DEMOGRAPHIC

AND

HEALTH

RESEARCH

DHS WORKING PAPERS

2010 No. 68

Kazuyo Machiyama

September 2010

This document was produced for review by the United States Agency for 

International Development.

A Re-examination of Recent 

Fertility Declines in Sub-Saharan Africa
The DHS Working Papers series is a prepublication series of papers reporting on research in progress that 

is based on Demographic and Health Surveys (DHS) data. This research is carried out with support 

provided by the United States Agency for International Development (USAID) through the MEASURE 

DHS project (#GPO-C-00-08-00008-00). The views expressed are those of the authors and do not 

necessarily reflect the views of USAID or the United States Government. 

MEASURE DHS assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about the MEASURE DHS project can 

be obtained by contacting MEASURE DHS, ICF Macro, 11785 Beltsville Drive, Suite 300, Calverton, 

MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@measuredhs.com; internet: 

www.measuredhs.com).
A Re-examination of Recent Fertility Declines in Sub-Saharan Africa 

Kazuyo Machiyama 

ICF Macro 

Calverton, Maryland, USA 

September 2010 

Corresponding author: Kazuyo Machiyama, Department for Population Studies London School of 

Hygiene and Tropical Medicine, Room 210 51 Bedford Square London WC1B 3DP UK; E-mail: 

Kazuyo.machiyama@lsthm.ac.uk 
ACKNOWLEDGMENTS

This study was supported by a fellowship to Kazuyo Machiyama from the United States 

Agency for International Development (USAID) through the MEASURE DHS project at ICF 

Macro. The author thanks John Cleland, Andy Sloggett, Simona Bignami, Sunita Kishor, and 

Shanxiao Wang for useful comments. The author also thanks Bryant Robey for editing and Yuan 

Gu for formatting the working paper. 

Suggested citation: 

Machiyama, Kazuyo. 2010. A Re-examination of Recent Fertility Declines in Sub-Saharan 

Africa. DHS Working Papers No. 68. Calverton, Maryland, USA: ICF Macro. 
ABSTRACT

Some studies have suggested that the fertility decline in several sub-Saharan African 

countries has stalled in recent years. These studies have reached contradictory conclusions about 

the extent and mechanisms of fertility stalls, however, and the actual number of stalls may be 

overstated, due to limitations of the available data. This report re-examined recent fertility trends 

in Benin, Cameroon, Ghana, Kenya, Nigeria, Rwanda, Tanzania, Uganda, and Zambia, where 

national total fertility rates (TFRs) appear to have failed to decline around 2000. In our reexamination we used three sets of data quality assessments to examine the impact of surveyrelated errors on the estimation of recent fertility trends. The smoothed long-term TFR trends 

resulting from our analysis provided compelling evidence of a stall in Kenya’s fertility decline. 

Benin, Rwanda, and Zambia also appeared to have stalled fertility declines. However, other stalls 

in fertility decline reported by previous studies appeared to be overstated. This is likely due to 

age displacement of children, omission, or differences between the surveys in the late 1990s and 

those in the early 2000s. The report recommends careful assessment of fertility trends that take 

into account the quality of survey data, as well as efforts to maintain the high level of DHS data 

quality. 

1

INTRODUCTION

Sub-Saharan Africa is the only region of the world where fertility is persistently high. 

Since the 1980s, fertility has declined in several sub-Saharan countries, and most parts of the 

region are now considered to be in the middle of the fertility transition. However, recent studies 

have observed unexpected stalling in the fertility decline in several sub-Saharan countries 

(Bongaarts 2006, 2008; Garenne 2008; Schoumaker 2009; Sneeringer 2009; Westoff and Cross 

2006). 

The studies have reached contradictory conclusions about the extent and mechanisms of 

these apparent fertility stalls (Table 1). Bongaarts (2006, 2008) identified 12 sub-Saharan 

African countries with stalled fertility decline around 2000.1

 Schoumaker (2009) found fertility 

stalls only in Kenya and possibly in Rwanda, while Garenne (2008) reported that fertility decline 

has stalled in six countries.2

 Westoff and Cross (2006) also found evidence of a stall in fertility 

decline in Kenya. Sneeringer (2009) examined cohort fertility trends and suggested that only in 

Congo (Brazzaville) and Madagascar have fertility transitions slowed. 

 

1

The 12 countries are Cameroon, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Mozambique, Nigeria, Rwanda, Tanzania, 

Uganda, Zambia, and Zimbabwe.

2

 Ghana, Kenya, Madagascar (urban), Nigeria, Rwanda (rural), and Tanzania (rural). 
2

Table 1: Comparison of literature on stalling fertility

Country/Period Bongaarts(1) Garenne 2008 Schoumaker 2009 Sneeringer 2009(2)

Benin

 2001-06 Stall Decline Early transition Decline

Cameroon

 1998-04 Stall Decline Decline Decline

Ghana

 1998-03 Stall Stall Decline Decline

 2003-08 Decline - - -

Kenya

 1998-03 Stall Stall Stall Decline

Nigeria

 1999-03 Stall

Stall Early transition Decline

 2003-08 - - -

Rwanda

 2000-05 Stall Stall Stall(3) Decline

 2005-07/8(4) Decline - - -

Tanzania

 1999-04 Stall Stall Decline -

Uganda

 1995-00/1 Stall

Decline

Pre-transition Decline

 2000/1-06 -

Zambia

 1996-01/2 Stall

Decline Decline Decline

 2001/2-08 - - -

- The latest DHS survey was not included for the study.

(1) The trends are determined using Bongaarts' method (Bongaarts 2008).

(2) Cohort fertility was used.

(3) Stall in Rwanda was less certain.

(4) The dataset for Rwanda interim DHS 2007/8 has not been released as of 31 May, 2010.
3

In this study we hypothesized that the number of observed fertility stalls might be 

overstated due to limitations of analysis related to the quality and nature of the DHS data used in 

making the estimates. The fertility stalls estimated in earlier studies often relied on selected 

average TFRs derived from DHS STATcompiler (Askew et al. 2009; Bongaarts 2006, 2008; 

Ezeh, Mberu and Emina 2009; Shapiro and Gebreselassie 2008; Westoff and Cross 2006). This 

analytic approach can produce a misleading impression of fertility trends. 

The DHS provides the most reliable information on national fertility levels, but no survey 

is immune to errors, and there is abundant evidence that such errors tend to be more pervasive in 

sub-Saharan Africa than elsewhere (Arnold 1990; Johnson et al. 2009; Pullum 2006; Rutstein 

and Bicego. 1990). The most serious and measurable problem in using this type of crosssectional household survey data to examine demographic trends is age displacement of children. 

In the DHS protocol, women with children born during a predetermined number of years prior to 

the survey (generally three or five years) are asked a range of questions on maternal and child 

health related to the children, which women are not asked to answer for children age the 

predetermined year or older. As a result, interviewers could be motivated to transfer dates of 

childbirths backward to avoid asking the additional questions and thus to reduce their workloads. 

In fact, births occurring 0-5 years before a survey tend to be pushed backwards, resulting in 

underestimation of births during the period and in overestimation of births six and more years 

before the survey. 

This pattern of age transfer can exaggerate the speed of fertility decline (Goldman, 

Rutstein and Singh 1985; Potter 1977). A published TFR in a DHS report is usually an average 

of TFRs during the three years before a survey, to avoid the underestimation of births in the 

boundary year (Rutstein and Rojas 2006). Nevertheless, a number of children may be 

underestimated throughout the five-year period after the boundary year. 

In the past few years, concern has been growing that the increasing number of questions 

in the DHS questionnaire may adversely affect the quality of the data (Murray et al. 2007), but 

the effect of the data quality on the estimation of recent fertility trends in sub-Saharan Africa has 

not been widely studied. If one survey has poorer data quality than the successive survey(s), it 

may distort the measurement of fertility trends, causing an erroneous impression of a fertility 

decline or stall. Furthermore, few studies have proposed methods for adjusting for age 
4

displacement (Sullivan 2008). Thus there is a clear need for a more rigorous method of 

examining fertility trends that allows for the errors inherent in the surveys. 

This paper makes three sets of data assessments to identify drawbacks of the current 

method of identifying fertility stalls, re-examines fertility changes in light of these assessments, 

and provides plausible interpretations of the recent fertility declines in the sub-Saharan countries 

studied. Adapting Gendell’s definition, we define a fertility stall as a trend when the average 

annual pace of fertility decline during a DHS inter-survey period is less than half the pace in the 

previous inter-survey period, in the countries where TFRs have dropped by over 20 percent from 

the highest observed TFR (Gendell 1985). The highest estimates of TFRs were obtained from the 

UN 2008 World Population Prospects Data between 1950-2000 (United Nations 2009). 
5

DATA and METHODS 

Data 

Our study used individual, birth, and household member datasets from 33 DHS surveys 

in nine sub-Saharan African countries. The countries were selected because the national TFRs 

appeared to have levelled off between successive DHS surveys conducted around 2000. These 

countries and survey years are Benin (1996, 2001, 2006), Cameroon (1991, 1998, 2004), Ghana 

(1988, 1993, 1998, 2003, 2008), Kenya (1989, 1993, 1998, 2003),3

 Nigeria (1990, 1999, 2003, 

2008), Rwanda (1992, 2000, 2005), Tanzania (1991/92, 1996, 1999, 2004/05), Uganda (1995, 

2000/01, 2006),4 and Zambia (1992, 1996, 2001/02, 2007). Uganda and Tanzania were included 

despite the fact that their TFRs have not declined substantially based on the definition described 

earlier. 

Methods 

As mentioned, our analysis involved three sets of data assessment and a re-examination 

of recent fertility trends in sub-Saharan Africa. First, the paper extended the work by Pullum on 

assessment of age and date misreporting, published as DHS Methodological Report 5 (Pullum 

2006), because age and dates of birth of respondents and their children are critically important 

for estimating fertility. Second, retrospective annual fertility rates were estimated for each survey 

in order to identify the effects of children’s age displacement on fertility changes, as well as 

discrepancies in the estimates derived from two successive surveys. We also used these rates to 

derive detailed fertility estimates and the long-term smoothed fertility trends for each country. 

Third, possible reasons for the discrepancies in fertility rates were explored by comparing the 

composition of women according to educational attainment and average parity. These various 

analyses led us to provide plausible interpretations of recent trends (see the discussion section). 

STATA SE/11 was used for the entire analysis. 

 

3

 To ensure comparability in the data for the entire period, North-East Province and four other northern districts that 

were covered only in the 2003 Kenya DHS survey were excluded from this analysis. 

4

 The Uganda 1988 DHS was excluded on account of the limited geographical area. Also the current two western 

and four northern districts were excluded from Uganda datasets to ensure comparability.
6

Assessment of age and date misreporting 

In the first part of our analysis, we updated Pullum’s assessment of age and date 

misreporting in DHS data (Pullum 2006) by focusing on: (a) the incompleteness of reporting age 

and birth dates of women and children; (b) digit preference in age reporting; and (c) age 

displacement of women and children, for the countries included in the study. 

