DHS METHODOLOGICAL 

REPORTS 12

Quality and Consistency of 

DHS Fertility Estimates, 

1990 to 2012

SEPTEMBER 2014

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Bruno Schoumaker of ICF International.

DHS Methodological Reports No.12 

Quality and Consistency of 

DHS Fertility Estimates, 1990 to 2012 

Bruno Schoumaker 

Université catholique de Louvain 

Louvain-la-Neuve 

September 2014 

Corresponding author: Bruno Schoumaker, Centre de recherche en démographie et sociétés, Université 

catholique de Louvain, Place Montesquieu, 1 bte L2.08.03 B-1348-Louvain-la-Neuve, BELGIUM; 

telephone: +32 10–474136; fax: +32 10–472952; email: bruno.schoumaker@uclouvain.be 
Acknowledgment: The author acknowledges the support of USAID for this study, and would like to 

thank Stephane Helleringer and Shea Rutstein for useful comments and suggestions, as well as Tom 

Pullum, Stan Becker, and Sarah Bradley for discussions in the preparation of this study. 

Editor: Sidney Moore 

Document Production: Yuan Cheng 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through the MEASURE DHS project (#GPO-C-00-08-000080-00). The views 

expressed are those of the author and do not necessarily reflect the views of USAID or the United States 

Government. 

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

population, health, and nutrition programs. For additional information about The DHS Program contact: 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500, fax: 301-407-6501, email: reports@dhsprogram.com, Internet: www.dhsprogram.com. 

Recommended citation: 

Schoumaker, Bruno. 2014. Quality and Consistency of DHS Fertility Estimates, 1990 to 2012. DHS 

Methodological Reports No. 12. Rockville, Maryland, USA: ICF International. 
iii 

Contents 

Tables ........................................................................................................................................................... v

Figures ......................................................................................................................................................... vi

Preface ......................................................................................................................................................... ix

Abstract ....................................................................................................................................................... xi

Executive Summary ................................................................................................................................. xiii

1. Introduction and Rationale ................................................................................................................ 1

1.1. Literature Review: Quality of Birth History Data .................................................................... 2

2. Data and Methods ............................................................................................................................... 7

2.1. DHS Data Used in the Study ..................................................................................................... 7

2.2. Methods ................................................................................................................................... 11

3. Results ................................................................................................................................................ 13

3.1. Reconstructed Fertility Trends by Calendar Year: Regularity and Consistency across 

Surveys .................................................................................................................................... 13

3.2. Discrepancies between Published Fertility and Reconstructed Fertility ................................ 16

3.3. Proximate Causes of Differences between Published and Reconstructed TFRs ..................... 20

4. Conclusion ......................................................................................................................................... 45

References .................................................................................................................................................. 47

Appendix .................................................................................................................................................... 49

v 

Tables 

Table 1. Description of main data quality problems affecting fertility estimates ......................................... 3 

Table 2. Distribution of 69 countries included in this study, by number of DHS surveys conducted in 

the country ...................................................................................................................................... 7 

Table 3. Number of DHS surveys used in this report, by region and phase ................................................. 7 

Table 4. Percentage of DHS surveys with a short reference period (<4 years) for the child health 

section in the questionnaire, by region and phase ........................................................................ 11 

Table 5. Evaluation of data quality based on consistency of fertility trends across surveys in countries 

with more than one DHS survey and on the regularity of fertility trends in countries with one 

DHS survey .................................................................................................................................. 16 

Table 6. Surveys in which the relative difference between the published TFR and the reconstructed 

TFR is greater than or equal to 10 percent, DHS surveys, 1991-2005 ......................................... 19 

Table 7. Mean of relative and actual differences between published estimates of TFR (15-49 years, 

last three years) and estimates from reconstructed fertility trends (15-49 years, centered on 

the same date), by region and DHS phase .................................................................................... 20

Table 8. Average index of displacement of births around the cutoff year for birth histories in the DHS 

questionnaire, by region and by phase, 182 DHS surveys ............................................................ 27 

Table 9. Evaluation of proximate causes of discrepancies between published fertility and 

reconstructed fertility in 26 DHS surveys, 1991-2005 ................................................................. 41 

Appendix Table A1. Description of the 182 DHS surveys (1990-2012) used in this report ...................... 99 
vi 

Figures 

Figure 1. Birth history table ......................................................................................................................... 9

Figure 2. Distribution of 182 DHS surveys by length of the reference period (months) for the child 

health section in the DHS questionnaire. (The length of the reference period is computed as 

the difference between the mean date of the survey and the cutoff date for the child health 

section.)....................................................................................................................................... 10

Figure 3. Reconstructed fertility trends (TFR 15-49) and comparison with published TFRs 

(Philippines, Colombia, Niger, Ethiopia, Ghana, and Bolivia) .................................................. 14

Figure 4. Distribution of actual and relative differences between published estimates of TFRs 

(women 15-49, last three years) and estimates from reconstructed fertility trends (women 

15-49, centered on the same date), 112 DHS surveys ................................................................ 17

Figure 5. Relationship between published estimates of TFRs (women 15-49, last three years) and 

estimates from reconstructed fertility trends (women 15-49, centered on the same date), 

112 DHS surveys ........................................................................................................................ 18

Figure 6. Patterns of relative TFRs around the cutoff year of the child health section in the DHS 

questionnaire, DHS surveys in Colombia (2010), Bangladesh (1996-97), Mozambique 

(1997), and Pakistan (1990) ........................................................................................................ 22

Figure 7. Average patterns of relative TFRs around the cutoff year of the child health section in the 

DHS questionnaire, by region, by phase, by groups of data quality, and by level of 

discrepancy between published and reconstructed TFRs ........................................................... 24

Figure 8. Distribution of births and total fertility rates (TFRs) by single years around the cutoff date 

for the child health section in the DHS questionnaire, Uganda 1995 ......................................... 25

Figure 9. Distribution of the displacement index in 182 DHS surveys ...................................................... 26

Figure 10. Reconstructed fertility trends and fertility for the last three years with and without 

correcting for displacement of births, DHS surveys in Mozambique and Cameroon ............... 28

Figure 11. Correlation between displacement of births in 112 DHS surveys and relative difference 

between published and reconstructed TFRs (r=0.14) ............................................................... 29

Figure 12. Average patterns of relative TFRs around the cutoff year of the child health section in the 

DHS questionnaire, by region, by phase, by groups of data quality, and by level of 

discrepancy between published and reconstructed TFRs (birth histories corrected for 

displacement of births) .............................................................................................................. 30

Figure 13. Reconstructed fertility trends (TFR 15-49) in six countries by three-year periods preceding 

each survey, fertility for the last three years, and reconstructed fertility with pooled birth 

histories (birth histories corrected for birth displacement) ....................................................... 32
vii 

Figure 14. Reconstructed fertility at young ages (TFR 15-24) in six countries by three-year periods 

preceding each survey, with and without correcting for a possible Potter effect (10 percent 

increase of birth intervals) ......................................................................................................... 35

Figure 15. Reconstructed fertility trends (TFR 15-49) in six countries by three-year periods preceding 

each survey, fertility for the last three years, and reconstructed fertility with pooled birth 

histories with (black thick line) and without (grey thick line) correction for the Potter 

effect ......................................................................................................................................... 39

Appendix Figure A1. Reconstructed fertility trends by single calendar year, published fertility (last 

three years), and reconstructed fertility with pooled birth histories in 69 countries (181 

surveys) ..................................................................................................................................... 49

Appendix Figure A2. Reconstructed fertility trends by three-year periods preceding each survey, 

published fertility (last three years), and reconstructed fertility with pooled birth histories 

(birth histories corrected for birth displacement), in 69 countries (181 surveys) ..................... 65

Appendix Figure A3. Reconstructed fertility trends (women 15-24) by three-year periods over the 30 

years preceding each survey, with and without adjusting for a Potter effect (increase of 

birth intervals by 10 percent), in 18 countries (61 surveys) ...................................................... 83

Appendix Figure A4. Reconstructed fertility trends (TFR 15-49) by three-year periods preceding each 

survey, published fertility (last three years), and reconstructed fertility with pooled birth 

histories with (black thick line) and without (grey thick line) correction for the Potter 

effect, 18 countries (61 surveys) ............................................................................................... 89

Appendix Figure A5. Comparison across surveys of the percentage of women (15-34) who have 

completed at least six years of education, reconstructed for the 15 years preceding each 

survey, 18 countries (61 surveys) ............................................................................................. 94

ix 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to continually assess and improve the methodology and 

procedures used to carry out national-level surveys as well as to offer additional tools for analysis. 

Improvements in methods used will enhance the accuracy and depth of information collected by The DHS 

Program and relied on by policymakers and program managers in low- and middle-income countries. 

While data quality is a main topic of the DHS Methodological Reports series, the reports also examine 

issues of sampling, questionnaire comparability, survey procedures, and methodological approaches. The 

topics explored in this series are selected by The DHS Program in consultation with the U.S. Agency for 

International Development. 

It is hoped that the DHS Methodological Reports will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries, and will be used to 

enhance the quality and analysis of survey data. 

Sunita Kishor 

Director, The DHS Program 

xi 

Abstract 

This report evaluates the quality of birth history data from 182 DHS surveys conducted in 69 countries 

since the 1990s (DHS Phase II onward). It focuses on levels and trends in estimated fertility. Fertility 

trends are reconstructed using birth history data from DHS surveys; the regularity of trends in fertility and 

their consistency across surveys draw a broad picture of the quality of data from the 69 countries. The 

results of the reconstruction indicate that fertility data are very good in some countries (e.g., Armenia, 

Colombia, Indonesia, Morocco), acceptable in many countries (e.g., Jordan, Kenya, Zimbabwe), and poor 

in other countries (e.g., Benin, Ethiopia, Niger, Nigeria, Pakistan). The study identified discrepancies 

between the fertility estimates, published in the survey reports (for the three years preceding the survey) 

and the reconstructed fertility estimates, and explored various data quality issues that may explain these 

discrepancies. Displacement of recent births in the birth history table of the DHS questionnaire because of 

the child health section is widespread, but it is only marginally related to discrepancies between published 

and reconstructed fertility trends. Three other factors may account for the differences: 1) omission of 

recent births, 2) the Potter effect, and 3) differences in sample composition. Overall, the analyses 

presented in this study indicate that DHS fertility estimates are of good or acceptable quality in the 

majority of surveys, but that taking published fertility figures at face value could be risky in some 

contexts. Inferring fertility trends by comparing recently published fertility data from successive surveys 

may lead to erroneous trend results. 

KEY WORDS: fertility, data quality, displacement, omission, Potter effect, sample 

xiii 

Executive Summary 

Birth histories provide key data for the measurement of fertility indicators (age-specific fertility rates, 

total fertility rates, length of birth intervals) and child mortality indicators. These indicators are central to 

designing and evaluating health and population policy, establishing population projections, and 

documenting the dynamics of population. Such objectives require good quality data. 

This report presents an evaluation of the quality of birth history data in 182 DHS surveys conducted in 69 

countries since the 1990s (DHS Phase II onward). It focuses on levels and trends in the total fertility rate 

(TFR) and the reliability of published fertility (three years preceding the survey). The first step in the 

study was reconstructing fertility trends in each of the 69 countries, using birth history data from DHS 

surveys. The regularity of the trends observed and their consistency across surveys draw a broad picture 

of the quality of the fertility data. In countries with several surveys, fertility trends are reconstructed by 

pooling birth histories from successive surveys. The study identified discrepancies between the published 

fertility estimates and the reconstructed fertility estimates and explored various data quality issues—

displacement of recent births, omission of recent births, the Potter effect, and sampling—that may explain 

these discrepancies. By manipulating birth histories to correct for potential data quality problems, we 

evaluated whether some of these issues/problems have an impact on the discrepancies or can be ruled out. 

