Probabilistic Projections of the Total Fertility Rate for All

Probabilistic Projections of the Total Fertility Ratefor All Countries

Leontine Alkema, Adrian E. Raftery, Patrick Gerland,Samuel J. Clark, Francois Pelletier, Thomas Buettner 1

Working Paper no. 97Center for Statistics and the Social Sciences

University of Washington

January 28, 2010

1Leontine Alkema, Department of Statistics and Applied Probability, National University

of Singapore, Singapore 117546; Email: [email protected]. Adrian E. Raftery, Depart-

ments of Statistics and Sociology, University of Washington, Seattle, WA 98195-4320; Email:

[email protected]. Patrick Gerland, Population Estimates and Projections Section, United

Nations Population Division, New York, NY 10017; Email: [email protected]. Samuel J. Clark, De-

partment of Sociology, University of Washington, Seattle, WA 98195-3340; MRC/Wits University

Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health,

University of Witwatersrand, South Africa and INDEPTH Network; Email: [email protected].

Francois Pelletier, Mortality Section, United Nations Population Division, New York, NY 10017;

Email: [email protected]. Thomas Buettner, United Nations Population Division, New York, NY

10017; Email: [email protected]. The project described was partially supported by Grant Number

R01 HD054511 (Adrian E. Raftery, Principal Investigator) and HD057246 (Samuel J. Clark, Princi-

pal Investigator) from the National Institute of Child Health and Human Development. Its contents

are solely the responsibility of the authors and do not necessarily represent the official views of the

National Institute of Child Health and Human Development or those of the United Nations. Its

contents have not been formally edited and cleared by the United Nations. The authors are grate-

ful to John Bongaarts, Jennifer Chunn, Joel Cohen, Timothy Dyson, Taeke Gjaltema, Gerhard

Heilig, Peter Johnson, Nico Keilman, Nan Li and Peter Way for helpful discussions and insightful

comments, and to Hana Sevcıkova for software development. Alkema thanks the United Nations

Population Division for hospitality.

Abstract

We describe a Bayesian projection model to produce country-specific projections of the totalfertility rate (TFR) for all countries. It decomposes the evolution of TFR in three phases:pre-transition high fertility, the fertility transition, and post-transition low fertility. Themodel for the fertility decline builds on the United Nations Population Division’s currentdeterministic projection methodology, which assumes that fertility will eventually fall belowreplacement level. It models the decline in TFR as the sum of two logistic functions thatdepend on the current TFR level, and a random term. A Bayesian hierarchical model isused to project future TFR based on both the country’s TFR history and the pattern of allcountries. It is estimated from United Nations estimates of past TFR in all countries usinga Markov chain Monte Carlo algorithm. The post-transition low fertility phase is modeledusing an autoregressive model, in which long-term TFR projections converge toward andoscillate around replacement level. The method is evaluated using out-of-sample projectionsfor the period since 1980 and the period since 1995, and is found to be well calibrated.

Population forecasts predict the future size and composition of populations, based on

projections of fertility, mortality and migration. They are used for many purposes, including

predicting the demand for food, water, education, medical services, labor markets, pension

systems, and future impact on the environment. It is important for decision makers to not

only have a point forecast that states the most likely scenario for a future population, but

also to know the uncertainty around it, that is, the possible future values of an outcome,

and how likely each set of possible future values is.

Fertility is a key driver of the size and composition of the population. Fertility decline

has been a primary determinant of population aging and projected levels of fertility have

important implications on the age structure of future populations, including the pace of

population aging. The total fertility rate (TFR) is one of the key components in population

projections; it is the average number of children a woman would bear if she survived through

the end of the reproductive age span, experiencing at each age the age-specific fertility rates

of that period.

In this paper we propose a new methodology for probabilistic projections of the total

fertility rate for all the countries of the world. The goal is to produce probabilistic projections

that would ultimately be part of the population projections produced by the United Nations

Population Division. Earlier versions of this methodology were described by Alkema (2008),

Alkema et al. (2008, 2009), and Raftery et al. (2009).

Until now, most operational population projections have been deterministic, produced

using the standard cohort-component population projection model. Future fertility rates

have typically been assigned in a deterministic way. The main organizations that have

produced population projections for all or most of the world’s countries are the United

Nations, the World Bank (Bos et al. 1994) and the United States Bureau of the Census (U.

S. Census Bureau 2009), all of which use the standard deterministic approach.

Probabilistic projections of the TFR have been proposed, mostly for developed coun-

tries with low fertility. Lee (1993) and Lee and Tuljapurkar (1994) proposed a time series

approach based on the probabilistic mortality projection method of Lee and Carter (1992),

for decomposing and projecting fertility in the U.S. Methods based on expert judgement

have been developed and applied by Lutz, Sanderson, and Scherbov (2001). The Uncertain

Population in Europe project (Alho et al. 2006; Alders, Keilman, and Cruijsen 2007; Alho,

Jensen, and Lassila 2008) developed probabilistic TFR projections for 18 European countries

using the ex-post method based on the errors of previous projections (Keyfitz 1981; Stoto

1983). Hyndman and Booth (2008) developed a method using functional data models and

applied it to Australia, as well as France (Booth et al. 2009).

These methods were developed mainly for low fertility countries that have largely finished

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going through the fertility transition and for which fertility is fluctuating in a fairly stable,

stationary way. They do not apply easily to countries that are still going through the fertility

transition. Here we develop a methodology for all countries.

A great deal of the demographic literature over the past sixty years has been concerned

with explaining trends in fertility; see the reviews by Hirschman (1994) and Mason (1997).

This literature is dominated by controversy over why fertility has declined in most countries.

Proposed explanations include socioeconomic and educational development, declines in child

mortality, contraceptive programs and ideational changes; see Raftery, Lewis, and Aghaja-

nian (1995) for one empirical comparison between competing theories. In spite of this, there

has been a general consensus that, whatever the causes, the evolution of fertility includes

three broad phases: a high-fertility pre-transition phase, the fertility transition itself and a

low-fertility post-transition phase during which fertility remains close to replacement level.

Our methodology is based on this sequence of change. For countries that are going

through the fertility transition from high fertility toward replacement fertility, we decom-

pose the pace of the fertility decline into a systematic decline and random distortion terms.

The pace of the systematic decline in TFR is modeled as a function of its level, based on the

current UN methodology. We propose a Bayesian hierarchical model to estimate the parame-

ters of the decline function. A time series model is used for projecting trends in fertility after

reaching replacement level, assuming that in the long term the TFR will fluctuate around

replacement-level fertility. The results are country-specific projections that are reproducible

and take into account past trends.

We build on the deterministic methodology currently used by the United Nations Pop-

ulation Division for projecting the TFR. The UN Population Division produces projections

of the total fertility rate for 196 countries that are revised every two years and published

in the World Population Prospects (United Nations, Department of Economic and Social

Affairs, Population Division 2009). The UN produces deterministic TFR projections. It

then decomposes them into projected age-specific fertility rates using fertility schedules, and

finally combines them with projections of mortality and international migration using the

cohort-component projection method, to yield the Medium variant of the official United

Nations population projections.

The effect of lower or higher fertility is illustrated with the Low and High variants of

the projections. In the high variant, half a child is added to the TFR projections in order

to examine the influence of higher fertility on the population projections. Similarly, for

the low variant, half a child is subtracted from the TFR projections. The high and low

variants highlight the sensitivity of demographic outcomes to different assumptions about

future TFR, but they do not assess the uncertainty in future fertility levels (Bongaarts and

2

Bulatao 2000), nor to what extent the low or high fertility variants are more likely.