(a) Incompleteness of birth dates of women and children 

To calculate proportions of women and children who possess the essential information on 

date of birth and current age, we used a variable in the individual and birth datasets that indicates 

whether all three pieces of information (current age and year and month of birth) were provided, 

or if imputation of this information was needed. 

(b) Digit preference in age reporting 

Myers’ blended Index was used to detect digit preference in women’s age reporting. This 

commonly used index measures proportions of women whose current age have been shifted from 

one final digit to another. There is often a tendency to round down or up current age and report 

another age ending in 0 or 5. The blended index adjusts for the fact that there are more people at 

age x than at x+1, due to births and deaths (Myers 1940; Siegel and Swanson 2004). The higher 

the value, the more dissimilarity exists. The STATA command myers, developed by Pullum and 

Rodríguez, was used to compute the indices (Rodríguez 2006). This analysis focused on digit 

preference in reporting current age among women age 15-44, to have equal representation of all 

final digits (0-9). 

(c) Age displacement of women and children 

Age displacement refers to the systematic transfer of respondents and children across an 

age boundary for eligibility for specific survey questions. The method formulated by Pullum was 

used here to estimate the proportion of downward and upward age displacement of women, and 

age displacement of children (Pullum 2006). Pullum reformulated the conventional method to 

measure age displacement, i.e. age ratio, to make it interpretable and to estimate the number and 

proportions of women transferred, using individual-level data (Pullum 2006). This method was 

used to quantify the displacement and also used later to adjust for the errors to re-estimate 

fertility rates. We could not apply this method to estimate women’s transfer to the 1988 Ghana 
7

DHS and the 1989 Kenya DHS, since the ages of household members are not stored in these 

datasets. 

Estimation of recent fertility trends by single calendar year 

Retrospective TFRs by single calendar year were obtained by reconstructing birth 

histories for the 10 years preceding each survey. This aimed to depict the detailed levels and 

trends of fertility and to identify age displacement of children, as well as discrepancies in the 

estimates derived from two successive surveys during overlapping periods. A similar method has 

been used for data assessments in the 1980s and in a few recent studies (Garenne 2008; 

Schoumaker 2009). Partial TFRs were computed by cumulating age-specific fertility rates among 

women age 15-39, because women age 40-49 at 10 years prior to the time of a survey (i.e. 

women age 50-59 at the time of the survey) were truncated from the individual dataset.5,6

 The 

confidence limits for the annual estimates were computed by Jackknife repeated replication, a 

method that DHS surveys use to report confidence intervals for TFRs. In the Jackknife method, 

TFRs are repeatedly calculated for replications of the dataset, each of which includes all but one 

cluster, and the standard error on TFRs is then calculated.7

Long-term trends in each country were presented by employing locally weighted scatter 

point smoothing (Loess), which produces a new smoothed value for each data point by running a 

linear regression with the highest weight on the data point and less weight on other points 

according to their distance from the data point. This procedure was repeated to obtain smoothed 

values for every point, and the smoothed values were connected with a line. This Loess 

regression is an increasingly used robust technique to perform locally-weighted smoothing. The 

smoothing window for this analysis was 0.8. 

 

5

 The data in the year that data collection ended covers a small fraction of births and exposure that would have 

occurred in the whole year. Consequently, the estimated rate would not be representative of the fertility rate in the 

calendar year and the reference period is likely to be distorted (Becker, S.and T.W. Pullum. 2007. "External 

Evaluation of the Peru Continuous Survey Experiment." Washington, DC.: USAID.). Therefore, these estimates 

were not reported in this paper. For Loess smoothing the estimates in the year of latest survey were not included 

because they might heavily affect directions of the smoothing line. 

6

 Births born to mothers under age 10 (including births born in the month of mother’s 10-year birthday) were 

excluded. 

7

 The details are described in the appendix section of final DHS reports. 
8

Furthermore, the partial TFRs were re-estimated by adjusting for age displacement of 

children and downward displacement of women. The estimated proportions of women age 15-19 

misreported as age 10-14 were transferred back to 15-19 age group. Subsequently, partial TFRs 

were re-estimated under an assumption that no births had occurred among the women transferred 

back to the 15-19 age group, a reasonable assumption given their younger age. Births in a year 

preceding a boundary year and in the boundary year itself were adjusted using the proportions of 

children transferred between these two years, which were estimated earlier using Pullum’s 

method. A limitation is that adjustment was made for only two years across the boundary year. 

This is because Pullum’s method assumes that the numbers of births in the two years before and 

after the boundary year are correct, and that the sum of births in a year before and in the 

boundary year is also correct (Pullum 2006). 

Inter-survey comparison: women’s educational attainment 

DHS surveys generally select nationally representative samples of women and men. 

Therefore, some socio-demographic characteristics, such as women’s educational levels, are 

expected to remain constant or to show a gradual change within the same birth cohort across 

surveys. Yet if one survey over-represents a group of women with lower fertility, due to different 

sampling, fertility rates are likely to be lower compared with estimates from successive surveys. 

A fertility stall may thus be artificially created if successive surveys have different compositions 

of respondents. To assess difference in the compositions, this analysis compared proportions of 

women who attended secondary or higher levels of schooling, by 10-year birth cohort for each 

survey. 

Inter-survey comparison: average parity 

To assess differences in the composition of women, we also compared average parity by 

birth cohort. This method was often used to compare fertility estimates derived from World 

Fertility Survey data with estimates obtained from census data in the 1980s, in order to detect 

omissions and age displacement of children (Goldman et al. 1985). In our analysis, births after 

the first month of fieldwork of an earlier survey were subtracted from the data of the later survey, 

and the maternal history was reconstructed to compute average parity by 10-year birth cohort. 
9

The two estimates of average parity should match, as the reference time is the same. However, if 

recent births were pushed backward in the later survey, the later survey may show higher average 

parity than the earlier survey. Also, omissions of births can be detected. This procedure was 

repeated for the three latest surveys for each country in our study. 
10

RESULTS 

Age and Date Misreporting 

This section presents levels of age and birth date misreporting in the 33 surveys. The 

results, shown in Appendix 1, suggest that the levels of errors varied markedly by survey and by 

country. The most noteworthy points are that there was large age displacement of children in 

most of the surveys, including the latest surveys, and that the levels of data quality were not 

constant across the successive surveys. 

The proportions of women who did not provide the calendar month and year of their 

births significantly decreased over the years in all countries. The observed improvement is more 

likely due to recent advancement of women’s educational attainment (Arnold 1990; Pullum 

2006). Nonetheless, in the most recent surveys in Benin and Rwanda more than half of the 

women did not report full information. Children’s birth dates were substantially better reported 

and have dramatically improved in all countries studied. 

One might expect that digit preference in women’s age reporting would also improve as 

more women provide information on their ages and birth dates. However, noticeable increases in 

this misreporting were found in Benin, Ghana, Nigeria, and Tanzania. The 2006 Benin DHS 

showed strong digit preference ending in 0 or 5, particularly at ages 20, 25, and 30, as indicated 

in the DHS report (INSAE and Macro International Inc. 2007). The results suggest that the 

improvement in completeness of age and date reporting did not necessarily imply more accurate 

reporting. 

Age displacement of women appeared to have improved in the recent surveys, but Benin 

worsened in both upward and downward age displacement of women. Also, in Nigeria, 

Tanzania, Uganda, and Zambia the latest survey suffered from more downward displacement of 

women than the preceding surveys. 

The level of age displacement of all children did not improve over the survey years. 

Among the latest surveys, the 2006 Benin survey contained 19 percent of children transferred, 

while the previous two surveys had less than 10 percent of the displacement. In the 1993 Ghana 

DHS survey, about 20 percent of all children born were misreported to be born a year before the 
11

boundary year. This is not a negligible level, and it is crucially important to take into account this 

type of error for estimating fertility trends. 

Displacement among deceased children was considerably higher than among living 

children. Seven surveys contained more than a 30 percent displacement of children who had 

died—an error that may have led to large overestimation of the decline in child mortality in 

recent years. It is noteworthy that the displacement was found in several Phase 5 DHS surveys 

conducted between 2003 and 2008. In the questionnaire for Phase 5 surveys, the number of 

questions on pregnancy, postnatal care, and breastfeeding increased from 53 to 71, and 37 

questions were newly added in the section on immunization, health, and nutrition (Macro 

International Inc. 2008; ORC Macro 2001). As a result, displacement might have occurred more 

widely because many interviewers were reluctant to ask women the health questions about their 

deceased children (Sullivan 2008). 

Recent Fertility Trends by Single Calendar Year 

Appendix 2 displays for individual countries recent changes in partial TFRs, age 

displacement of children, and the discrepancies in the estimates. First, general trends over the 

past 15 years are clearly shown by the smoothing lines. Despite various fluctuations by country, 

the overall trends show at least some fertility decline in all countries except Uganda and 

Tanzania, with only a slight decline in Zambia. Fertility declines were most rapid in Ghana and 

Kenya, where fertility dropped by about 40 percent over the past 20 years. In Benin, Cameroon, 

Nigeria, and Rwanda the decline was about 20 percent, although in Nigeria and Rwanda fertility 

seemed overestimated in the earliest surveys. 

These results suggest that some of the apparent fertility stalls or reversals were 

overstated. For instance, Ghanaian fertility decline has been clearly continuous. The false 

impression of stall may have arisen due to the discrepancies between the estimates from the 1998 

and the 2003 surveys, as shown in Figure 2.3.1 in Appendix 2. The estimate of partial TFR in 

1996 from the 1998 survey was 0.8 lower than the estimate from the 2003 survey. In contrast, 

Kenya clearly decelerated in the pace of fertility decline after 1996, and the decline levelled off 

at a partial TFR of 4.5. Since Kenya has very good agreement in the estimates across the surveys, 

the observed stall in fertility decline is likely to be valid. 
12

Second, the results clearly illustrate age displacement of children across the boundary 

year of eligibility for the additional survey questions on child health. The rate in the earliest year 

of a window dropped sharply, and the estimate of a year before the window was grossly inflated. 

In Uganda, for instance, large spikes and deficits were found in the two years across the 

boundary year in each of three surveys (see Figure 2.8.1 in Appendix 2). The partial TFR in 2000 

was 7.7 and dropped sharply by 2.0 children in the following year. 

These displacements disappeared and the agreement in the estimates from the 

consecutive surveys became better in the adjusted trends, shown in the second graphs for each 

country. Uganda clearly showed better agreement, for instance, as the adjusted trends in Figure 

2.8.2 show fewer fluctuations than unadjusted trends in Figure 2.8.1. These findings support the 

reliability of Pullum’s model for estimating age displacement. Nevertheless, as mentioned 

earlier, displacements or omissions more than two years across the boundary year were not 

adjusted. 

Third, most surveys showed higher estimates in later surveys compared with earlier 

surveys during the overlapping periods. This may be due to a combination of overstatement of 

births in the later surveys and understatement or omissions, particularly of deceased children, in 

the earlier surveys, or to either one of these misstatements. Another possible explanation of the 

discrepancies may be differences in sample compositions between surveys, resulting in different 

slopes and levels of fertility estimates in overlapping periods. The next sections will explore 

potential reasons for such discrepancies. 