The overall picture emerging from these analyses is that fertility estimates appear to be very good in some 

countries (e.g., Armenia, Colombia, Indonesia, Morocco), are acceptable in many countries (e.g., Jordan, 

Kenya, Zimbabwe), and are poor in other countries (e.g., Benin, Ethiopia, Niger, Nigeria, Pakistan). 

Displacement of recent births in the birth history table of the DHS questionnaire is widespread but the 

problem is only marginally related to the discrepancies between published and reconstructed fertility 

trends. Three other factors were explored in 26 surveys where discrepancies were large. First, the Potter 

effect may account for a portion of the discrepancies in some surveys; second, differences in sample 

implementation appear to be an important issue in a few surveys. Finally, after correcting for these two 

data quality problems, omission of recent births may best explain the differences between reconstructed 

fertility and published fertility. 

Overall, these analyses identify broad patterns of data quality that affect birth histories and may be 

possible sources of errors. Even though consistency across DHS surveys is very good in a majority of 

countries, the results of this report indicate that data quality is an important consideration. Taking 

published figures on fertility at face value could be risky in some contexts; likewise, inferring fertility 

trends by comparing published recent fertility data from successive surveys may lead to erroneous trends 

results. 

1 

1. Introduction and Rationale 

Birth histories provide key data for the measurement of fertility indicators (age-specific fertility rates, 

total fertility rates, length of birth intervals) and child mortality indicators. These indicators are central to 

designing and evaluating health and population policies, establishing population projections, and 

documenting the dynamics of population. Such objectives require good quality data (Alkema et al. 2012). 

Over the past four decades, birth histories collected first by the World Fertility Survey (WFS) and since 

the mid-1980s by the Demographic and Health Surveys (DHS) have allowed tremendous progress in 

knowledge of fertility and child mortality in developing countries. More than 200 surveys have been 

conducted as part of the DHS program in more than 70 countries. Despite the large investment in efforts 

to collect high quality data, all surveys are to some extent affected by data quality issues. The collection 

of retrospective data (full birth histories)—on issues that may be sensitive in contexts where respondents 

have limited education—is affected by problems such as 1) child’s date of birth or age at death is 

unknown, 2) displacement of date of birth or age at death, and 3) omission of births and deaths. The 

intensity of these problems depends on a variety of factors including the size and design of the 

questionnaire, the training of interviewers, and the quality control during fieldwork. Ultimately, data 

quality problems can influence fertility and mortality indicators; however, while some indicators may be 

severely biased by data quality problems, other indicators may experience only benign effects. 

The objective of this report is to evaluate the quality of birth history data in 182 DHS surveys conducted 

in 69 countries since the 1990s (DHS Phase II onward). It focuses on levels and trends in the total fertility 

rate (TFR) and the reliability of published fertility data (three years preceding the survey). The first step 

in the study was reconstructing fertility trends in each of the 69 countries, using birth history data from 

DHS surveys. The regularity of the trends observed and their consistency across surveys draw a broad 

picture of the quality of the fertility data. In countries with several surveys, fertility trends are 

reconstructed by pooling birth histories from successive surveys. The study identified discrepancies 

between the reconstructed fertility estimates and the published estimates and explored various data quality 

issues—displacement of recent births, omission of recent births, Potter effects, and sampling issues1

—that 

may explain these discrepancies. By manipulating birth histories to correct for potential data quality 

problems, we evaluate whether some of these issues/problems have an impact on the discrepancies or can 

be ruled out. Rather than quantifying in a precise way the role of each data quality issue, we evaluate the 

plausibility of different types of errors. Compared to previous analyses of consistency of fertility 

estimates across surveys (Arnold 1990; Marckwardt and Rutstein 1996; Pullum 2006), this study focuses 

on the causes of inconsistencies across surveys and the discrepancies between published fertility estimates 

and fertility estimated from pooled survey data; it also covers a much larger set of surveys than was the 

case in previous reports. 

The large number of countries and surveys included in this report allows drawing a broad picture of the 

data quality issues found in these surveys. However, covering such a large number of surveys precludes 

an in-depth analysis of each country, let alone each survey. Data from specific countries are used to 

illustrate the variety of situations found in DHS surveys, but most results and comments presented in the 

text refer to broad patterns and groups of countries or surveys, rather than to specific cases. Nevertheless, 

figures are provided for each country and survey in the appendix, and may reassure users on the quality of 

the data or draw attention to specific problems and the need for more detailed investigation in specific 

cases. 

 

1

 Because this approach relies on combining data from more than one survey, it can only be carried out in countries 

with at least two surveys, and the most recent published fertility estimates (latest survey) cannot be evaluated in this 

way. 
2 

The study is organized as follows: A brief literature review is presented in section 1. The data and 

methods are presented in section 2. Results are presented in section 3, starting with the reconstructed 

fertility trends and their consistency across surveys, followed by the discrepancies between published 

fertility and reconstructed fertility, and last, by an exploration of the possible causes of these 

discrepancies. Conclusions are presented in section 4. 

1.1. Literature Review: Quality of Birth History Data 

Since the 1970s, birth histories have become the major tool for collecting fertility data in less developed 

countries. Over the last four decades, analyses of WFS and DHS data quality have shown that birth 

history data are subject to various types of errors. Four broad types of errors that can lead to biases are 

usually distinguished (Goldman, Rutstein, and Singh 1985): misreporting of date of birth, omission of 

births, selection bias, and sampling design. Some errors can be further distinguished according to whether 

they concern recent births or births that took place some distance in the past. In addition, errors in data not 

collected in the birth histories may also influence fertility estimates. The main types of data quality 

problems are shown in Table 1 and are discussed below. Four of these problems (Table 1, shaded text) 

will be explored in this report. 

Misreporting of date of birth occurs for various reasons but generally because the respondent does not 

know the child’s exact date of birth. The problem is usually more common in low income countries and 

among less educated women (Pullum 2006). Incomplete information on date of birth is also more 

common for dead children (Curtis 1995). When respondents do not know exact birth dates, these are 

estimated using probing techniques; when no estimate is available, imputation methods are used to 

provide birth dates. Not knowing exact date of birth can lead to several types of misreporting of birth 

dates. Heaping on year of birth (e.g., 2000, 2005, 2010, etc.) or on duration since birth (e.g., 5 years, 10 

years, etc.) is frequently an issue. Pullum (2006) found that heaping of date of birth on years with final 

digits 0 or 5 was common in surveys from sub-Saharan Africa. Gage (1995) also showed considerable 

heaping of year of first birth and duration since first birth on digits 0 and 5 in the early DHS surveys 

(Phase 2) in sub-Saharan Africa. Misreporting of date of birth can translate into the so-called “Potter 

effect.” The Potter effect results from a tendency among women to report that their first birth occurred 

later than it actually did, while recent births are reported correctly. As a result, recent fertility will be 

correct, fertility in more distant periods will be underestimated, and fertility in the intermediate period 

will be overestimated (Goldman 1985; Moultrie 2013). Misreporting may also be intentional. There is 

considerable evidence that recent births tend to be displaced backward in some DHS surveys, especially 

in sub-Saharan Africa (Arnold 1990; Curtis 1995; Pullum 2006). This is linked to the fact that some 

interviewers may change the birth date of certain children to avoid administering the lengthy child health 

section of the DHS questionnaire (Arnold 1990; Pullum 2006). The effects of displacement of births on 

fertility indicators depends on the extent of displacement and on the correspondence between the cutoff 

date for the collection of child health information and the period for which rates are computed (Arnold 

1990). In the DHS program, fertility rates are most often reported for the three years preceding the survey, 

and births are thought to be transferred mainly from the fifth to the sixth year before the survey, so 

displacement would have little or no impact on estimates of recent fertility (Marckwardt and Rutstein 

1996). 

 
3 

Table 1. Description of main data quality problems affecting fertility estimates 

Type of problem 

Possible causes and 

mechanisms Impact on fertility 

Misreporting of date of birth 

Heaping Heaping of year of birth or age 

(e.g., final digit 0 or 5). More likely 

in contexts where exact date of 

birth/death is not known by 

respondent. 

Irregular fertility trends; underestimation 

of recent fertility if heaping implies 

displacement of births outside the 

window of eligibility for computation of 

rates. 

Displacement of recent births 

before the cutoff date 

Displacement of births by 

interviewer or respondent in order 

to avoid additional questions to 

complete the child health section in 

the DHS questionnaire. 

Irregular fertility trends; underestimation 

of recent fertility if births are displaced 

outside the window of eligibility for 

computation of rates. 

Potter effect Tendency to report distant births as 

closer to the survey than actually 

occurred. More likely when birth 

histories are collected starting with 

first birth. 

Underestimation of fertility in earlier 

periods and overestimation in 

intermediate periods (U-shape trend in 

fertility); apparent decrease in median 

age at first birth. 

Underreporting of births 

Omission of distant births Involuntary omission of early births, 

especially by older respondents 

and for deceased children. 

Underestimation of fertility in earlier 

periods; apparent decrease in median 

age at first birth. 

Omission of recent births Omission of births by interviewers 

or respondents in order to avoid 

additional questions to complete 

the child health section in the DHS 

questionnaire. 

Omission of deceased children in 

order to avoid sensitive questions. 

Underestimation of recent fertility. 

Sample implementation 

Sample implementation Accidental oversampling or 

undersampling of some groups of 

women; this may be due to an 

outdated sampling frame. 

Overestimation or underestimation of the 

level of fertility, including the published 

TFR. 

Selection bias 

Mortality Only surviving women are 

interviewed about their past fertility. 

Slight underestimation of recent fertility if 

fertility and mortality are positively 

correlated. 

Fertility at young ages among older 

women may be underestimated if high 

fertility women experience higher 

mortality rates. 

International migration Only women who have survived to 

international migration are 

interviewed about their past fertility. 

International migrants are also 

included in the computation of past 

fertility. 

Likely to be small, unless the share of 

international migrants is large and the 

correlation with fertility is strong. 

(Continued...)
4 

Table 1. – Continued

Type of problem 

Possible causes and 

mechanisms Impact on fertility 

Other factors 

Misreporting of women’s age Heaping on year of birth or age 

(e.g., final digit 0 or 5). Women may 

also be reported as younger or 

older than they actually are, to 

avoid being interviewed. 

Possible impact on fertility rates if 

misreporting of age is related to fertility. 

All women factor The percentage of ever-married 

women by age is needed to 

compute exposure in surveys 

conducted among ever-married 

women. The proportion of 

ever-married women at younger 

ages may be underestimated. 

Underestimation of ever-married women 

will lead to underestimation of fertility 

rates. 

Note: Shaded lines indicate data quality problems explored in this report. 

Omission of births is another potential data quality issue in birth histories (Blacker 1994; Goldman 1985; 

Schoumaker 2011). Early evaluations of the quality of birth histories in the World Fertility Survey (WFS) 

led to the conclusion that omission of distant births was most likely to occur among older women 

(United Nations 1987). Omission of distant births translates into an underestimation of past fertility and 

an apparent decrease in age at first birth (Arnold 1990). A decrease in the median age at first birth was 

found in 9 of the first 11 DHS surveys in sub-Saharan Africa (Arnold 1990), indicating possible 

omissions among older women. Gage (1995) found similar results in DHS Phase 2 surveys in subSaharan Africa. Such patterns may suggest omission of distant births but are also consistent with forward 

displacement of age at first birth and the Potter effect (Arnold 1990; Gage 1995). 