Our projection intervals vary by country. The 80% projection intervals are wider than

the current UN low-high intervals in most high-fertility countries, and narrower in most

low-fertility countries. On average, the 80% projection intervals are slightly narrower than

the UN high-low intervals. For high-fertility countries, the pace of fertility decline in our

projections is slower compared to corresponding UN projections, but about the same for

most other countries.

The paper is organized as follows. The next section describes the Bayesian projection

model. The following section summarizes the results and the out-of-sample projection valida-

tion. The paper concludes with a discussion of the methodology and results, and additional

details of the model are given in the Appendix.

BAYESIAN PROJECTION MODEL

The three phases

We base our projections on the five-year UN estimates of TFR for 1950 to 2010 from the 2008

revision of the UN World Population Prospects (United Nations, Department of Economic

and Social Affairs, Population Division 2009). The outcome in each five-year period (t, t+5)

is computed between July 1st of year t and July 1st of year t+5, and centered on January 1st

in year t+3. Following standard fertility transition theory, the change in TFR is modeled in

three phases, illustrated in Figure 1. Phase I is the stable pre-transition high fertility phase;

the fertility transition has not started yet and fertility fluctuates around high TFR levels.

In some countries there was an increase before the decline started. Phase II is the fertility

transition from high fertility to replacement-level fertility or below, and Phase III consists

of the post-transition low fertility, which includes recovery from below-replacement fertility

toward replacement fertility and oscillations around replacement-level fertility.

We do not model Phase I explicitly because in all countries, the TFR has now started

to decline, so Phase I is not relevant for projections. Instead we use a deterministic rule to

identify the periods during which a country was in Phase I. If the TFR was below 5.5 in

1950–1955, we define Phase I for that country as having ended before 1950–1955, i.e. before

the beginning of our data set. For the other countries, we define the start period of the

fertility decline as the most recent period with a local maximum that is within 0.5 children

of the global maximum of the TFR within that country. The use of half child is a more

stringent threshold than the 5% decline proposed by other authors to detect the early stage

of the fertility transition (Bongaarts 2002b; Casterline 2001).

Phase III starts after the fertility transition has been completed. To operationalize this

3

Figure 1: Illustration of the three phases of the evolution of the TFR.

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3 Phases

Time

TFR

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23

45

67

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Phase IPhase IIPhase III

Start Phase II Start Phase III

for countries where this happened during the observation period, we say that Phase III

started the first time two 5-year increases below a TFR of 2 were observed. More specifi-

cally, the start of Phase III is the earliest period t for which: (i) fc,t > fc,t−1, (ii) fc,t+1 > fc,t,

and (iii) fc,p < 2 for p = t − 1, t, t + 1. Thus the start of Phase III is in the middle of two

increments below a TFR of 2 children. By this definition, in 2005–2010 the start of phase

III has been observed in 20 countries (Belgium, Bulgaria, Channel Islands, Czech Repub-

lic, Denmark, Estonia, Finland, France, Germany, Italy, Latvia, Luxembourg, Netherlands,

Norway, Russian Federation, Singapore, Spain, Sweden, United Kingdom, United States).

Fertility transition model

When a country is in Phase II, the five-year decline in its TFR is modeled as a sum of

two logistic functions and a random distortion term. Specifically, fc,t, the TFR in five-year

period t in country c, is modeled by a random walk model with drift:

fc,t+1 = fc,t − dc,t + εc,t, for τc ≤ t < λc, (1)

where dc,t is the decrement term that models the systematic decline during the fertility

transition, εc,t are random distortions that model the deviations from the systematic decline,

τc is the start period of the fertility decline, and λc is the start period of the post-transition

phase. The decrement dc,t is modeled as a function of the level of the TFR, as follows:

dct = d(θc,λc, τc, fc,t) =

�g(θc, fc,t) for τc ≤ t < λc and fc,t ≥ 1;0 otherwise,

(2)

4

where g(·, ·) is a parametric decline function. This function specifies a five-year decrement

(decrease) as a function of the current level of the TFR and parameter vector θ.

The decline function is the sum of two logistic functions, i.e. a double logistic or bi-

logistic function (Meyer 1994). The first logistic function describes a pace of fast decline at

high total fertility rates that decreases toward a slower pace as fertility becomes lower. The

second function describes the opposite force, namely a slowing down of the pace of fertility

decline over time. The sum of the two is a parametric function that describes a decline in

fertility that starts with a slow pace at high TFR values, peaks and slows down again at

lower TFR values, the general shape of the trend in TFR observed in many countries. This

model underlies the current deterministic UN methodology (United Nations, Department of

Economic and Social Affairs, Population Division 2006), and it is flexible enough to represent

a wide range of fertility decline patterns.

The double logistic function with country-specific parameter vector

θc = (�c1,�c2,�c3,�c4, dc), is

−dc

1 + exp�−2 ln(9)

�c1(fc,t −

�i �ci + 0.5�c1)

� +dc

1 + exp�−2 ln(9)

�c3(fc,t −�c4 − 0.5�c3)

� .

Figure 2 illustrates the parametrization of the double logistic function. The five-year decre-

ments as given by the decline function are plotted against TFR. The TFR is reversed, i.e.

plotted from high to low outcomes on the horizontal axis so that the five-year decrements

during the fertility transition are given by the decline curve in chronological order when fol-

lowing the curve from left to right. The maximum possible pace of the decline (the maximum

five-year decrement) is dc. However, the actual maximum pace tends to be slightly smaller

than dc; it depends on the four �ci’s, which describe the TFR ranges in which the pace of

the fertility decline changes.

The decline starts at TFR level Uc =�4

i=1 �i, where the decrement is between 0 and

10% of its maximum pace. Between TFR levels Uc and Uc − �c1, the pace of the decline

increases from around 0.1dc to over 0.8dc. During the TFR range denoted by �c2, the TFR

declines at a higher pace than during the rest of the transition: its five-year decrements

range between 0.8dc and dc. In �c3 the pace of the fertility decline decreases further to 0.1dc

at TFR level �c4. The decline is set to zero if the TFR is smaller than one.

The parameters of the decline function are estimated for each country. For countries

in which the transition started before period 1950–1955, the start level Uc =�

�ci of the

fertility decline is added as a parameter to the model. For countries in which the fertility

decline started after 1950–1955, the start period τc is within the observation period. For these

countries, the start level Uc is fixed at the TFR in that period, Uc = fc,τc . The systematic

decline in the start period is between 0 and 10% of the maximum decline, with a “start

5

Figure 2: Five-year decrements as given by the double logistic function g(θc, fc,t) plottedagainst the TFR. The horizontal TFR axis is negatively oriented (i.e. decreasing from leftto right).

fct (decreasing)

0ΔΔc1 ΔΔc2 ΔΔc3 ΔΔc4

0ΔΔc4Uc=ΣΣΔΔci

dc

0.1dc

0.8dcg((θθc,, fct))

period” random distortion term εc,τc added to it, to allow a bigger decrease in that specific

period:

εc,τc ∼ N(mτ , s2τ ), (3)

where mτ is the mean and sτ is the standard deviation of the distortion in the start period.

