N
OR
SW
BEL
AU
ITA
T
FIN
N
LD
C
D
H
N
E
K
D
EU
0
2
8
4
2
0
0
2
4
ISL
N ZL BR B
C
AN
IR
L
U
SA
MLT
AU
S
PR T
N
LD
POL
U
RY
NFIN
FR
OR
A
ESP
GBR
AU
D
N
KT
BEL
C
H
E
ITA
GR
SW
E
H UCN
J PN
RWA
KEN
J OR
C IV AFG
LBY D ZA
SYR
YEM
NBD
ER
Q
MD
G
OMN
I MD VMW
I
HZMB
NSEN
DTGO
KWZW
TIR E
U
GA
C
OM
ETH
GH
QATSLB
CSW
PV
SDMR
N
IC
MLI
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TPAK
TZA
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MEX
MAR
LBR
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ER
IIR
BTN
BW
A
AR
E
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DN
BH
R
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AM
M
TU
N
PER
PH
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L
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GTM
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C
MR
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D
OM
GIN
PN
G
EC
U
LC
A
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VC Y
T
N PL
TON
MMR
C AFEGY
LSO
H
TI
PR
C
TH
OL
A
TU
R
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AM C H NID N IN D
PAN
GU
BR A
C RYI KGZ
LBN
MYS
KOR
FJLKA
I
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BC HSL
IR LCTTO
BH S
N ZL
BR
B
AR
ROM
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R
AU
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ESP
ISL
N
LD
FR
AT C
OR
U
SA
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LTU
GR
ITA
AU
BEL
AN
POL
BGR
JD
PN
CC
H
EU
D
MLT
EU
R
H
USN
N
K
SW
E
FIN
TFR
6
TFR
6
8
RWA
KEN
J OR
D ZA
D
OM
LBY
SYR
D
NHMAR
ICN
C IV
KW
TTU
VU
EZMB
PH LZW
BH
RI
GA
SEN
BD
AR
PER
C MEX
OL
TZA
TTGO
GH
SLV
AE
SW
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SD
EC
NMR
U
BOL
EGY MLI
VEN
BW
A
PAK
PR
Y M BR
FJ
IVN
H TILBR
TU R
BEN
PNGTM
IR
Q
AGBFA
MYS
MU
KOR
STH ASLB
SLE
N
PL
PAN
C OGLSO
D GIN
CIN
AF
LBN
GU
Y
ID
N
C
MR
C
H
GMB
L
J LKA
AM
TTO
10
1980
10
1970
10
1960
0
50
150
200
250
0
50
100
150
200
250
200
250
10
2013
10
2000
10
1990
100
AFG
NSOM
ER
TC
D
TMP
BD
IZAR
U
GA
AGO
MLI
BFA
ETH
YEM
MW
I
SSD
N
GA
ZMB
BEN
ER
ITZA
GIN
GMB
R
LBR
W
GN
GN
MOZ
QBASLE
C
MR
SEN
MD
G
SD
N
C
AF
MR
C
T
IV
CC
OM
TGO
OG
KEN
IR
QSTP
GTM
SLB
GH
A
GAB
W
SM
PN
G
D
PAK
J
I
VU
T
FSM
HLSO
TI Z
TON
SW
LAO
BOL
N
ZW
PL
E
JPH
OR
AM
SAU
H
NN
SYR
TJ
KM
LD
KH
OMN
C
PV
PR
YKIR
BLZ
BTN
BW
EGY
N
MD
VBGD
QAT
INA
D
FJ
I IC
LBY
EC
U
ISR
SLV
PER
D
OM
KW
T
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MYS
VEN
PAN
TKM
BH
R
SU
RR
MAR
MEX
AR
E
OL
JC
AM
GR
GU
D
U
Y
ZB
D
ZA
ID
N
AR
TU
G
MMR
BR
C
R
N
I
KGZ
VC
ALB
T
BR
A
ATG
LC
A
LKA
U
LBN
R
Y
IR
N
MN
CMN
H
L
ISL
BH
TU
S
N
U
SA
N
MU
ZL
VN
S
PR
MAZE
KG
FR
IR
LTTO
A
N
OR
EA
KAZ
D
AU
BR
N
S
K
B
FIN
LD
C
MLT
YP
MKD
TH
AR
M
GBR
U
GEO
B
PR
MD
T
A
SW
E
C
H
AN
C
H
KOR
D
H
BIH
LTU
EU
R
V
JBEL
AU
POL
EST
PN
T
H
U
N
SVK
BLR
R
OM
GR
C
ITA
SVN
LVA
BGR
ESP
R
U
SN
C
ZE
U
KR
TFR
6
4
TFR
6
4
0
50
100 150
IMR
200
250
0
0
2
TFR
6
4
2
0
N ER
MLI
SOM
TC
D
BD
NI ZAR
GA
UGMB
GA
AGO
ZMB
BFA
MW
I
TZA
TMP
MOZ
C
OG
SEN
SSD
GN
GIN
AFG
C
IV
BEN
GN
LBR
MRTQBSLE
ER
C
ICG
MR
TGO
R
ETH
W
AOM
MD
SD
N
KEN
C
AF
WSLB
SM
STP
GAB
YEM
IR
Q
GH
TJ
KGA
TON
GTM
PN
ZW
D PAK
J IE
VU
T AM
SW
Z
FSM
JH
OR
BOL
KGZ
H
N
ISR
PH
LLAO
LSO
N
D
SYR
OMN
PR
KH
YKIR
MATI
D
ZA
EGY
MAR
BLZ
SAU
KAZ
KW
T
BW
FJ
IIN
EC
GU
U
Y
N
D
IC
OM
PAN
D
MN
G
PER
VEN
Z
AF
LBY
LKA
ID
N
TKM
C
C
OL
N
PV
PL
MD
J
SU
AM
V
R
TU
BTN
N
U
ZB
GR
AR
MEX
SLV
BGD
G
D
ATG
BH
R
ISL
U
TU
RS
Y
R
FR
IR
QAT
L
A
AZE
BR
VC
PR
N
TM
K
N
MYS
ZL
AU
GBR
IR
MMR
SW
BH
LC
E
S
U
SA
N
OR
B
AR
C
GEO
BR
H
L
A
FIN
BEL
TTO
R
I
ALB
D
AR
N
LD
VN
K
M
R
U
S
MN
C
H
E
N
BLR
C
AN
SVN
LTU
EST
R
H
OM
H
LBN
U
BGR
R
V
YP
AU
C
LVA
MU
MD
ZE
UKR
T
B
A
JC
ITA
MLT
MKD
PN
D
TH
EU
AS
ESP
H
SVK
N
POL
PR
GR
BIH
TE
C
KOR
2
N ER
AFG
BD
SOM
TC
DMW I
RBFA
W
AIMLI
ETH
AGO
UZAR
GA
SSD
BEN
BLBR
SEN
GIN
SLE
ER
IIVG
NGN
GA
ZMB
CTGO
MR
C
MD
MOZ
TZA
SD
N
LAO
GMB
MD
D
V
J
I
KEN
PAK
MR
T
IRSW
Q
SLB
SAU
CGN
AF Q
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BTN
GH
A
M
GTM
C
OM
JSYR
OR
H
TI
GAB
STP
C
OG
C
PV
N
AM
ZW
EBOL
TJNKPLTMP
W
SM
H
N
D
LBY
FSM
VU
T
LSO
N
PN
G
DIR
N
ZA
IC
BW
A
TON
KIR
PR
Y
BLZ
AR
EPHU
EGY
TKMBGD
LZB
MAR
QAT
SLV
ING
D
GR
D
PER
EC
UMN
BH
R
KGZ
ZAF
VN
M
BR
NMEX
MYS
VEN
OM
FJ
ID
TU
N MMR
CLC
R
IA
CAM
OL
ID
N
TU
R
PAN
LBN
AR
ALB
G
VC
J
T
ISR
BR
A
AZE
SU
KAZ
R
C
BH
HRT
S
LS
AR
M
U
Y
LKA
C
H
NY
TTO
GU
C
YP
MD
A
KW
MU
ISL
PR
K
MKD
N
ZL
GEO
SW
TH
EUE
IR
SVK
LZE
U
POL
SA
ATG
MLT
LTU
EST
LVA
N
OR
AU
C
BLR
R
S
SA
MN
H
U
C
GBR
U
AN
R
OM
BGR
FR
C
U
A
BR
BN
BIH
D
N
K
N
BEL
H
LD
RKR
V
C
H
KOR
PR
TB
JFIN
PN
D
AU
SVN
EU
TE
GR
C
ESP
ITA
8
8
8
YEM
OMN
0
50
100 150
IMR
200
250
0
50
100 150
IMR
FIGURE 8
Total Fertility Rate and Child Mortality Rate
0
100
200
300
400
500
4
TFR
6
8
YEM
OMN R W A
LBY C IV AFG
MW
I ER
KENZMB
BD
I N
SEN
ETH
JSYR
OR
SAU
TGO
AGO
C
OM
BFA
GA
ZWSD
MD
VU
SLE
MLI
BEN
LBR
TC
D
DE
ZA
SSD
NBTN
N
GAGIN
SLB
SW
Z
TZA
MR
ZAR
IR
Q
GH
AGIGMB
MD
NDDPAK
MOZ
NIR
AM
JCER
ILAO
MR
T
CSTP
PV
BGD
H
N
GN
B
BW
AIC
MN
G
C
GTM
OG
N
H
TI
C
AF
BLZ
QAT
N
PL
PN
GAB
MAR
G
KH
M
TJ
K
VU
T
LSO
TON
KW
T
BOL
ARPR
E
EGY
TU
N
Y
PH
LMMR
SLV
U
ZB
KIR
VN
M
PER
TKM
BH
R
ZAF
EC
U IN D
LCMEX
AD
OM
ID
TUNR
VEN
BR
KGZ
LBN
VC
C
T A
IOL
PAN
JFJ
AM
ALB
GU
Y
CMYS
R
I
LKA
THGA
AR
TTO
ISR
IR
L
BH
SH
KAZ
KOR
UMD
R
YN
C
CMU
H
LS
ISL
R
OM
AR
C
YP
GEO
POL
PR
T
GR
C AM
ESP
N
BLR
BGR
ZL
BR
EST
B
MLT
LTU
U
KR
GBR
H
U
AU
C
R
U
S
U
BNS
FR
LVA
A
U
SA
J
C
PN
AN
N
OR
SW
BEL
E
ITA
AU
T
FIN
N
LD
D
C
N
H
K
E
D EU
0
2
8
4
2
0
0
2
4
ISL
N ZLBR B
C
AN
IR
L
U
SA
MLT
AU
S
N
LD
POL
U
R PR
Y T
N
FR
OR
ESP
A
FIN
GBR
AU
D
N
KT
CBEL
H
ITA
GR
SW
EEUC
H
N
J PN
WA
KEN R C
J OR
LBY
AFG
DIV
ZA
SYR
ENZMB
NMW
ER I
IR
QMD
SEN
G
OMN
BD
I VYEM
DGA
KWZW
TH
U
C
ETH
GH
AMD
QAT
SLB
CSW
PVBGD
SD
N
IC
N
ZTGO
SSD
MR
TOM
TZA
MEX
MAR
LBR
SLEMLI
ER
IBEN
BTN
BW
A
AR
E
PAK
BFA
BOL
MOZ
TC
D
BH
R
STP
N GA
VN
NVU
AM
IR
N
TU
N
BLZ
PER
PH
LM
C
T
OG
GTM
SLV
C
MR
ZAR
D
OM
GIN
PN
G
EC
U
LC
A
GMB
KIR
GN
B
VC
T
N
PL
TON
MMR
C EGY
AF
LSO
H
TI
PR
Y
C
TH
OL
A
TU
R
IN
D
JVEN
AMC H N
ID N
PAN
GU
Y
C RKGZ
IBR A
LBN
MYS
KOR
FJ
I
LKA
BH LS
MU
IRCTTO
LUC
BH S
N ZL
BR
BYTG
AR
PR
UR
ROM
AU
S
ESP
ISL
LD
FR
ATC
N
OR
U
SA
LTU
GR
ITA
AU
C
BEL
AN
POL
BGR
JGBR
PN
C
H
EN
D
MLT
EU
R
U
S
D
N
K
SW
E
FIN
TFR
6
TFR
6
8
RWA
KEN
J OR
AFG
D
OM
LBY
SYR
HIC
NDDZA
N
C IV OMN
YEM
T
VU
T
ZW
E
PHKW
L
BH
R
ZMB
UMAR
GA
BD
IBOL
SEN
AR
E
C
MEX
OL
TZA
MR
T SSD
SLV
GH
ATGO
SW
Z
BGD
MLI
SD
EC
N
U
EGY
VEN
BW
APER
PAK
GTM
PR
Y
FJMYS
IKOR
SLB
LBR
VNTH
M
HBEN
TI
TU
R
BFA
PN
IR
BR
AQG
MU
S
A
GIN
SLE
N
PL
PAN
C
OG
IN
LSO
CD
AF
GN
B GMB
LBN
GU
Y
ID
N
C
MR
C
H
L
LKA
J AM
TTO
10
1980
10
1970
10
1960
0
100
300
400
500
0
100
200
400
500
200 300 400
under5_mort
500
300
10
2013
10
2000
10
1990
200
AFG
N ER
SOM
TC
D
TMP
ZAR
BD
I AGO
U
GA
MLI
BFA
YEMETH
MW
I
SSD
N
GA
ZMB
BEN
ERGMB
ITZA
GIN
LBR
R
W
GN
BA
GN
MOZ
Q SLE
C
MR
MD
SEN
G
N
C
AF
MR
C
T
IV
CSD
OM
TGO
C OG
KEN
IR
QSTP
GTM
SLB
GH
GAB
W
SM
PN
G
D
PAK
JTIIAZ
VU
TBOL
FSM
H
TON
SW
LAO
N
ZW
PL