Omission of recent births has been less studied but is potentially a very serious issue for the estimation of 

recent fertility. The same reason that may cause interviewers to displace births in the birth history table—

i.e., the lengthy child health section in the DHS questionnaire—may lead them to omit births altogether 

(Marckwardt and Rutstein 1996). Omission of recent births leads to underestimating recent fertility; if 

omissions of deceased children are more likely than omissions of surviving children, mortality estimates 

will also be underestimated (Curtis 1995; Sullivan, Bicego, and Rutstein 1990). Previous analyses of data 

quality have suggested that a few DHS surveys were affected by severe omission but that, overall, 

omission of births was not widespread. Marckwardt and Rutstein (1996) analyzed the distribution of 

births by calendar year in 25 DHS surveys and concluded that three surveys (Indonesia 1991, Pakistan 

1990-91, and Yemen 1991-92) were affected by substantial omission. Omissions of deceased children 

were confirmed in Pakistan with a reinterview survey (Curtis and Arnold 1994). Evidence of severe 

omission was also reported in more recent DHS surveys. For instance, in the 1999 Nigeria DHS, “[...] 

omission of births in the three-year period immediately prior to the survey [...resulted] in an 

underestimate of current fertility of about 16-17 percent” (National Population Commission 2000:36). 

However, identification and quantification of omission is not an easy task unless underreporting is severe 

(Arnold 1990; Marckwardt and Rutstein 1996). Displacement and omission may lead to similar 

distortions in fertility trends (Arnold 1990; Goldman 1985; Sullivan, Bicego, and Rutstein 1990), making 

it difficult to distinguish these two broad issues. Internal consistency checks such as the measurement of 

sex ratio at birth and the ratio of neonatal mortality to infant mortality also have limitations because they 

can only detect selective omissions (Curtis 1995). When two or more surveys are available, a useful 

approach to evaluating the data quality of birth histories consists of comparing the level of fertility in one 

survey with the level of fertility estimated for the same period in the subsequent survey. Good consistency 
5 

in fertility levels across surveys provides solid evidence of good quality of birth histories. In contrast, 

discrepancies may reflect several types of data quality issues, including omission. This approach has been 

used in several DHS country reports as well as in comparative papers (Arnold 1990; Machiyama 2010; 

Pullum 2006). Overall, discrepancies between TFRs (for women age 15-44) from successive surveys may 

be large, especially in sub-Saharan Africa (Pullum 2006). Omission of recent births may account for part 

of these discrepancies. 

Selection bias is related to the fact that only surviving women are interviewed about their past fertility. If 

fertility and mortality are correlated, and if a large proportion of women have died before a given age, 

fertility estimates may be biased. In most instances, this should not have a strong impact; the risk of dying 

in the few years preceding the survey at adult ages is usually low. Combined with moderate fertility 

differences between surviving and deceased women, the overall impact should be small. In contrast, 

fertility at young ages among older women may be underestimated if high fertility women experience 

high mortality rates. Selection may also occur because of migration (out-migration and in-migration), but 

the overall impact on national fertility estimates should be small because, in most cases, levels of 

international migration are low.2

 

Fertility estimates also depend on the sample design. While this is not a characteristic of birth histories, it 

can potentially substantially influence fertility estimates. Accidental oversampling or undersampling of 

women with high fertility—e.g., rural areas are overrepresented or underrepresented in the sample—will 

translate into overestimated or underestimated fertility rates. Such situations are more likely to occur if 

the sampling frame is outdated. Unless the data can be compared with other sources, these problems are 

difficult to detect. 

Finally, other types of data quality issues may affect fertility estimates. The quality of age reporting of 

mothers may influence fertility estimates if misreporting of mother’s age relates to fertility. Information 

on marital status may influence the quality of estimates in surveys conducted among ever-married 

women. In these surveys, all women factors are derived from the percentage of ever-married women by 

age, to estimate exposure for the computation of general age-specific fertility rates (Rutstein and Rojas 

2006). This factor is sensitive to age reporting and may thus also influence fertility estimates (Curtis and 

Arnold 1994). 

Overall, the number of potential data quality errors in estimating fertility is large. Errors may occur 

simultaneously, or different errors may have similar effects on fertility estimates. As a result, evaluating 

the existence of these errors and, more important, quantifying their extent, has no ready-made solution. 

Instead, a variety of approaches should be used to reach an acceptable assessment of the quality of birth 

histories and the sources of possible data problems. This report aims to contribute to this assessment by 

implementing a combined approach to the problem of data quality errors. The consistency of estimates 

from several sources is a key element in this approach. The causes of inconsistencies are investigated with 

specific attention to displacement of recent births, omission of recent births, sample implementation, and 

Potter effects. 

 

2

 Impacts may be larger for subnational estimates. 

7 

2. Data and Methods 

2.1. DHS Data Used in the Study 

The data for this study come from 182 DHS surveys conducted from the early 1990s to 2012, for which 

access to data was unrestricted. These surveys were conducted in 69 countries under DHS Phase 2 

through Phase 6. The number of surveys carried out by each country varied: 23 countries conducted one 

survey during the study period while 24 countries had four or more surveys (Table 2). Only standard DHS 

surveys are used in this report; full birth histories have been collected in some MIS and AIS surveys, but 

these surveys were not included. 

Table 2. Distribution of 69 countries included in this study, by number of DHS surveys conducted 

in the country 

Number of DHS surveys Number of countries 

1 23 

2* 11 

3 11 

4 18 

5 4 

6 2 

Total 69 

* Two surveys were conducted in Brazil, but the geographic coverage of the two surveys in Brazil is not 

comparable. 

Table 3 presents the surveys by broad regions and by DHS phase. With 93 surveys, sub-Saharan Africa 

includes approximately half of the surveys used in this report, and is by far the most represented region. 

Thirty-six surveys were conducted in Asia, and 31 surveys were conducted in Latin America. A total of 

15 surveys were carried out in the Middle-East and North Africa (MENA). A few surveys (7) have also 

been conducted in Eastern Europe since Phase 4. 

Table 3. Number of DHS surveys used in this report, by region and phase 

 Phase 

Region Total 

II 

(1988-1993) 

III 

(1992-1997) 

IV 

(1997-2003) 

V 

(2003-2008) 

VI 

(2008-2013) 

Sub-Saharan Africa (SSA) 11 22 26 20 14 93 

Asia (ASIA) 3 11 9 8 5 36 

Europe (EU) 0 0 1 5 1 7 

Latin America (LA) 5 9 7 7 3 31 

Middle-East and North Africa 

(MENA) 

4 3 5 3 0 15 

Total 23 45 48 43 23 182 
8 

2.1.1. Birth histories 

The analyses in this study use data from birth histories collected with the DHS Woman’s Questionnaire. 

In the DHS Program, birth history data are collected using the birth history table that records “[...] all the 

births the respondent has had in the order in which they occurred starting with her first birth” 

(ICF International 2012:52). This “forward approach” is the same as that used in the World Fertility 

Survey (Goldman 1985).3

 Each line in the table corresponds to a live birth, and the 10 columns 

correspond to the 10 questions related to each live birth (Figure 1). 

Figure 1 shows the information collected in birth histories in DHS Phase 6. The content of birth histories 

in DHS has changed slightly since the 1990s, but the core of the birth history table (questions Q212 to 

Q218 and Q220) has been collected in a fairly consistent manner.4

 In some surveys, birth histories were 

collected as part of pregnancy histories (e.g., Ghana 1998), with a few additional questions to distinguish 

between live births, stillbirths, and pregnancy losses. The phrasing of questions may also have varied 

across surveys but, overall, the manner in which birth history data have been collected in DHS surveys 

makes it highly comparable. Most DHS surveys administer the individual questionnaire to all women age 

15-49, regardless of marital status; in some countries (e.g., Morocco, Bangladesh), only ever-married 

women are interviewed. As mentioned earlier, in these countries, an all women factor is used to compute 

general fertility rates from data collected among ever-married women (Rutstein and Rojas 2006). 

 

 

3

 This is one element that may lead to the Potter effect (Potter 1977). 

4

 Some of these questions have not changed (Q216, Q220) or have changed only slightly (Q214). For some of these 

questions, some precisions were added or removed. For instance, for the date of birth (Q215), the following 

precision “OR In what season was he/she born?” was removed from the questionnaire in DHS Phase 5 and Phase 6. 

The order of some questions was changed (Q213 comes before Q214 in DHS Phase 6, but not in previous phases). 

Some questions were also added, and some were removed. Q219 (household line number of the child) was added in 

DHS Phase 4. Q221 (any other live births since the previous birth) was added in DHS Phase 3, in combination with 

a question on the duration of the preceding birth interval. The question on the preceding birth interval was removed 

in DHS Phase 4, and a further precision (“including any children who died after birth?”) was brought to Q221 in 

DHS Phase 5. Overall, the changes have been limited. 
9 

Figure 1. Birth history table 

Source: ICF International (2012) 

 
10 

2.1.2. Cutoff date for the health sections 

At the end of the birth history, the interviewer is asked to count the number of live births the respondent 

has had since a specified cutoff date. The births between the cutoff date and the date of the survey are 

eligible for the health questions administered in various parts of the questionnaire, particularly the lengthy 

child health section. The number of eligible births reported by the interviewer comes directly from the 

data collected in the birth history table and may strongly influence the quality of the birth history data. As 

discussed in section 1.1 above, interviewers may be tempted to change the date of birth of children to 

limit their workload (i.e., avoid asking the same series of questions for each eligible birth), leading to 

displacement of births backward. Interviewers may also omit births to avoid the time-consuming child 

health section. 

In most surveys, the cutoff date corresponds to the month of January for the year of the beginning of the 

survey minus five years. For instance, for a survey conducted from March to August 2001, the cutoff date 

would be January 1996. The reference period for the child health sections is, on average, around 5.5 years 

in these surveys (Figure 2). In approximately 1 in 10 surveys, the reference period was shorter than five 

years (an average of 3.5 years), with the cutoff date defined as the month of January for the survey year 

minus three years (Table 4). These shorter reference periods were used in the Phase 3 and Phase 4 surveys 

and mainly involved countries in sub-Saharan Africa and Asia (e.g., Bangladesh 1994, Ghana 1993, 

Mozambique 1997, Togo 1998, Zimbabwe 1994, etc.). 

Figure 2. Distribution of 182 DHS surveys by length of the reference period (months) for the child 

health section in the DHS questionnaire. (The length of the reference period is computed as the 

difference between the mean date of the survey and the cutoff date for the child health section.) 

 

Reference period (months)

Number of surveys

20 40 60 80 100

0 5 10 15 20 25 30 35
11 

Table 4. Percentage of DHS surveys with a short reference period (<4 years) for the child health 

section in the questionnaire, by region and phase 

 Phase 

Region Total 

II 

(1988-1993) 

III 

(1992-1997) 

IV 

(1997-2003) 

V 

(2003-2008) 

VI 

(2008-2013) 

Sub-Saharan Africa (SSA) 0% 55% 4% 0% 0% 14% 

Asia (ASIA) 0% 45% 11% 0% 0% 17% 

Europe (EU) 0% 0% 0% 0% 0% 0% 

Latin America (LA) 0% 0% 14% 0% 0% 6% 

Middle-East and North Africa 

(MENA) 

0% 0% 0% 0% 0% 0% 

Total 0% 38% 6% 0% 0% 11% 

2.2. Methods 

This methodological report relies to a large extent on reconstructed fertility trends as a tool to evaluate the 

quality of birth histories and fertility estimates. Two broad approaches are used in this study to 

reconstruct fertility trends from birth history data. The first consists of reconstructing trends in the total 

fertility rate (TFR) for each survey separately. In countries where several DHS surveys have been 

conducted, the consistency of fertility trends from successive surveys provides a powerful way to evaluate 

data quality. The second approach consists of pooling all the surveys conducted in the same country and 

reconstructing smoothed fertility trends from the pooled dataset. The pooled reconstructed trend is then 

used as a reference for evaluating the quality of published fertility estimates. The possible impact of 

specific types of errors on fertility estimates is evaluated by manipulating birth histories to correct for data 

quality problems. 