The distribution of the distortion term εc,t after start period τc is

εc,t ∼ N(0, σ(fc,t)2), for t �= τc. (4)

The expression for its standard deviation σ(fc,t) is based on examination of the absolute

distortions as a function of the TFR level (which shows a higher variance around a TFR of

4-5, and over time), and is given in the Appendix.

Given Uc, the five parameters that determine the pace of the fertility decline and the

time that the transition takes in country c are �c4, {�ci/(Uc −�c4) : i = 1, 2, 3}, and dc.

Estimating these five parameters presents a challenge, because for any one country there

are at most 11 observed five-year decrements in Phase II, often fewer. We use a Bayesian

hierarchical model (Lindley and Smith 1972; Gelman et al. 2004) which allows us to borrow

strength from the observations in all countries when estimating the parameters for country

c, and also to assess the uncertainty of the estimates.

The model assumes that for all countries, each of their unknown decline parameters is

drawn from a probability distribution that represents the range of outcomes of that decline

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parameter across all countries. For a specific country, the posterior distribution of its decline

parameters is then determined by the world-level experience for all countries combined, as

well as the observed declines in that country. The resulting estimate can be viewed as based

on a weighted average of a “world pattern” and information from the country data. In

the hierarchical model, transformations of parameters are used to restrict their outcomes to

realistic values.

The maximum five-year decrement, dc, is transformed to restrict its outcomes to between

0.25 and 2.5 child: d∗c = log�

dc−0.252.5−dc

�. The upper bound of 2.5 reflects the maximum pace

of fertility decline observed in the past, i.e. around 2 children per five-year period in China.

The hierarchical distribution of the transformed dc’s is given by d∗c ∼ N(χ,ψ2), where χ is

the world mean of the d∗c ’s, and ψ2 is their variance.

A similar approach yields world distributions of the other decline parameters. The world

distribution for �c4 is given by �∗c4 ∼ N(�4, δ24), where �∗

c4 is a logit-transform of �c4,

namely �∗c4 = log

��c4−12.5−�c4

�, to restrict it to be between 1 and 2.5 children.

To define the world distributions of {�ci/(Uc − �c4) : i = 1, 2, 3}, we first define

pci = �ci

Uc−�c4for i = 1, 2, 3, such that

�3i=1 pci = 1. For the purpose of computation,

new parameters γci, i = 1, 2, 3 are introduced, with the pci’s defined as a function of these

parameters (Gelman et al. 1996): pci =exp(γci)�j exp(γcj)

. The hierarchical model for the γci’s is

given by γci ∼ N(αi, δ2i ), where αi is the hierarchical mean of the γci’s and δ2i is their variance.

To summarize, the complete Phase II model is defined by the random walk with drift as

described in Eq.(1), the distributions of the random distortion terms given by Eq.(3) and

(4), hierarchical distributions for country-specific decline parameters, prior distributions on

the variance parameters of the distortion terms and hierarchical parameters, and a prior

distribution on Uc for the countries in which the transition started before 1950–1955. A

Markov Chain Monte Carlo (MCMC) algorithm is used to get samples of the posterior

distributions of each of the parameters of the fertility transition model (Gelfand and Smith

1990). The full model and details of the MCMC algorithm are given in the Appendix.

Estimated decline curves

Figure 3 shows the observed five-year decrements during the fertility transition, against

decreasing TFR, for all countries combined. The world mean of the double logistic decline

curve is plotted in red.

For comparison, we also show the double logistic curves currently used by the UN. The

UN currently uses three specific double logistic curves, each corresponding to one set of

double logistic parameters. Each of these is chosen to describe a different observed pace of

decline; they are called “slow-slow,” “fast-slow” and “fast-fast.” For each country, the UN

7

analyst chooses the decline curve that seems most reasonable for the future fertility decline

in that country, based on what has been observed to date in that country or its region. The

TFR is kept constant after it decreases to 1.85. Our method generalizes the current UN

approach because we allow a wide range of parameter values for the double logistic curve

rather than restricting it to a small number of possibilities.

The UN decline functions corresponding to the fast/fast and slow/slow declines are shown

in blue in Figure 3. The Bayesian world decline curve differs from the UN decline curves at

high TFR values, but the maximum decrements are comparable between the Bayesian and

UN curves. The Bayesian world decline curve increases rapidly to its maximum outcome

after the start of the decline, while the UN decline curves increase more slowly as TFR

declines and peak around a TFR of 5.

Figure 4 shows the Bayesian estimates of the double logistic decline curves for Thailand,

India and Mozambique, together with the observed decrements in those countries and the

fast/fast and slow/slow UN decline curves. The shapes of the curves for Thailand and India

are very different. Thailand has had a very fast fertility transition, and its median decline

curve peaked at a five-year decrement of around one child per five-year period. In India the

transition has been much slower, with a maximum decrement of around 0.4 children every

five years. The UN decline curves do not capture the extent of the variation between these

two countries. In Mozambique, the observed and projected decrements are smaller than the

decrements as given by the UN decline curves. Only a short part of its transition has been

observed so far, which leads to large uncertainty about the pace of its future transition.

The 95% projection interval includes decline curves with a maximum decrement that ranges

between about 0.3 and 0.8 children per five-year period.

Post-transition model

In long-term projections the TFR is assumed to converge toward and fluctuate around

replacement-level fertility (around 2.1 children per woman for low-mortality countries). As

proposed by Lee and Tuljapurkar (1994), this is modeled with a first order autoregressive

time series model, an AR(1) model, with its mean fixed at the approximate replacement-level

fertility, µ = 2.1:

fc,t ∼ N(µ+ ρ(fc,t−1 − µ), s2) for t > λc, (5)

where ρ is the autoregressive parameter with |ρ| < 1 and s is the standard deviation of the

random errors. This can also be written as fc,t = fc,t−1 + (1 − ρ)(µ − fc,t−1) + ec,t, with

errors ec,t ∼ N(0, s2). In this model the expected increase or decrease toward 2.1 is larger

if the current TFR is farther from 2.1, and depends on ρ. For example, at a TFR of 1.5,

the expected next TFR is 2.1 − 0.6ρ; a smaller ρ will give a larger expected increase. The

8

Figure 3: World (hierarchical) mean of the double logistic decline curve (red), plotted againstdecreasing TFR. The five-year decrements for all countries during the fertility transition areplotted in grey. The UN fast/fast and slow/slow decline curves are shown in blue.

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8 6 4 2

−0.5

0.0

0.5

1.0

1.5

2.0

TFR (reversed)

5−ye

ar d

ecre

men

t

● DecrementsUN declinesHierarchical mean

Figure 4: Double logistic decline curves for Thailand, India and Mozambique, plotted againstdecreasing TFR. Examples of the decline curves simulated from the posterior distributionare shown in grey. The median and 95% projection intervals for the five-year decrements areshown in red. The observed five-year decrements are shown by black dots and the fast/fastand slow/slow UN scenarios are shown in blue.