LSO
JH
OR
N
AM
SAU
N
SYR
TJ
KE
KIR
PH
LD
KH
M
OMN
C
PV
PR
YBW
BLZ
BTN
EGY
N
MD
IC
V
QAT
IN DA
BGD
FJ
IT
LBY
EC
U
ISR
SLV
PER
D
OM
KW
ZAF
MYS
VEN
PAN
TKM
BH
R
SU
R
MAR
MEX
AR
C
OL
E
JALB
AM
GR
GU
U
DMMR
ZB
Y
D
ZA
ID
N
AR
TU
G
R
BR
C
R
KGZ
NA
IT
VC
BR
A
ATG
LC
LKA
U
LBN
R
Y
IR
N
MN
G
C
H
L
ISL
BH
TU
S
N
U
SA
AZE
N
MU
ZL
VN
PR
S
MK
FR
IR
LKAZ
A
N
OR
MN
E
D
AU
BR
TTO
N
S
K
B
FIN
LD
C
MLT
YP
MKD
TH
AR
A
M
GBR
U
GEO
B
PR
MD
T
SW
E
C
H
AN
C
H
KOR
D
H
BIH
LTU
EU
R
V
JBEL
AU
POL
EST
PN
T
UKR
NSAN
SVK
BLR
R
OM
GR
C
ITA
SVN
LVA
BGR
ESP
R
U
C
ZE
U
TFR
6
4
TFR
6
4
0
100
200 300 400
under5_mort
500
0
0
2
TFR
6
4
2
0
N ER
MLI
SOM
TC D
BD
I GA
N
ZAR
UGMB
GA
AGO
ZMB
BFA
MW
I
TZA
TMP
MOZ
C
OG
SEN
SSD
GN
B
AFG
GIN
C
IV
BEN
GN
LBR
C
MR
ER
C
IOM
MR
TQSLE
TGO
R
ETH
W
ANC
MD
G
SD
KEN
AF
W
SM
YEM
STP
GAB
SLB
IRTJ
QG
KH
TON
GTM
PN
G AE
ZW
J
I
VU
TD
SW
Z
FSM
J
OR
BOL
KGZ
PAK
H
TI
AM
ISR
PH
LAO
L
H
N
D
SYR
KIR
OMN
PR
KH
Y
MLSO
D
ZA
EGY
MAR
BLZ
SAU
KAZ
KW
BW
TPL
FJ
IV
EC
GU
U
YA
N
D
IC
OM
PAN
IN
D
MN
G
PER
VEN
ZAF
LBY
LKA
ID
TKM
C
C
N
OL
PV
MD
J
SU
AM
R
TU
BTN
N
U
ZB
GR
AR
MEX
SLV
BGD
G
D
ATG
BH
R
ISL
U
TU
R
Y
R
FR
IR
QAT
AZE
L
A
BR
VC
PR
N
T
K
N
MYS
ZL
AU
GBR
IR
N
MMR
SW
BH
LC
S
U
SA
N
OR
B
AR
C
BR
GEO
H
E
A
L
FIN
BEL
TTO
R
IS
ALB
D
AR
VN
LD
N
K
M
M
R
U
MN
C
HS
E
N
BLR
C
AN
SVN
LTU
R
H
OM
E
H
LBN
U
BGR
R
KR
V
YP
C
AU
LVA
MD
MU
ZE
U
T
B
A
JEST
ITA
MKD
MLT
PN
D
TH
EU
A
ESP
H
SVK
NS
POL
PR
GR
BIH
TC
KOR
2
AFG
I D N ER
SOM
TC
RBD
W
A
ETH
AGO
ZAR
U
GA
MLII
BFA
MW
SSD
BEN
GN
B
SEN
SLE
ER
IN
GA
LBR
C
MR
CMD
IVZMB
TGO
GGIN
MOZ
TZA
SD
LAO
N
GMB
MD
D
J
V
I
KEN
PAK
MR
T
IR
Q
GN
SLB
SAU
C
AFQ
SW
Z
BTN
KH
GH
M
A
GTM
C
OM
J
OR
H
TI
GAB
STP
C
OG
TMP
SYR
CN
PV
AM
ZW
TJ
EN
K PL
W
H
SM
N
DBOL
LBY
FSM
VU
T
LSO
IR
N
PN
G
D
NZA
IC
BW
A BGD
TON
KIR
PR
Y
BLZ
AR
EUEGY
TKM
PH
LMN G
ZB
MAR
QAT
SLV
IN D
GR
D
PER
EC
U
BH
R
KGZ
ZAF
VN
M
BR
N
MYS
VEN
D
OM
LC
FJ
A
IITU
MEX
TU
NMMR
C
R
C
OL
N
R
PAN
AR
ALB
GID
VC
JLBN
AM
T
ISR
BR
AZE
SU
KAZ
R
C
BH
H
S
L
AR
MA
UTTO
R
Y
LKA
CGU
H
NY
C
MD
YP
A
KW
T
MU
S
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K
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N
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TH
E
A
IR
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U
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EST
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N
OR
AU
C
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ZE
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H
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U
AN
R
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U
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R
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H
E
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D
AU
SVN
EU
TT
GR
C
ESP
ITA
8
8
8
YEM
OMN
0
100
200 300 400
under5_mort
15
500
0
100
FIGURE 9
Total Fertility Rate and Risk of Maternal Death
1990
2000
8
8
YEM
NERBDIAFG
SOM
ETH AGO TCD
ZAR
UGA MWI
MLIRWA
BEN BFA
GNBGIN
SEN
LBR ERI SSD SLE
NGA
ZMB
CMR
CIV
TGO
MDG
TZA
SDN
GMB LAOMOZ GNQ
DJI
MDV
KEN
PAK
MRT
IRQGTM
SAU
SLB
CAF
SWZ
BTN TMP
KHM
GHA
COM
JOR
HTI
GAB
STP
COG
SYR
NAM
TJ
KCPV
ZWE
NPL
HND
WSM
LBY
FSM
VUT
LSO
BOL
IRN
PNG
DZA
NIC
BWA
TON
KIR
PRY
BGD
BLZ
ARE
TKM
EGY
PHL
UZB
MNG
MAR
QAT
SLV
IND
GRD
PER
ECU
BHR
KGZ
ZAF
VNM
BRN
MYS
DOM
VEN
MMR
LCA
FJ
I IDN
TUN
MEX
CRI
COL
TUR
PAN
LBN
ARG
ALB
VCT
JAM
ISR
BRA
AZE
SUR
KAZ
BHS
CHL
ARM
URY
CHN
LKA
GUY
TTO
CYP
MDA
KWT
MUS
ISL
PRK
MKD
PRI
NZL
GEO
SWE
IRL
THA
USA
SVK
POL
MLT
LTU
EST
LVA
NOR
BLR
AUS
CZE
RUS
MNE
HUN
UKR
CAN
GBR
BGR
ROM
FIN
FRA
CUB
BRB
BIH
DNK
HRV
BEL
NLD
CHE
KOR
PRT
JPN
DEU
AUT
SVN
GRC
ESP
ITA
NER AFG
SOM
TMPMLI
BDI ZAR
UGA
AGO
BFA
ETH
YEMZMB
MWI
NGA
BEN
ERI
GIN
GMB
RWA
LBRSSD
GNB
GNQ
MOZ
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SEN
MDG
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CIVCMR CAF
COM
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COG
KEN
IRQ
GTM
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PNG
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HTI
TON
SWZ
LAO
NPL
ZWE
LSO
JOR
NAM
HND
SAU
SYR
TJ
KBOL
KIR
PHL
KHM
OMN
CPV
PRY
BTN
BLZ
BWA
EGY
MDV
QAT
NIC
BGD
FJ
I IND
LBY
ECU
ISR
SLV
PER
DOM
KWT
ZAF
TKM
MYS
PAN
VEN
BHR
SUR
MAR
MEX
ARE
COL
JAM
GUY
GRD
UZB
DZA
ARG
IDN
TUR
MMR
BRN
CRI
KGZ
ALB
VCT
BRA
LCA
LBN
URY
LKA
IRN
MNG
ISL
CHL
TUN
USA
BHS
PRI
NZL
MUS
AZE
VNM
PRK
IRL
FRA
NOR
MNE
KAZ
DNK
BRB
AUS
TTO
FIN
CYP
NLD
MLT
ARM
MKD
BEL
THA
GBR
CUB
GEO
MDA
PRT
SWE
CHE
CHN
CAN
KOR
HRV
BIH
POL
DEU
LTU
AUT
JPN
EST
HUN
SVK
BLR
ROM
GRC
ITA
SVN
BGR
LVA
ESP
RUS
CZE
UKR
TFR
4
6
2
TCD
SLE
0
0
2
TFR
4
6
OMN
0
5
10
15
0
TFR
4
6
NER
MLI SOM
TCD
BDI
NGA
ZAR
UGA
AGO
GMB
ZMB
BFA
MWI
TMP
TZA
MOZ
COG
SEN
GNB
GIN
SSD SLE
AFG
CIV
GNQ
BEN
CMR
LBR
COM
ERI
MRT
TGO
RWA
ETH
MDG
SDN
KEN CAF
WSM
GAB
STP
YEM
SLB
IRQ
GHA
TJ
K
TON
GTM
PNG
ZWE
VUT
DJI
SWZ
FSM
JOR
BOL
KGZ
PAK
HTI
PHL
NAM
ISR
LAO
LSO
HND
SYR
KIR
PRY
KHM
OMN
DZA
EGY
MAR
BLZ
SAU
KAZ
BWA
KWT
FJ
I
ECU
GUY
NIC
DOM
PAN
IND
MNG
PER
VEN
ZAF
LBY
LKA
TKM
IDN
NPL
CPV
COL
MDV
TUN
JAM
SUR
BTN
UZB
GRD
MEX
SLV
ARG
BGD
BHR
ISL
TUR
URY
FRA
IRL
QAT
AZE
BRN
VCT
PRK
MYS
NZL
MMR
AUS
GBR
SWE
IRN
LCA
BHS
NOR
USA
BRB
CHL
ARE
FIN
CRI
GEO
BRA
TTO
BEL
ALB
ARM
VNM
DNK
NLD
RUS
MNE
CHN
PRI
BLR
CAN
SVN
LTU
EST
ROM
HRV
BGR
CHE
UKR
LBN
CYP
CZE
MDA
CUB
MKD
JPN
LVA
ITA
MLT
AUT
MUS
THA
DEU
HUN
SVK
ESP
POL
GRC
PRT
BIH
KOR
0
0
2
TFR
4
6
15
2013
NER SOM
TCD
AFG
BDIZAR
AGO
UGA MLI
TMP
BFA
NGA
ZMB
MWI
GMB
ETH
SSD
MOZ
TZA
GIN
BEN
GNQ
ERI
GNB
LBR
SLE
YEM
RWA
CMR
SEN
COM
MRT
COG
SDN
MDG
TGO
CIV CAF
KEN
IRQ
STP
SLB
WSM
GHA
GTM
GAB
PNG
TON
DJI
ZWE
SWZ
VUT
FSM
PAK
HTI
JOR
BOL
TJ
K
NAM
LAO
LSO
PHL
HND
SYR
NPL
KIR
PRY
SAU
KHM
BLZ
EGY
BWA
OMN
CPV
FJ
I
ISR
ECU
NIC
IND
LBY
BTN
DOM
GUY
PAN
PER
ZAF
KWT
VEN
TKM
BGD
QAT
SUR
MDV
DZA
COL
KGZ
IDN
JAM
BHR
MEX
SLV
MAR
UZB
GRD
LKA
ARG
TUR
KAZ
MYS
MNG
BRN
VCT
ARE
URY
MMR
CRI
BRA
ISL
USA
LCA
TUN
PRK
AZE
NZL
FRA
CHL
ALB
IRN
MUS
VNM
IRL
BHS
NOR
FIN
DNK
AUS
BRB
SWE
LBN
PRI
TTO
MNE
GBR
BEL
ARM
NLD
GEO
CHN
CUB
CYP
CAN
THA
MKD
HRV
EST
MDA
CHE
PRT
AUT
LVA
MLT
ROM
DEU
ITA
GRC
BGR
ESP
HUN
LTU
CZE
RUS
JPN
SVK
SVN
POL
BLR
UKR
BIH
KOR
2
10
8
8
2005
5
0
5
10
Risk of maternal death
15
0
5
10
Risk of maternal death
15
Notes: Figures 7, 8, and 9 show the scatter plots and smoothed polynomial relationship
between TFR and infant mortality, child mortality and the risk of maternal death at the
start of each decade (data for risk of maternal death is only available from 1990). Infant
and child mortality are calculated as the number of infant (less than 1 year old) or child
(less than 5 years-old) deaths per 1,000 lives births. The lifetime risk of maternal death is
the probability that a 15-year-old female will eventually die from a maternal cause
assuming that current levels of fertility and mortality (including maternal mortality) do
not change in the future, taking into account competing causes of death.