2.2.1. Reconstruction of fertility trends from a single survey 

Fertility is reconstructed using Poisson regression. For a single survey the method consists of creating a 

table of births and exposure by periods and by 5-year age groups of mothers from the birth history data. A 

Poisson model is then fitted with the number of births as the dependent variable, exposure as an offset, 

and two independent variables: 1) age measured with dummy variables for 5-year age groups, and 2) 

periods measured with dummy variables. The total fertility rate between age 15 and age 49 can be 

reconstructed for each period from the regression coefficients. This age-period model makes the 

assumption that the age pattern of fertility is constant over time, i.e., that there is no interaction between 

age and time periods. Although this does not hold strictly, simulations show that the assumption is 

reasonable for periods up to 15 years. Two types of periods are used for the reconstructions. In the first 

series of reconstructions fertility is computed by single calendar years over 15 years. In the second series, 

fertility is computed by three-year periods preceding the survey for the last 15 years. A similar approach 

is used to reconstruct partial total fertility rates (15-24) over long periods (30 years) to evaluate the 

existence of a Potter effect. The method for reconstructing fertility in this way is presented in detail in 

Schoumaker (2013b), and a Stata module implementing the method is available.5

 

 

5

 The Stata module can be installed directly from Stata by typing ssc install tfr2. 
12 

2.2.2. Reconstruction of fertility trends from pooled surveys 

Trends in total fertility rates (15-49) can also be reconstructed over longer periods by pooling birth 

histories from several surveys. The method, which is an extension of the method for a single survey, is 

presented in detail in Schoumaker (2013a). For each survey in a country, a table of births and exposure 

for the 15 years preceding the survey is prepared. These tables are then simply appended to create a table 

covering a longer period. As with the previous method, fertility rates are estimated with Poisson 

regression, with age and time periods included as independent variables. Contrary to the single survey 

approach, the age pattern of fertility is not considered to be constant for the entire period, but is

considered to be constant for each survey. This is done by computing a pattern of proportional 

age-specific fertility rates for each survey; the pattern is multiplied by exposure and controlled for in the 

offset.6

 Total fertility rates are also smoothed by using restricted cubic splines (Schoumaker 2013a). This 

method is useful to provide an average TFR estimate based on data from all the surveys combined. When 

successive surveys are highly consistent, the reconstructed fertility trend for successive surveys will 

correspond to estimates from separate surveys. When the surveys do not match, the reconstructed trend 

will provide a reference for quantifying discrepancies. 

2.2.3. Manipulating birth histories 

Individual birth histories can be manipulated in different ways to evaluate the impact of data quality on 

fertility estimates. One approach consists of introducing measurement errors in good quality birth 

histories (e.g., displacing births, removing births) to evaluate the impact of different types of errors on 

fertility levels and trends and on consistency across surveys. Another approach consists of trying to 

correct for measurement errors in birth histories that are thought to be affected by data quality issues. 

Births may be displaced (or added) to evaluate the extent to which correcting for some types of errors 

improves consistency across surveys. In this study the second approach is used to explore the possible 

impact of two specific problems: displacement of recent births and the Potter effect. For the first problem, 

a percentage of births are displaced from the cutoff year of the child health sections to the year just 

before, to correct for the backward displacement of births. Births are randomly selected and displaced in 

individual birth histories to correct for the distribution of births at the aggregate level.7

 For the second 

problem, the Potter effect, all the births occurring before the cutoff year are displaced backward. This is 

done by increasing the length of birth intervals by a given percentage (10 percent). In this way, births that 

occurred a longer time before the survey—those at young ages among older cohorts—are displaced to a 

larger extent than those that occurred more recently. These approaches are further explained in sections 

3.3.2. and 3.3.4. 

 

6

 Several age patterns are thus used for periods in which several surveys overlap. As a result, the age pattern is an 

average of several age patterns for these periods. Simulations and applications to data from the Human Fertility 

Database indicate the method is able to reproduce known fertility trends accurately (Schoumaker 2013a). 

7

 One should note that consistency within birth histories is not necessarily respected using this approach; however, 

because the objective of this method is to evaluate errors in birth histories on aggregate measures, inconsistencies 

within birth histories created in this way are not a problem for our purpose. 
13 

3. Results 

3.1. Reconstructed Fertility Trends by Calendar Year: Regularity and Consistency across 

Surveys 

Total fertility rates for each of the 182 DHS surveys in this study were reconstructed by single calendar 

year for the last 15 years (Appendix Figure A1).8

 Published values of total fertility rates (computed over 

the three years preceding the survey) are represented on these figures as red dots.9

 The smoothed 

reconstructed fertility trend with pooled birth histories is also represented on these figures. The quality of 

fertility data is first interpreted by visual inspection of fertility trends. With good quality data, we expect 

the fertility trends from successive surveys to match, fluctuations to be small, and the red dots (published 

fertility) to be located on or close to the reconstructed trends. In countries with only one survey, the 

evaluation of data quality is less straightforward. The regularity of the fertility trends is used as an 

indicator of quality. In contrast, large fluctuations10 and a sharp decrease a few years before the survey—

at the cutoff date for the child health section—are interpreted as data quality problems. 

Six reconstructed fertility trends for the Philippines, Colombia, Niger, Ethiopia, Ghana, and Bolivia are 

described below and compared with published fertility data (Figure 3). The results for all 69 countries are 

reported in Appendix Figure A1. Overall, these trends indicate that data quality varies greatly across 

surveys and countries from excellent to very poor. Data from the Philippines and Colombia illustrate 

highly consistent fertility trends. Annual variations in the TFR are small; the reconstructed trends match 

quite well and are very close to the smoothed trend (thick black line); and the published TFRs (last three 

years) are located along the fertility trend. Even though the retrospective estimates do not match perfectly, 

they are close to one another and the published estimates do not depart from the overall trend in a 

significant way. In contrast, fertility trends for Niger and Ethiopia are affected by severe data quality 

problems. Fertility estimates from successive surveys are not very consistent; annual values of the TFRs 

vary widely; and estimates of recent fertility (last three years) are much lower than the retrospective 

estimates at the same time from the following surveys and are lower than the TFRs estimated with pooled 

survey data. The published three-year TFR in the second survey in Niger is almost two children lower that 

the retrospective estimate from the third survey. A similar situation is seen in Ethiopia, where fertility 

decreases sharply in the few years before the survey and published TFRs are well below other estimates. 

For the most recent surveys in Niger and Ethiopia, the reconstructed estimate and the published estimate 

are very close to each other because only one survey is available. In countries with questionable data 

quality, the reconstructed trend for the most recent period should not be given too much confidence. 

Intermediate fertility trends are illustrated by data from Ghana and Bolivia. In Ghana, TFRs fluctuate 

more than in the Philippines and Colombia, and the published TFR in the 1998 Ghana survey (second 

survey shown) is below the fertility trend. In Bolivia, fluctuations are also larger than in the Philippines 

and Colombia and the first two estimates of recent fertility appear to be well below the fertility trend. In 

addition to possible displacement and/or omission of births, the estimate from the second survey in 

Bolivia may be lower than the other estimates because of differences in sample implementation. 

 

8

 In Brazil, only the survey covering the entire country is shown, as the two surveys are not comparable. As a result, 

181 surveys are represented on the figures. 

9

 We refer to rates for the three years preceding the survey as “published fertility rates.” Published TFRs in country 

reports may sometimes be computed for the five years preceding the survey, but TFRs on the STATcompiler cover 

the three years preceding the survey. 

10 Some fluctuations may reflect real fluctuations in the TFR, some are due to sampling errors, and some reflect 

heaping on some dates or ages. Given that sample sizes are fairly similar across surveys, the larger fluctuations are 

interpreted as reflecting data quality problems.
14 

Figure 3. Reconstructed fertility trends (TFR 15-49) and comparison with published TFRs 

(Philippines, Colombia, Niger, Ethiopia, Ghana, and Bolivia) 

(Continued...) 

1975 1980 1985 1990 1995 2000 2005 2010

2 4 6 8 10

Philippines

Year

TFR (15-49)

Published estimates

survey-specific estimates

Reconstructed smoothed estimates

Periods only covered by the latest survey

1975 1980 1985 1990 1995 2000 2005 2010

2 4 6 8 10

Colombia

Year

TFR (15-49)

1975 1980 1985 1990 1995 2000 2005 2010

2 4 6 8 10

Niger

Year

TFR (15-49)

1975 1980 1985 1990 1995 2000 2005 2010

2 4 6 8 10

Ethiopia

Year

TFR (15-49)
15 

Figure 3. – Continued

Note: Because only one survey is available for the recent period, the most recent published estimate necessarily falls 

on the trend line. This is a structural feature, not an indication that data are of good quality. For this reason, the 

section of the reconstructed trend based on a single survey for the most recent period is shown in a different color. 

Overall, in the 45 countries with more than one survey, consistency across surveys is very good in 16 

countries, moderate in 16 countries, and low in 13 countries (Table 5). Most countries with severe 

inconsistencies are in sub-Saharan Africa. In the 24 countries with only one survey, fertility trends show 

considerable variation in data quality across countries. In 10 of these countries, fluctuations in fertility are 

small and changes are regular. In 9 other countries, fluctuations are larger and changes are less regular, 

especially in the few years preceding the survey. Finally, fertility in 5 countries appears very erratic and 

the reconstructed trend indicates a sharp decrease a few years before the survey, reflecting possible 

omission and/or displacement of births. 

This visual evaluation of reconstructed trends is to some extent subjective but clearly shows that data 

quality ranges from excellent to very poor. Overall, birth histories in about a quarter of the 69 countries 

are affected by serious data quality issues, mainly in sub-Saharan Africa and in a few Asian countries 

(Pakistan, Timor Leste, and Yemen). Fertility trends appear of good quality in 26 of the 69 countries, and 

quality is intermediate in 25 countries. 