The same analysis of estimating partial TFRs was repeated for urban and rural areas, as 

shown in the third graph for each country. These estimates are not adjusted for age displacement. 

The patterns of decline were different across countries. The gaps between urban and rural areas 

have been widening in Benin, Cameroon, Nigeria, Tanzania, and Zambia. In Ghana and Uganda 

the differences widened and then have been constant since around 2000. In contrast, the gap has 

been constant in Kenya, although the difference has been slightly widening. Only in Rwanda has 

the gap narrowed and then recently become constant. Age displacement was more apparent in 

rural areas in all countries, and particularly in Ghana, Nigeria, Uganda, and Zambia. 
13

Inter-survey Comparison: Women’s Educational Attainment 

This section explores possible explanations for the discrepancies in estimates derived 

from the data for two successive survey periods, mentioned earlier. Appendix 3 presents the 

proportions of women with secondary or higher education, by birth cohort and by survey. 

Generally, the proportions were fairly similar across surveys, which implies that the composition 

of women by educational levels was quite similar and comparable across the surveys. It is 

unlikely that these slight differences have greatly affected trends of the fertility declines. 

However, Nigeria showed a clear difference across the surveys. Apparently, the 1999 

survey contained higher proportions of educated woman in almost all birth cohorts. Birth cohort 

1960-69 in the 1999 survey included about 31 percent of women with secondary or higher 

education, while the 1990 and 2003 surveys encompassed about 23 percent, although the 

difference was not significant. This difference may have led to lower estimates of partial TFRs in 

the 1999 survey, probably due to omissions of births in the recent period or displacement across 

the boundary year (National Population Commission [Nigeria] 2000). 

Similarly, the 1970-79 birth cohort in the 2003 Ghana DHS survey contained a lower 

proportion of educated women. Although the difference was not statistically significant, it might 

contribute to the discrepancy in the estimates from the two surveys. The difference in the 

proportions of educated women in the youngest cohort in Kenya is presumably due to the 

increase in the number of women going on to higher levels of schooling between the survey 

periods. 

Inter-survey Comparison: Average Parity 

Appendix 4 shows average parity by birth cohort and by survey. Parities were remarkably 

similar across the successive surveys. For instance, in Kenya parity in 1998 and the estimates 

from the 2003 DHS survey have 0.1 or less differences among all birth cohorts. The composition 

of women in terms of average parity were fairly similar as well, and the small differences were 

unlikely to substantially affect estimation of fertility trends. Yet, in the 1999 Nigeria DHS all 

birth cohorts had lower parity than the estimates from two other surveys, probably reflecting 

substantial omissions of births in the 1999 survey. 
14

DISCUSSION 

This section discusses recent fertility decline over 15 years, allowing for the impact of the 

errors identified from the above results for each country. Table 2 presents the trend for each 

country, comparing the results from the prior studies. 

Table 2: Summary results and comparison of literature on stalling fertility

Country/Period Results Bongaarts(1) Garenne 2008 Schoumaker 2009

Benin

 2001-06 Stall Stall Decline Early transition

Cameroon

 1998-04 Decline Stall Decline Decline

Ghana

 1998-03 Decline

Stall Stall Decline

 2003-08 Decline - -

Kenya

 1998-03 Stall Stall Stall Stall

Nigeria

 1999-03 Decline Stall

Stall Early transition

 2003-08 - -

Rwanda

 2000-05 Stall Stall Stall Stall(3)

 2005-07/8(4) - Decline - -

Tanzania

 1999-04 Early-transition Stall Stall Decline

Uganda

 1995-00/1 Early-transition Stall

Decline

Pre-transition

 2000/1-06 -

Zambia

 1996-01/2 Decline

Stall

Decline Decline

 2001/2-08 Stall - -

- The latest DHS survey was not included for the study.

(1) Partial TFR (15-39) was used. 

(2) The trends are determined using Bongaarts' method (Bongaarts 2008).

(3) Stall in Rwanda was less certain.

(4) The dataset for Rwanda interim DHS 2007/8 has not been released as of 31 May, 2010.
15

Benin displays a modest fertility decline since the late 1990s. However, the adjusted 

smoothed trends suggest that the average annual pace of decline fell from 0.08 children to 0.03 

children per year in the recent inter-survey period, and the pace of fertility decline in urban area 

appears particularly to have slowed in recent years (see Appendix 5). Digit preference and age 

displacement of women and children were more significant in the 2006 survey than in earlier 

surveys. 

Cameroon exhibits similar modest but continuous fertility decline in the last 20 years. 

The graphs clearly show underestimation of births during the window of the second-to-last 

survey. As described in the DHS reports, the underestimation of births apparently caused 

overestimation of the pace of fertility decline in the 1990s. The fertility decline in urban areas 

was more rapid than in rural areas. 

Ghana’s fertility transition is similar to the other two West African countries studied, but 

the pace of decline is markedly more rapid. The observed fertility stall between the 1998 and 

2003 surveys is likely to be spurious, because the estimates from the two surveys are not quite 

the same. The reason for the discrepancy is not clear. One potential explanation is that the 2003 

survey might include a slightly lower proportion of educated women than the survey before it, in 

1998, although the differences were not significant. This may be because of use of different 

sampling frame. The 2003 survey used the 2000 census, while the 1984 census was used 

previously. Also, there may be underestimation or omission of births in the 1998 survey, or a 

combination of both the underestimation and overestimation in the 2003 survey in the 

overlapping period. The pace of fertility decline in urban areas might have been slowing since 

2000. 

Kenya clearly shows fertility stall, as several prior studies have suggested. As the 

estimates from successive surveys are in good agreement, except the first survey, the stall is 

evidently genuine. Both urban and rural areas have been experiencing stalls since around 1996. 

The long-term trend in Nigeria is difficult to interpret because of the poor data quality. As 

discussed earlier, the 1999 survey reported extensive omission of births (National Population 

Commission [Nigeria] 2000). There are also substantial discrepancies between the 1999 and 

2003 surveys in partial TFR estimates and the average parity by birth cohort, as shown earlier. 

But the overall smoothed trends indicate modest fertility decline over the years. 
16

The partial TFRs (15-39) in Rwanda have stabilized at around five children per woman in 

the past 15 years. The adjusted estimates suggest that the average pace of decline fell from 0.06 

to 0.02 children (see Appendix 5). It is consistent with the 2005 Rwanda DHS report indicating 

that the TFR (15-49) remained at around six children per woman since 1990. No obvious 

explanation for the drop in partial TFR in 1998 can be offered. Despite little change in fertility in 

the past, the 2007-08 Rwanda Interim DHS report indicates a decrease in TFR from 6.2 to 5.5 at 

the national level since the previous survey in 2005.8

Tanzania, Uganda, and Zambia show similar patterns. National and rural partial TFRs 

fluctuated above five children per woman and show little decline over the past 20 years, as 

described in the DHS reports. In Uganda and Zambia, despite a large age displacement of 

children, these errors did not seriously affect patterns of the long-term trends. Further 

examination is needed in the next Tanzania and Zambia surveys to validate a slight upward trend 

in fertility, as observed in the DHS report and in this paper. In Uganda and Zambia the urban 

areas showed clear fertility declines, reaching a partial TFR of 4.0, while the published TFRs in 

urban areas appeared to stall between two latest surveys. 

 

8

 The dataset had not been released as of the end of June, 2010.
17

CONCLUSIONS 

This report began by proposing a hypothesis that the recent findings of fertility stalls in 

sub-Saharan Africa may be overstated, because they did not account adequately for limitations of 

DHS data quality. Our report assessed age and date misreporting in the surveys and their effects 

on estimation of recent fertility declines. We then estimated annual fertility rates, adjusting for 

misreporting in nine sub-Saharan countries where national TFRs appear to have failed to decline 

between successive DHS surveys around 2000. 

The results suggest that some of the apparent fertility stalls observed earlier were 

exaggerated due to discrepancies in estimates between successive surveys in the late 1990s and 

early 2000s. The differences are likely to have resulted from age displacement, omissions of 

children, or different composition of women respondents across the successive surveys. 

The partial TFRs by single calendar year showed clear evidence of fertility stall in 

Kenya, following a rapid fertility decline. Additionally, adjusted smoothed estimates suggests 

that TFRs in Benin, Rwanda, and Zambia declined little in recent years and remained constant at 

above five children per woman, despite the fact that these countries are considered to have 

started fertility transitions. These plateaus in fertility levels and the slow pace of decline at such 

levels are alarming. 

The main conclusion drawn here is at variance with earlier findings by Bongaarts 

suggesting that fertility declines stalled in all the selected countries (Bongaarts 2006). The 

different results probably reflect differences in methods of assessing the pace of fertility decline 

and in consideration of the nature and quality of survey data used in the estimates. Our findings 

demonstrate that the levels of the error were not negligible and that these errors clearly distorted 

recent fertility trends in several surveys. Nonetheless, fertility decline appears clearly to have 

stalled in Kenya and is declining very slowly in the region as a whole, as suggested by earlier 

research (Bongaarts 2002; Casterline 2001). 

A limitation of our study is that only the countries where fertility levelled off between 

two DHS surveys around 2000 were selected for analysis. Also, interpretation of fertility rates 

for about five years preceding the last survey needs some care, because the estimates can be 

affected by the errors but there is no new survey to validate the trends. Moreover, adjustment of 
18

age displacement of children for partial TFRs were made for only two years across the boundary 

year, while children transferred more than two years across the boundary year could not be 

displaced back. 

This study leads to three sets of recommendations for data collection, data analysis, and 

family planning programs. First, it is crucial to maintain a high quality of DHS data, particularly 

the quality of key indicators, such as fertility rates. The DHS has provided the highest quality of 

nationally representative demographic and health data in developing countries for over 25 years. 

Nonetheless, some errors cannot be avoided in this type of cross-sectional national household 

survey. This paper suggests the importance of balancing the length of the survey questionnaire 

with the need to ensure data quality, as the demand of information on maternal and child health 

increases. Also, strengthening supervision of fieldwork may be needed to ensure the accuracy of 

surveys. It is noteworthy that the level of age displacement of children is much lower in DHS 

surveys in Lesotho, where supervision was strongly performed by Ministry of Health and Social 

Welfare and Bureau of Statistics (Ministry of Health and Social Welfare (MOHSW) [Lesotho], 

Bureau of Statistics (BOS) [Lesotho] and ORC Macro 2005). 

Second, data assessment of fertility estimates across successive surveys is crucial for 

examining fertility trends and for making good policy and program decisions based on observed 

trends. More accurate estimates allowing for errors inherent in survey taking can help 

policymakers and program leaders to set priorities and to evaluate family planning programs 

appropriately. 

Third, fertility in Benin, Nigeria, Rwanda, Tanzania, Uganda, and Zambia remains quite 

high, particularly in the rural areas. In these and other sub-Saharan countries, there is an urgent 

need to understand the determinants of persistently high fertility and to provide appropriate 

family planning programs that meet the needs of the people. 
19

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Arnold, F. 1990. Assessment of the Quality of Birth History Data in the Demographic and Health 

Surveys. An Assessment of DHS-I Data Quality. DHS Methodological Reports No. 1. 

Columbia, Maryland: Institute for Resource Development / Macro System, Inc. 