The second piece of evidence suggesting that the reduction in fertility rates was not driven simply
by a decline in mortality rates concerns the number of wanted children. The WDI reports on “wanted
fertility”, meaning the average number of children reportedly desired by a woman in the survey. The
data are available for di¤erent countries at di¤erent years. The number of wanted children has declined
over time. Remarkably the “unwanted fertility”, that is, the di¤erence between actual TFR and wanted
fertility is almost invariably positive and positively correlated with actual TFR. This means that in
countries with high fertility rates, people are not achieving their target: indeed, countries systematically
err on the positive side, meaning that they always have more than the desired number of children.
To some extent, this suggests that there is insu¢ cient access to or use of contraception. Figure 10
illustrates this relationship.
16
FIGURE 10
-2
-1
Unwanted fertility
0
1
2
3
Actual vs. Unwanted Fertility
0
2
4
6
Total f ertility
8
10
Notes: The …gure plots TFR against unwanted fertility. TFR and wanted fertility are from
the WDI database and unwanted fertility is calculated as the di¤erence between TFR
and wanted fertility. The WDI publishes wanted fertility obtained from Demographic and
Health Surveys from 90 developing countries.
Taken together, these two additional pieces of evidence suggest that the TFR is (or was) high,
but not necessarily because of unconstrained rational decision making. In a rational setting it is hard
to reconcile i) why increased risk of maternal death would increase fertility rates (unless of course,
one was willing to argue that the infant mortality rate causes high fertility rates, but that maternal
mortality rates are instead a consequence of high fertility rates); and ii) why people systematically
have more than the desired number desired of children (e.g., the error is always one-sided). As already
states, the latter suggests that there is insu¢ cient access to or use of contraception.
In the next section, we discuss how the global e¤ort to reduce fertility rates surged in response
to the lower overall mortality rates and took as part of its mission the goal of decreasing “wanted
fertility”by establishing a small family as a new ideal.
17
IV
The Global Family Planning Movement and its Consequences
The following section provides a brief overview of the global family planning program, discussing the
historical context as well as outlining some of its characteristics. We then examine more systematically
the link between fertility policy adoption and declines in fertility.10
A
Evolution of the Global Family Planning Program
After World War II, there was growing preoccupation with the unprecedented levels of population
growth observed in most of the developing world due to the combined e¤ect of declining mortality
rates and high fertility rates. The problem was identi…ed early on in several of the world’s most populous nations such as India (the …rst country to introduce a national population policy) and Egypt,
though a prevalent belief among most developing nations held that larger populations translated into
having greater political power.11 A growing concern about the population explosion in developing
countries was particularly notable in the United States. A neo-Malthusian population-control movement developed, led by, among others, John D. Rockefeller III, whose main preoccupations were both
the growing imbalance between population and resource growth, and the potential for political instability as most of the population growth was concentrated in the poorest countries of the world. In
1952, Rockefeller founded the Population Council, aimed at providing research and technical assistance
for population programs across the world. The same year, India started the …rst national population
program and, in parallel, the International Planned Parenthood Federation was established.12 Private
foundations including the Rockefeller and Ford Foundations, provided seed funding for research and
planning programs, but it was in the mid-1960s when large-scale funding became available and the
planning movement really took o¤. The …rst large-scale intervention was carried out by the Swedish
government, which supported family planning e¤orts in Sri Lanka (then Ceylon), India, and Pakistan,
starting in 1962 (Sinding 2007).13
10
This and the following section draw heavily on Robinson and Ross (2007) who provide a compilation of case studies
for 22 countries across the world on their family planning programs.
11
This sentiment was observed in countries including Turkey, Indonesia, and Egypt in the 1950s and 1960s.
12
An early birth-control movement led by feminist Margaret Sanger in the United States (who set up the …rst birthcontrol clinic in the USA in 1916) and Elise Ottesen-Jensen in Sweden was another force leading to the e¤orts for fertility
reduction This movement initially had a di¤erent focus: its goal was to promote individual control over fertility rather
than an explicit population policy to avert explosive global population growth.
13
Over time, several international organizations, like USAID and the World Bank, joined in providing funds and
support for family planning programs around the world.
18
By the 1960s, there was a strong consensus in development policy circles that curbing population
growth was a high priority. Funding agencies began to be actively involved in providing …nancial and
technical assistance for population programs in developing countries. The invention of the modern intrauterine device (IUD) and the oral contraceptive pill around the same time allowed for the possibility
of easy-to-use and e¤ective contraceptive methods becoming widely available for public use.
These early family planning e¤orts showed rapid success in East Asian countries, with Hong Kong,
South Korea, Singapore, and Thailand leading the rankings. Program implementation and success
would take longer in other developing countries partly due to the di¢ culty of overcoming cultural
inhibitions and religious opposition towards birth control, as well as operational problems including
inadequate transport infrastructure and insu¢ cient funding. However, the World Population Conference in 1974 appeared to be a turning point for the global family planning movement. In 1976, 93
governments were providing direct support for family planning (some governments provided support
for family planning for other than demographic reasons), while explicit policies to limit fertility were
introduced in 40 countries (data on number of countries by policy comes from the UN World Population Policy database).14 Between 1976 and 2013, 114 countries adopted policies to reduce fertility
rates. The number of countries with policies to reduce fertility rates in a given year increased over
the decades, with some countries eventually needing to reverse course in order to keep their population stable. This is clearly illustrated by the fact that at present, the number of countries wishing
to maintain their level of fertility, or even raise it, is increasing, as birth rates have fallen below the
replacement fertility rates. Together with this trend, the number of countries with state support for
family planning has also continued to rise steadily (see Tables 3 and 4).15
14
For instance, in Latin America, the adverse e¤ects of illegal abortions was the key rationale for establishing family
planning programmes.
15
Note that while Table 3 refers to the number of countries by type of support for family planning by the government,
it does not necessarily include the countries with private sector involvement in the provision of family planning services.
19
TABLE 3
Number of Countries by Fertility Policy Goals
Year
Lower
Maintain
No
Raise
Total
Fertility
Fertility
Intervention
Fertility
1976
40
19
78
13
150
1986
54
16
75
19
164
1996
82
19
65
27
193
2005
78
31
47
38
194
2013
84
33
26
54
197
Notes: The table shows the number of countries by fertility policy implemented.
The data is obtained from the U.N. World Population Policies database and begins
in 1976. Countries are categorized according to whether they had a policy to
lower, maintain or raise fertility or if they had no intervention to change fertility.
TABLE 4
Number of Countries by Government Support for Family Planning
Year
Direct
Indirect
No
Limit
Not
Total
support
support
support
1976
95
17
28
10
0
150
1986
117
22
18
7
0
164
1996
143
18
26
2
0
193
2005
143
35
15
1
0
194
2013
160
20
16
0
1
197
permitted
Notes: The table shows the number of countries by the type of support extended by the
state for family planning services. The data is obtained from the U.N. World Population
Policies database and begins from 1976. Countries are categorized by whether their
governments directly supported, indirectly supported or did not support family planning
as well as if the government limited family planning services or did not permit family
planning in the country.
20
In 1976, the countries that had policies to reduce fertility covered nearly one-third of East Asian
countries, a quarter of Latin American and Caribbean countries and nearly two-thirds of South Asian
countries. By contrast, only a …fth of countries in North Africa, the Middle East, and Sub-Saharan
Africa had a fertility reduction policy in 1976. By 2000, 88 countries had implemented a fertility
reduction policy at some point (by this tiem, some of them had reached their fertility reduction
targets and changed to policies of maintaining fertility rates) including half of the countries in East
Asia and Latin America, and more than two-thirds of the countries in Sub-Saharan Africa and South
Asia. These countries represent 70 percent of the world’s population.
B
Features of Family Planning Programs
The early phases of planning programs in most developing countries typically addressed the technological side of the population problem by attempting to provide a range of contraception methods (oral
contraceptives, IUD, condoms, sterilization, and abortion) and information on their use. Increases in
the supply of contraceptives soon proved insu¢ cient to lower fertility rates, particularly in poorer or
more traditional societies. This failure led to concerted e¤orts to change public attitudes and beliefs
and establish a new small-family norm through active mass-media campaigns. We discuss these two
faces in turn.
