Transcript
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.
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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
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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
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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).
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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
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6
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10
.2 0
.05
Fraction .1 .15
.2 Fraction .1 .15 .05 0
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.05
Fraction .1 .15
.2
.25
1980
.25
1970
.25
1960
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6 TFR
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.25 0
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Fraction .1 .15
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.25 .2 .05 0
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2013
Fraction .1 .15
.2 Fraction .1 .15 .05 0
0
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2000
.25
1990
4
0
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4
6 TFR
8
10
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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.
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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.
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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.)
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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.
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FIGURE 3 The Fertility-Income Relation in 1960 and 2013
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2
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8
RWA KEN NER SYRHND DOM DZA CRI MDG OMNPHLCIVNIC VCT ZWE NER ZMB BDI SEN MWI PER MEX MLI COL MRT GHA BGD SDN BOL ECU VEN SSFBLZ PAK GTMFJI T GOBWALBR PRY NGA BFA BEN PNG T CD T CD BRAT UR ZAF MYS KOR BDI SLE SAS ZAR NPL NGA LCN ZAR COG PAN AGO IND LMC UGA CAF MICGUY CHN LSO GMB UMC ZMB IDN CHL LKA BFACMR SGP EAS MWI T TO T ZA MOZ SSF COG WLD SEN GNB GIN AFG CIV BEN GNQ LBR CMR COM SLE ERI ETHT GO MRT PRI RWA BHS MDG SDN KEN GAB BRB CAF ISL WSM GAB STP LIC YEM SLB WBG IRQ T JK GHAPNG CAN GTM T ON ISR NAC USA ZWE AUS DJI VUT SWZ FSM JOR BOL PAK PRT HTIKGZ NLD ISR ARG PHL NAM LSO LAO HND KIR HIC URY PRY KHM LMC ESP FRA NOR ECS FIN DZA EGY MAR MEA AUT GBR BLZ SAU KAZ BWA FJI DNK SAS ECU GUY BEL NICMNG IND DOM PAN WLD PER SYC VEN MICTTKM ZAF IT A LBY IDN LKACPV NPL BGDUZB LUX SUR MDV UN BTN GRC SLVCOL MEX GRD SWE KSV LCN ATG BHRBRN URY T UR FRA IRLISL QAT AZEUMC JPN VCT MYS NZL IRN AUS GBR LCA SWE BHS USANOR NAC GEO EAS BRA ARE FIN CRI T TOPRI BEL ALB VNM ARM CHN ECS DNK LUX NLD RUSCHL HIC MNE BLR CAN LT U SVN EST ROM CHE HRV BGRMUS LBN MDA UKR BIH CYP SRB CZE LVA AUT MLT IT A JPN TMKD HA DEU HUN SVK ESP POL GRC PRT 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 AGO ZMB BFA COM UGA MDV ZWE SYR J OR MLI SLE BEN SOM LBR TCD DZA SSD SDN NGA SLB TZA SWZ ERI CMR ZAR BTN IRQ GIN PAK GHA MDG MOZ IRN NAM DJ I MRT STP CPV BGD GMB GNB HND LAO BWA W FSM SM MNG GTM COG NIC HTI CAF BLZ QAT NPL GNQ KHM PNG MAR GAB TJK LSO VUT TON BOL ARE KWT EGY TUN PRY PHL UZB SLV KIR VNM MMR TKM BHR ZAF PER TMP ECU LCA MEX IND IDNGRD DOM TUR BRN VEN BRA KGZ VCT COL LBN SUR FJI MYS J PAN AMPYF ALB GUY CRI LKA NCL THA ARG TTO AZE ISR GUM IRL VIR BHS KAZ KOR URY CHN MDA MUS PRK PRI CHL MKD ISL ROM ABW ARM CYP SVK GEO MNE POL PRT GRC ESP ATG BIH CZE SVN BLR BGR NZL HKG BRB HRV EST LTU MLT UKR HUN GBR CUB RUS AUS LVA FRA USA CAN J PN NOR MAC AUT ITA FIN NLD CHE DNKBEL CHI DEUSWE
0
2
4
TFR
6
8
RWA KEN CIV J OR LBY AFG DZA SYR MNG YEM ZMB NER ZWE IRQ SEN BDI MDG OMN MWI AGO SAU HND MDV KWT W SOM SM UGA TGO COM ETH BGD GHA SLB CPV FSM QAT MLI SDN NIC MEX SSD SWZ TJK MRT DJ I ARE TZA BEN SLE LBR MAR BTN ERI BWA BFA PAK MOZ BOL TCD BHR KHM NGA STP UZB VNM NAM IRN TUN TKM BLZ PER VUT PHL COG GTM CMR ZAR SLV GIN DOM PNG ECU LCA GMB GNB KIR VCT NPL LAO TON MMR CAF TMP EGY LSO HTI PRY BRN GNQ SUR THA ZAF COL IND TUR IDN CHN J AM VEN PAN VIR GAB GUY ALB BRA CRI PYF LBN KGZ MYS AZE GUM FJI KOR LKA GRD PRK NCL MUS IRLCUB CHL ATG TTO KAZ BHS ISR HKG ARM ARG NZL PRI BRB PRT MKD ABW BIH ROM AUS ESP URY ISL MNE CYP MDA GEO NLD FRA NOR USA GBR BEL SVK LTU GRC ITA BLR SVN AUT CAN BGR EST MAC JDEU PN CHI UKR CHE MLT HRVPOL HUN RUS LVA DNK CZE SWE FIN