Askew, I., A. Ezeh, J. Bongaarts, and J. Townsend. 2009. Kenya’s Fertility Transition: Trends, 

Determinants and Implications for Policy and Programmes. Nairobi: Population Council. 

Becker, S. and T.W. Pullum. 2007. External Evaluation of the Peru Continuous Survey 

Experiment. Washington, DC.: USAID. 

Bongaarts, J. 2002. The End of the Fertility Transition in the Developing World. New York: 

Population Council. 

—. 2006. The Causes of Stalling Fertility Transitions. Studies in Family Planning 37(1): 1-16.

—. 2008. Fertility Transitions in Developing Countries: Progress or Stagnation? Studies in 

Family Planning 39(2): 105-110.

Casterline, J. 2001. The Pace of Fertility Transition: National Patterns in the Second Half of the 

Twentieth Century. Population and Development Review 27 (Supplement): 17-52. 

Ezeh, A.C., B.U. Mberu, and J.O. Emina. 2009. Stall in Fertility Decline in Eastern African 

Countries: Regional Analysis of Patterns, Determinants and Implications. Philosophical 

Transactions of the Royal Society B: Biological Science 27(364): 2991-3007.

Garenne, M. 2008. Fertility Changes in Sub-Saharan Africa. DHS Comparative Reports No. 18.

Calverton, Maryland: Macro International Inc.

Gendell, M. 1985. Stalls in the fertility decline in Costa Rica, Korea, and Sri Lanka. Pp. vii, 112.

World Bank Staff Working Papers No. 693; Population and Development Series No. 18:

Washington, D.C., World Bank, 1985.
20

Goldman, N., S.O. Rutstein, and S. Singh. 1985. Assessment of the Quality of Data in 41 WFS 

Surveys: A Comparative Approach. WFS Comparative Studies No. 44. Voorburg, 

Netherlands: International Statistical Institute.

INSAE and Macro International Inc. 2007. Enquête Démographique et de Santé (EDSB-III) - 

Bénin 2006. Calverton, Maryland, USA: INSAE (Institut National de la Statistique et de 

l’Analyse Économique ), Macro International Inc.

Johnson, K., M. Grant, S. Khan, Z. Moore, A. Armstrong, and Z. Sa. 2009. Fieldwork-Related 

Factors and Data Quality in the Demographic and Health Surveys Program. DHS 

Analytical Studies No. 19. Calverton, Maryland: ICF Macro. 

Macro International Inc. 2008. Model Woman's Questionnaire with HIV/AIDS and Malaria 

Modules. DHS Basic Documentations. Calverton, Maryland: Macro International Inc.

Ministry of Health and Social Welfare (MOHSW) [Lesotho], Bureau of Statistics (BOS) 

[Lesotho], and ORC Macro. 2005. Lesotho Demographic and Health Survey 2004.

Calverton, Maryland: MOH, BOS, and ORC Macro. 

Murray, C.J., T. Laakso, K. Shibuya, K. Hill, and A.D. Lopez. 2007. Can We Achieve 

Millennium Development Goal 4?: New Analysis of Country Trends and Forecasts of 

under-5 mortality to 2015. The Lancet 370(9592): 1040-1054.

Myers, R.J. 1940. Errors and Bias in the Reporting of Ages in Census Data. Transactions of the 

Actuarial Society of America 41 Part II (104): 395-415.

National Population Commission [Nigeria]. 2000. Nigeria Demographic and Health Survey 

1999. Calverton, Maryland: National Population Commission and ORC Macro. 

ORC Macro. 2001. Model "B" Questionnaire: With Commentary for Low Contraceptive 

Prevalence Countries. MEASURE DHS+ Basic Documentation. Calverton, Maryland: 

ORC Macro.

Potter, J.E. 1977. Problems in Using Birth History Analysis to Estimate Trends in Fertility. 

Population Studies 31(2): 335-364. 
21

Pullum, T.W. 2006. An Assessment of Age and Date Reporting in the DHS Surveys 1985-2003.

Methodological Reports No. 5. Calverton, Maryland: Macro International Inc.

Rodríguez, G. 2006. Digit Preference. Research Methods in Demography: Office of Population 

Research, Princeton University. 

Rutstein, S.O. and G.T. Bicego. 1990. Assessment of the Quality of Data Used to Ascertain 

Eligibility and Age in the Demographic and Health Surveys. An Assessment of DHS-I 

Data Quality. DHS Methodological Reports No. 1. Columbia, Maryland: Institute for 

Resource Development / Macro System, Inc. 

Rutstein, S.O. and G. Rojas. 2006. Guide to DHS Statistics. Calverton, Maryland: Demographic 

and Health Surveys, ORC Macro. 

Schoumaker, B. 2009. Stalls and Reversals in Fertility Transitions in Sub-Saharan Africa: Real 

or Spurious? Document de travail SPED. Louvain-la-Neuve: Université catholique de 

Louvain. 

Shapiro, D. and T. Gebreselassie. 2008. Falling and Stalling Fertility in Sub-Saharan Africa.

International Union for the Scientific Study of Population International Seminar on 

Human Fertility in Africa. Cape Coast, Ghana.

Siegel, J. and D. Swanson. 2004. The Methods and Materials of Demography. San Diego, 

California: Esevier Academic Press. 

Sneeringer, S.E. 2009. Fertility Transition in Sub-Saharan Africa: A Comparative Analysis of 

Cohort Trends in 30 Countries. DHS Comparative Reports No. 23. Calverton, Maryland: 

ICF Macro.

Sullivan, J. 2008. An Assessment of the Credibility of Child Mortality Declines Estimated from 

DHS Mortality Rates (Working Draft). Edited by Inter-Agency Coordination Group on 

Child Mortality Estimation: UNICEF. 

United Nations. 2009. World Population Prospects: The 2008 Revision. New York: United 

Nations. 
22

Westoff, C.F. and A.R. Cross. 2006. The Stall in the Fertility Transition in Kenya. DHS 

Analytical Studies No. 9. Calverton, Maryland: ORC Macro.
23

APPENDIX 1: AGE AND DATE MISREPORTING

Table1.1: Age and Date Misreporting in 9 sub-Saharan African countries 

Country/ Year 

of Survey Phase

Incompleteness 

of birth dates

Digit 

preference/Age 

heaping

Age 

displacement 

of women

Age displacement 

of children:

Estimated % 

of women 

displaced

Estimated % of births in 

boundary year misreported 

in preceding year

Women's 

birth 

dates(%)

Children's 

birth 

dates(%)

Myer's blended 

Index for 

women

Women 

15-19

Women 

45-49

Alive 

children

Dead 

children

All 

children

Benin

 1996 3 78.3 41.5 5.9 11.2 6.6 1.7 11.3 3.4

 2001 4 73.2 39.0 14.2 7.7 12.6 6.7 21.7 9.3

 2006 5 62.6 13.5 17.8 13.7 15.6 17.3 30.2 19.1

Cameroon

 1991 2 50.9 30.7 8.5 4.7 27.3 7.8 18.7 9.3

 1998 3 36.6 5.8 7.1 5.8 8.8 8.5 19.3 9.8

 2004 4 30.7 6.3 5.8 1.4 11.6 5.7 17.0 7.5

Ghana

 1988 1 51.3 20.6 13.1 na na -1.3 -9.1 -2.2

 1993 3 45.9 17.8 10.2 17.8 20.6 24.0 16.8 23.4

 1998 4 36.7 13.3 9.5 14.1 6.0 7.2 19.2 8.7

 2003 4 30.8 2.3 5.2 9.5 11.5 11.2 38.4 14.1

 2008 5 21.2 4.0 7.4 4.8 9.3 17.3 19.5 17.5

Kenya

 1989 1 37.0 2.8 6.7 na na 17.8 30.2 18.9

 1993 3 34.3 8.3 7.6 16.1 28.5 7.7 19.2 8.7

 1998 3 27.6 2.1 5.3 13.3 15.9 5.4 17.9 6.5

 2003 4 26.7 4.8 4.8 6.9 14.0 3.8 7.2 4.2

Nigeria

 1990 2 44.7 16.6 26.1 19.7 28.8 15.9 32.5 19.3

 1999 4 36.2 16.1 19.2 -17.4 17.9 8.4 16.8 9.5

 2003 4 23.9 8.4 15.7 1.2 14.1 -2.2 6.8 -0.1

 2008 5 24.8 2.7 16.8 4.7 9.0 9.8 28.6 13.0

Rwanda

 1992 2 61.6 6.6 5.7 5.3 14.7 0.0 11.7 1.9

 2000 3 46.9 4.4 6.3 10.0 2.7 11.5 31.6 15.7

 2005 5 54.0 1.1 5.3 3.6 6.1 1.1 2.8 1.3

Tanzania

 1991/92 2 57.4 16.7 5.1 5.1 10.8 3.8 30.0 8.1

 1996 3 40.4 6.7 5.9 9.9 6.2 9.4 25.8 11.8

 1999 4 35.7 2.9 4.1 3.7 3.1 2.4 3.8 2.6

 2004/05 4 26.6 1.6 5.5 6.9 2.4 -0.2 33.6 4.1

Uganda

 1995 3 44.1 6.1 7.7 10.3 31.3 15.8 25.3 17.3

 2000/01 4 41.6 5.4 7.5 12.0 15.5 13.0 21.0 14.1

 2006 5 36.8 2.7 5.5 14.3 7.5 11.7 22.0 13.5

Zambia

 1992 2 11.8 1.8 4.8 -1.3 17.3 4.1 7.2 4.7

 1996 3 18.7 1.0 5.2 6.7 7.4 10.2 24.9 13.1

 2001/02 4 16.4 1.5 3.2 5.2 5.8 8.8 13.1 9.5

 2007 5 8.6 0.8 2.1 11.7 2.0 -1.6 -0.6 -1.6
24

APPENDIX 2: RECENT FERTILITY TRENDS BY SINGLE CALENDAR YEAR 

Figure 2.1.1: Benin: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1990 1995 2000 2005

year

BJDHS1996 BJDHS2001

BJDHS2006 lowess smoothing

Partial TFR (15-39), Benin, 1986-2005
25

Figure 2.1.2: Benin: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1990 1995 2000 2005

year

BJDHS1996 BJDHS2001

BJDHS2006 lowess smoothing

Adjusted Partial TFR (15-39), Benin, 1986-2005

Figure 2.1.3: Benin: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1990 1995 2000 2005

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Benin, 1986-2005
26

Table 2.1: Benin: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI]

Adjusted 

smoothed 

TFR 

1996 2001 2006 (15-39)