8 6 4 2 0

0.0

0.5

1.0

1.5

2.0

Thailand

TFR (reversed)

TFR

dec

rem

ent

●

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● 5−year decrementsUN declines

Median95 % PIExample curves

8 6 4 2 0

0.0

0.5

1.0

1.5

2.0

India

TFR (reversed)

TFR

dec

rem

ent

●

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●

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8 6 4 2 0

0.0

0.5

1.0

1.5

2.0

Mozambique

TFR (reversed)

TFR

dec

rem

ent

●●●●

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9

smaller ρ, the more quickly the TFR will increase toward replacement-level fertility. The

asymptotic 100(1− α)% projection interval is�2.1− zα

s�1− ρ2

, 2.1 + zαs�

1− ρ2

�, (6)

where zα is the (1 − α2 ) quantile of the standard normal distribution. For example, for an

80% projection interval, zα = 1.28 and for a 95% projection interval, zα = 1.96. Equation

(6) is the projection interval for the TFR in the distant future.

The AR(1) parameters ρ and s are estimated using maximum-likelihood estimation based

on all the data points after and including period λc. The start of Phase III has been observed

in 20 countries, giving 52 post-transition outcomes (fc,t−1, fc,t) to estimate the parameters

of the AR(1) process. The maximum-likelihood estimate for ρ is 0.906, and the estimated

standard deviation of the residuals is 0.09. The 52 post-transition outcomes and fitted

regression line are shown in Figure 5; the fitted regression line fits the data well. The

asymptotic 80% projection interval is [1.83, 2.37] and the asymptotic 95% projection interval

is [1.68, 2.52].

The estimated value of ρ gives expected increases that are similar to those from the

current UN methodology. The UN projects increments of 0.05 child for each five-year period,

until the TFR equals 1.85, as shown in blue in the same figure.

Figure 5: Observed UN estimates in Phase III, with the TFR in period t plotted againstits predecessor. The expected outcome for the TFR in the AR(1) model, conditional on itsprevious outcome, is shown in red, with the limits of its 95% projection interval (dashedlines). The current UN projection methodology is illustrated in blue.

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0.9 1.2 1.5 1.8 2.1

0.9

1.2

1.5

1.8

2.1

f(t) = f(t−1)UN: f(t) = f(t−1) + 0.05AR(1): f(t) = f(t−1) + 0.1(2.1−f(t−1))

10

TFR projections

TFR projections during the fertility transition are based on the Phase II model, using the

sample from the posterior distribution of the model parameters. The result is a sample from

the predictive distribution of future TFR trajectories for each country. For example, consider

projecting fc,t+1, the TFR in country c in period (t+1), assuming that it is in Phase II. The

predictive distribution is represented by a sample {f (i)c,t+1 : i = 1, . . . , I}. The i-th member

of the sample, f (i)c,t+1, is then given by f (i)

c,t+1 = f (i)c,t − d(θ(i)

c , f (i)c,t ) + ε(i)c,t, where θ(i)

c is the i-th

sample of parameter vector θc, and ε(i)c,t is a random draw from N(0, σ(fc,t)(i)).

In each trajectory, Phase III is projected to start when the TFR has decreased to a TFR

level which is around replacement-level fertility, and after the pace of the fertility decline has

decreased to zero. These assumptions are incorporated into the definition of the projected

start of Phase III, which is given by the earliest period t such that (i) mint{f (i)c,t } ≤ �(i)

c4 , and

(ii) f (i)c,t > f (i)

c,t−1. The TFR level �(i)c4 is the TFR outcome at which the pace of the fertility

declines has decreased to 10% of the maximum five-year decrement, and is restricted to be

between 1 and 2.5 children, to ensure that the fertility transition ends around replacement-

level fertility. The TFR increases in period t when the sum of the expected fertility decrement

(given by the decline parameters) and the random distortion term is positive. The chance of

an increasing TFR in period t increases as the expected fertility decrements decrease, and is

thus more likely to occur at the end of the fertility transition. This definition of the start of

Phase III means that the projected start period depends on the decline parameters, as well

as the random distortion terms. The start of Phase III will vary between trajectories, which

quantifies the uncertainty in the start of the recovery phase. For projections during Phase

III, the AR(1) model is used, with the parameters as described in the previous section.

In all projections, an additional prior distribution is put on future TFR outcomes, fc,t+1 ∼U [0, Uc]. The upper bound is used to exclude TFR trajectories in which the fertility transition

does not take off. The lower bound ensures positive TFR outcomes. This prior is enforced

by resampling any future distortion term that results in TFR outcomes outside its prior

bounds.

After constructing a large sample of TFR trajectories, the “best” TFR projection is given

by the median outcome of the TFR trajectories in each period, and the bounds of the 80%

projection intervals are given by the 10th and 90th percentiles.

11

RESULTS

Probabilistic TFR projections

We now illustrate the results of the Bayesian projection model (BPM) for several countries.

The results for all countries are given in the Appendix. Figure 6 shows the projection intervals

for future TFR in six countries with different fertility pasts and prospects, namely Italy,

China, the USA, India, Israel and Mozambique. We also show the current UN projections

and the result of adding and subtracting half a child to and from the median BPM projection,

by analogy with how the UN constructs its high and low variants. The most recent UN

estimates for 2005–2010 and projections for 2045–2050 and 2095–2100 for these countries

are shown in Table 1.

Table 1: Projection results for 2045–2050 and 2095–2100 for selected countries, ordered byincreasing TFR in 2005–2010. “Low” and “High” refer to the lower and upper bounds ofthe 80% projection interval.

Country UN BPM 2045–2049 BPM 2095–21002005–09 2045–50 Low Median High Width Low Median High Width

Italy 1.38 1.74 1.52 1.76 2.02 0.50 1.68 1.97 2.26 0.57China 1.77 1.85 1.08 1.66 2.03 0.95 1.56 1.92 2.22 0.66USA 2.09 1.85 1.86 2.10 2.36 0.50 1.84 2.10 2.38 0.54India 2.76 1.85 1.37 1.83 2.31 0.94 1.28 1.75 2.13 0.85Israel 2.81 1.90 1.58 2.06 2.57 0.99 1.30 1.80 2.23 0.92Mozambique 5.11 2.41 1.85 2.61 3.42 1.57 1.32 1.83 2.41 1.10

Italy: Italy is one of the countries, mostly in Europe, that have had “lowest-low” fertility

during the past two decades, as documented by Kohler, Billari, and Ortega (2002), Billari and

Kohler (2004) and Frejka and Sobotka (2008). Various possible explanations of this have

been reviewed by Morgan and Taylor (2006). After some recovery over the past decade,

Italy’s TFR is currently 1.38. Our method projects a slow recovery, with relatively tight

80% projection intervals of about plus or minus a quarter of a child, much tighter in this

case than the UN’s scenarios of plus or minus half a child. The UN projects a recovery of

TFR toward 1.85 while our method projects movement toward 2.1. However, the average

recovery projected by our model is so slow that the UN medium projection and the Bayesian

median projection are essentially the same for 2045–50 (1.77 and 1.76). Even by 2100 the

Bayesian median projection is still below 2.1 (1.97), although there is a real possibility of

recovery by then (the upper bound is 2.26).

12

Figure 6: Projection intervals for Italy, China, United States of America, India, Israel andMozambique. The median projection (solid red line), 80% projection intervals (purple dashedlines) and 95% projection intervals (red dashed lines) are shown, as well as the medianprojection plus and minus half a child (green dashed lines). The UN estimates and projections(2008 revision) are shown in blue.