0
0
2
4
TFR
6
8
RWA KEN J OR AFG WHND SM DZA DOM LBY SYR TON CIV NIC AGO CRI YEM MDG OMN SOM KWT VCT SAU VUT ZWE PHL BHR NER MAR TUN MDV ZMB UGA KHM LCA QAT BDI KIR SEN MNG MWI FSM IRN ARE ERI ETH CPV PER TZA COM COL MEX MRT GHA GRD SLV SWZ BGD SSD MLI UZB S DN ECU BOL BTN EGY BWA MOZ PAK SUR TGO GTM PRY BLZ BRN FJI DJ I VEN TKM SLB LBR TMP VNM NGA HTI BFA BEN TUR PNG NCL TCD STP TJK IRQ BRA MYS ALB NAM KOR MUS ZAF THA GIN MMR GUM SLE NPL ZARCOG LAO IND PAN LSO CAF GNB CHN LBN IDN GUY PYF CMR VIR GMB CHL LKA GNQ JKGZ AM AZE TTO HKG MAC ABW PRI PRK KAZ ARM BHS ISL ATG GAB BRB CUB NZL BIH ISR CAN IRL MKD USAAUS MLT MNE CYP MDA PRT NLD ARG SVK POL GEO URY NOR ESP FRA FIN AUT GBR BLR LTUCHE DNK BEL RUS CHI ITA DEU SVN HRV ROM BGR UKR GRC CZE HUN EST J PNSWE LVA
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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
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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.
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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.
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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
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100
0
20
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100
8
8
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2013
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2 0
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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 ZABGD TPAK TZA BEN MEX MAR LBR SLE ER IIR BTN BW A AR E BFA BOL TC DN BH R STP NMOZ GA VN NBLZ AM M TU N PER PH VU L C T OG GTM SLV C MR ZAR D OM GIN PN G EC U LC A GMB KIR VC Y T N PL TON MMR C AFEGY LSO H TI PR C TH OL A TU R JVEN AM C H NID N IN D PAN GU BR A C RYI KGZ LBN MYS KOR FJLKA I UMU BC HSL IR LCTTO BH S N ZL BR B AR ROM YTG R AU SUPR ESP ISL N LD FR AT C OR U SA GBR 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 ZBGD 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
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250
0
50
100
150
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200
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10
2013
10
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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 ZAF 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 ZKH 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
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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
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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
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500
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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 ISL PR K MKD N ZL GEO SW TH E A IR SVK L U POL ATG SA MLT LTU EST LVA N OR AU C BLR RLD ZE SK MN H UU E NS C GBR U AN R KR OM BGR FIN FR C U A BR B BIH D N BEL H R V C H E KOR PR JN PN D AU SVN EU TT GR C ESP ITA
8
8
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YEM OMN
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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 TZA SEN MDG SDN MRT CIVCMR CAF COM TGO COG KEN IRQ GTM SLB STP GHA GAB PNG WSM PAK DJI VUT FSM 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
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OMN
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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.
Data Sources DHS Program (2015). The DHS Program STATcompiler. ICF International. http://www.statcompiler.com (accessed June 18 2015). 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 through 1981. The Population Council. pp 61-63. 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) The World Bank (2015). World Development Indicators. The World Bank. http://data.worldbank.org/datacatalog/world-development-indicators (accessed July 20, 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