1986 7.4 ( 6.94 - 7.77 ) 6.7

1987 6.0 ( 5.61 - 6.44 ) 6.6

1988 6.5 ( 6.09 - 6.99 ) 6.6

1989 6.4 ( 5.96 - 6.78 ) 6.4

1990 7.0 ( 6.54 - 7.46 ) 6.1 ( 5.61 - 6.51 ) 6.3

1991 5.8 ( 5.46 - 6.22 ) 6.9 ( 6.43 - 7.36 ) 6.2

1992 6.5 ( 6.12 - 6.92 ) 5.9 ( 5.51 - 6.34 ) 6.1

1993 5.7 ( 5.32 - 6.13 ) 6.6 ( 6.15 - 7.09 ) 6.0

1994 4.7 ( 4.32 - 5.02 ) 5.8 ( 5.37 - 6.15 ) 5.9

1995 5.7 ( 5.28 - 6.07 ) 6.5 ( 5.96 - 6.94 ) 5.3 ( 5.06 - 5.59 ) 5.8

1996 5.2 ( 4.87 - 5.62 ) 6.6 ( 6.39 - 6.90 ) 5.7

1997 5.4 ( 5.03 - 5.71 ) 5.5 ( 5.22 - 5.69 ) 5.6

1998 5.2 ( 4.84 - 5.52 ) 6.4 ( 6.15 - 6.67 ) 5.5

1999 5.1 ( 4.77 - 5.40 ) 5.8 ( 5.57 - 6.06 ) 5.4

2000 4.8 ( 4.50 - 5.16 ) 6.7 ( 6.44 - 7.01 ) 5.3

2001 4.4 ( 4.20 - 4.60 ) 5.3

2002 5.2 ( 5.04 - 5.45 ) 5.2

2003 5.6 ( 5.34 - 5.77 ) 5.2

2004 5.0 ( 4.85 - 5.25 ) 5.2

2005 5.3 ( 5.13 - 5.52 ) 5.2

Figure 2.2.1: Cameroon: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

CMDHS1991 CMDHS1998

CMDHS2004 lowess smoothing

Partial TFR (15-39), Cameroon, 1981-2003
27

Figure 2.2.2: Cameroon: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

CMDHS1991 CMDHS1998

CMDHS2004 lowess smoothing

Adjusted Partial TFR (15-39), Cameroon, 1981-2003

Figure 2.2.3: Cameroon: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR(15-39) by residence, Cameroon, 1981-2003
28

Table 2.2: Cameroon: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI]

Adjusted 

smoothed 

1991 1998 2004 TFR (15-39)

1981 5.8 ( 5.17 - 6.35 ) 6.1

1982 6.4 ( 5.78 - 6.94 ) 6.0

1983 6.1 ( 5.62 - 6.60 ) 6.0

1984 6.3 ( 5.67 - 6.93 ) 6.0

1985 6.5 ( 6.04 - 7.04 ) 5.9

1986 5.3 ( 4.74 - 5.78 ) 5.8

1987 6.0 ( 5.57 - 6.48 ) 6.0 ( 5.44 - 6.55 ) 5.8

1988 5.3 ( 4.91 - 5.72 ) 6.1 ( 5.68 - 6.51 ) 5.7

1989 5.1 ( 4.75 - 5.49 ) 5.6 ( 5.17 - 6.06 ) 5.6

1990 5.3 ( 4.91 - 5.79 ) 6.3 ( 5.86 - 6.76 ) 5.5

1991 5.1 ( 4.70 - 5.58 ) 5.5

1992 6.2 ( 5.79 - 6.63 ) 5.4

1993 5.0 ( 4.64 - 5.46 ) 5.3 ( 4.96 - 5.63 ) 5.3

1994 5.5 ( 5.05 - 5.94 ) 5.9 ( 5.51 - 6.28 ) 5.2

1995 4.3 ( 3.89 - 4.72 ) 5.0 ( 4.70 - 5.36 ) 5.1

1996 4.5 ( 4.07 - 4.84 ) 5.8 ( 5.47 - 6.16 ) 5.1

1997 4.5 ( 4.14 - 4.92 ) 5.2 ( 4.84 - 5.50 ) 5.0

1998 5.2 ( 4.90 - 5.51 ) 4.9

1999 4.7 ( 4.41 - 5.00 ) 4.9

2000 5.4 ( 5.12 - 5.71 ) 4.9

2001 4.4 ( 4.11 - 4.67 ) 4.8

2002 5.0 ( 4.72 - 5.28 ) 4.8

2003 4.5 ( 4.23 - 4.80 ) 4.7

Figure 2.3.1: Ghana: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000 2005

year

GHDHS1988 GHDHS1994 GHDHS1999

GHDHS2003 GHDHS2008 lowess smoothing

Partial TFR (15-39), Ghana, 1978-2007
29

Figure 2.3.2: Ghana: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000 2005

year

GHDHS1988 GHDHS1994 GHDHS1999

GHDHS2003 GHDHS2008 lowess smoothing

Adjusted Partial TFR (15-39), Ghana, 1978-2007

Figure 2.3.3: Ghana: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Ghana, 1978-2007
Table 2.3: Ghana: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI]

Adjusted 

smoothed 

TFR 

1988 1993 1998 2003 2008 (15-39)

1979 6.3 ( 5.80 - 6.72 ) 6.0

1980 6.1 ( 5.61 - 6.51 ) 5.9

1981 5.5 ( 5.09 - 5.91 ) 5.8

1982 6.0 ( 5.57 - 6.43 ) 5.7

1983 5.8 ( 5.41 - 6.23 ) 5.6

1984 5.4 ( 4.98 - 5.84 ) 5.1 ( 4.70 - 5.48 ) 5.5

1985 5.7 ( 5.26 - 6.13 ) 5.7 ( 5.23 - 6.08 ) 5.4

1986 5.4 ( 5.03 - 5.81 ) 5.5 ( 5.07 - 5.91 ) 5.3

1987 5.6 ( 5.18 - 5.93 ) 5.2 ( 4.78 - 5.58 ) 5.2

1988 4.9 ( 4.55 - 5.25 ) 5.1

1989 6.5 ( 6.07 - 6.92 ) 4.8 ( 4.43 - 5.19 ) 5.0

1990 4.0 ( 3.68 - 4.32 ) 5.5 ( 5.07 - 5.89 ) 4.9

1991 4.5 ( 4.17 - 4.85 ) 4.4 ( 4.00 - 4.76 ) 4.8

1992 4.4 ( 4.03 - 4.70 ) 4.8 ( 4.42 - 5.25 ) 4.7

1993 4.1 ( 3.73 - 4.39 ) 5.1 ( 4.74 - 5.54 ) 4.6

1994 4.3 ( 3.95 - 4.65 ) 4.6 ( 4.18 - 4.92 ) 4.5

1995 4.2 ( 3.79 - 4.54 ) 4.5 ( 4.14 - 4.91 ) 4.4

1996 3.8 ( 3.49 - 4.17 ) 4.6 ( 4.18 - 4.96 ) 4.3

1997 4.2 ( 3.85 - 4.55 ) 4.8 ( 4.38 - 5.15 ) 3.6 ( 3.24 - 4.03 ) 4.3

1998 3.5 ( 3.23 - 3.84 ) 4.8 ( 4.36 - 5.28 ) 4.2

1999 4.3 ( 3.92 - 4.62 ) 4.1 ( 3.70 - 4.50 ) 4.1

2000 4.2 ( 3.90 - 4.55 ) 4.6 ( 4.16 - 4.96 ) 4.1

2001 3.7 ( 3.35 - 4.02 ) 3.9 ( 3.56 - 4.32 ) 4.0

2002 4.1 ( 3.71 - 4.40 ) 4.7 ( 4.30 - 5.13 ) 3.9

2003 3.2 ( 2.94 - 3.54 ) 3.9

2004 4.3 ( 3.86 - 4.66 ) 3.8

2005 3.4 ( 3.02 - 3.69 ) 3.7

2006 3.5 ( 3.13 - 3.79 ) 3.6

2007 3.8 ( 3.46 - 4.15 ) 3.6

3

0
31

Figure 2.4.1: Kenya: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000

year

KEDHS1989 KEDHS1993 KEDHS1998

KEDHS2003 lowess smoothing

Partial TFR(15-39), Kenya, 1979-2002

Figure 2.4.2: Kenya : Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000

year

KEDHS1989 KEDHS1993 KEDHS1998

KEDHS2003 lowess smoothing

Adjusted Partial TFR (15-39), Kenya, 1979-2002
32

Figure 2.4.3: Kenya: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Kenya, 1979-2002
Table 2.4: Kenya: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI]

Adjusted 

smoothed 

TFR 

1989 1993 1998 2003 (15-39)

1980 8.2 ( 7.57 - 8.80 ) 7.1 

1981 5.7 ( 5.22 - 6.19 ) 6.9 

1982 8.1 ( 7.74 - 8.55 ) 6.8 

1983 5.9 ( 5.40 - 6.33 ) 6.6 ( 6.12 - 7.09 ) 6.6 

1984 6.4 ( 6.01 - 6.86 ) 7.2 ( 6.78 - 7.63 ) 6.4 

1985 5.6 ( 5.16 - 6.01 ) 6.1 ( 5.70 - 6.54 ) 6.2 

1986 6.5 ( 6.02 - 6.90 ) 6.8 ( 6.36 - 7.19 ) 6.1 

1987 5.7 ( 5.30 - 6.05 ) 6.5 ( 6.10 - 6.84 ) 5.8 

1988 6.0 ( 5.63 - 6.40 ) 5.2 ( 4.82 - 5.57 ) 5.5 ( 5.14 - 5.91 ) 5.6 

1989 5.5 ( 5.12 - 5.83 ) 5.6 ( 5.24 - 6.01 ) 5.4 

1990 5.3 ( 4.91 - 5.59 ) 5.7 ( 5.33 - 6.12 ) 5.2 

1991 4.7 ( 4.40 - 4.99 ) 4.7 ( 4.34 - 5.02 ) 5.1 

1992 4.9 ( 4.53 - 5.20 ) 5.4 ( 5.09 - 5.75 ) 4.9 

1993 4.3 ( 3.95 - 4.61 ) 4.4 ( 4.09 - 4.81 ) 4.8 

1994 4.6 ( 4.28 - 4.89 ) 4.7 ( 4.33 - 5.15 ) 4.7 

1995 4.0 ( 3.73 - 4.33 ) 4.7 ( 4.30 - 5.03 ) 4.5 

1996 4.5 ( 4.18 - 4.81 ) 4.8 ( 4.42 - 5.17 ) 4.5 

1997 4.4 ( 4.09 - 4.66 ) 4.6 ( 4.28 - 4.99 ) 4.5 

1998 4.2 ( 3.93 - 4.55 ) 4.5 

1999 4.8 ( 4.43 - 5.15 ) 4.5 

2000 4.6 ( 4.31 - 4.97 ) 4.5 

2001 4.3 ( 3.94 - 4.63 ) 4.5 

2002 4.6 ( 4.30 - 4.98 ) 4.5 

3

3
34

Figure 2.5.1: Nigeria: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000 2005

year

NGDHS1990 NGDHS1999 NGDHS2003

NGDHS2008 lowess smoothing

Partial TFR (15-39), Nigeria, 1980-2007

Figure 2.5.2: Nigeria: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1980 1985 1990 1995 2000 2005

year

NGDHS1990 NGDHS1999 NGDHS2003

NGDHS2008 lowess smoothing

Adjusted Partial TFR (15-39), Nigeria, 1980-2007
35

Figure 2.5.3: Nigeria: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000 2005

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Nigeria, 1980-2007
Table 2.5: Nigeria: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI]