1.0

1.5

2.0

2.5

Italy

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098

● UN estimatesUN projection

●

●

●

●

Median projectionMedian +/− 0.5 child80% PI95% PI

0.5

1.0

1.5

2.0

2.5

China

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●● ●


1.5

2.0

2.5

3.0

United States of America

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

India

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Israel

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●●

●


12

34

56

Mozambique

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


13

Our projection of a recovery is supported by the more detailed analysis by region, age

and social group conducted by Caltabiano, Castiglioni, and Rossina (2009). One possible

explanation is given by Myrskyla, Kohler, and Billari (2009), who postulate a recent reversal

of the negative relationship between fertility and socioeconomic factors that prevailed during

the fertility transition.

This paper is concerned with projecting period TFR, which UN incorporates into its

population projections. Bongaarts and Feeney (1998) have pointed out that current below-

replacement period TFRs may be lower than the cohort TFRs for the currently fertile cohorts,

reflecting a tempo rather than a quantum effect. Our AR(1) model for the low fertility Phase

III predicts a recovery from below-replacement period TFR, as does the Bongaarts-Feeney

work, and so it may to some extent capture this phenomenon. Bongaarts (2002a), Lutz,

O’Neill, and Scherbov (2003) and Sobotka (2005) have estimated the average tempo effect

on TFR in Europe to be in the range of 0.2–0.3 children, and as a result Lutz et al. (2003)

predicted that average TFR in Europe would increase from about 1.5 to about 1.8. This is

compatible with our projections.

The probabilistic TFR projections of the Uncertain Population in Europe project (Alho

et al. 2006; Alders, Keilman, and Cruijsen 2007; Alho, Jensen, and Lassila 2008) yield

much wider intervals than our method for the below replacement European countries, with

widths of their 80% intervals averaging around 1.7 compared with our 0.6 or so. They based

their intervals on past projection errors by projecting agencies, and these tended to greatly

underestimate the pace of decline, leading to large errors. Our projection method would

have made much smaller errors in the past, leading to tighter projection intervals for the

future.

China: According to the UN estimates, Chinese TFR fell precipitously from the 1960s to

the late 1990s, but has remained constant for the past 15 years or so, at its current level of

1.77. The Bayesian median projection is that the fertility transition is not yet finished, and

that a further small decline will be followed by a slow recovery. The UN projection is quite

different, remaining essentially constant at 1.85.

Our projection intervals for China are much wider than for Italy — one child compared

to half a child — even though both countries are currently below replacement. This reflects

the unusual form of the Chinese data, with a steep fall followed by the abrupt end of the

decline.

In fact there is controversy about whether the sudden halt to the decline over the past 15

years is real. The UN estimate of 1.77 for China’s TFR in 2005–2010 was based on official

estimates. However, Gu and Cai (2009) argue that current Chinese TFR is lower, at about

14

1.5, summarizing several other studies (Retherford et al. 2005; Zhang and Zhao 2006; Cai

2008; Morgan, Guo, and Hayford 2009). A decline of the TFR to 1.5 over the past 15 years

would also be more in line with the experience of other countries than the reported abrupt

end of the decline. Reflecting this, our 80% projection intervals are also compatible with a

lower current TFR value.

United States: The United States is the only country so far that has recovered to re-

placement level from a below-replacement turnaround (about 1.75 in the mid-1970s). Our

projection is flat, with a width of about half a child. The UN projection declines fairly

quickly to 1.85, and stays there. This value of 1.85 is on the edge of our 80% interval. By

comparison, the US Census Bureau projects a TFR of 2.0 in 2045–2049, which is higher than

the UN projection and well within our interval.

Preston and Hartnett (2008) analyzed the effect of population changes on fertility, and

concluded that the two most predictable changes in population composition, educational

attainment and ethnicity, are expected to induce relatively small changes in fertility by

2025–29, and that these changes essentially cancel one another out. Their estimated changes

in the TFR related to the tempo effect, increasing ratio of female to male earnings, and

possible restrictions on abortion access (0.15, -0.24 and 0.1 respectively) would yield TFR

projections that are within our projection interval.

Israel: Israel is an unusual case because it has a technologically advanced market economy,

but its TFR of 2.8 is much higher than in most other such countries. Over the past 60 years

its fertility has declined, but more slowly than in most other countries. Our projection

reflects this, with a likely slow decline for the rest of the century. In contrast, the UN

projects a faster decline.

DellaPergola (2007, 2009) and Nahmias and Stecklov (2007) analyzed proximate deter-

minants of fertility in Israel, including fertility intentions, and concluded that future fertility

decline would be slow, or perhaps non-existent. Our projection is compatible with this, with

an upper bound for 2045–2050 of 2.6, only 0.2 children below the current level. The unusual

nature of the Israeli fertility transition is reflected in a relatively wide projection interval of

one child in 2045–2049.

India: India has a TFR of 2.8 and has had a typical, fairly rapid fertility decline. The

Bayesian median projection indicates that the steady decline will continue to below replace-

ment level, bottoming out late in the century and followed by a gradual recovery that will

likely continue into the next century. As with other countries with mid-level fertility, there

15

is considerable uncertainty about the timing, leading to fairly wide projection intervals of

slightly less than one child.

Mozambique: Like most other countries in sub-Saharan Africa, Mozambique’s TFR of

5.1 is high and has experienced a late and slow fertility decline. Reflecting this, our method

projects a continued slow decline. It also reflects considerable uncertainty, with an interval

of width 1.6 children in 2045–2049.

The UN projects a faster decline than our method, for Mozambique as well as most other

high-fertility countries. This is because the UN method is restricted to a small number of

double logistic decline functions, none of which fit the Mozambique experience well, as shown

in Figure 4(c). In contrast, the double logistic curve estimated by the Bayesian method does

fit the Mozambique experience well.

This is the largest difference between the UN medium projection and the Bayesian me-

dian projection for the selected countries. For 2045–50 in Mozambique these are 2.4 (UN),

and 2.6 (Bayesian). The US Census Bureau projection is much higher than either, at 3.2,

anticipating an even slower decline. Guengant and May (2002) analyzed the proximate de-

terminants of fertility, including contraceptive use, in sub-Saharan Africa, and concluded

that the fertility decline was likely to continue to be slow. Timaeus and Moultrie (2008) and

Moultrie and Timaeus (2009) analyzed the evidence for stopping, spacing and postponing

in these countries, and also concluded that continued slow decline is the most likely future.

These analyses lead to conclusions similar to those from our method.

Regional averages: A summary of the average results by region for 2045–50 is given in

Table 2. Western, Middle and Eastern Africa have the highest projected fertility in 2045–50

and the widest projection intervals, because of high current levels of fertility. The mean

uncertainty as given by the 80% projection interval is around 1.4 children. The widths of

the 80% projection intervals are around 1 child for Asia, Latin America and the Pacific, and

around 0.6 children for Europe. The widths of the 95% projection intervals are about 50%

wider than the widths of the 80% projection intervals.

Differences by 2045–2050 between the UN medium TFR projection and the BPM median

TFR projection are overall small for regional averages (|12|% at most). Main differences be-

tween the two approaches can be roughly summarized as follows; for regions with countries

that have experienced fast fertility declines in recent decade(s), the BPM projects lower

fertility by 2045–2050 than in the current UN projection (e.g., Asia, Northern and Southern

Africa), while for other regions with high-fertility countries in 2005–2010 that have experi-

enced slower fertility declines, the BPM projects a slower fertility decline than in the UN

16

projection (e.g., Western, Eastern and Middle Africa and part of Oceania).