1975 1980 1985 1990 1995 2000 2005 2010

2 4 6 8 10

Ghana

Year

TFR (15-49)

1975 1980 1985 1990 1995 2000 2005 2010

2 4 6 8 10

Bolivia

Year

TFR (15-49)
16 

Table 5. Evaluation of data quality based on consistency of fertility trends across surveys in 

countries with more than one DHS survey and on the regularity of fertility trends in countries with 

one DHS survey 

Subjective evaluation of quality 

(Degree of consistency/regularity)

Countries with more than one survey 

(45 countries) 

Countries with one survey 

(24 countries) 

Good quality 

(High consistency/regularity) 

16+10 countries 

Armenia, Colombia, Egypt, Gabon, 

Honduras, Indonesia, Kazakhstan, 

Morocco, Lesotho, Nicaragua, Namibia, 

Nepal, Peru, Philippines, Vietnam, 

Zimbabwe 

Albania, Azerbaijan, Brazil, 

Guyana, Kyrgyz Republic, 

Moldova, Maldives, Paraguay, 

Ukraine, Uzbekistan 

Moderate quality 

(Moderate consistency/ regularity) 

16+9 countries 

Bangladesh, Bolivia, Cambodia, Côte 

d’Ivoire, Dominican Republic, Ghana, 

Haiti, India, Jordan, Kenya, Malawi, 

Rwanda, Senegal, Tanzania, Turkey, 

Zambia 

Burundi, DR Congo, Congo 

Brazzaville, Guatemala, 

Comoros, Sao Tome, Togo, 

Swaziland, South Africa 

Poor quality 

(Low consistency/regularity) 

13+5 countries 

Burkina Faso, Benin, Cameroon, Chad, 

Ethiopia, Guinea, Madagascar, Mali, 

Mozambique, Nigeria, Niger, Pakistan, 

Uganda 

Central African Republic, 

Liberia, Sierra Leone, Timore 

Leste, Yemen 

3.2. Discrepancies between Published Fertility and Reconstructed Fertility 

Published total fertility rates are of central importance in DHS publications. In most survey reports the 

TFR is presented for the three years preceding the survey and indicates the level of recent fertility. 

Fertility changes are often evaluated by comparing recent fertility from successive surveys. However, as 

shown in Figure 3, published fertility (red dots) can be much lower than reconstructed fertility, suggesting 

that published fertility may be underestimated. Variations in the degree of underestimation across surveys 

may lead to erroneous trends. 

In this section, reconstructed, smoothed fertility trends are used as a reference for the comparison of 

published fertility rates. The procedure is as follows: for all surveys except the most recent survey in each 

country—i.e., 112 surveys in the 45 countries with more than one survey—the published estimate (red 

dot) is compared to the reconstructed smoothed estimate (black line).11 Countries with only one survey 

are, by definition, excluded and the last survey is also excluded in all the countries. The difference 

between the published fertility rate and the reconstructed fertility rate is used as a metric of discrepancy 

across surveys. Although the reconstructed estimate is not the true value of the TFR it is expected to be 

closer (on average) to the true value than the published value estimated from a single survey.12 It is 

important to emphasize that a published fertility lower than the reconstructed fertility may result from 

underestimation of the published TFR and/or overestimation of the reconstructed trend (and thus 

overestimation of fertility at the same period in subsequent surveys). As shown in Figure 3, when 

consistency across surveys is good, the published estimates will be close to the reconstructed estimate. 

When consistency across surveys is poor, as in Niger and Ethiopia, the published TFR can be much lower 

than the estimate from the reconstructed trend (Figure 3). 

 

11 The number of surveys should be 113 (182 surveys minus the most recent one in each of the 69 countries); and the 

number of countries with two surveys is 46. Since consistency of fertility trends cannot be analyzed in Brazil 

because the surveys do not cover the same geographic areas, only 112 surveys from 45 countries are used. 

12 The reconstructed estimate at one point in time is a weighted average of fertility estimated from different surveys. 
17 

Both the actual difference and the relative difference between the published and the reconstructed fertility 

estimates are computed.13 Figure 4 shows the distribution of actual and relative differences in all the 

surveys. Overall, differences are negative in the large majority of surveys, showing a clear tendency of 

published TFRs to be lower than the estimates from the reconstructed trend. However, differences are 

moderate in most surveys. The average actual difference is -0.34 children and the relative difference is 

around -6 percent (Table 7). Half of the surveys have actual differences between 0 and -0.3 children and 

half of the surveys have relative differences of less than 5 percent. Yet, some surveys are characterized by 

large differences. In 26 surveys—out of 112 surveys from Phase 2 to Phase 5—published fertility is lower 

than reconstructed fertility by more than 10 percent and actual differences are greater than one child in 

seven surveys (Table 6). The 1990-1991 Pakistan survey and the 1999 Nigeria survey are especially 

problematic but surveys in Bangladesh, Ethiopia, Guinea, and India are also affected by serious problems. 

As expected, actual differences between published and reconstructed fertility increase with fertility levels 

(Figure 5); in contrast, relative differences do not vary with fertility levels. 

Figure 4. Distribution of actual and relative differences between published estimates of TFRs 

(women 15-49, last three years) and estimates from reconstructed fertility trends (women 15-49, 

centered on the same date), 112 DHS surveys 

 

 

13 The actual difference is computed as ACT=TFR(p)-TFR(r), and the relative difference is REL=TFR(p)/TFR(r)-1, 

where TFR(p) is the published TFR for the three years preceding the survey and TFR(r) is the value from the 

smoothed reconstructed trend at the average date of the published TFR. 

Actual differences (n=112)

Actual difference

Number of surveys

-1.5 -1.0 -0.5 0.0 0.5

0 10 20 30 40 50

Relative differences (n=112)

Relative difference

Number of surveys

-0.3 -0.2 -0.1 0.0 0.1

0 10 20 30 40
18 

Figure 5. Relationship between published estimates of TFRs (women 15-49, last three years) and 

estimates from reconstructed fertility trends (women 15-49, centered on the same date), 112 DHS 

surveys 

2468

2468

Reconstructed TFR (15-49)

Published TFR (15-49)

equality

-10%

-20%
19 

Table 6. Surveys in which the relative difference between the published TFR and the 

reconstructed TFR is greater than or equal to 10 percent, DHS surveys, 1991-2005 

Country Year Relative difference Actual difference 

Bangladesh 1994 -0.18 -0.78 

Bangladesh 1997 -0.14 -0.55 

Bangladesh 2000 -0.12 -0.44 

Bangladesh 2004 -0.11 -0.37 

Benin 2001 -0.10 -0.65 

Bolivia 1998 -0.12 -0.58 

Burkina Faso 1993 -0.13 -1.00 

Burkina Faso 2003 -0.14 -0.99 

Cameroon 1998 -0.14 -0.82 

Cameroon 2004 -0.12 -0.65 

Chad 1997 -0.14 -1.05 

Dominican Republic 1999 -0.17 -0.54 

Ethiopia 2000 -0.18 -1.23 

Ethiopia 2005 -0.11 -0.70 

Guinea 1999 -0.16 -1.05 

Haiti 1994 -0.14 -0.78 

India 1999 -0.17 -0.59 

Mali 1996 -0.13 -0.98 

Mozambique 1997 -0.15 -0.94 

Niger 1992 -0.10 -0.81 

Niger 1998 -0.14 -1.16 

Nigeria 1999 -0.23 -1.45 

Nigeria 2003 -0.11 -0.71 

Pakistan 1991 -0.23 -1.49 

Peru 1992 -0.13 -0.52 

Turkey 1993 -0.11 -0.33 

Note: Shaded lines indicate surveys in which the relative difference is less than or equal to -0.15. 

Table 7 shows the mean values of these indicators by region and DHS phase. The relative differences are 

highest in sub-Saharan Africa and Asia. Actual differences are highest in sub-Saharan Africa (-0.44 

children), reflecting both the higher level of relative differences and the high level of fertility. Overall, 

differences increased slightly between DHS Phase 2 and Phase 3 (late 1990s) but have decreased since 

then. No estimate is available for Phase 6; estimates for Phase 5 suggest an improvement in the 

measurement of fertility, but the discrepancies may be underestimated.14 

 

14 The overall reconstructed trend may be pulled down by the latest observations, so that the difference in the nextto-last survey is underestimated. 
20 

Table 7. Mean of relative and actual differences between published estimates of TFR (15-49 years, 

last three years) and estimates from reconstructed fertility trends (15-49 years, centered on the 

same date), by region and DHS phase 

 Relative difference Actual difference 

 Mean Min Max Mean Min Max N 

Region 

Sub-Saharan Africa -6.8% -23.4% 7.4% -0.44 -1.45 0.37 58 

Asia -6.7% -23.3% 3.1% -0.27 -1.49 0.12 23 

Europe 2.1% -5.6% 9.8% 0.03 -0.10 0.15 2 

Latin America -6.3% -16.9% 1.4% -0.25 -0.79 0.04 19 

MENA -3.8% -11.5% 1.1% -0.14 -0.33 0.04 10 

 

DHS Phase 

II -5.2% -23.2% 7.4% -0.31 -1.49 0.37 20 

III -7.7% -18.5% -2.1% -0.41 -1.16 -0.06 37 

IV -6.9% -23.4% 6.3% -0.36 -1.45 0.25 42 

V -1.9% -11.5% 9.8% -0.11 -0.70 0.15 13 

 

Total -6.3% -23.4% 9.8% -0.34 -1.49 0.37 112 

3.3. Proximate Causes of Differences between Published and Reconstructed TFRs 

The discrepancies between published TFRs and reconstructed TFRs may have several proximate causes, 

which were discussed in section 1.1.15 Some recent births may have been displaced backward and some 

may have been omitted (Table 1). These two causes will lead to underestimating recent fertility. Another 

possible source of discrepancies is the “Potter effect”; in this case, retrospective estimates of fertility will 

be overestimated, pulling the reconstructed trend upward. Finally, differences in sample implementation 

across surveys may lead to higher or lower fertility than expected. While it is difficult to reach a definitive 

conclusion as to the causes of these discrepancies, it is possible to determine which are plausible, which 

are possible, and which to rule out. We first look at patterns of fertility around the cutoff year to identify 

general evidence of displacement and/or omission of recent births. Each cause is then examined more 

specifically in the next stage. 

 

 

15 Proximate causes refer to the types of data quality problems that cause these differences. Ultimate causes, e.g., 

design of questionnaire, education of interviewers, and interviewees, etc., are not discussed in this report. 
21 

3.3.1. Patterns of fertility around the cutoff year of the health module 

Patterns of fertility around the cutoff year for the child health section in the DHS questionnaire are useful 

as a first step in detecting displacement and omission of recent births. In each survey fertility rates are 

computed by single year for the four years preceding the cutoff date and the three years following the 

cutoff date.16 This approach is similar to examining the distribution of births by calendar year 

(Marckwardt and Rutstein 1996); however, the evaluation of trends is facilitated with total fertility rates 

because exposure is controlled. In the absence of omission or displacement of births around the cutoff 

year for the child health section, we expect total fertility rates to vary in a regular way, i.e., with small 

fluctuations and no disruption in the fertility trend at the cut-off year. Displacement should cause an 

excess of fertility in the year before the cutoff date for the health section, reduced fertility in the first year 

covered by the health section, followed by a return to average fertility levels. In contrast, omission should 

cause a sudden drop in fertility at the cutoff year of the health section, with no recovery (Marckwardt and 

Rutstein 1996). 

Relative total fertility rates (annual TFRs divided by the average TFR over the seven-year period—i.e., 

the four years preceding and the three years following the cutoff date of the birth history) are computed to 

compare patterns across surveys. Figure 6 illustrates four cases that show distorted fertility patterns based 

on the relative TFR data from Colombia, Bangladesh, Mozambique, and Pakistan. In all four surveys, 

there is a more rapid drop in fertility between the year just before the cutoff date (year 1) and the first year 

covered by the health section (year 0). The distortion in the fertility trend is small in Colombia and 

moderate in Bangladesh. In these two countries some displacement of births is likely but it is difficult to 

conclude anything about underreporting of births. In contrast, the sharp drop in Pakistan—fertility 

decreases by 50 percent in one year—that was not followed by a recovery, suggests births were 

underreported (Marckwardt and Rutstein 1996). In Mozambique underreporting of births is also possible, 

but the pattern is less obvious than in Pakistan. 