Adjusted 

smoothed 

TFR 

1990 1999 2003 2008 (15-39)

1980 7.8 ( 7.29 - 8.22 ) 6.9

1981 5.8 ( 5.41 - 6.22 ) 6.7

1982 7.3 ( 6.75 - 7.90 ) 6.5

1983 6.3 ( 5.82 - 6.77 ) 6.3

1984 7.8 ( 7.29 - 8.26 ) 6.2

1985 5.1 ( 4.76 - 5.49 ) 6.1

1986 5.8 ( 5.43 - 6.27 ) 6.0

1987 5.7 ( 5.25 - 6.22 ) 5.9

1988 5.0 ( 4.54 - 5.39 ) 5.8

1989 5.4 ( 5.10 - 5.75 ) 5.8 ( 5.40 - 6.12 ) 5.7

1990 5.8 ( 5.48 - 6.22 ) 5.7

1991 5.4 ( 5.09 - 5.76 ) 5.7

1992 5.9 ( 5.53 - 6.26 ) 5.6

1993 5.7 ( 5.36 - 6.00 ) 5.9 ( 5.45 - 6.29 ) 5.6

1994 5.5 ( 5.17 - 5.78 ) 5.9 ( 5.44 - 6.31 ) 5.6

1995 5.5 ( 5.16 - 5.84 ) 6.1 ( 5.58 - 6.69 ) 5.6

1996 4.2 ( 3.92 - 4.50 ) 6.0 ( 5.52 - 6.41 ) 5.5

1997 4.2 ( 3.92 - 4.44 ) 5.5 ( 5.07 - 5.97 ) 5.5

1998 5.0 ( 4.69 - 5.35 ) 5.2 ( 4.76 - 5.66 ) 6.6 ( 6.41 - 6.89 ) 5.5

1999 5.3 ( 4.88 - 5.70 ) 5.0 ( 4.83 - 5.21 ) 5.4

2000 5.6 ( 5.16 - 6.02 ) 6.8 ( 6.59 - 7.11 ) 5.4

2001 4.6 ( 4.21 - 4.96 ) 5.3 ( 5.08 - 5.49 ) 5.4

2002 5.6 ( 5.10 - 6.06 ) 6.2 ( 5.99 - 6.45 ) 5.4

2003 4.8 ( 4.65 - 5.02 ) 5.3

2004 5.4 ( 5.17 - 5.55 ) 5.3

2005 5.3 ( 5.10 - 5.49 ) 5.2

2006 5.0 ( 4.80 - 5.15 ) 5.1

2007 5.2 ( 4.98 - 5.36 ) 5.1

3

6
37

Figure 2.6.1: Rwanda: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

RWDHS1992 RWDHS2000

RWDHS2005 lowess smoothing

Partial TFR (15-39), Rwanda, 1982-2004

Figure 2.6.2: Rwanda: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

RWDHS1992 RWDHS2000

RWDHS2005 lowess smoothing

Adjusted Partial TFR (15-39), Rwanda, 1982-2004
38

Figure 2.6.3: Rwanda: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Rwanda, 1982-2004

Table 2.6: Rwanda: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI] Adjusted 

smoothed 

1992 2000 2005 TFR (15-39)

1982 7.0 ( 6.51 - 7.42 ) 7.1

1983 6.9 ( 6.47 - 7.32 ) 7.0

1984 6.9 ( 6.45 - 7.32 ) 6.9

1985 6.9 ( 6.49 - 7.28 ) 6.7

1986 7.2 ( 6.79 - 7.64 ) 6.5

1987 6.6 ( 6.24 - 6.96 ) 6.4

1988 5.8 ( 5.44 - 6.18 ) 6.2

1989 5.8 ( 5.42 - 6.17 ) 6.0

1990 5.2 ( 4.84 - 5.51 ) 6.8 ( 6.45 - 7.20 ) 6.0

1991 5.3 ( 4.92 - 5.60 ) 5.0 ( 4.65 - 5.28 ) 5.9

1992 6.1 ( 5.76 - 6.44 ) 5.8

1993 5.6 ( 5.34 - 5.94 ) 5.7

1994 6.9 ( 6.52 - 7.20 ) 5.6

1995 5.1 ( 4.79 - 5.32 ) 5.9 ( 5.54 - 6.16 ) 5.6

1996 6.1 ( 5.75 - 6.35 ) 6.4 ( 6.10 - 6.73 ) 5.5

1997 5.2 ( 4.98 - 5.47 ) 5.8 ( 5.46 - 6.05 ) 5.5

1998 4.3 ( 4.07 - 4.61 ) 5.0 ( 4.70 - 5.23 ) 5.5

1999 5.3 ( 5.04 - 5.64 ) 5.9 ( 5.59 - 6.21 ) 5.4

2000 5.5 ( 5.24 - 5.82 ) 5.3

2001 4.6 ( 4.33 - 4.81 ) 5.3

2002 5.9 ( 5.62 - 6.19 ) 5.3

2003 5.2 ( 4.92 - 5.40 ) 5.3

2004 5.1 ( 4.85 - 5.37 ) 5.2
39

Figure 2.7.1: Tanzania: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

TZDHS1992 TZDHS1996 TZDHS1999

TZDHS2005 lowess smoothing

Partial TFR (15-39), Tanzania, 1982-2004

Figure 2.7.2: Tanzania: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

TZDHS1992 TZDHS1996 TZDHS1999

TZDHS2005 lowess smoothing

Adjusted TFR (15-39), Tanzania, 1982-2004
40

Figure 2.7.3: Tanzania: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Tanzania, 1982-2004
Table 2.7: Tanzania: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI]

Adjusted 

smoothed 

TFR 

1991/2 1996 1999 20004/5 (15-39)

1983 6.2 ( 5.78 - 6.58 ) 6.0

1984 6.0 ( 5.56 - 6.44 ) 5.9

1985 6.3 ( 5.92 - 6.67 ) 5.3 ( 4.88 - 5.69 ) 5.9

1986 5.3 ( 4.90 - 5.62 ) 6.7 ( 6.31 - 7.10 ) 5.8

1987 5.7 ( 5.37 - 6.07 ) 5.6 ( 5.25 - 5.92 ) 5.8

1988 5.2 ( 4.85 - 5.49 ) 5.9 ( 5.53 - 6.33 ) 5.3 ( 4.57 - 5.98 ) 5.7

1989 5.4 ( 5.05 - 5.69 ) 5.7 ( 5.38 - 6.05 ) 5.7 ( 4.98 - 6.47 ) 5.6

1990 5.8 ( 5.44 - 6.13 ) 6.6 ( 6.21 - 6.95 ) 5.5 ( 4.81 - 6.10 ) 5.6

1991 5.5 ( 5.13 - 5.78 ) 5.1 ( 4.72 - 5.44 ) 5.8 ( 5.16 - 6.35 ) 5.5

1992 5.4 ( 5.01 - 5.70 ) 5.9 ( 5.09 - 6.63 ) 5.4

1993 5.4 ( 5.01 - 5.73 ) 5.6 ( 4.95 - 6.21 ) 5.4

1994 4.8 ( 4.52 - 5.12 ) 5.2 ( 4.48 - 5.97 ) 5.6 ( 5.21 - 5.98 ) 5.3

1995 5.5 ( 5.10 - 5.81 ) 5.0 ( 4.37 - 5.63 ) 5.5 ( 5.09 - 5.89 ) 5.3

1996 4.7 ( 4.08 - 5.40 ) 5.4 ( 5.00 - 5.76 ) 5.2

1997 5.2 ( 4.54 - 5.82 ) 5.5 ( 5.16 - 5.86 ) 5.1

1998 4.8 ( 4.13 - 5.47 ) 5.4 ( 5.02 - 5.76 ) 5.1

1999 4.8 ( 4.48 - 5.12 ) 5.1

2000 5.3 ( 4.92 - 5.62 ) 5.1

2001 5.3 ( 5.01 - 5.65 ) 5.1

2002 5.0 ( 4.73 - 5.37 ) 5.1

2003 5.1 ( 4.80 - 5.50 ) 5.2

2004 5.3 ( 5.02 - 5.67 ) 5.2

41
42

Figure 2.8.1: Uganda: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1990 1995 2000

year

UGDHS1995 UGDHS2001

UGDHS2006 lowess smoothing

Partial TFR (15-39), Uganda, 1985-2005

Figure 2.8.2: Uganda: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1990 1995 2000

year

UGDHS1995 UGDHS2001

UGDHS2006 lowess smoothing

Adjusted Partial TFR (15-39), Uganda, 1985-2005
43

Figure 2.8.3: Uganda: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2

3

4

5

6

7

8

9 

1990 1995 2000

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Uganda, 1985-2005

Table 2.8: Uganda: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI]

Adjusted 

smoothed 

TFR 

1995 2000/1 2006 (15-39)

1985 7.2 ( 6.72 - 7.67 ) 7.0

1986 7.0 ( 6.53 - 7.38 ) 6.8

1987 6.7 ( 6.25 - 7.08 ) 6.7

1988 6.6 ( 6.24 - 7.04 ) 6.6

1989 6.4 ( 5.99 - 6.75 ) 6.5

1990 7.9 ( 7.40 - 8.35 ) 6.4

1991 5.2 ( 4.85 - 5.58 ) 6.1 ( 5.70 - 6.56 ) 6.4

1992 6.0 ( 5.62 - 6.33 ) 7.2 ( 6.80 - 7.63 ) 6.4

1993 6.4 ( 6.04 - 6.75 ) 6.8 ( 6.39 - 7.24 ) 6.4

1994 6.8 ( 6.41 - 7.17 ) 7.8 ( 7.33 - 8.19 ) 6.4

1995 5.7 ( 5.32 - 6.06 ) 6.6 ( 6.20 - 7.01 ) 6.5

1996 6.6 ( 6.23 - 7.04 ) 7.3 ( 6.94 - 7.70 ) 6.5

1997 6.4 ( 6.04 - 6.75 ) 6.4 ( 6.00 - 6.80 ) 6.5

1998 5.9 ( 5.55 - 6.29 ) 6.9 ( 6.56 - 7.28 ) 6.5

1999 6.6 ( 6.17 - 6.93 ) 6.8 ( 6.46 - 7.21 ) 6.4

2000 6.3 ( 5.93 - 6.71 ) 7.7 ( 7.32 - 8.08 ) 6.4

2001 5.7 ( 5.33 - 6.02 ) 6.3

2002 6.5 ( 6.19 - 6.89 ) 6.2

2003 6.3 ( 5.91 - 6.63 ) 6.2

2004 6.0 ( 5.64 - 6.27 ) 6.1

2005 6.1 ( 5.78 - 6.48 ) 6.0
44

Figure 2.9.1: Zambia: Partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000 2005

year

ZMDHS1992 ZMDHS1997 ZMDHS2002

ZMDHS2007 lowess smoothing

Partial TFR (15-39), Zambia, 1982-2006

Figure 2.9.2: Zambia: Adjusted partial Total Fertility Rates (15-39) by single calendar year 