Table 2: Mean projection results by region for 2045–50; UN projection and median projec-tion with Bayesian projection model, and the mean widths of the 80% and 95% projectionintervals (PI). Note that regional averages are not population weighted, the results are thearithmetic means of the country-specific outcomes within the region.

Region UN 2008 Projection Mean widthUN BPM 95% PI 80% PI

Eastern Africa 4.6 2.3 2.6 2.1 1.4Middle Africa 5.0 2.4 2.5 2.2 1.4Northern Africa 2.7 1.9 1.7 1.4 1.0Southern Africa 3.2 2.0 1.8 1.4 0.9Western Africa 5.1 2.5 2.8 2.3 1.5Eastern Asia 1.4 1.6 1.6 1.2 0.7South-Central Asia 2.9 2.0 1.8 1.5 1.0South-Eastern Asia 2.8 2.0 1.8 1.5 1.0Western Asia 2.8 1.9 1.8 1.5 1.0Eastern Europe 1.3 1.8 1.7 0.9 0.5Northern Europe 1.7 1.8 1.9 0.9 0.6Southern Europe 1.4 1.8 1.7 1.1 0.7Western Europe 1.6 1.8 1.8 0.9 0.5Caribbean 2.1 1.9 1.7 1.4 0.9Central America 2.8 1.8 1.8 1.4 1.0South America 2.5 1.9 1.8 1.4 0.9Northern America 1.8 1.8 1.9 1.0 0.6Australia/New Zealand 1.9 1.8 1.7 1.4 0.9Melanesia 3.4 2.0 2.2 1.6 1.0Micronesia 3.1 1.8 2.0 1.4 0.9Polynesia 3.4 2.1 2.3 1.6 1.1

Model validation

We validated our projection model using out-of-sample projections. In the first set of out-

of-sample projections, we used the Bayesian projection model to construct projections for

1980–2010 based on the UN estimates up to and including the five-year period 1975–1980.

In the second set of out-of-sample projections, we used the BPM to construct projections

for 1995–2010 based on the UN estimates up to and including the five-year period 1990–

1995. The first set of projections was compared to the UN estimates for the six five-year

periods from 1980–1985 up to 2005–2010, and the second set of projections was compared to

the UN estimates for the three five-year periods 1995–2000, 2000–2005 and 2005–2010. In

17

both comparisons, we did not include countries that were still in Phase I at the start of the

projection period. This is because currently there are no countries in Phase I, so predictive

performance for these countries is not relevant to the current projection task.

Table 3 shows the proportion of left-out UN estimates that fall outside their projection

intervals. If the projection model is valid, on average we expect about 10% of the values

to fall above the upper bound of the 80% projection interval and about 10% to fall below

its lower bound. Similarly, we expect about 2.5% to fall above the upper bound of the

95% interval and about 2.5% to fall below its lower bound. The projection intervals were

reasonably well calibrated in both out-of-sample projections, although the TFR was slightly

overpredicted in the more recent periods.

Table 3: Model validation results: Mean squared error (MSE) and proportion of left-out UNestimates that falls above the median projected TFR, and above or below their 80% and 95%projection intervals in future periods, when projecting from 1975–1980 and from 1990–1995.

Data until 1980 MSE Above Proportion of obs.Median Above Below Above Below

95%PI 95%PI 80%PI 80%PI1980–1985 0.11 0.49 0.05 0.01 0.11 0.111985–1990 0.22 0.51 0.03 0.05 0.11 0.101990–1995 0.38 0.45 0.04 0.07 0.08 0.141995–2000 0.59 0.38 0.03 0.10 0.07 0.212000–2005 0.63 0.38 0.02 0.07 0.07 0.212005–2010 0.59 0.39 0.02 0.04 0.07 0.15

Data until 1995 MSE Above Proportion of obs.Median Above Below Above Below

95%PI 95%PI 80%PI 80%PI1995–2000 0.07 0.33 0.02 0.08 0.04 0.192000–2005 0.17 0.37 0.02 0.05 0.06 0.192005–2010 0.21 0.39 0.02 0.03 0.05 0.11

DISCUSSION

We have proposed a new unified approach to projecting the total fertility rate for all countries

of the world. It uses a Bayesian projection model that incorporates worldwide fertility

experience over the last 60 years. The method produces a full predictive distribution of

future fertility trajectories. We assessed it in terms of out-of-sample predictive performance,

and it yielded reasonably well-calibrated intervals.

To summarize our results in this paper we have presented the median projections and

18

80% and 95% projection intervals. For policy planning purposes, the 80% projection interval

may often give enough insight into future scenarios. It has the appealing interpretation that

there is one chance in ten of the true value being above the interval, and one chance in ten

of it being below. The 95% projection interval is wider and can be viewed as encompassing

most plausible possibilities.

The projection intervals as presented here are based on the UN estimates of past and

current TFR. However, the UN estimates themselves measure the true TFR with some error,

and to that extent our projections may underestimate uncertainty about future true TFR.

They can be viewed validly as probabilistic projections of future UN TFR estimates. Ideally,

uncertainty about the UN estimates would be incorporated into our uncertainty assessment,

but the wide variation in data availability and quality around the world makes this difficult.

For one effort to assess uncertainty about the UN TFR estimates, see Alkema et al. (2008b).

It is possible that the projections could be improved by including information about

future trends in determinants or predictors of fertility, such as child mortality and economic

development. However, this would require good projections of these variables themselves far

into the future, and they are arguably harder to predict than fertility itself. This has been

done for several countries at the national level (Lutz 1994; Sanderson 1998). However, it

would be hard to do this for all countries on a comparable basis.

The projections produced by our method could in practice provide a basis for UN pro-

jections of future TFR, to be evaluated and if necessary revised by the UN analysts. Recent

values and short-term projections of proximate determinants such as contraceptive use and

fertility intentions could be especially useful for doing this. As these are not available on

a uniform basis for all countries, modifications of the results of our method based on these

could be incorporated in a more informal way by analysts, as is currently done with the UN’s

deterministic projections.

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24

APPENDIX: METHODOLOGY

The complete fertility transition model is given by:

fc,t+1 = fc,t − dc,t + εc,t, for c = 1, . . . , C, t = τc, . . . ,λc − 1,

εc,t ∼ N(0, σ(fc,t)2), for t �= τc,

εc,τc ∼ N(mτ , s2τ ),

dc,t = d(θc,λc, τc, fc,t),

d(θc,λc, τc, fc,t) =

�g(θc, fc,t) for τc ≤ t < λc and fc,t ≥ 1;0 otherwise,

θc = (�c1,�c2,�c3,�c4, dc),

g(θc, fc,t) =−dc

1 + exp�−2 ln(9)

�c1(fc,t −

�i �ci + 0.5�c1)

�

+dc

1 + exp�−2 ln(9)

�c3(fc,t −�c4 − 0.5�c3)

� ,

τc =

�max{t : (Mc − Lc,t) < 0.5}, if Lc,t > 5.5;1950− 1955, otherwise.

Uc

�= fc,τ , if τc ≥ 1950− 1955;∼ U(min{5.5,max

tfc,t}, 8.8) for τc < 1950− 1955,

where Mc = maxt

fc,t is the maximum observed TFR outcome in country c, and Lc,t denote

local maxima. The upper bound of the prior distribution for Uc for countries in which the

decline had possibly already started before 1950–1955 is based on the observed maximum in

the UN estimates, namely 8.7. Its lower bound is the minimum of the maximum observed

TFR value and 5.5 children (5.5 children is based on examining decline curves, the minimum

level at which the decline starts is slightly under 6).