 

16 Three years were used because the cutoff date was three years before the survey in a few cases (e.g., Ghana 1998, 

Kenya 1998). 
22 

Figure 6. Patterns of relative TFRs around the cutoff year of the child health section in the DHS 

questionnaire, DHS surveys in Colombia (2010), Bangladesh (1996-97), Mozambique (1997), and 

Pakistan (1990) 

 

-4 -2 0 2

0.6 0.8 1.0 1.2 1.4

Colombia 2010

Year (0=cutoff date)

Relative TFR (15-49)

-4 -2 0 2

0.6 0.8 1.0 1.2 1.4

Bangladesh 1996-1997 

Year (0=cutoff date)

Relative TFR (15-49)

-4 -2 0 2

0.6 0.8 1.0 1.2 1.4

Mozambique 1997

Year (0=cutoff date)

Relative TFR (15-49)

-4 -2 0 2

0.6 0.8 1.0 1.2 1.4

Pakistan 1990

Year (0=cutoff date)

Relative TFR (15-49)
23 

Fertility trends and patterns of relative TFRs around the cutoff year were computed for the 182 surveys; 

average patterns for several groups of surveys (regions, phase, data quality groups, differences between 

published and reconstructed fertility) are shown in Figure 7. On average, the relative fertility level drops 

from 1.08 just before the cutoff year to 0.90 in the first year covered by the child health section, and 

remains at that level for three years. This pattern shows that the cutoff year for the child health section is 

associated with serious distortions in fertility levels and trends. In sub-Saharan Africa the highly distorted 

pattern is consistent with both displacement and omission of births. In the other regions evidence is less 

conclusive. The pattern in the MENA countries is also highly distorted, but this is largely because of 

Yemen. In Asia and Latin America the average patterns are less distorted than those in the previous two 

regions. Birth histories in the European surveys are least affected by the cutoff date. Comparisons of 

average patterns by DHS phase also indicate that this pattern of fertility is an enduring characteristic of 

the DHS surveys (Figure 7). 

Further analysis is done using two other classifications. First, average patterns are computed for the three 

groups of countries categorized in Table 5 by subjective evaluation of data quality (182 surveys). The 

poor quality group comprises 18 countries where consistency of fertility trends across surveys is low or—

when only a single survey is available—where the trend is very irregular. The high quality group 

comprises 26 countries, and the moderate quality group consists of 25 countries. As expected, patterns are 

much more distorted in countries with poor data quality, and this pattern is compatible with both 

displacement and omission of births. Patterns are less distorted in the countries with good data quality; 

there may be some omission, but this could be due to moderate displacement and reflect the overall 

fertility trend. The pattern is intermediate in countries with moderate data quality; omission of births is 

possible, but this pattern could result from displacement and decreasing fertility. 

Patterns are also compared across five groups of surveys based on the difference between published 

fertility and reconstructed fertility (112 surveys, Figure 7). In the least favorable group—which has the 

greatest amount of discrepancy—published fertility is lower than reconstructed fertility by at least 15 

percent (eight surveys, Table 6). In the most favorable group—which has the least amount of 

discrepancy—published fertility is greater than or equal to reconstructed fertility. Again, distortions are 

most severe in surveys where differences between published and reconstructed TFRs are large. In the 

least favorable group, fertility in the three years following the cutoff year is well below fertility in the four 

years preceding the cutoff year (by more than 20 percent); this pattern suggests serious omission of births 

in these surveys. In countries with smaller discrepancies the pattern is also distorted but, again, this could 

result from displacement and reflect the overall fertility trend.

In summary, the patterns of fertility around the cutoff year for the child health section in the DHS 

questionnaire indicate that the reporting of births has had an impact on DHS fertility rates. Countries 

where fertility trends are less consistent or less regular show more distorted patterns of fertility around the 

cutoff year. The distortion is also greater for surveys in which published fertility is much lower than 

reconstructed fertility, indicating these two problems—distortions in fertility trend and difference between 

published fertility and reconstructed fertility—are related. 
24 

Figure 7. Average patterns of relative TFRs around the cutoff year of the child health section in the 

DHS questionnaire, by region, by phase, by groups of data quality, and by level of discrepancy 

between published and reconstructed TFRs 

 

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

Region (n=182)

Year (0=cutoff date)

relative TFR (15-49)

SSA

ASIA

EU

LA

MENA

Total

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

DHS phase (n=182)

Year (0=cutoff date)

relative TFR (15-49)

Phase 2

Phase 3

Phase 4

Phase 5

Phase 6

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

Groups of data quality (n=182)

Year (0=cutoff date)

relative TFR (15-49)

Poor

Moderate

Good

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

Level of discrepancy (n=112)

Year (0=cutoff date)

relative TFR (15-49)

>0

]-0.05;0]

]-0.10;-0.05]

]-0.15;-0.10]

<=-0.15
25 

3.3.2. Does displacement of recent births account for the difference between published and 

reconstructed fertility estimates? 

We now look at evaluating the extent of displacement of recent births and the potential impact of 

displacement on the difference between published and reconstructed fertility estimates. The measurement 

of displacement of births is based on the distribution of births by calendar years (the cutoff date for the 

child health section in the DHS questionnaire usually corresponds to the beginning of a calendar year). 

Using the 1995 Uganda DHS as an example, Figure 8 shows the distribution of births by calendar year 

around the cutoff date for the child health section in the questionnaire. The number of births in the first 

year covered by the health section (year 0) appears exceptionally low compared with the year preceding 

the cutoff date (year -1). There was a decrease from 1,567 births in year -1 to 1,174 births in year 0. This 

pattern is mirrored by a strong decrease in the TFR between year -1 (8.6 children) and year 0 (6.0 

children). 

Figure 8. Distribution of births and total fertility rates (TFRs) by single years around the cutoff 

date for the child health section in the DHS questionnaire, Uganda 1995 

An index of displacement of births from year 0 (cutoff year) to year -1 (year preceding cutoff) is 

computed using the same method as Pullum (2006). The expected numbers of births in year 0 (ܿ̂) and in 

year -1 (ܾ෠) are estimated in the following way: 

 ܾ෠ = ሺܾ+ܿሻ. ଵ

ଵାቀ೏

ೌቁ

భ/య (1) 

 ܿ̂ = ሺܾ+ܿሻ. ቀ

೏

ೌቁ

భ/య

ଵାቀ೏

ೌቁ

భ/య (2) 

 

-5 -4 -3 -2 -1 0 1 2

600 1000 1400 1800

Births

Year (0=cutoff date)

Number of births per year

-4 -3 -2 -1 0 1 2 -5 -4 -3 -2 -1 0 1 2 23456789

TFR (15-49)

Year (0=cutoff date)

Total Fertility Rate (15-49)

-4 -3 -2 -1 0 1 2
26 

Where a is the number of births in year -2, b is the number of births in year -1, c is the number of births in 

year 0, and d is the number of births in year 1. The number of displaced births (DB) is obtained as 

follows: 

 (3) ܿ ܿ̂−	= ෠ܾ − ܾ = ܤܦ 

The displacement index (interpreted as a percentage of births displaced from year 0 to year -1) is 

computed as follows: 

௖̂ି௖ = ܮܵܲܫܦ 

௖̂

 (4) 

Displacement of births appears to be common. The displacement index is positive in seven out of eight 

surveys, and is above 10 percent in three out of ten surveys (Figure 9). Overall, displacement is most 

severe in sub-Saharan Africa (Table 8), a finding that is consistent with previous analyses of DHS data 

quality (Arnold 1990; Marckwardt and Rutstein 1996; Pullum 2006). In contrast, displacement is less 

pronounced in Latin America and in the European surveys. No clear trend in displacement is visible 

across DHS phases. 

Figure 9. Distribution of the displacement index in 182 DHS surveys 

 

Displacements

Number of surveys

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

0 10 20 30 40 50 60
27 

Table 8. Average index of displacement of births around the cutoff year for birth histories in the 

DHS questionnaire, by region and by phase, 182 DHS surveys 

Mean Min Max N 

Region 

Sub-Saharan Africa 10.0% -9.3% 25.1% 93 

Asia 5.4% -4.1% 25.0% 36 

Europe 2.8% -2.0% 8.1% 7 

Latin America 3.7% -6.6% 13.0% 31 

MENA 6.7% -0.4% 15.2% 15 

 

DHS Phase 

II 7.5% -0.6% 25.0% 23 

III 7.6% -6.4% 23.4% 45 

IV 7.0% -6.6% 22.3% 48 

V 8.4% -2.0% 25.1% 43 

VI 6.3% -9.3% 16.9% 23 

 

Total 7.5% -9.3% 25.1% 182 

Although displacement is widespread and sometimes severe, several factors indicate that the 

discrepancies between published TFRs and reconstructed TFRs described in section 3.2 do not result from 

the displacement of births. First, the published TFRs are computed over the three years preceding the 

survey, whereas the reference period for the child health section is more than five years in most surveys 

(Figure 2). Even if respondents or interviewers displace dates of recent births by two years, this will have 

no effect (or a very limited effect) on recent fertility. In surveys with shorter reference periods, as in DHS 

Phase 3 (Table 4), displacement may potentially have greater influence.17 

To evaluate the impact of displacement on the measurement of fertility, births were displaced in the birth 

histories to obtain the corrected distribution of births around the cutoff year estimated using equation (1) 

and equation (2). This correction was done for each age group separately.18 Total fertility rates over the 

last three years are then computed after correcting for birth displacements in all 182 DHS surveys. As 

expected, displacement of births has no effect on the TFR in surveys with a long reference period for the 

child health section (5 or more years) and has a very limited effect in most surveys with a short reference 

period. In only six surveys does displacement have an impact on the TFR greater than 2 percent, and the 

impact is greater than 3 percent in just two surveys (Mozambique 1997 and Cameroon 1998). 

Reconstructed fertility trends with pooled birth histories were computed using the corrected birth 

histories; fertility trends are virtually identical to trends obtained with the original data (examples from 

Mozambique and Cameroon, Figure 10). 

 

17 In such cases, the TFRs published in the country reports often refer to the five years before the survey so that 

displacement of births should have limited impact. However, published TFRs on the DHS STATcompiler are 

computed for a three-year period and may be affected. 

18 In practice, the percentage of births to displace back (DISPL) was computed using equation (4) in each five-year 

age group. A random sample (with a sampling rate equal to DISPL) of births that had occurred in the cutoff year 

was selected, and 12 months were removed from these dates of birth. 
28 

Figure 10. Reconstructed fertility trends and fertility for the last three years with and without 

correcting for displacement of births, DHS surveys in Mozambique and Cameroon 

Legend: Red dots represent TFRs for the three years preceding the surveys with the original data (published 

TFRs). Grey dots are TFRs for the three years preceding the surveys with date of birth corrected for displacement. 

The red line is the reconstructed fertility trend with the original data. The grey line is the reconstructed trend with 

date of birth corrected for displacement. The red line and the grey line are almost impossible to distinguish. 

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Mozambique

Year

TFR (15-49)

Published estimates (last 3 years)

Estimates corrected for displacements (last 3 years)

Reconstructed smoothed estimates

Reconstructed smoothed estimates (corrected for displacements)

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Cameroon

Year

TFR (15-49)

Published estimates (last 3 years)

Estimates corrected for displacements (last 3 years)

Reconstructed smoothed estimates

Reconstructed smoothed estimates (corrected for displacements)
29 

A further indication of the limited role of birth displacement on the discrepancy between published 

fertility and reconstructed fertility is the weak correlation between the index of displacement of births and 

the difference between published and reconstructed fertility (r=-0.14); the correlation is too weak for 

displacement to be a major explanation of the discrepancies. In summary, displacement is widespread but 

has limited impact on the measurement of recent fertility and cannot account for differences between 

published fertility and reconstructed fertility. Other factors must explain the differences. 