The implementation of the family planning programs varied vastly across countries. Di¤erences
included the price at which contraception was o¤ered (public versus commercial provision, subsidies to
production or sales, and so on.), the delivery system through which services were provided, the outlets
for the mass-media campaigns, and the supplementary policies that accompanied the core measures
(Freedman and Berelson 1976).
Most countries began their family planning programs with a clinic-based approach that took advantage of the existing health infrastructure to provide modern contraceptive methods. This approach
was supplemented by the deployment of trained …eld workers who would make house calls, particularly
in rural areas. Many countries also implemented postpartum programs in hospitals, to advise women
on the use of contraception, often after giving birth or undergoing an induced abortion. However, this
approach had limited success in regions where a large proportion of women gave birth outside of the
formal health care system.16 In some nations such as Iran and Malaysia, family-planning programs
were linked to maternal and child health services at an early stage, which allowed for better integrating
16
In fact, this was the case in countries like India and Iran.
21
the program into the country’s health system.17
Many of the programs established in the 1950s and 1960s just focused on enhancing service provision, but it became apparent that this approach was insu¢ cient, particularly in countries where
populations were very conservative or mostly uneducated and poor. For instance, countries with a
predominantly Catholic or Muslim population had di¢ culty gaining wide acceptance for their programs, so planners had to work on achieving a balance between these cultural factors and their policy
targets. Indonesia o¤ers a good example of a program that worked around this issue. Early on, the
program published a pamphlet titled “Views of Religions on Family Planning” that documented the
general acceptance of family planning by four of Indonesia’s …ve o¢ cial religions— Islam, Hinduism,
and Protestant and Catholic Christianity— to illustrate that family planning did not go against religious beliefs. To overcome fears that husbands would resist male doctors or health professionals
working with their wives, the family planning program In Bangladesh relied heavily on female health
workers to visit women in their homes to educate them about and supply them with contraceptive
methods. This modality ensured a greater di¤usion of contraceptive knowledge and methods in rural
Bangladesh.
Mass communication was commonly used to educate the population on family planning, and most
important, to change public viewes by establishing a small-family norm. Most countries used television, radio, and print media to publicize and promote their programs. In India, the family planning
program’s slogan, “Have only two or three children, that’s enough,”was widely publicized on billboards
and even on the sides of buildings. Other slogans in India were “A small family is a happy family”and
“Big family: problems all the way; small family: happiness all the way” (Khanna 2009). Bangladesh
publicized the slogans “Boy or girl, two children are enough”and “One child is ideal, two children are
enough” (Begum 1983). South Korea run the slogan “Stop at two, regardless of sex” (Kim and Ross
2007). Hong Kong chose “Two is enough” (Fan 2007), and so on. China took population planning
to the extreme in 1979, when it imposed a coercive one-child policy. But the Chinese TFR actually
started falling signi…cantly before the one-child policy was implemented. Indeed, the sharp decline
started after 1973, with mass-media messages such as “Later, longer, fewer” (Tien 1980) and “One
is not too few, two, just right, and three, too many” (Chang, Lee, McKibben, Poston and Walther,
2005). In Singapore, bumper stickers, coasters, calendars and key chains reinforcing the family planning message were distributed free of charge; in Bangladesh, a television drama to highlight the value
of family planning outreach work was aired (Piotrow and Kincaid, 2000). The Indonesian program is
17
Towards the 1990s, with the rebranding of family planning as sexual and reproductive wellbeing, more countries
followed this approach.
22
particularly noteworthy in its collaboration between the government and community groups in getting
the messages of the program across. In Latin America, the Population Media Centre (PMC), a non
pro…t organization, collaborates with a social marketing organization in Brazil to ensure the inclusion
of social and health themes in soap operas airing on TV Globo, the most popular television network
in Brazil. (TV Globo’s programming is estimated to currently reach 98 percent of Brazil’s population,
and 65 percent of all of Spanish-speaking Latin America.) PMC studied how programs like “Paginas
da Vida” (Pages of Life) in‡uenced Brazilians: about two-thirds of women interviewed said “Paginas da Vida” had helped them take steps to prevent unwanted pregnancy. Brazil’s telenovelas have
been popular across Latin America since the 1980s; they tend to depict the lives of characters from
invariably small families, who were also very rich and glamorous.18
Stronger inducements such as monetary or in-kind incentives and disincentives were also used in
some countries as means of encouraging families to practice birth control. In Tunisia, for example,
government family allowances were limited to the …rst four children; in Singapore, income tax relief
was restricted to the …rst three children as was maternity leave, the allocation of public apartments,
and preferred school places. Incentives for female or male sterilization was a common feature of family
planning programs in India, Bangladesh, and Sri Lanka and resulted in a large number of sterilizations
taking place during the 1970s. In Bangladesh, …eld health workers were paid for accompanying an
individual to a sterilization procedure, while in Sri Lanka and India both the sterilization provider and
patient were given compensation. In Kerala, India, individuals undergoing serilization were given a
payment equal to a week’s worth of food for their entire family and entered into a ra- e to win about
a month’s of income of a typical person. This type of incentivized compensation scheme, combined
with increased regional sterilization targets, led to a drastic increase in sterilization procedures.19
In addition to increased provision of information on and access to family planning methods, attempts were also made to delay marriage and childbearing or to increase birth spacing as a means of
controlling fertility. For example, the legal age of marriage was increased to 18 years for women and
21 years for men in India, and to 17 years for women and 20 years for men in Tunisia. China raised
the legal age for marriage in urban areas— to 25 years for women and 28 years for men— and rural
areas— 23 years for women and 25 years for men. China also imposed a minimum gap of three to
18
The main force behind the anti-natalist movement in Brazil was BEMFAM, an a¢ liate of the International Planned
Parenthood Federation. The military regime of the 70s, and the Church hierarchy were opposed to birth control,
though the local Clergy and multiple non govermental organizations advised and informed in favour of contraceptive
use. Telenovelas were arguably a good counterbalance to the religious and military opposition. In other Latin American
countries, such as Colombia and Chile, family planning had strong support from the government.
19
Critics alleged that many acceptors were coerced by o¢ cials who stood to gain from higher numbers, both in
monetary and political terms.
23
four years between births and restricted the number of children to three per couple until it decided to
accelerate the decline even more aggressively by implementing the draconian one-child policy in the
1980s.
More recently, likely as a result of the sizable decline in birth rates, fertility control has been
put on the back burner. In fact, the current HIV/AIDS epidemics have somewhat overshadowed
fertility control, particularly in African countries, while family planning did not even warrant being
a sub-goal in the Millennium Development Goals agreed to in 2000. Many countries are also now
below replacement-level fertility rates and more are attempting to raise fertility rates to avoid the
consequences of large aging populations. Nonetheless, it appears that family planning programs have
been incorporated into the broader framework of sexual and reproductive health services and become
…rmly entrenched in health care systems around the world.
A natural question of course is whether the type of less coercive intervention carried out by most
countries can be e¤ective in helping to rapidly change norms and in overcoming other socioeconomic
in‡uences that a¤ect fertility rates. Recent experimental (or quasi-experimental) studies suggest that
this may indeed be the case. La Ferrara, Chong, and Duryea (2012) …nd that Brazilian regions covered
by a television network showing soap operas that portray small families experienced a bigger reduction
in fertility rates. Bandiera, Buehren, Burgess, Goldstein, Gulesci, Rasul, and Sulaiman (2014) …nd
that in Uganda, adolescent girls who received information on sex, reproduction, and marriage reported
wanting a smaller number of children. Evidence of family planning programs in the United States appears to be more mixed, though recently Bailey (2013) has shown that a U.S. family planning program
signi…cantly reduced fertility. In the next section we explore the question using cross-country data on
spending and implementation e¤ort of the program and their relationship with fertility reduction.
C
Fertility Policies and the Decline in Fertility Rates
It is challenging to assess the quantitative e¤ect of the fertility programs on the basis of cross-country
data, as clearly there are a number of omitted variables that could blur the estimation of a causal
e¤ect. The task is particularly di¢ cult since di¤erent countries opted for a wide and varied range
of policies, with the speci…c choice of measures partly dictated by their feasibility in each country’s
institutional and cultural setting.
The following section includes a descriptive analysis of the relationships between fertility rates,
population policy, funds for family planning, and family planning program e¤orts across countries.
Fertility rates are obtained as before from the World Bank’s WDI. Data on the existence of a fertility
24
policy and government support for family planning come from the U.N. World Population Policies
Database. Data on funds for family planning are taken from Nortman and Hofstatter (1978), Nortman
(1982), and Ross, Mauldin and Miller (1993) which, taken together, cover funding for family planning
by source for 58 countries over various years starting in 1972 and going up to 1992. Finally, family
planning program e¤ort is measured using the Family Planning Program E¤ort Index published in
Ross and Stover (2001); this indicator, based on work by Lapham and Mauldin (1984), measures the
strength of a given country’s program on four dimensions (policies, services, evaluation, and method
access). The score has a potential range of 0–300 points, based on 1–10 points for each of 30 items,
and has been calculated for 1972, 1982, 1989, 1994, and 1999.
Comparing the trends in mean TFR by the fertility policy observed in 1976 paints a striking picture
(see Figure 11).20 While fertility has fallen in all regions (even in the group of predominantly European
countries that wanted to increase fertility!), the countries that had identi…ed the need to reduce fertility
in 1976 recorded by far the highest average fertility rates before 1976 but the second-lowest average
fertility rates by 2013. The countries where there was no intervention had the second-highest average
fertility rates in 1976 and became the highest fertility group by 2013.
20
The data on fertility policy begins from 1976 but there were several countries that had already adopted fertility
reduction policies beforehand.
25
FIGURE 11
2
3
average TFR
4
5
6
Evolution of Fertility Rates by Policy in 1976
1960
1970
1980
1990
Year
Lower
Raise
2000
2010
Maintain
No interv ention
Notes: The …gure illustrates the evolution of weighted average TFR, with countries grouped by
the fertility policy observed in 1976. The policy could be to lower, maintain, or raise fertility;
there also could be no intervention.
We use funding data for family planning and family planning program e¤ort as measures of the
inputs into programs around the world. Table 5 reports the amount of funds (in real terms) available
for family planning by source over the 1970s and 1980s for each country. Latin American countries
appear to have the largest amount of per capita funds, exceeding US$ 2 per capita (in 2005 U.S. dollars)
of total funding in Costa Rica, El Salvador, and Puerto Rico. (The per capita …gures in Table 5 are
expressed in terms of 2005 U.S. dollar cents). The region also has the highest proportion of nonstate
funding for family planning, more than double the state-funding in some countries. By contrast, in
Asia, the funding is predominantly state-led. As a percentage of GDP, total funds for family planning
averaged at around 0.05 percent in the 1970s and 0.07 percent in the 1980s, but was as high as 0.47
percent in Bangladesh and 0.46 in Korea in the 1980s.
26
TABLE 5
Funds for family planning by country
Country
Total per capita
Government per capita
Nongoven per capita
Total funds as a
funds (in U.S. cents)
funds (in U.S. cents)
funds (in U.S. cents)
% of GDP
1970s
1980s
1970s
1980s
1970s
1980s
1970s
1980s
Afghanistan
n.a
2.56
n.a
0.00
n.a
2.56
n.a
n.a
Bangladesh
41.02
186.56
16.39
36.24
24.63
150.32
0.07
0.47
Hong Kong, China
54.65
66.00
26.74
48.42
27.91
17.57
0.01
0.00
India
68.42
99.55
64.10
89.67
4.32
9.88
0.08
0.16
Indonesia
74.75
101.37
39.52
71.38
35.23
29.99
0.09
0.11
Korea, Rep.