The expression for the standard deviation σ(fc,t) of the distortion terms after start period

τc is:

σ(fc,t) = c1975(t)�σ0 + (fc,t − S)

�−aI[S,∞)(fc,t) + bI[0,S)(fc,t)

��,

where σ0 is the maximum standard deviation of the distortions, attained at TFR level S,

and a and b are multipliers of the standard deviation, to model the linear decrease for larger

and smaller outcomes of the TFR. The constant c1975(t) is added to model the higher error

variance of the distortions before 1975, and is given by:

c1975(t) =

�c1975, t ∈ [1950− 1955, 1970− 1975];1, t ∈ [1975− 1980,∞).

(7)

25

The hierarchical part of the transition model is given by:

d∗c = log

�dc − 0.25

2.5− dc

�,

d∗c ∼ N(χ,ψ2),

�∗c4 = log

��c4 − 1

2.5−�c4

�,

�∗c4 ∼ N(�4, δ

24),

pci =�ci

Uc −�c4for i = 1, 2, 3,

pci =exp(γci)�3j=1 exp(γcj)

,

γci ∼ N(αi, δ2i ).

The country-specific parameters in the model are given by {γci, Uc, dc,�c4}, i = 1, 2, 3.

The hyperparameters in the model are given by {χ,ψ2,�4, δ4,α, δ} and {a, b, S, σ0, c1975,mτ , sτ}.The prior distributions on the hyperparameters are given by:

χ ∼ N(−1.5, 0.62),

1/ψ2 ∼ Gamma(1, 0.62),

α1 ∼ N(−1, 1),

α2 ∼ N(0.5, 1),

α3 ∼ N(1.5, 1),

1/δ2i ∼ Gamma(1, 1), for i = 1, . . . , 4

δ4 ∼ N(0.3, 1),

a ∼ U [0, 0.2],

b ∼ U [0, 0.2],

σ0 ∼ U [0.01, 0.6],

c1975 ∼ U [0.8, 2],

S ∼ U [3.5, 6.5],

mτ ∼ N(−0.25, 0.42),

1/s2τ ∼ Gamma(1, 0.42),

The prior distributions on the hyperparameters are chosen based on (i) initial least-squares

fits to fertility declines in countries that had observed most of the transition and/or (ii)

guesses of reasonable outcomes. All priors are more spread out than their posterior distri-

butions (i.e. the posterior is determined mostly by the data).

26

A Markov Chain Monte Carlo (MCMC) algorithm is used to get samples of the posterior

distributions of each of the parameters of the fertility transition model (Gelfand and Smith

1990). This algorithm is a combination of Gibbs sampling, Metropolis-Hastings and slice

sampling steps (Neal 2003).

The MCMC sampling algorithm was implemented in the statistical package R. The results

are based on a run of 102,000 iterations, discarding the first 2,000. Convergence of all model

parameters was assessed using the run length diagnostic of Raftery and Lewis (1992, 1996).

The length of the MCMC chain exceeded the required sample size for estimating the 2.5%

and 97.5% percentiles of the posterior distributions of all model parameters to within +/-

0.0125 accuracy with probability 0.95. Convergence of αi, i = 1, 2, 3, was assessed on the

transformed scale, i.e. αi/(�3

j=1 αj), as these parameters are only weakly identified on

their original scale (the likelihood of the data conditional on these parameters, and thus the

projections, are not altered when adding a constant to all three αi’s). Similarly for the γci’s,

i = 1, 2, 3, c = 1, . . . , C, convergence was assessed for γci/(�3

j=1 γcj), i = 1, 2, 3, c = 1, . . . , C.

APPENDIX: RESULTS

27

12

34

56

7

Burundi

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Comoros

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Djibouti

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Eritrea

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Ethiopia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Kenya

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


Figure 7: Projections for Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya

28

12

34

56

Madagascar

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Malawi

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Mauritius

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Mayotte

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Mozambique

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Reunion

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


Figure 8: Projections for Madagascar, Malawi, Mauritius, Mayotte, Mozambique, Reunion

29

12

34

56

Rwanda

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Somalia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ● ●●


12

34

56

7

Uganda

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


12

34

56

United Republic of Tanzania

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


12

34

56

7

Zambia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


12

34

5

Zimbabwe

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 9: Projections for Rwanda, Somalia, Uganda, United Republic of Tanzania, Zambia,Zimbabwe

30

12

34

56

7

Angola

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Cameroon

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Central African Republic

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Chad

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●●

●


12

34

5

Congo

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


12

34

56

7

Democratic Republic of the Congo

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


Figure 10: Projections for Angola, Cameroon, Central African Republic, Chad, Congo,Democratic Republic of the Congo

31

12

34

56

Equatorial Guinea

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●

●

●


12

34

5

Gabon

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Sao Tome and Principe

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Algeria

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Egypt

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


01

23

4

Libyan Arab Jamahiriya

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 11: Projections for Equatorial Guinea, Gabon, Sao Tome and Principe, Algeria,Egypt, Libyan Arab Jamahiriya

32

12

34

Morocco

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Sudan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Tunisia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Western Sahara

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Botswana

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Lesotho

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 12: Projections for Morocco, Sudan, Tunisia, Western Sahara, Botswana, Lesotho

33

12

34

5

Namibia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

South Africa

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Swaziland

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Benin

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Burkina Faso

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Cape Verde

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 13: Projections for Namibia, South Africa, Swaziland, Benin, Burkina Faso, CapeVerde

34

12

34

56

Cote d'Ivoire

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Gambia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Ghana

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Guinea

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Guinea−Bissau

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●●

●


12

34

56

7

Liberia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 14: Projections for Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia

35

12

34

56

7

Mali

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Mauritania

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


24

68

Niger

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Nigeria

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Senegal

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Sierra Leone

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


Figure 15: Projections for Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone

36

12

34

56

Togo

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

China

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●● ●

Median projectionMedian +/− 0.5 child80% PI95% PI0.

51.

01.

52.

02.

5

China, Hong Kong SAR

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


0.5

1.0

1.5

2.0

2.5

China, Macao SAR

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Dem. People's Republic of Korea

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Japan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


Figure 16: Projections for Togo, China, China, Hong Kong SAR, China, Macao SAR, Dem.People’s Republic of Korea, Japan

37

12

34

Mongolia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


0.5

1.0

1.5

2.0

2.5

Republic of Korea

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


24

68

Afghanistan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●

●

●


12

34

Bangladesh

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Bhutan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

India

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 17: Projections for Mongolia, Republic of Korea, Afghanistan, Bangladesh, Bhutan,India

38

12

34

Iran (Islamic Republic of)

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Kazakhstan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●

●


12

34

Kyrgyzstan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


01

23

45

Maldives

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Nepal

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Pakistan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 18: Projections for Iran (Islamic Republic of), Kazakhstan, Kyrgyzstan, Maldives,Nepal, Pakistan

39

0.5

1.0

1.5

2.0

2.5

3.0

Sri Lanka

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


12

34

5

Tajikistan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Turkmenistan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Uzbekistan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Brunei Darussalam

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Cambodia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 19: Projections for Sri Lanka, Tajikistan, Turkmenistan, Uzbekistan, Brunei Darus-salam, Cambodia