Figure 11. Correlation between displacement of births in 112 DHS surveys and relative difference 

between published and reconstructed TFRs (r=0.14) 

 

-0.1 0.0 0.1 0.2 0.3

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

Displacement index

Published TFR-Reconstructed TFR

Regression line
30 

3.3.3. Is there evidence of omission of recent births? 

Because displacement of recent births cannot account for discrepancies between published and 

reconstructed fertility, underreporting of births becomes a serious candidate. Two approaches are used in 

this study to evaluate possible omission of recent births. 

The first approach looks at patterns of fertility around the cutoff year after correcting for displacement of 

births. As in section 3.4.1, average patterns were computed for all the surveys by region, by phase, by 

groups based on subjective evaluation of data quality, and by groups based on differences between 

published and reconstructed fertility. The average patterns look much more regular than the original 

patterns and do not differ substantially across categories of countries or surveys (Figure 12). There is one 

notable exception however: in the eight surveys for which the difference between published fertility and 

reconstructed fertility is large (>15 percent),19 the decrease in fertility is very steep and the pattern 

remains clearly distorted after correcting for displacement. Both the difference between published fertility 

and reconstructed fertility and the distorted patterns of fertility suggest underreporting of births is likely in 

these surveys. However, this method is not sufficient to rule out underreporting of recent births in surveys 

with more regular patterns. First, the correction for displacement may in fact “overcorrect” displacement 

of births, and the reconstructed trend may be more regular than it actually is. Second, a regular downward 

trend in one survey may turn out to be much lower than the reconstructed trend with pooled surveys. 

Figure 12. Average patterns of relative TFRs around the cutoff year of the child health section in 

the DHS questionnaire, by region, by phase, by groups of data quality, and by level of discrepancy 

between published and reconstructed TFRs (birth histories corrected for displacement of births) 

(Continued...) 

 

19 Bangladesh 1994, Dominican Republic 1999, Ethiopia 2005, Guinea 1999, India 1999, Mozambique 1997,

Nigeria 1999, and Pakistan 1990-91. 

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

Region (n=182)

Year (0=cutoff date)

relative TFR (15-49)

SSA

ASIA

EU

LA

MENA

Total

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

DHS phase (n=182)

Year (0=cutoff date)

relative TFR (15-49)

Phase 2

Phase 3

Phase 4

Phase 5

Phase 6
31 

Figure 12. – Continued

The second approach to evaluating possible omission of recent births looks at the consistency of 

reconstructed fertility trends after correcting for other types of problems. In the previous step, birth 

displacement was corrected. Trends were reconstructed in the 69 countries for each survey, separately by 

three-year periods (Appendix Figure A2); three-year periods provide a clearer picture of differences 

across surveys. Because displacement is accounted for, the remaining differences between the 

reconstructed trends and the recent TFRs (red dots) are due to other factors. Omission of births is one of 

these factors; it is expected to be reflected in a sharp drop of the TFR in the last or two last three-year 

periods. Fertility is expected to be lower than fertility at the same period in subsequent surveys, while 

estimates from different surveys should match in periods not influenced by omission of recent births. 

Six cases are illustrated in Figure 13; the six countries—Bangladesh, Burkina Faso, Cameroon, Ethiopia, 

Haiti, and Niger—all include at least one survey for which the difference between the published fertility 

and the reconstructed fertility was greater than 10 percent (Table 6). The evaluation is essentially 

qualitative and is based on visual inspection of reconstructed fertility trends. In Bangladesh, Burkina 

Faso, Cameroon, Ethiopia, and Niger the patterns are suggestive of omission of recent births in all or 

several surveys. In Haiti, fertility trends for the three most recent surveys match quite well, but estimates 

from the first survey are well below the other estimates, and the drop in the TFR a few years before the 

survey could reflect omission of births. Among the 45 countries with at least two surveys, 26 countries20

show at least one survey with inconsistencies that are characteristic of omission of births (Appendix 

Figure A2). Among the 26 surveys in Table 6, underreporting of recent births appears as plausible in most 

of the countries, and possible in all of them. However, some inconsistencies may also be due to at least 

two other factors: differences in sample composition and Potter effect.

 

20 They correspond to a large extent to the countries in the poor and moderate data quality categories of Table 5. 

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

Groups of data quality (n=182)

Year (0=cutoff date)

relative TFR (15-49)

Poor

Moderate

Good

-4 -2 0 2

0.8 0.9 1.0 1.1 1.2

Level of discrepancy (n=112)

Year (0=cutoff date)

relative TFR (15-49)

>0

]-0.05;0]

]-0.10;-0.05]

]-0.15;-0.10]

<=-0.15
32 

Figure 13. Reconstructed fertility trends (TFR 15-49) in six countries by three-year periods 

preceding each survey, fertility for the last three years, and reconstructed fertility with pooled 

birth histories (birth histories corrected for birth displacement) 

(Continued...) 

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Bangladesh

Year

TFR (15-49)

Published estimates

Survey-specific estimates

Reconstructed smoothed estimates

Periods only covered by the latest survey

1975 1980 1985 1990 1995 2000 2005 2010

23456789

Burkina Faso

Year

TFR (15-49)

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Cameroon

Year

TFR (15-49)

Published estimates

Survey-specific estimates

Reconstructed smoothed estimates

Periods only covered by the latest survey

1975 1980 1985 1990 1995 2000 2005 2010

23456789

Ethiopia

Year

TFR (15-49)
33 

Figure 13. – Continued

3.3.4. Are these differences compatible with a Potter effect? 

A further step consists of evaluating if the differences between published and reconstructed trends may be 

due to a Potter effect (Moultrie 2013; Potter 1977). According to the Potter effect, first births tend to be 

erroneously reported at higher ages (i.e., older ages and later dates). As a result, if all the births are 

reported, birth intervals will be shorter than they actually are and fertility trends will be distorted. In this 

case, discrepancies between reconstructed fertility rates would not stem from underestimation of recent 

fertility but from overestimation of fertility in the intermediate periods that pulls the reconstructed trend 

upward. 

The approach used here consists of evaluating if the observed discrepancies are compatible with a Potter 

effect. Two steps are applied in this approach. First, fertility rates are reconstructed over a long period (30 

years) for young age groups (women 15-24) to evaluate the possibility of a Potter effect. In the presence 

of a Potter effect, fertility (15-24) should increase in the past, because of the underestimation of fertility at 

young ages. The second step consists of manipulating the birth histories to “correct” for a possible Potter 

effect. The total number of births for each woman is left unchanged, but the dates of birth are changed so 

that the births are spread over a longer period of time. The duration between each birth and the cutoff year 

is increased by a factor of 10 percent.21 This correction (10 percent) was set arbitrarily but is a plausible 

upper limit for a Potter effect. It corresponds roughly to a median age at first birth that is around 2.5 years 

lower in the oldest cohort than when the Potter effect is not corrected.22 This is a crude approach because 

it considers that the same factor is applicable to all surveys. A further step would be to evaluate more 

 

21 Only dates of births before the cutoff year of the birth history in the DHS questionnaire are changed because it is 

expected in the Potter effect that dates of recent births are reported accurately. Corrections for displacement of births 

described earlier are also taken into account. 

22 For instance, correcting for the Potter effect in Burkina Faso (2003) leads to a median age at first birth of 17.5 

years among women 45-49, compared with 19.75 years without correcting for the Potter effect. In the 2004 DHS in 

Cameroon, this correction leads to a median age at first birth of 16.4 years instead of 18.8 years. 

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Haiti

Year

TFR (15-49)

Published estimates

Survey-specific estimates

Reconstructed smoothed estimates

Periods only covered by the latest survey

1975 1980 1985 1990 1995 2000 2005 2010

3 4 5 6 7 8 9 10

Niger

Year

TFR (15-49)
34 

precisely the extent of the Potter effect for each survey and correct fertility trends accordingly. This 

process is beyond the scope of this report. The objective here is to evaluate 1) if a Potter effect is a 

possible source of discrepancy, and 2) if discrepancies between published and reconstructed fertility are 

still visible after correcting for the upper value of the Potter effect. Remaining discrepancies, after 

correcting for a Potter effect, point to other sources of errors such as omission of births and differences in 

sample composition. 

The six countries shown in Figure 13 are used again in Figure 14 to illustrate this method; Ghana is also 

added as a contrasting example. Trends in (partial) total fertility rates (15-24) for the 30 years preceding 

the surveys (by three-year periods) are reconstructed using the same method as that used for the total 

fertility rate (15-49). Restricting the analysis to young women allows for both reconstructing fertility over 

a long period and identifying unusual fertility trends at early ages. In Figure 14, successive surveys from 

the same country are shown on dual figures; the figure on the left shows partial TFRs based on birth 

histories that were not corrected for the Potter effect; the figure on the right shows the partial TFRs with 

corrected birth histories. It can be seen from these figures that correcting for the Potter effect has a strong 

influence on fertility trends and, as a result, on the consistency of fertility estimates across surveys. 

In Burkina Faso, Cameroon, Haiti, and Niger the trends in fertility (15-24) show an increasing trend in the 

past, consistent with a Potter effect. In Bangladesh and Ethiopia the trend is less defined. Interestingly, 

when correcting for a Potter effect, consistency across surveys improves substantially in Burkina Faso, 

Cameroon, and Niger as well as in Bangladesh. In Haiti, estimates from the last three surveys are closer to 

each other after correcting for the Potter effect, but fertility in the first survey is lower, reflecting possible 

differences in sample composition discussed earlier. In Ethiopia, consistency is not improved by 

correcting for a Potter effect. The Ghana example illustrates a similar situation; no Potter effect is visible 

and “correcting” for it causes deterioration in consistency across surveys. Results are available in 

Appendix Figure A3 for the 18 countries where large discrepancies were found in 26 surveys. In most 

countries Potter effects are possible (Table 9); in only a few do they lead to deterioration in consistency 

across surveys.23 

 

23 These results only indicate that Potter effects are possible, not that they are plausible. Decreasing trends in median 

age at first birth by cohorts of women (not shown) suggest Potter effects are the more plausible in Burkina Faso, 

Mali, Niger, Guinea, and Mozambique. 
35 

Figure 14. Reconstructed fertility at young ages (TFR 15-24) in six countries by three-year periods 

preceding each survey, with and without correcting for a possible Potter effect (10 percent 

increase of birth intervals) 

(Continued...) 

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Bangladesh

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Bangladesh (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Burkina Faso

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Burkina Faso (adjusted Potter effect)

Year

TFR (15-24)
36 

Figure 14. – Continued

(Continued...) 

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Cameroon

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Cameroon (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Ethiopia

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Ethiopia (adjusted Potter effect)

Year

TFR (15-24)
37 

Figure 14. – Continued

(Continued...) 

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Haiti

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Haiti (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Niger

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Niger (adjusted Potter effect)

Year

TFR (15-24)
38 

Figure 14. – Continued

The second step in evaluating if observed discrepancies are compatible with a Potter effect consists of 

reconstructing fertility trends (TFR 15-49) using birth histories that are corrected for the upper value of 

the Potter effect. The approach we follow here consists of “correcting” all the birth histories for a Potter 

effect to the same extent, regardless of the plausibility of the Potter effect. This allows measuring residual 

discrepancies between published and reconstructed estimates in a worst case scenario for Potter effects. 