108.63
147.06
85.32
132.12
23.32
14.94
0.04
0.46
Malaysia
165.63
105.86
102.10
95.60
63.53
10.26
0.04
0.03
Mongolia
n.a
6.60
n.a
n.a
n.a
6.60
n.a
0.00
Nepal
28.06
35.94
15.67
27.93
12.40
8.02
0.07
0.12
Pakistan
76.01
41.58
32.21
18.07
43.79
23.51
0.13
0.07
Philippines
145.58
62.43
79.85
37.85
65.73
24.58
0.11
0.05
Singapore
134.12
97.74
132.62
97.38
1.50
0.36
0.01
0.01
Sri Lanka
16.11
16.68
n.a
11.76
n.a
4.92
0.02
0.02
Taiwan
50.88
89.44
46.52
89.35
4.36
0.10
n.a
n.a
Thailand
44.54
42.87
11.33
26.70
33.21
16.17
0.03
0.03
Vietnam
n.a
n.a
n.a
5.81
n.a
n.a
n.a
n.a
Asia
Latin America and Caribbean
Bolivia
13.20
n.a
0.96
n.a
12.25
n.a
0.01
n.a
Brazil
n.a
8.70
2.28
0.00
n.a
8.70
n.a
n.a
Colombia
59.18
47.40
n.a
23.70
n.a
23.70
0.02
0.02
Costa Rica
184.92
203.73
52.57
132.81
132.35
70.92
0.05
0.06
Dominican Rep.
91.42
n.a
43.28
n.a
48.15
n.a
0.04
n.a
El Salvador
300.66
324.76
237.06
235.47
63.60
89.29
0.15
0.22
Honduras
n.a
125.80
n.a
0.00
n.a
125.80
n.a
0.08
Nicaragua
n.a
n.a
n.a
204.57
n.a
n.a
n.a
n.a
Panama
n.a
59.59
n.a
14.29
n.a
45.30
n.a
0.01
27
TABLE 5 (contd.)
Country
Total per capita
Government per capita
Nongovern per capita
Total funds as a
funds (in U.S. cents)
funds (in U.S. cents)
funds (in U.S. cents)
% of GDP
1970s
1980s
1970s
1980s
1970s
1980s
1970s
1980s
897.43
n.a
390.17
n.a
507.26
n.a
0.09
n.a
Trinidad and Tobago
n.a
n.a
n.a
26.51
n.a
n.a
n.a
n.a
Venezuela
n.a
n.a
123.35
1.50
n.a
n.a
n.a
n.a
Puerto Rico
North Africa and Middle East
Egypt
16.33
n.a
1.81
11.96
14.51
n.a
0.01
n.a
Iran
248.01
n.a
243.34
0.07
4.67
n.a
0.05
n.a
Iraq
n.a
3.26
n.a
2.25
n.a
1.02
n.a
0.00
Jordan
n.a
61.82
n.a
21.45
n.a
40.37
n.a
0.02
Morocco
n.a
55.53
n.a
45.49
n.a
10.05
n.a
0.03
Tunisia
124.05
130.23
36.10
73.57
87.96
56.66
0.05
0.06
Turkey
23.03
23.58
21.81
20.51
1.22
3.06
0.01
0.01
Botswana
n.a
15.40
n.a
7.48
n.a
7.93
n.a
0.01
Burkina Faso
n.a
23.93
n.a
6.70
n.a
17.23
n.a
0.05
Central African Rep.
n.a
35.21
n.a
16.93
n.a
18.28
n.a
0.05
Congo, Rep.
n.a
n.a
n.a
0.37
n.a
n.a
n.a
n.a
Ethiopia
n.a
6.66
n.a
n.a
n.a
n.a
n.a
0.02
Ghana
49.70
n.a
40.64
n.a
9.06
n.a
0.04
n.a
Guinea
n.a
15.24
n.a
0.71
n.a
14.53
n.a
0.02
Kenya
n.a
43.36
n.a
12.25
n.a
31.11
n.a
0.07
Liberia
n.a
48.34
n.a
n.a
n.a
n.a
n.a
0.08
Madagascar
n.a
3.78
n.a
1.46
n.a
2.32
n.a
0.01
Mauritania
n.a
29.51
n.a
0.76
n.a
28.75
n.a
0.04
356.05
385.87
180.29
244.30
175.76
141.58
0.11
0.12
Nigeria
n.a
9.39
n.a
n.a
n.a
n.a
n.a
0.02
Rwanda
n.a
55.90
n.a
29.90
n.a
25.99
n.a
0.10
Somalia
n.a
2.00
n.a
n.a
n.a
n.a
n.a
0.01
Tanzania
7.52
n.a
0.35
n.a
7.17
n.a
n.a
n.a
Sub-Saharan Africa
Mauritius
28
TABLE 5 (contd.)
Country
Total per capita
Government per capita
Nongovern per capita
Total funds as a
funds (in U.S. cents)
funds (in U.S. cents)
funds (in U.S. cents)
% of GDP
1970s
1980s
1970s
1980s
1970s
1980s
1970s
1980s
Uganda
5.63
n.a
n.a
n.a
n.a
n.a
0.01
n.a
Zambia
n.a
23.26
n.a
3.53
n.a
19.73
n.a
0.03
51.70
142.60
45.47
100.50
6.23
42.10
0.02
0.10
Zimbabwe
Notes: The table reports the total funds for family planning per capita and per capita funds for family
planning by source: government or nongovernment for the 1970s and 1980s. (We compute averages
for the two decades as di¤erent countries have data for di¤erent years.) Averages for the 1970s and
1980s are computed in constant 2005 U.S.$cents for comparability. The …nal two columns report the total
funds for family planning as a percentage of GDP (both in nominal terms) averaged for the 1970s and
1980s. Data on funding for family planning are taken from Nortman and Hofstatter (1978), Nortman
(1982), and Ross, Mauldin, and Miller (1993), while data on the price index (for conversion to real terms)
and nominal GDP are from the World Development Indicators.
We next examine the relationship between (logged) total funds for family planning per capita (in
constant 2005 US dollars) and the percentage reduction in total fertility rate over the 1960–2013 period
(see Figure 12). Despite the small number of observations available (27 for the 1970s and 40 for the
1980s), there is a clear positive relationship, indicating that the countries with more funding for family
planning experienced greater reductions in fertility rates. Regressions of the percentage decline in
fertility on the total funding for family planning per capita for the two periods show highly signi…cant
positive coe¢ cients with R-square values over 30 percent (see Table 6). Quantitatively, the results
indicate that a 1 percent increase in funding per capita in the 1970s is associated with an 8.3 percent
reduction in the total fertility rate.
29
FIGURE 12
Percentage reduction in fertility and funds for family planning
1980s
KO R
HKG SG P
THA
CRI MUS
IRN
MYS
TUN
TURBGCO
D DO
L M SLV
80
80
1970s
BRA
60
MAR
BWA NPL
HND
IDN
PAN
JOR
LKA
IND
PHL
PAK
% decline in TFR
40
60
% decline in TFR
40
PAK
ZWE
G HA
ZWE
RWA
KEN
MDG
AF
G
IRQ
ET H
MRT
CAF
LBR
20
TZ A
20
TUN
TURCO L MYS
BG D
SLV
MNG
PRI
NPL
IDN PHL
EG Y
LKA
IND
BO L
KO R
HKG
SG P
THA
CRI MUS
G IN
ZMB
UG A
BF A
SO M
0
0
NG A
0
2
4
6
log of total funds per capita
% decline in TFR
8
0
Fitted values
2
4
6
log of total funds per capita
% decline in TFR
8
Fitted values
Notes: The …gure shows the scatter plot and linear …t of the percentage decline in TFR from 1960 to
2013 and the log of total per capita funds (in constant 2005 US cents) for family planning for the
1970s and 1980s. Total funds are converted to 2005 US$ before averaging for each decade. Data on
TFR and consumer price index for the USA (used to convert the funds to real terms) are from the WDI
and the data on funds for family planning are from Nortman and Hofstatter (1978), Nortman (1982)
and Ross, Mauldin and Miller (1993).
30
TABLE 6
Decline in Fertility Rates and Funding for Family Planning Programs
% change in TFR (1960–2013)
Regressor
1973
1989
1970s
1980s
Log of total funds for family planning
7.35*
8.88**
(per capita)
(2.85)
(1.82)
Log of average funds for family planning
8.29**
9.35**
(per capita)
(2.66)
(1.73)
Constant
R2
Observations
32.82*
21.23*
27.03*
18.28*
(12.53)
(8.66)
(12.09)
(7.98)
0.36
0.31
0.41
0.34
24
27
27
40
Notes: The table reports the results of regressions of the percentage change in TFR from 1960 to 2013 on
the logged value of total per capita funds for family planning for di¤erent years. The …rst two columns use
the log of total per capita funds for family planning as the regressor for regressions in 1973 and 1989 (the
two years with the highest number of observations). The third column uses the log of the average total per
capita funds for the 1970s as regressor, while the fourth column uses the log of the 1980s average of total
per capita funds. All funds are measured in constant 2005 US cents. The values in parentheses are robust
standard errors. Data on TFR and the consumer price index for the United States (used to convert funds
to real terms) is from the WDI and the data on funds for family planning are from Nortman and Hofstatter
(1978), Nortman (1982) and Ross, Mauldin, and Miller (1993).
* Signi…cant at 5% level ** Signi…cant at 1% level
An alternative measure of program inputs is the family planning program e¤ort index published
by Ross and Stover (2001). The regional averages of family planning program e¤ort indicate that
East Asia and South Asia have, in general, had the strongest family planning programs over time (see
Table 7). Latin America, North Africa, and the Middle East seem to have caught up on program e¤ort
over the three decades but the greatest improvement appears to have been in Sub-Saharan Africa
in 1989-1999. (It is worth emphasizing that Sub-Saharan African countries were the latest to adopt
family planning programs.)
31
TABLE 7
Program E¤ort Score by Region
Region
1972 1982 1989 1994 1999
Europe and Central Asia
20.0
27.0
46.0
42.2
53.0
East Asia and the Paci…c
39.4
46.1
52.5
55.7
58.5
Latin America and the Caribbean
30.2
39.0
50.6
50.3
50.0
North Africa and the Middle East
11.4
17.9
40.5
41.8
58.3
South Asia
24.3
46.3
55.6
56.8
64.4
Sub Saharan Africa
5.0
15.5
36.7
43.9
51.1
Total
19.3
28.5
44.3
47.8
53.6
89
94
92
95
88
Number of countries
Notes: The table reports the average family planning program e¤ort score for each region.
The regional averages are calculated using data from Ross and Stover (2001).
We next examine the relationship between the observed percentage reduction in fertility over the
1960–2013 period and the program e¤ort score. Figure 13 indicates that there is a clear positive
relationship, with larger fertility declines in countries with higher program e¤ort, consistent with the
results of the preceding section, where we found a strong positive correlation between funding for
family planning and reduction in fertility. The relationship between the program e¤ort score and the
decline in fertility rates is strongest in 1972: the R-square coe¢ cient of the regression of the percentage
decline in TFR on the program e¤ort score is 0.48 for 1972.21
In all, there appears to be a strong association between either the amoung of funding or program
e¤ort and the decline in fertility rates. Most Sub-Saharan African countries were the latest to adopt
family planning programs and their e¤orts only caught up recently to the rest of the world. Perhaps
not surprisingly in light of the strong correlations, the countries in Sub-Saharan Africa are the ones
where fertility rates still remain above the world’s average.
21
We report the results using program e¤ort scores for 1972 and 1982. Program e¤ort scores in 1989, 1994, and 1999
also have signi…cantly positive relationships with the percentage reduction in TFR but the R-square coe¢ cients are
lower than in 1972.