2

3

4

5

6

7

8

9 

1985 1990 1995 2000 2005

year

ZMDHS1992 ZMDHS1997 ZMDHS2002

ZMDHS2007 lowess smoothing

Adjusted Partial TFR (15-39), Zambia, 1982-2006
45

Figure 2.9.3: Zambia: Partial Total Fertility Rates (15-39) by single calendar year by residence 

2 3 4 5 6 7 8 9 

1985 1990 1995 2000 2005

year

Urban Rural

lowess smoothing(urban) lowess smoothing(rural)

Partial TFR (15-39) by residence, Zambia, 1982-2006
Table 2.9: Zambia: Partial Total Fertility Rates (15-39) by year and survey 

Year/

Survey

TFR (15-39) [95% CI] Adjusted 

smoothed 

TFR 

1992 1996 2001/2 2007 (15-39)

1983 6.4 ( 6.07 - 6.79 ) 6.7

1984 6.4 ( 6.03 - 6.80 ) 6.5

1985 6.3 ( 5.96 - 6.66 ) 6.3

1986 6.5 ( 6.18 - 6.90 ) 6.6 ( 6.22 - 7.01 ) 6.1

1987 5.5 ( 5.15 - 5.83 ) 5.8 ( 5.44 - 6.23 ) 6.0

1988 5.2 ( 4.88 - 5.53 ) 5.7 ( 5.39 - 6.09 ) 5.9

1989 5.8 ( 5.38 - 6.13 ) 5.6 ( 5.26 - 6.01 ) 5.8

1990 5.5 ( 5.16 - 5.83 ) 6.7 ( 6.28 - 7.10 ) 5.8

1991 6.0 ( 5.64 - 6.31 ) 5.1 ( 4.80 - 5.49 ) 6.0 ( 5.62 - 6.43 ) 5.8

1992 5.9 ( 5.55 - 6.23 ) 6.3 ( 5.93 - 6.77 ) 5.7

1993 5.7 ( 5.44 - 6.02 ) 6.4 ( 6.01 - 6.76 ) 5.7

1994 5.7 ( 5.40 - 5.97 ) 6.2 ( 5.84 - 6.58 ) 5.7

1995 5.6 ( 5.32 - 5.98 ) 6.3 ( 5.90 - 6.68 ) 5.7

1996 5.2 ( 4.84 - 5.46 ) 5.9 ( 5.46 - 6.31 ) 5.6

1997 5.7 ( 5.35 - 6.01 ) 6.0 ( 5.60 - 6.43 ) 5.6

1998 5.2 ( 4.89 - 5.53 ) 5.6 ( 5.22 - 6.02 ) 5.5

1999 5.3 ( 4.94 - 5.65 ) 5.1 ( 4.75 - 5.50 ) 5.5

2000 5.6 ( 5.25 - 5.95 ) 6.7 ( 6.28 - 7.13 ) 5.4

2001 5.1 ( 4.73 - 5.41 ) 5.2 ( 4.80 - 5.63 ) 5.4

2002 5.0 ( 4.64 - 5.33 ) 5.4

2003 5.5 ( 5.14 - 5.89 ) 5.4

2004 5.6 ( 5.18 - 5.97 ) 5.4

2005 5.6 ( 5.17 - 5.97 ) 5.4

2006 5.6 ( 5.23 - 5.97 ) 5.4

4

6
47

APPENDIX 3: INTER-SURVEY COMPARISON: COMPOSITION OF RESPONDENTS

Table 3.1: Benin: Proportion of women with secondary or higher education by birth cohort and 

survey (%) 

Survey /

Birth 

Cohort

Proportion of women with secondary or higher education [95% CI]

1996 2001 2006

1950-59 4.3 ( 2.89 - 6.20 ) 8.5 ( 6.52 - 10.90 ) 7.6 ( 5.57 - 10.34 )

1960-69 12.0 ( 10.20 - 14.16 ) 12.9 ( 10.90 - 15.14 ) 11.3 ( 10.04 - 12.74 )

1970-79 10.5 ( 8.72 - 12.51 ) 11.1 ( 9.41 - 12.98 ) 9.9 ( 8.93 - 10.91 )

1980-89 13.8 ( 9.81 - 19.05 ) 20.7 ( 18.1 - 23.44 ) 21.6 ( 20.03 - 23.16 )

Table 3.2: Cameroon: Proportion of women with secondary or higher education by birth cohort 

and survey (%) 

Survey /

Birth 

Cohort

Proportion of women with secondary or higher education [95% CI]

1991 1998 2004

1950-59 15.8 ( 12.39 - 19.84 ) 15.2 ( 12.17 - 18.74 ) 18.6 ( 15.75 - 21.85 )

1960-69 28.9 ( 24.07 - 34.33 ) 29.7 ( 25.67 - 34.07 ) 27.9 ( 25.42 - 30.53 )

1970-79 39.7 ( 34.09 - 45.64 ) 42.5 ( 37.88 - 47.15 ) 39.9 ( 37.36 - 42.39 )

1980-89 na 37.8 ( 32.16 - 43.72 ) 46.4 ( 43.93 - 48.85 )
Table 3.3: Ghana: Proportion of women with secondary or higher education by birth cohort and survey (%) 

Survey /

Birth Cohort

Proportion of women with secondary or higher education [95% CI]

1993 1998 2003 2008

1940-49 22.3 ( 17.84 - 27.58 ) 38.9 ( 27.86 - 51.18 ) na na

1950-59 42.5 ( 39.00 - 46.09 ) 41.0 ( 37.20 - 44.86 ) 42.4 ( 37.95 - 47.06 ) 53.1 ( 38.89 - 66.80 )

1960-69 46.3 ( 43.06 - 49.46 ) 48.7 ( 45.22 - 52.10 ) 43.5 ( 39.80 - 47.17 ) 46.1 ( 42.27 - 49.93 )

1970-79 58.3 ( 54.82 - 61.74 ) 56.1 ( 53.00 - 59.08 ) 49.7 ( 46.45 - 53.03 ) 51.5 ( 48.14 - 54.92 )

1980-89 na 67.4 ( 63.27 - 71.22 ) 62.4 ( 59.38 - 65.25 ) 65.5 ( 62.71 - 68.26 )

Table 3.4: Kenya: Proportion of women with secondary or higher education by birth cohort and survey (%) 

Survey /

Birth Cohort

Proportion of women with secondary or higher education [95% CI]

1989 1993 1998 2003

1940-49 4.1 ( 2.92 - 5.70 ) 5.7 ( 3.91 - 8.36 ) * na

1950-59 15.4 ( 13.08 - 17.96 ) 16.0 ( 13.48 - 18.77 ) 19.7 ( 16.96 - 22.80 ) 21.93 ( 18.46 - 25.84 )

1960-69 31.3 ( 28.79 - 33.82 ) 32.3 ( 29.36 - 35.35 ) 33.0 ( 30.14 - 35.98 ) 32.09 ( 28.73 - 35.65 )

1970-79 20.5 ( 15.83 - 26.05 ) 26.2 ( 23.97 - 28.63 ) 36.2 ( 33.56 - 38.91 ) 32.01 ( 29.27 - 34.89 )

1980-89 na na 18.2 ( 15.13 - 21.71 ) 27.16 ( 24.91 - 29.53 )

Table 3.5: Nigeria: Proportion of women with secondary or higher education by birth cohort and survey (%) 

Survey /

Birth Cohort

Proportion of women with secondary or higher education [95% CI]

1990 1999 2003 2008

1950-59 7.8 ( 6.22 - 9.72 ) 12.5 ( 10.25 - 15.19 ) 11.4 ( 8.40 - 15.26 ) 16.3 ( 12.75 - 20.66 )

1960-69 23.4 ( 19.41 - 27.89 ) 30.6 ( 27.14 - 34.19 ) 23.7 ( 20.03 - 27.71 ) 27.0 ( 24.87 - 29.33 )

1970-79 30.7 ( 26.19 - 35.52 ) 43.6 ( 40.02 - 47.32 ) 38.6 ( 33.58 - 43.84 ) 39.5 ( 36.96 - 42.04 )

1980-89 na 38.1 ( 35.19 - 41.19 ) 48.8 ( 43.99 - 53.67 ) 51.3 ( 48.69 - 53.89 )

4

8
Table 3.6: Rwanda: Proportion of women with secondary or higher education by birth cohort and survey (%) 

Survey /

Birth Cohort

Proportion of women with secondary or higher education [95% CI]

1992 2000 2005

1950-59 4.8 ( 3.66 - 6.23 ) 4.5 ( 3.40 - 5.84 ) 4.6 ( 3.16 - 6.51 )

1960-69 9.7 ( 8.04 - 11.72 ) 10.1 ( 8.50 - 11.91 ) 9.1 ( 7.81 - 10.48 )

1970-79 10.0 ( 8.28 - 11.98 ) 15.5 ( 13.59 - 17.64 ) 12.4 ( 11.10 - 13.87 )

1980-89 na 8.9 ( 7.64 - 10.44 ) 9.4 ( 8.40 - 10.47 )

Table 3.7: Tanzania: Proportion of women with secondary or higher education by birth cohort and survey (%) 

Survey /

Birth Cohort

Proportion of women with secondary or higher education [95% CI]

1991/2 1996 1999 2004/5

1950-59 3.2 ( 2.28 - 4.60 ) 2.6 ( 1.77 - 3.82 ) 3.2 ( 1.87 - 5.35 ) 2.9 ( 1.91 - 4.43 )

1960-69 4.9 ( 3.28 - 7.20 ) 5.2 ( 4.19 - 6.49 ) 4.9 ( 3.44 - 7.00 ) 5.2 ( 3.86 - 6.93 )

1970-79 7.2 ( 5.21 - 9.93 ) 7.3 ( 5.84 - 9.17 ) 7.0 ( 5.10 - 9.62 ) 9.0 ( 7.33 - 10.88 )

1980-89 na * 4.3 ( 2.96 , 6.21 ) 10.8 ( 9.20 , 12.64 )

Table 3.8: Uganda: Proportion of women with secondary or higher education by birth cohort and survey (%) 

Survey /

Birth Cohort

Proportion of women with secondary or higher education [95% CI]

1995 2000/1 2006

1950-59 8.7 ( 6.75 - 11.04 ) 9.6 ( 7.46 - 12.24 ) 9.1 ( 6.20 - 13.25 )

1960-69 14.0 ( 11.73 - 16.52 ) 12.6 ( 10.57 - 15.05 ) 11.4 ( 9.43 - 13.64 )

1970-79 16.0 ( 13.86 - 18.45 ) 20.9 ( 18.49 - 23.60 ) 17.5 ( 15.40 - 19.73 )

1980-89 * 23.2 ( 20.34 - 26.31 ) 29.9 ( 27.20 - 32.64 )

Table 3.9: Zambia: Proportion of women with secondary or higher education by birth cohort and survey (%) 

Survey /

Birth Cohort

Proportion of women with secondary or higher education [95% CI]

1992 1996 2001/2 2007

1950-59 22.8 ( 19.89 - 25.96 ) 23.3 ( 20.00 - 26.94 ) 17.7 ( 14.86 - 21.00 ) 24.0 ( 17.31 - 32.33 )

1960-69 28.1 ( 25.61 - 30.64 ) 28.0 ( 25.30 - 30.92 ) 23.7 ( 21.00 - 26.58 ) 21.8 ( 18.34 - 25.58 )

1970-79 25.4 ( 22.93 - 28.10 ) 32.7 ( 30.06 - 35.36 ) 33.2 ( 30.08 - 36.53 ) 31.3 ( 28.53 - 34.29 )

1980-89 na 21.0 ( 17.86 - 24.40 ) 34.1 ( 31.27 - 37.07 ) 41.9 ( 38.87 - 44.98 )

* an asterisk indicates a figure is based on fewer than 25 unweighted cases and has been suppressed.