40

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Indonesia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Lao People's Democratic Republic

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Malaysia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Myanmar

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Philippines

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.0

1.5

2.0

2.5

Singapore

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 20: Projections for Indonesia, Lao People’s Democratic Republic, Malaysia, Myan-mar, Philippines, Singapore

41

0.5

1.0

1.5

2.0

2.5

Thailand

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


12

34

56

7

Timor−Leste

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●

●


12

3

Viet Nam

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Armenia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Azerbaijan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Bahrain

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 21: Projections for Thailand, Timor-Leste, Viet Nam, Armenia, Azerbaijan, Bahrain

42

0.5

1.0

1.5

2.0

2.5

Cyprus

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Georgia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


12

34

56

Iraq

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Israel

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●●

●


12

34

5

Jordan

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Kuwait

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 22: Projections for Cyprus, Georgia, Iraq, Israel, Jordan, Kuwait

43

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Lebanon

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

7

Occupied Palestinian Territory

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


01

23

45

67

Oman

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Qatar

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Saudi Arabia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Syrian Arab Republic

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 23: Projections for Lebanon, Occupied Palestinian Territory, Oman, Qatar, SaudiArabia, Syrian Arab Republic,

44

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Turkey

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

United Arab Emirates

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


24

68

Yemen

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Belarus

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


1.0

1.5

2.0

2.5

Bulgaria

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


1.0

1.5

2.0

2.5

Czech Republic

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


Figure 24: Projections for Turkey, United Arab Emirates, Yemen, Belarus, Bulgaria, CzechRepublic

45

0.5

1.0

1.5

2.0

2.5

Hungary

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Poland

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


0.5

1.0

1.5

2.0

2.5

Republic of Moldova

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


0.5

1.0

1.5

2.0

2.5

Romania

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


1.0

1.5

2.0

2.5

Russian Federation

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.0

1.5

2.0

2.5

Slovakia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 25: Projections for Hungary, Poland, Republic of Moldova, Romania, Russian Feder-ation, Slovakia

46

1.0

1.5

2.0

2.5

Ukraine

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.0

1.5

2.0

2.5

3.0

Channel Islands

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ● ●


1.5

2.0

2.5

3.0

Denmark

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ● ●

●


1.0

1.5

2.0

2.5

3.0

Estonia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.5

2.0

2.5

3.0

Finland

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●

●


0.5

1.0

1.5

2.0

2.5

3.0

Iceland

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 26: Projections for Ukraine, Channel Islands, Denmark, Estonia, Finland, Iceland

47

0.5

1.0

1.5

2.0

2.5

Ireland

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


1.0

1.5

2.0

2.5

Latvia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.0

1.5

2.0

2.5

Lithuania

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.5

2.0

2.5

3.0

Norway

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.5

2.0

2.5

3.0

Sweden

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


1.5

2.0

2.5

3.0

United Kingdom

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●

●


Figure 27: Projections for Ireland, Latvia, Lithuania, Norway, Sweden, United Kingdom

48

0.5

1.0

1.5

2.0

2.5

3.0

Albania

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Bosnia and Herzegovina

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●

●

●


0.5

1.0

1.5

2.0

2.5

Croatia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


1.0

1.5

2.0

2.5

3.0

Greece

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


1.0

1.5

2.0

2.5

Italy

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Malta

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 28: Projections for Albania, Bosnia and Herzegovina, Croatia, Greece, Italy, Malta

49

0.5

1.0

1.5

2.0

2.5

Montenegro

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


0.5

1.0

1.5

2.0

2.5

Portugal

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


0.5

1.0

1.5

2.0

2.5

Serbia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


1.0

1.5

2.0

2.5

Slovenia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


1.0

1.5

2.0

2.5

Spain

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

TFYR Macedonia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 29: Projections for Montenegro, Portugal, Serbia, Slovenia, Spain, TFYR Macedonia

50

0.5

1.0

1.5

2.0

2.5

Austria

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●● ●


1.0

1.5

2.0

2.5

3.0

Belgium

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●

●

●


1.5

2.0

2.5

3.0

France

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


1.0

1.5

2.0

2.5

Germany

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●● ●

●


1.0

1.5

2.0

2.5

3.0

Luxembourg

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


1.0

1.5

2.0

2.5

3.0

Netherlands

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

● ●


Figure 30: Projections for Austria, Belgium, France, Germany, Luxembourg, Netherlands

51

0.5

1.0

1.5

2.0

2.5

Switzerland

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


0.5

1.0

1.5

2.0

2.5

Aruba

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Bahamas

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Barbados

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●●


0.5

1.0

1.5

2.0

2.5

Cuba

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Dominican Republic

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 31: Projections for Switzerland, Aruba, Bahamas, Barbados, Cuba, Dominican Re-public

52

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Grenada

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Guadeloupe

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


● ●●

●


12

34

5

Haiti

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Jamaica

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Martinique

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Netherlands Antilles

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


Figure 32: Projections for Grenada, Guadeloupe, Haiti, Jamaica, Martinique, NetherlandsAntilles

53

0.5

1.0

1.5

2.0

2.5

Puerto Rico

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

● ●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Saint Lucia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


0.5

1.0

1.5

2.0

2.5

3.0

Saint Vincent and the Grenadines

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

Trinidad and Tobago

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

United States Virgin Islands

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Belize

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 33: Projections for Puerto Rico, Saint Lucia, Saint Vincent and the Grenadines,Trinidad and Tobago, United States Virgin Islands, Belize

54

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Costa Rica

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

El Salvador

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

56

Guatemala

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Honduras

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Mexico

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Nicaragua

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 34: Projections for Costa Rica, El Salvador, Guatemala, Honduras, Mexico,Nicaragua

55

0.5

1.0

1.5

2.0

2.5

3.0

Panama

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Argentina

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Bolivia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Brazil

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Chile

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Colombia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 35: Projections for Panama, Argentina, Bolivia, Brazil, Chile, Colombia

56

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Ecuador

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

French Guiana

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Guyana

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Paraguay

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

Peru

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Suriname

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 36: Projections for Ecuador, French Guiana, Guyana, Paraguay, Peru, Suriname

57

0.5

1.0

1.5

2.0

2.5

3.0

Uruguay

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Venezuela (Bolivarian Republic of)

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Canada

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


1.5

2.0

2.5

3.0

United States of America

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

Australia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●●

●


0.5

1.0

1.5

2.0

2.5

3.0

New Zealand

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●

●


Figure 37: Projections for Uruguay, Venezuela (Bolivarian Republic of), Canada, UnitedStates of America, Australia, New Zealand,

58

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Fiji

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

New Caledonia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Papua New Guinea

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●●

●

●


12

34

56

Solomon Islands

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Vanuatu

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

Guam

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


Figure 38: Projections for Fiji, New Caledonia, Papua New Guinea, Solomon Islands, Van-uatu, Guam,

59

12

34

5

Micronesia (Fed. States of)

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


0.5

1.0

1.5

2.0

2.5

3.0

3.5

French Polynesia

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

●

●

●


12

34

5

Samoa

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●

●


12

34

5

Tonga

Period

TFR

1993 2008 2023 2038 2053 2068 2083 2098


●

● ●

●


Figure 39: Projections for Micronesia (Fed. States of), French Polynesia, Samoa, Tonga

60

Date post:	12-Sep-2021
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Probabilistic Projections of the Total Fertility Rate for All

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