32
FIGURE 13
Fertility Decline and Program E¤ort
1982
80
80
1972
ZWE
HTI
PAK
GHA
KEN
GTM
ARE
SAU
OMN
60
AFG
SDN
20
LBR
TZA
BEN
20
KOR
HKG
SGP
THA
MUS
CRI
LBN
VNM
IRN
BRA CHN
MYS
TUN
MMR
MEX
TURCHL
BGD
SLV
DOM
COL
NIC
TTO
CUB
PER
KWT
VEN
DZA
ECU
NPL
MAR
BWA
SYR
FJI IDN
HND
JAM
PAN
JOR CYP EGY
IND
LKA
PHL
PRK
PRY
GUY
BOL
PAK
ZWE
HTI
LSO
RWA
KEN
YEM GHA
GTM
PNG
MDG
AFG
IRQ
ETH
CIVSDN
MRT
TGO SEN
CAF
LBR
TZA
BEN
MWI
SLE
MOZ
GIN
ZMB
UGA
COG
CMR GNB
% decline in TFR
40
% decline in TFR
40
60
KOR
HKG
SGP
THA MUS
CRI
IRNVNM
CHN
MYS
MEXTUR TUN
BGD
SLV
CHL
DOM
COL
TTO
CUB
VEN
DZA
ECU
MARNPL
HND IDN
JAM
EGYLKAPHL
PAN
IND
PRY
BFA
SOM
NGA
NGA
0
0
ZAR
TCD
ZAR
MLI
GMB
NER
0
1
2
3
4
log program effort score
dtfr_percent
5
0
Fitted values
1
2
3
4
log program effort score
dtfr_percent
5
Fitted values
Notes: The …gure shows the scatter plot and linear …t for percentage decline in TFR (from 1960–2013)
and program e¤ort score for the years 1972 and 1982. Data on fertility decline is from the WDI database
while program e¤ort scores are from Ross and Stover (2001).
V
Conclusion
This paper has argued that the meteoric convergence in fertility rates in the past four decades cannot
be accounted for by convergence in other economic variables. The timing and speed of the decline
coincides with the growth of a neo-Malthusian global population-control movement that designed
and advocated a number of policy measures aimed at lowering fertility rates across the world. The
precise measures chosen by di¤erent countries varied in nature, scope, and intensity, depending on the
individual country’s socioeconomic context. Common to all programs was an enhanced provision of
(di¤erent) contraceptive methods and a mass-media campaign to establish a new small-family norm.
The global convergence in fertility rates to near replacement fertility rates will eventually ensure a
constant world population,22 reducing Malthusian and environmental concerns regarding the imbalance
of resources and population growth. To the extent that lower fertility rates are associated with higher
22
Higher life expectancy implies that it will take another few decades to reach a constant population level.
33
investment in human capital, the trends bode well for development and living standards in the world’s
poorest regions. The coordinated e¤orts taken by most countries to contain population growth are an
instructive example on how to reach a cooperative solution to a global economic problem.
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DHS Program (2015). The DHS Program STATcompiler. ICF International. http://www.statcompiler.com
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Nortman, Dorothy L. and Ellen Hofstatter (1978). Population and Family Planning Programs.
The Population Council. pp 38-41.
Nortman, Dorothy L. (1982). Population and Family Planning Programs: A compendium of data
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Ross, John and John Stover (2001). The family planning program e¤ort index: 1999 cycle: Dataset.
International Family Planning Perspectives. https://www.guttmacher.org/pubs/journals/2711901.pdf
(accessed July 20, 2015).
Ross, John A., Mauldin, W. Parker and Vincent C. Miller (1993). Family Planning and Population:
A compendium of international statistics. The Population Council. pp 123-131.
United Nations Population Division (2015). World Population Policies Database: 2013 revision.
United Nations. http://esa.un.org/PopPolicy/wpp_datasets.aspx (accessed July 20, 2015).
United Nations Population Division (2013). World Population Prospects: The 2012 revision. DVD
edition. United Nations. http://esa.un.org/wpp/Excel-Data/population.htm (accessed July 22, 2015)
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38
Appendix
TABLE A1
Total fertility rate by country
Country
1960 1970 1980 1990 2000 2010 2013
Afghanistan
7.67
7.67
7.67
7.69
7.73
5.66
4.90
Albania
6.19
5.05
3.68
2.97
2.38
1.74
1.77
Algeria
7.65
7.64
6.89
4.76
2.51
2.82
2.80
Andorra
1.22
Angola
7.32
7.30
7.20
7.17
6.84
6.22
5.86
Antigua and Barbuda
4.43
3.68
2.12
2.06
2.32
2.13
2.09
Argentina
3.11
3.07
3.33
2.99
2.48
2.22
2.18
Armenia
4.55
3.21
2.39
2.54
1.69
1.74
1.74
Aruba
4.82
2.91
2.39
2.25
1.87
1.70
1.67
Australia
3.45
2.86
1.89
1.90
1.76
1.93
1.92
Austria
2.69
2.29
1.65
1.46
1.36
1.44
1.44
Azerbaijan
5.57
4.61
3.29
2.74
2.00
1.92
2.00
Bahamas
4.50
3.53
2.99
2.64
2.07
1.90
1.89
Bahrain
7.09
6.50
4.92
3.74
2.77
2.14
2.08
Bangladesh
6.73
6.95
6.36
4.55
3.12
2.28
2.18
Barbados
4.33
3.11
2.00
1.74
1.77
1.84
1.85
Belarus
2.67
2.31
2.03
1.91
1.31
1.44
1.62
Belgium
2.54
2.25
1.68
1.62
1.67
1.86
1.79
Belize
6.50
6.30
5.85
4.51
3.59
2.80
2.68
Benin
6.28
6.75
7.03
6.74
5.98
5.10
4.85
1.74
1.76
1.63
Bermuda
Bhutan
6.67
6.67
6.55
5.64
3.61
2.38
2.23
Bolivia
6.70
6.58
5.52
4.91
4.14
3.36
3.22
Bosnia and Herzegovina
4.05
2.88
2.09
1.71
1.38
1.24
1.28
Botswana
6.62
6.64
6.22
4.70
3.41
2.76
2.62
Brazil
6.21
5.02
4.07
2.81
2.36
1.84
1.80
39
TABLE A1 (contd.)
Country
1960 1970 1980 1990 2000 2010 2013
Brunei Darussalam
6.49
5.75
4.25
3.53
2.40
2.05
1.99
Bulgaria
2.31
2.17
2.05
1.82
1.26
1.57
1.50
Burkina Faso
6.29
6.62
7.13
7.01
6.59
5.87
5.61
Burundi
6.95
7.31
7.45
7.54
7.06
6.30
6.03
Cabo Verde
6.89
6.94
6.38
5.31
3.70
2.43
2.29
Cambodia
6.97
6.48
5.69
5.62
3.75
2.97
2.86
Cameroon
5.65
6.21
6.61
6.43
5.62
5.02
4.78
Canada
3.81
2.26
1.74
1.83
1.49
1.63
1.61
Central African Rep.
5.84
5.95
5.95
5.78
5.45
4.63
4.37
Chad
6.25
6.53
6.96
7.31
7.35
6.60
6.26
Channel Islands
2.42
2.12
1.45
1.46
1.40
1.44
1.46
Chile
5.58
4.02
2.68
2.62
2.09
1.86
1.82
China
5.76
5.47
2.71
2.51
1.51
1.65
1.67
Colombia
6.81
5.60
3.99
3.10
2.64
2.38
2.29
Comoros
6.79
7.06
7.13
5.57
5.32
4.92
4.71
Congo, Dem. Rep.
6.00
6.21
6.59
7.13
7.09
6.25
5.93
Congo, Rep.
5.88
6.26
6.18
5.35
5.13
5.07
4.97
Costa Rica
7.31
5.01
3.62
3.18
2.41
1.85
1.80
Cote d’Ivoire
7.35
7.91
7.60
6.36
5.38
4.91
4.87
Croatia
2.33
2.01
2.00
1.63
1.39
1.55
1.51
Cuba
4.18
4.03
1.89
1.75
1.63
1.47
1.45
Curacao
2.20
Cyprus
3.50
2.61
2.35
2.41
1.71
1.48
1.46
Czech Rep.
2.09
1.92
2.08
1.90
1.15
1.51
1.45
Denmark
2.57
1.95
1.55
1.67
1.77
1.87
1.73
Djibouti
6.46
6.80
6.44
6.09
4.47
3.60
3.39
Dominican Rep.
7.56
6.18
4.42
3.47
2.89
2.58
2.48
Ecuador
6.69
6.13
4.74
3.77
3.07
2.66
2.56
Egypt
6.63
5.94
5.37
4.35
3.31
2.88
2.77
El Salvador
6.73
6.20
5.14
3.95
2.93
2.26
2.18
Equatorial Guinea
5.51
5.68
5.73
5.90
5.77
5.14
4.85
40
TABLE A1 (contd.)
Country
1960 1970 1980 1990 2000 2010 2013
Eritrea
6.90
6.65
6.63
6.49
5.94
4.97
4.70
Estonia
1.98
2.17
2.02
2.05
1.36
1.72
1.56
Ethiopia
6.88
6.98
7.32
7.25
6.53
4.90
4.52
Fiji
6.46
4.54
3.91
3.40
3.09
2.67
2.59
Finland
2.72
1.83
1.63
1.78
1.73
1.87
1.80
France
2.85
2.55
1.85
1.77
1.89
2.03
2.01
French Polynesia
5.66
5.06
3.99
3.40
2.46
2.11
2.06
Gabon
4.38
5.08
5.68
5.42
4.60
4.21
4.09
Gambia
5.57
6.09
6.34
6.11
5.92
5.80
5.75
Georgia
2.96
2.60
2.32
2.18
1.61
1.82
1.82
Germany
2.37
2.03
1.44
1.45
1.38
1.39
1.38
Ghana
6.75
6.95
6.54
5.62
4.67
4.05
3.86
Greece
2.23
2.40
2.23
1.40
1.27
1.51
1.29
2.44
2.31
2.20
2.05
Greenland
Grenada
6.74
4.60
4.25
3.84
2.58
2.24
2.17
Guam
6.05
4.37
3.25
3.01
2.82
2.47
2.41
Guatemala
6.53
6.24
6.18
5.58
4.80
3.97
3.78
Guinea
6.10
6.20
6.53
6.58
5.94
5.17
4.92
Guinea-Bissau
5.83
6.07
6.32
6.65
5.85
5.12
4.93
Guyana
5.67
5.07
3.65
2.47
2.59
2.68
2.55
Haiti
6.32
5.76
6.06
5.43
4.30
3.35
3.15
Honduras
7.46
7.27
6.31
5.14
4.00
3.15
3.00
Hong Kong, China
5.16
3.42
2.05
1.27
1.04
1.13
1.12
Hungary
2.02
1.98
1.91
1.87
1.32
1.25
1.34
Iceland
4.29
2.81
2.48
2.30
2.08
2.20
2.04
India
5.87
5.49
4.68
3.88
3.15
2.56
2.48
Indonesia
5.67
5.47
4.43
3.12
2.48
2.43
2.34
Iran
6.93
6.44
6.48
4.82
2.19
1.90
1.92
Iraq
6.25
7.36
6.57
5.88
4.97
4.21
4.03
Ireland
3.78
3.85
3.21
2.11
1.89
2.05
2.01
Israel
3.87
3.78
3.24
2.82
2.95
3.03
3.03
41
TABLE A1 (contd.)
Country
1960 1970 1980 1990 2000 2010 2013
Italy
2.37
2.38
1.64
1.33
1.26
1.46
1.43
Jamaica
5.42
5.48
3.73
2.95
2.60
2.33
2.26
Japan
2.00
2.14
1.75
1.54
1.36
1.39
1.43
Jordan
7.69
7.93
7.26
5.54
4.05
3.46
3.24
Kazakhstan
4.56
3.54
2.90
2.72
1.80
2.60
2.64
Kenya
7.95
8.08
7.46
6.04
5.01
4.62
4.38
Kiribati
6.95
6.05
5.07
4.63
3.88
3.05
2.95
Korea, Dem. Rep.