49
APPENDIX 4: INTER-SURVEY COMPARISON: AVERAGE PARITY 

Table 4.1: Benin: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1996

2001 reconstructed for 

1996 2001

2006 reconstructed for 

2001

1950-59 6.6 ( 6.42 - 6.78 ) 6.5 ( 6.29 - 6.72 ) 6.9 ( 6.67 - 7.14 ) 6.6 ( 6.43 - 6.84 )

1960-69 4.2 ( 4.05 - 4.31 ) 4.3 ( 4.12 - 4.39 ) 5.4 ( 5.19 - 5.51 ) 5.4 ( 5.29 - 5.49 )

1970-79 1.1 ( 1.07 - 1.20 ) 1.2 ( 1.15 - 1.31 ) 2.5 ( 2.41 - 2.62 ) 2.7 ( 2.65 - 2.77 )

1980-89 * * 0.4 ( 0.35 - 0.44 ) 0.4 ( 0.35 - 0.40 )

Table 4.2: Cameroon: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1991

1998 reconstructed for 

1991 1998

2004 reconstructed for 

1998

1950-59 5.3 ( 5.14 - 5.55 ) 5.3 ( 5.05 - 5.49 ) 6.1 ( 5.82 - 6.32 ) 6.2 ( 5.93 - 6.38 )

1960-69 3.0 ( 2.88 - 3.16 ) 2.8 ( 2.70 - 2.95 ) 4.4 ( 4.22 - 4.59 ) 4.6 ( 4.50 - 4.75 )

1970-79 0.6 ( 0.51 - 0.66 ) 0.0 ( 0.33 - 0.42 ) 1.6 ( 1.49 - 1.69 ) 1.7 ( 1.66 - 1.80 )

1980-89 na na 0.2 ( 0.16 - 0.24 ) 0.1 ( 0.08 - 0.11 )

Table 4.3: Ghana: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1998

2003 reconstructed for 

1998 2003

2008 reconstructed for

2003

1950-59 5.6 ( 5.45 - 5.83 ) 5.5 ( 5.23 - 5.70 ) 5.8 ( 5.51 - 6.01 ) 5.9 ( 5.21 - 6.60 )

1960-69 3.7 ( 3.62 - 3.86 ) 3.9 ( 3.72 - 3.99 ) 4.6 ( 4.43 - 4.74 ) 4.6 ( 4.46 - 4.81 )

1970-79 1.3 ( 1.26 - 1.40 ) 1.3 ( 1.26 - 1.41 ) 2.3 ( 2.20 - 2.39 ) 2.4 ( 2.26 - 2.47 )

1980-89 0.1 ( 0.07 - 0.11 ) 0.0 ( 0.03 - 0.05 ) 0.4 ( 0.32 - 0.38 ) 0.4 ( 0.34 - 0.42 )

50
Table 4.4: Kenya: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1993

1998 reconstructed for 

1993 1998

2003 reconstructed for 

1998

1940-49 7.7 ( 7.37 - 7.96 ) 6.9 ( 6.43 - 7.40 ) 7.0 ( 6.53 - 7.49 ) na

1950-59 6.1 ( 5.96 - 6.30 ) 5.9 ( 5.78 - 6.12 ) 6.3 ( 6.14 - 6.50 ) 6.2 ( 5.91 - 6.39 )

1960-69 3.3 ( 3.17 - 3.38 ) 3.3 ( 3.14 - 3.36 ) 4.2 ( 4.09 - 4.35 ) 4.3 ( 4.12 - 4.43 )

1970-79 0.5 ( 0.49 - 0.57 ) 0.5 ( 0.48 - 0.56 ) 1.4 ( 1.37 - 1.51 ) 1.5 ( 1.43 - 1.59 )

1980-89 na na 0.1 ( 0.07 - 0.12 ) 0.0 ( 0.04 - 0.06 )

Table 4.5: Nigeria: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1990

1999 reconstructed for 

1990 1999

2003 reconstructed for 

1999

1940-49 6.8 ( 6.48 - 7.08 ) 6.3 ( 5.63 - 7.05 ) 6.8 ( 5.99 - 7.70 ) na

1950-59 5.3 ( 5.06 - 5.45 ) 4.9 ( 4.73 - 5.11 ) 6.1 ( 5.87 - 6.31 ) 6.9 ( 6.50 - 7.21 )

1960-69 2.7 ( 2.62 - 2.86 ) 2.3 ( 2.17 - 2.37 ) 4.4 ( 4.29 - 4.58 ) 5.3 ( 5.10 - 5.52 )

1970-79 0.5 ( 0.44 - 0.58 ) 0.2 ( 0.22 - 0.27 ) 1.7 ( 1.61 - 1.79 ) 2.0 ( 1.87 - 2.19 )

1980-89 na na 0.1 ( 0.09 - 0.14 ) 0.1 ( 0.11 - 0.16 )

Table 4.6: Rwanda: Average parity by birth cohort and survey 

Survey /

Birth 

Cohort

Average parity [95% CI]

1992

2000 reconstructed for 

1992 2000

2005 reconstructed for 

2000

1950-59 5.9 ( 5.72 - 6.03 ) 5.7 ( 5.56 - 5.81 ) 6.9 ( 6.78 - 7.06 ) 6.8 ( 6.57 - 6.98 )

1960-69 2.5 ( 2.40 - 2.62 ) 2.6 ( 2.53 - 2.71 ) 4.8 ( 4.66 - 4.89 ) 4.9 ( 4.77 - 4.99 )

1970-79 0.3 ( 0.22 - 0.28 ) 0.2 ( 0.19 - 0.24 ) 1.8 ( 1.72 - 1.86 ) 1.9 ( 1.79 - 1.92 )

1980-89 na na 0.1 ( 0.09 - 0.13 ) 0.1 ( 0.07 - 0.10 )

51
Table 4.7: Tanzania: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1996

1999 reconstructed for 

1996 1999

2004/5 reconstructed for 

1999

1940-49 7.4 ( 7.07 - 7.74 ) 8.3 ( 6.46 - 10.23 ) * na

1950-59 6.5 ( 6.31 - 6.67 ) 6.4 ( 6.08 - 6.75 ) 6.6 ( 1.87 - 5.35 ) 6.6 ( 6.32 - 6.84 )

1960-69 4.0 ( 3.84 - 4.07 ) 3.9 ( 3.70 - 4.13 ) 4.5 ( 4.25 - 4.78 ) 4.7 ( 4.58 - 4.87 )

1970-79 1.2 ( 1.13 - 1.26 ) 1.2 ( 1.11 - 1.30 ) 2.0 ( 1.83 - 2.09 ) 2.0 ( 1.90 - 2.05 )

1980-89 0.1 ( 0.03 - 0.08 ) 0.0 ( 0.01 - 0.03 ) 0.2 ( 0.17 - 0.25 ) 0.1 ( 0.11 - 0.14 )

Table 4.8: Uganda: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1995

2000/01 reconstructed for 

1995 2000/01

2006 reconstructed for 

2000/01

1950-59 6.7 ( 6.48 - 6.88 ) 6.7 ( 6.44 - 6.94 ) 7.2 ( 6.94 - 7.49 ) 7.5 ( 7.12 - 7.84 )

1960-69 4.3 ( 4.13 - 4.38 ) 4.5 ( 4.32 - 4.59 ) 5.8 ( 5.62 - 5.93 ) 6.0 ( 5.83 - 6.16 )

1970-79 1.2 ( 1.18 - 1.31 ) 1.3 ( 1.22 - 1.35 ) 3.0 ( 2.92 - 3.11 ) 3.2 ( 3.11 - 3.28 )

1980-89 * * 0.5 ( 0.46 - 0.56 ) 0.4 ( 0.32 - 0.48 )

Table 4.9: Zambia: Average parity by birth cohort and survey 

Survey /

Birth Cohort

Average parity [95% CI]

1996

2001/02 reconstructed for 

1996 2001/02

2007 reconstructed for 

2001/02

1950-59 6.6 ( 6.47 - 6.82 ) 6.9 ( 6.63 - 7.09 ) 7.3 ( 7.02 - 7.50 ) 6.7 ( 6.15 - 7.20 )

1960-69 4.2 ( 4.06 - 4.30 ) 4.4 ( 4.28 - 4.53 ) 5.5 ( 5.34 - 5.64 ) 5.5 ( 5.33 - 5.66 )

1970-79 1.3 ( 1.26 - 1.36 ) 1.4 ( 1.30 - 1.42 ) 2.7 ( 2.63 - 2.81 ) 2.8 ( 2.67 - 2.86 )

1980-89 0.1 ( 0.04 - 0.07 ) 0.0 ( 0.03 - 0.04 ) 0.6 ( 0.54 - 0.62 ) 0.4 ( 0.41 - 0.48 )

* an asterisk indicates a figure is based on fewer than 25 unweighted cases and has been suppressed. 

5

2
53

APPENDIX 5: AVERAGE PACE OF FERTILITY DECLINE

Table 5.1: Average pace of fertility decline 

Country Year

Adjusted 

smoothed TFR 

(15-39)

Average pace per 

year (in child)

Relative pace 

to previous 

period Trend

Benin 1996 - 2001 5.7 5.3 0.080 

2001 - 2005 5.3 5.2 0.025 0.3 Stall

Cameroon 1991 - 1998 5.5 4.9 0.086 

1998 - 2003 4.9 4.7 0.040 0.5 Decline

Ghana 1993 - 1998 4.6 4.2 0.080 

1998 - 2003 4.2 3.9 0.060 0.8 Decline

2003 - 2007 3.9 3.6 0.075 1.3 Decline

Kenya 1993 - 1998 4.8 4.5 0.060 

1998 - 2002 4.5 4.5 0.000 0.0 Stall

Nigeria 1990 - 1999 5.7 5.4 0.033 

1999 - 2003 5.4 5.3 0.025 0.8 Decline

2003 - 2007 5.3 5.1 0.050 2.0 Decline

Rwanda 1992 - 2000 5.8 5.3 0.063 

2000 - 2004 5.3 5.2 0.025 0.4 Stall

Tanzania 1996 - 1999 5.2 5.1 0.033 

1999 - 2004 5.1 5.2 -0.020 -0.6 

Earlytransition

Uganda 1990 - 1995 6.4 6.5 -0.020 

1995 - 2000 6.5 6.4 0.020 -1.0 

Earlytransition

2000 - 2005 6.4 6.0 0.080 4.0 

Earlytransition

Zambia 1991 - 1996 5.7 5.6 0.020 

1996 - 2001 5.6 5.4 0.040 2.0 Decline

2001 - 2006 5.4 5.4 0.000 0.0 Stall