4.58
4.33
2.68
2.29
1.99
2.00
1.99
Korea, Rep.
6.16
4.53
2.82
1.57
1.47
1.23
1.19
3.90
2.95
2.29
2.16
Kosovo
Kuwait
7.25
7.24
5.52
2.36
2.87
2.67
2.60
Kyrgyzstan
5.17
4.89
4.04
3.69
2.40
3.06
3.20
Laos
5.96
5.97
6.28
6.15
4.19
3.29
3.02
Latvia
1.94
1.96
1.86
2.02
1.25
1.36
1.44
Lebanon
5.74
4.95
4.00
3.00
2.23
1.51
1.50
Lesotho
5.84
5.81
5.59
4.92
4.09
3.21
3.04
Liberia
6.41
6.70
6.97
6.50
5.88
5.02
4.79
Libya
7.54
7.87
7.77
4.97
3.05
2.53
2.36
1.57
1.40
1.51
Liechtenstein
Lithuania
2.56
2.40
1.99
2.03
1.39
1.50
1.60
Luxembourg
2.29
1.97
1.50
1.60
1.76
1.63
1.57
Macao SAR, China
4.95
2.17
1.67
1.69
0.94
1.00
1.08
Macedonia, FYR
3.72
2.98
2.57
2.24
1.68
1.45
1.43
Madagascar
7.30
7.33
6.51
6.26
5.55
4.65
4.47
Malawi
6.91
7.30
7.62
7.00
6.25
5.64
5.39
Malaysia
6.19
4.87
3.79
3.52
2.83
2.00
1.96
Maldives
7.02
7.23
7.07
6.10
3.27
2.34
2.26
Mali
6.70
6.90
7.05
7.06
6.84
6.84
6.85
Malta
3.62
2.03
1.99
2.04
1.70
1.36
1.43
Mauritania
6.78
6.78
6.43
5.98
5.38
4.84
4.67
Mauritius
6.17
3.95
2.67
2.32
1.99
1.57
1.44
42
TABLE A1 (contd.)
Country
1960 1970 1980 1990 2000 2010 2013
Mexico
6.78
6.72
4.71
3.38
2.66
2.28
2.19
Micronesia, Fed. Sts.
6.93
6.94
6.22
4.96
4.30
3.46
3.29
Moldova
3.33
2.58
2.48
2.41
1.57
1.48
1.46
Mongolia
6.95
7.57
6.21
4.05
2.14
2.44
2.44
Montenegro
3.52
2.69
2.27
1.87
1.82
1.70
1.67
Morocco
7.07
6.69
5.68
4.06
2.70
2.58
2.74
Mozambique
6.60
6.59
6.49
6.24
5.78
5.41
5.19
Myanmar
6.05
5.96
5.00
3.42
2.43
2.00
1.94
Namibia
6.15
6.46
6.45
5.23
4.03
3.23
3.05
Nepal
5.99
5.97
5.76
5.17
4.07
2.62
2.30
Netherlands
3.12
2.57
1.60
1.62
1.72
1.79
1.72
New Caledonia
6.28
4.30
3.42
3.19
2.59
2.17
2.28
New Zealand
4.13
3.16
2.03
2.18
1.98
2.15
1.95
Nicaragua
7.34
6.89
6.13
4.75
3.25
2.63
2.50
Niger
7.05
7.42
7.71
7.76
7.73
7.58
7.56
Nigeria
6.35
6.47
6.78
6.49
6.10
6.02
5.98
Norway
2.85
2.50
1.72
1.93
1.85
1.95
1.85
Oman
7.25
7.31
8.30
7.16
3.72
2.90
2.85
Pakistan
6.60
6.60
6.54
6.02
4.47
3.43
3.19
2.76
1.54
Palau
Panama
5.87
5.17
3.88
3.08
2.82
2.55
2.47
Papua New Guinea
6.28
6.16
5.69
4.80
4.51
3.95
3.78
Paraguay
6.50
5.74
5.22
4.54
3.68
2.97
2.86
Peru
6.88
6.31
5.01
3.83
2.93
2.51
2.42
Philippines
7.15
6.26
5.18
4.32
3.81
3.15
3.04
Poland
2.98
2.20
2.28
2.06
1.37
1.38
1.30
Portugal
3.16
3.01
2.25
1.56
1.55
1.39
1.28
Puerto Rico
4.66
3.15
2.61
2.22
2.05
1.66
1.64
Qatar
6.97
6.92
5.81
4.02
3.24
2.09
2.02
Romania
2.34
2.89
2.43
1.83
1.31
1.54
1.53
Russia
2.52
1.99
1.89
1.89
1.21
1.57
1.70
43
TABLE A1 (contd.)
Country
1960 1970 1980 1990 2000 2010 2013
Rwanda
8.19
8.23
8.45
7.27
5.90
4.84
4.51
Samoa
7.65
7.19
6.20
5.12
4.50
4.34
4.15
Sao Tome and Principe
6.24
6.47
6.41
5.40
4.69
4.29
4.08
Saudi Arabia
7.22
7.28
7.21
5.84
3.99
2.83
2.64
Senegal
6.95
7.34
7.38
6.63
5.56
5.05
4.93
Serbia
1.48
1.40
1.45
Seychelles
2.08
2.10
2.40
5.92
4.94
4.71
1.15
1.19
Sierra Leone
6.03
6.70
7.06
6.53
Singapore
5.45
3.09
1.74
1.87
Slovak Republic
3.04
2.41
2.32
2.09
1.30
1.43
1.34
Slovenia
2.32
2.27
2.04
1.46
1.26
1.57
1.58
Solomon Islands
6.39
6.91
6.75
5.85
4.72
4.24
4.03
Somalia
7.25
7.18
7.01
7.40
7.61
6.87
6.56
South Africa
6.17
5.59
4.79
3.66
2.87
2.47
2.39
South Sudan
6.72
6.88
6.85
6.77
6.13
5.19
4.92
Spain
2.86
2.84
2.20
1.36
1.23
1.37
1.32
Sri Lanka
5.54
4.34
3.41
2.48
2.24
2.34
2.34
St. Lucia
6.97
6.10
4.70
3.40
2.31
1.98
1.91
2.12
1.83
1.82
1.81
2.96
2.38
2.07
2.00
6.53
5.40
4.22
4.01
St. Martin (French part)
St. Vincent & Grenadines
7.22
6.01
3.99
State of Palestine
Sudan
6.69
6.89
6.80
6.15
5.44
4.64
4.42
Suriname
6.61
5.65
3.92
2.73
2.74
2.35
2.27
Swaziland
6.72
6.88
6.66
5.74
4.21
3.56
3.33
Sweden
2.17
1.92
1.68
2.13
1.54
1.98
1.91
Switzerland
2.44
2.10
1.55
1.58
1.50
1.52
1.52
Syria
7.47
7.57
7.09
5.31
3.96
3.08
2.96
Tajikistan
6.24
6.88
5.66
5.18
3.95
3.78
3.82
Tanzania
6.81
6.77
6.65
6.21
5.69
5.43
5.21
Thailand
6.15
5.60
3.39
2.11
1.68
1.44
1.40
Timor-Leste
6.37
5.92
4.77
5.34
7.11
5.60
5.20
44
TABLE A1 (contd.)
Country
1960 1970 1980 1990 2000 2010 2013
Togo
6.52
7.08
7.21
6.33
5.29
4.79
4.64
Tonga
7.36
5.94
5.55
4.64
4.25
3.91
3.77
Trinidad and Tobago
5.26
3.55
3.28
2.45
1.75
1.80
1.80
Tunisia
7.04
6.44
5.35
3.38
2.08
2.13
2.25
Turkey
6.30
5.56
4.36
3.08
2.45
2.10
2.04
Turkmenistan
6.42
6.30
5.01
4.35
2.84
2.41
2.33
Uganda
7.00
7.12
7.10
7.09
6.87
6.16
5.87
Ukraine
2.24
2.09
1.95
1.84
1.11
1.45
1.51
United Arab Emirates
6.93
6.61
5.42
4.39
2.64
1.87
1.80
United Kingdom
2.69
2.44
1.90
1.83
1.64
1.92
1.92
United States
3.65
2.48
1.84
2.08
2.06
1.93
1.87
Uruguay
2.88
2.90
2.72
2.52
2.24
2.08
2.05
Uzbekistan
6.71
6.49
5.13
4.07
2.58
2.34
2.20
Vanuatu
7.20
6.27
5.58
4.93
4.37
3.50
3.38
Venezuela
6.62
5.40
4.20
3.45
2.82
2.47
2.39
Vietnam
6.35
6.47
5.05
3.56
1.98
1.82
1.74
Virgin Islands (U.S.)
5.62
5.17
3.14
2.95
2.06
1.81
1.77
Yemen, Rep.
7.29
7.54
8.99
8.67
6.36
4.50
4.08
Zambia
7.02
7.44
7.18
6.47
6.07
5.81
5.69
Zimbabwe
7.16
7.42
7.10
5.18
4.07
3.72
3.49
Notes: The table reports the total fertility rate for each country at the start of each decade and in
2013 (the most recent data). Data is from the World Development Indicators database.
45
TABLE A2
Summary statistics
Variable
Obs.
Mean
Std. Dev.
Min
Max
Source
Total fertility rate
10484
4.13
2.04
0.84
9.22
WDI
GDP per capita
8232
9231.08
15335.40
50.04
158802.50
WDI
Total population
11455
23.5 mn
98 mn
4279.00
1.36 bn
WDI
% of urban population
11388
49.40
25.57
2.08
100.00
WDI
Infant mortality rate
9139
55.92
47.68
1.60
269.50
WDI
Child mortality rate
9206
84.66
82.58
2.00
497.90
WDI
Risk of maternal death
1098
1.30
2.18
0.00
13.09
WDI
Wanted fertility
257
3.51
1.40
0.80
7.40
DHS
Female LFPR
6280
50.03
17.28
8.50
90.80
WDI
Total family planning funds (pc)
378
95.70
138.00
0.19
1180.34
(Complied from
Govt family planning funds (pc)
367
62.57
84.33
0.00
545.56
multiple sources)*
Family planning programme
458
38.72
23.66
0.00
92.00
Ross and Stover
e¤ort score
(2001)
Notes: The table reports summary statistics for total fertility rate, GDP per capita (in constant 2005 US$), total population,
proportion of urban population (%), infant and child mortality rates (per 1000 live births), risk of maternal death, wanted
fertility, female labour force participation rate (% of women aged 15+ participating in the labour force), total and
government funds for family planning per capita (in constant 2005 US cents) and family planning programe¤ort score.
WDI refers to World Development Indicators database and DHS to Demographic and Health Surveys.
* Total and government funds for family planning per capita (in 2005 US cents) are obtained from Nortman and
Hofstatter (1978), Nortman (1982) and Ross, Mauldin and Miller (1993).
mn: millions, bn: billions
46
TABLE A3
Fertility Rate Decomposition
Country
Fertility decline Between-e¤ect Within-e¤ect First year Last year
Albania
1.05
1.55%
98.45%
2002
2008
Armenia
0.04
-11.09%
111.09%
2000
2010
Azerbaijan
0.06
7.97%
92.03%
2001
2006
Bangladesh
0.99
6.75%
93.25%
1993
2011
Benin
1.10
6.85%
93.15%
1996
2011
Bolivia
1.66
15.56%
84.44%
1989
2008
Brazil
0.91
14.56%
85.44%
1986
1996
Burkina Faso
0.67
43.07%
56.93%
1993
2010
Burundi
0.48
19.16%
80.84%
1987
2010
Cambodia
0.69
2.03%
97.97%
2000
2010
Cameroon
0.71
29.33%
70.67%
1991
2011
Chad
0.05
4.07%
95.93%
1996
2004
Colombia
1.11
11.59%
88.41%
1986
2010
Comoros
0.23
-1.27%
101.27%
1996
2012
Congo Dem. Rep.
-0.13
-41.38%
141.38%
2007
2013
Cote d’Ivoire
0.38
57.77%
42.23%
1994
2011
Dominican Rep.
1.43
15.20%
84.80%
1986
2013
Ecuador
1.17
10.51%
89.49%
1987
2004
Egypt
1.74
-0.49%
100.49%
1988
2008
El Salvador
2.05
12.54%
87.46%
1985
2008
Eritrea
1.23
3.07%
96.93%
1995
2002
Ethiopia
0.57
15.44%
84.56%
2000
2011
Gabon
0.04
361.30%
-261.30%
2000
2012
Ghana
2.39
10.49%
89.51%
1988
2008
Guatemala
1.87
8.42%
91.58%
1987
2008
Guinea
0.49
18.97%
81.03%
1999
2012
Haiti
1.66
30.37%
69.63%
1994
2012
Honduras
2.11
8.00%
92.00%
1996
2011
India
0.70
4.39%
95.61%
1992
2005
47
TABLE A3 (contd.)
Country
Fertility decline Between-e¤ect Within-e¤ect First year Last year
Indonesia
0.58
24.33%
75.67%
1987
2012
Jordan
2.07
5.15%
94.85%
1990
2012
Kazakhstan
0.46
0.02%
99.98%
1995
1999
Kenya
2.00
7.75%
92.25%
1989
2008
Kyrgyzstan
-0.32
1.56%
98.44%
1997
2012
Lesotho
0.08
62.90%
37.10%
2004
2009
Liberia
1.63
4.13%
95.87%
1986
2013
Madagascar
1.49
10.45%
89.55%
1992
2008
Malawi
0.96
6.09%
93.91%
1992
2010
Mali
1.22
16.51%
83.49%
1987
2012
Moldova
0.22
-2.15%
102.15%
1997
2005
Morocco
2.19
6.83%
93.17%
1987
2003
Mozambique
-0.84
-4.38%
104.38%
1997
2011
Namibia
1.75
19.12%
80.88%
1992
2013
Nepal
1.99
4.52%
95.48%
1996
2011
Nicaragua
1.09
3.00%
97.00%
1998
2006
Niger
-0.66
-5.96%
105.96%
1992
2012
Nigeria
0.41
56.66%
43.34%
1990
2013
Pakistan
1.09
5.03%
94.97%
1990
2012
Paraguay
2.35
6.57%
93.43%
1990
2008
Peru
1.58
14.41%
85.59%
1986
2012
Philippines
1.07
-3.87%
103.87%
1993
2013
Rwanda
1.72
16.41%
83.59%
1992
2010
Senegal
1.34
6.12%
93.88%
1986
2010
Sierra Leone
0.21
15.80%
84.20%
2008
2013
Tanzania
0.89
19.59%
80.41%
1991
2010
Togo
1.33
8.81%
91.19%
1988
1998
Turkey
-0.12
-14.29%
114.29%
1993
1998
Uganda
1.05
10.59%
89.41%
1988
2011
Ukraine
0.41
0.13%
99.87%
1999
2007
48
TABLE A3 (contd.)
Country
Fertility decline Between-e¤ect Within-e¤ect First year Last year
Vietnam
0.42
4.22%
95.78%
1997
2002
Yemen
1.14
6.59%
93.41%
1991
1997
Zambia
0.30
-8.02%
108.02%
1992
2007
Zimbabwe
1.30
8.92%
91.08%
1988
2010
Notes: The table reports the overall decline in fertility, the percentage due to the between-region e¤ect
(urbanization e¤ect) and within-e¤ect of the change, and the years over which the overall change is
calculated. Data on urban and rural fertility rates are obtained from the Demographic and Health Surveys,
while the proportion of urban population is taken from the World Development Indicators database.
49
TABLE A4
Replacement Fertility Rates 2010
Country
RFR
TFR
Country
RFR
TFR
World
2.25
2.49
Italy
2.08
1.41
Kazakhstan
2.16
2.59
Kyrgyzstan
2.16
3.06
North America
Canada
2.08
1.63
Latvia
2.09
1.17
United States
2.08
1.93
Lithuania
2.08
1.55
Luxembourg
2.06
1.63
Europe and Central Asia
Albania
2.12
1.74
Macedonia, FYR
2.08
1.45
Armenia
2.20
1.74
Moldova
2.11
1.48
Austria
2.07
1.44
Montenegro
2.10
1.70
Azerbaijan
2.27
1.92
Netherlands
2.07
1.79
Belarus
2.09
1.44
Norway
2.07
1.95
Belgium
2.07
1.84
Poland
2.08
1.38
Bosnia and Herzegovina
2.10
1.24
Portugal
2.08
1.36
Bulgaria
2.10
1.49
Romania
2.10
1.33
Channel Islands
2.09
1.44
Russia
2.11
1.54
Croatia
2.08
1.46
Serbia
2.09
1.40
Cyprus
2.08
1.48
Slovak Republic
2.07
1.40
Czech Rep.
2.07
1.49
Slovenia
2.07
1.57
Denmark
2.07
1.87
Spain
2.08
1.38
Estonia
2.09
1.63
Sweden
2.07
1.98
Finland
2.06
1.87
Switzerland
2.07
1.52
France
2.07
2.03
Tajikistan
2.24
3.78
Georgia
2.17
1.82
Turkey
2.12
2.10
Germany
2.07
1.39
Turkmenistan
2.21
2.41
Greece
2.08
1.51
Ukraine
2.11
1.45
Hungary
2.08
1.25
United Kingdom
2.07
1.98
Iceland
2.07
2.20
Uzbekistan
2.19
2.50
Ireland
2.09
2.07
50
TABLE A4 (contd.)
Country
RFR
TFR
East Asia and Paci…c
Country
RFR
TFR
Tonga
2.12
3.91
Australia
2.07
1.87
Vanuatu
2.15
3.50
Brunei
2.08
2.05
Vietnam
2.17
1.82
Cambodia
2.23
2.97
Latin America and Caribbean
China
2.22
1.65
Antigua and Barbuda
2.07
2.13
Fiji
2.13
2.67
Argentina
2.08
2.22
French Polynesia
2.08
2.11
Aruba
2.10
1.70
Guam
2.09
2.47
Bahamas
2.11
1.90
Hong Kong, China
2.08
1.13
Barbados
2.08
1.84
Indonesia
2.14
2.43
Belize
2.12
2.80
Japan
2.07
1.39
Bolivia
2.24
3.36
Kiribati
2.18
3.05
Brazil
2.12
1.84
Korea, Dem. Rep.
2.15
2.00
Chile
2.07
1.86
Korea, Rep.
2.08
1.23
Colombia
2.12
2.38
Laos
2.20
3.29
Costa Rica
2.08
1.85
Macao, China
2.07
1.00
Cuba
2.08
1.47
Malaysia
2.08
2.00
Curacao
2.09
2.20
Micronesia, Fed. Sts.
2.21
3.46
Dominican Rep.
2.14
2.58
Mongolia
2.11
2.44
Ecuador
2.12
2.66
Myanmar
2.23
2.00
El Salvador
2.13
2.26
New Caledonia
2.09
2.19
Grenada
2.10
2.24
New Zealand
2.08
2.16
Guatemala
2.16
3.97
Papua New Guinea
2.30
3.95
Guyana
2.20
2.68
Philippines
2.14
3.15
Haiti
2.33
3.35
Samoa
2.15
4.34
Honduras
2.16
3.15
Singapore
2.09
1.15
Jamaica
2.14
2.33
Solomon Islands
2.23
4.24
Mexico
2.10
2.28
Thailand
2.12
1.44
Nicaragua
2.12
2.63
Timor-Leste
2.21
5.58
Panama
2.11
2.55
51
TABLE A4 (contd.)
Country
RFR
TFR
Country
RFR
TFR
Paraguay
2.16
2.97
Tunisia
2.10
2.13
Peru
2.13
2.51
United Arab Emirates
2.07
1.87
Puerto Rico
2.08
1.66
Yemen, Rep.
2.32
4.50
St. Lucia
2.09
1.98
South Asia
St. Vincent & Grenadines
2.09
2.07
Afghanistan
2.40
5.66
Suriname
2.16
2.35
Bangladesh
2.19
2.28
Trinidad and Tobago
2.13
1.80
Bhutan
2.22
2.38
Uruguay
2.09
2.08
India
2.32
2.56
Venezuela
2.11
2.47
Maldives
2.10
2.34
Virgin Islands (U.S.)
2.10
1.80
Nepal
2.22
2.62
Pakistan
2.31
3.43
2.09
2.34
Middle East and North Africa
Algeria
2.15
2.82
Sri Lanka
Bahrain
2.07
2.14
Sub Saharan Africa
Djibouti
2.36
3.60
Angola
2.69
6.22
Egypt
2.12
2.88
Benin
2.44
5.10
Iran
2.12
1.90
Botswana
2.37
2.76
Iraq
2.16
4.21
Burkina Faso
2.58
5.87
Israel
2.07
3.03
Burundi
2.58
6.30
Jordan
2.12
3.46
Cameroon
2.51
5.02
Kuwait
2.08
2.67
Cape Verde
2.08
2.43
Lebanon
2.08
1.51
Central African Rep.
2.70
4.63
Libya
2.11
2.53
Chad
2.71
6.60
Malta
2.08
1.38
Comoros
2.38
4.92
Morocco
2.16
2.58
Congo, Dem. Rep.
2.78
6.25
Oman
2.08
2.90
Congo, Rep.
2.42
5.07
Qatar
2.07
2.09
Cote d’Ivoire
2.70
4.91
Saudi Arabia
2.07
2.83
Equatorial Guinea
2.65
5.14
Syria
2.11
3.08
Eritrea
2.25
4.97
52
TABLE A4 (contd.)
Country
RFR
TFR
Country
RFR
TFR
Ethiopia
2.31
4.90
Nigeria
2.71
6.02
Gabon
2.31
4.21
Rwanda
2.29
4.84
Gambia
2.43
5.80
Sao Tome & Principe
2.24
4.29
Ghana
2.39
4.05
Senegal
2.31
5.05
Guinea
2.54
5.17
Seychelles
2.09
2.10
Guinea-Bissau
2.60
5.12
Sierra Leone
3.00
4.94
Kenya
2.34
4.62
Somalia
2.56
6.87
Lesotho
2.50
3.21
South Africa
2.31
2.47
Liberia
2.41
5.02
South Sudan
2.56
5.19
Madagascar
2.26
4.65
Sudan
2.36
4.64
Malawi
2.50
5.64
Swaziland
2.59
3.56
Mali
2.68
6.84
Tanzania
2.35
5.43
Mauritania
2.39
4.84
Togo
2.48
4.79
Mauritius
2.08
1.47
Uganda
2.41
6.16
Mozambique
2.64
5.41
Zambia
2.45
5.81
Namibia
2.18
3.23
Zimbabwe
2.32
3.72
Niger
2.53
7.58
The table reports replacement fertility rates (RFR) and total fertility rates (TFR) for all countries for 2010. TFR
data is from the WDI. Replacement fertility is calculated using the approximation given in Espenshade, Guzman
(1+sex ratio at birth)
and Westo¤ (2003): Replacement fertility rate = (Pr obability of survival to mean childbearing age)
Sex ratio at birth and probability of survival to age 25 for women are obtained from the UN World Population
Prospects (2013).
53