Figure 15 shows the results for the same six countries. The corrected reconstructed trend (black thick line) 

is now much closer to the published estimates (red dots) than the reconstructed estimates not correcting 

for the Potter effect (grey thick line). These new estimates suggest that the Potter effect affects the 

reconstruction of fertility estimates and that accounting for this possibility is important when explaining 

discrepancies between published and reconstructed estimates. In Bangladesh and Haiti, estimates are now 

much closer; improvements are also seen in Burkina Faso, Cameroon, and Niger,24 although discrepancies 

persist for a number of surveys. In Burkina Faso, for example, published estimates from the first and third 

surveys are still below the reconstructed trend. This difference and the sharp decrease in fertility in the 

last three years are consistent with omission of recent births. 

 

 

24 Discrepancies are also reduced in Ethiopia, even though evidence of a Potter effect was not found in fertility at 

young ages. This illustrates that a better agreement between fertility for the three years preceding the survey and 

reconstructed fertility should not be interpreted as evidence of a Potter effect. 

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Ghana

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Ghana (adjusted Potter effect)

Year

TFR (15-24)
39 

Appendix Figure A4 shows the results for the 18 countries, including the 26 surveys in Table 6. In most 

countries consistency between the reconstructed fertility trends and published fertility is improved, 

indicating that Potter effects may be part of the explanation for the discrepancies. However, in all 26 

surveys, correcting for a Potter effect leaves clear differences between published estimates and 

reconstructed estimates, and in half of the surveys, the difference is below -8 percent (Table 9). One may 

consider these residual differences as lower estimates,25 and omission of recent births thus appears as a 

plausible explanation for these differences in most of the 26 surveys. In a few cases, sample 

implementation is also a possible explanation.

Figure 15. Reconstructed fertility trends (TFR 15-49) in six countries by three-year periods 

preceding each survey, fertility for the last three years, and reconstructed fertility with pooled 

birth histories with (black thick line) and without (grey thick line) correction for the Potter effect 

(Continued...) 

 

25 First, an upper value of the Potter effect was used for the correction; with a smaller correction of the Potter effect, 

residual differences would be larger. Second, the reconstructed trend falls between published fertility and the 

estimate of fertility at the same time in subsequent survey. In this way, the reconstructed fertility trends already 

corrects partly for omission of recent births. 

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Bangladesh

Year

TFR (15-49)

Published estimates

Survey-specific estimates (correcting for Potter effect)

Reconstructed smoothed estimates (correcting for Potter effect)

Periods only covered by the latest survey

Reconstructed smoothed estimates (not correcting for Potter effect)

1975 1980 1985 1990 1995 2000 2005 2010

23456789

Burkina Faso

Year

TFR (15-49)
40 

Figure 15. – Continued

Legend: The black line represents the smoothed fertility trend correcting for a possible Potter effect. Retrospective 

estimates from each survey also correct for the Potter effect. The thick grey line is the smoothed fertility trend based 

on the data only correcting for displacement. 

 

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Cameroon

Year

TFR (15-49)

Published estimates

Survey-specific estimates (correcting for Potter effect)

Reconstructed smoothed estimates (correcting for Potter effect)

Periods only covered by the latest survey

Reconstructed smoothed estimates (not correcting for Potter effect)

1975 1980 1985 1990 1995 2000 2005 2010

23456789

Ethiopia

Year

TFR (15-49)

1975 1980 1985 1990 1995 2000 2005 2010

12345678

Haiti

Year

TFR (15-49)

Published estimates

Survey-specific estimates (correcting for Potter effect)

Reconstructed smoothed estimates (correcting for Potter effect)

Periods only covered by the latest survey

Reconstructed smoothed estimates (not correcting for Potter effect)

1975 1980 1985 1990 1995 2000 2005 2010

3 4 5 6 7 8 9 10

Niger

Year

TFR (15-49)
Table 9. Evaluation of proximate causes of discrepancies between published fertility and reconstructed fertility in 26 DHS surveys, 

1991-2005 

Country Year 

Relative 

difference 

(1) 

Displacements 

(2) 

Omission after 

correcting for 

displacements 

(3) 

Potter effect 

(4) 

Residual 

relative 

difference 

(5) 

Impact of 

sample 

implementation 

(6) 

Omission of recent 

births, after considering 

other errors 

(7) 

Bangladesh 1994 -0.18 No Yes Possible -0.13 Slight Strong Bangladesh 1997 -0.14 No Yes Possible -0.08 Slight Moderate Bangladesh 2000 -0.12 No Yes Possible -0.05 No Moderate Bangladesh 2004 -0.11 No Yes Possible -0.06 No Moderate Benin 2001 -0.10 No Yes Possible -0.05 No Moderate Bolivia 1998 -0.12 No Maybe Possible -0.07 Yes Low Burkina Faso 1993 -0.13 No Yes Possible -0.08 Slight Moderate Burkina Faso 2003 -0.14 No Yes Possible -0.09 No Moderate Cameroon 1998 -0.14 Slight Yes Possible -0.06 No Moderate Cameroon 2004 -0.12 No Yes Possible -0.07 No Moderate Chad 1997 -0.14 No Yes Possible -0.10 No Moderate Dom. Republic 1999 -0.17 No Yes No -0.15 No Strong Ethiopia 2000 -0.18 No Yes No -0.14 No Strong 

Ethiopia 2005 -0.11 No Yes No -0.07 No Moderate 

Guinea 1999 -0.16 No Yes Possible -0.11 Slight Strong 

Haiti 1994 -0.14 No Maybe Possible -0.08 Yes Low 

India 1999 -0.17 No Maybe Possible -0.11 Slight Moderate 

Mali 1996 -0.13 No Yes Possible -0.07 No Moderate 

Mozambique 1997 -0.15 Slight Yes Possible -0.06 No Moderate 

Niger 1992 -0.10 No Yes Possible -0.06 No Moderate Niger 1998 -0.14 Slight Yes Possible -0.05 Slight Moderate 

Nigeria 1999 -0.23 Slight Yes Possible -0.16 Yes Strong 

Nigeria 2003 -0.11 No Yes Possible -0.05 No Moderate 

Pakistan 1991 -0.23 No Yes Possible -0.18 No Strong 

Peru 1992 -0.13 No Maybe Possible -0.08 Yes Low Turkey 1993 -0.11 No Yes Possible -0.06 Slight Moderate 

(Continued...) 

41 
Table 9. – Continued

Note: Shaded lines indicate surveys in which the residual relative difference is below -0.10. (1) Difference between published fertility (last three years) and reconstructed fertility at the same date. (2) Extent to which displacement of recent births accounts for discrepancies between published fertility and reconstructed fertility. (3) Whether, based on visual inspection of fertility trends after correcting for displacement, omission of recent births is possible. 

(4) Whether, based on visual inspection of fertility trends (15-24), Potter effects are possible. (5) The residual relative difference measures the difference between fertility in the last three years and the reconstructed fertility trends, after correcting for

displacement and Potter effect (Potter effect set at 10 percent), as shown in Appendix Figure A4. 

(6) Whether, based on visual inspection of fertility trends in Appendix Figure A4 and trends in education in Appendix Figure A5, differences in sample 

implementation may explain the discrepancies. 

(7) This is a subjective evaluation of the degree of omission after taking into account other possible sources of discrepancies. Surveys with a residual relative 

difference under -0.10 are classified in the “strong” category. Surveys with residual relative differences between 0 and -0.05 are classified in the “low” category. 

42 
43 

3.3.4. Do variations in sampling implementation account for discrepancies? 

Some of the discrepancies in fertility estimates may be due to differences in sample implementation. If 

high fertility women are underrepresented or overrepresented in a survey by accident, we expect fertility 

in that survey to be below or above fertility in another survey for all time periods. The estimate of recent 

fertility will thus be underestimated or overestimated. This sampling problem will influence the 

reconstructed trend in the same direction, pulling the average trend downward or upward; however, 

because the reconstructed trend is based on pooled surveys, the impact will be less pronounced. This issue 

of sample composition is illustrated with the case of Haiti (Figure 13 and Figure 15). While fertility 

decreases in the few years preceding the first survey (sharply in Figure 13, less sharply in Figure 15), 

reflecting possible omission of births, Figure 15 also suggests that low fertility women were oversampled 

in the first survey, because the reconstructed trend appears below the trends of subsequent surveys. In this 

case, part of the difference between recent fertility in the first survey and reconstructed fertility may be 

due to differences in sample composition. Ethiopia is another example of possible differences in sample 

composition, with high fertility women possibly underrepresented in the first survey (lower fertility). 

Appendix Figure A4 shows that among the 26 surveys, differences in sample composition may have an 

impact in 6 to 8 surveys (in Bolivia, Ethiopia, Haiti, India, Niger, Nigeria, Pakistan, and Peru). In 

contrast, differences in sample composition appear less likely in the other countries. 

This issue can be further checked by estimating retrospectively the composition of the sample for some 

socio-demographic characteristics in a specific age group (characteristics that are time invariant, or for 

which time-varying characteristics are available). Here, the proportion of women age 15-34 with at least 

six years of education for the 15 years preceding the survey is computed.26 As for total fertility rates, the 

retrospective estimates of educational levels should match across surveys for the same periods. Results 

are presented in Appendix Figure A5 for the 18 countries with 26 surveys in Table 9. In half of the 

countries, education levels match very well across surveys, suggesting that differences in sample 

composition are not a significant issue. In six countries, differences are visible, but relatively small. In 

contrast, in four countries (Bolivia, Haiti, Nigeria, and Peru), differences in sample composition are large. 

The Nigerian case is particularly compelling. In the second survey (1999), the percentage of women with 

six years of education is much larger than in the first and third surveys; this difference helps to explain the 

much lower fertility in the 1999 survey. In Haiti, the first survey also included a larger proportion of 

educated women than the following surveys; this confirms the role of differences in sample composition 

in the lower fertility reported in the first survey in Haiti. In the 1991-92 survey in Peru, some rural areas 

could not be visited because of security reasons, and this sampling issue was acknowledged in the 1991-

92 DHS survey report.27 In Bolivia, the two most recent surveys show lower fertility levels, and also 

include higher proportions of educated women compared to the first two surveys, reflecting differences in 

sample implementation. Overall, these results indicate that sample composition is a potentially serious 

issue in four of the 26 surveys. Omission of recent births appears to be a plausible explanation in surveys 

where residual differences cannot be explained by sample composition, which is the case in most surveys. 

 

26 The method is simple: at the time of the survey, the percentage of women age 15-34 with secondary or higher 

education is computed directly. One year before the survey, the percentage of women age 15-34 with secondary or 

higher education is equal to the percentage of educated women age 16-35 at the time of the survey, who were age 

15-34 one year before the survey. This operation can be repeated for 15 years before the survey. Fifteen years before 

the survey, the percentage is estimated among women age 30-49 at the time of the survey. The percentages could be 

estimated over a longer time period if the age range were restricted; alternatively, a shorter time period should be 

taken if a larger age range was used. We selected the 15-34 age range, as a large part of fertility is achieved by age 

35, and this allows reliable comparisons across surveys. 

27 In the report of the 1996 Peru DHS, a new estimate of fertility for the 1991-92 survey (4.0 children, instead of 3.5) 

was computed using weights to account for areas that were not surveyed in 1991-92. 

45 

4. Conclusion 

In this report, reconstruction of fertility trends and comparison of trends across successive surveys were 

used to evaluate the quality of data from birth histories collected in DHS surveys. The overall picture 

emerging from these analyses is that DHS fertility estimates are very good in some countries (e.g., 

Armenia, Colombia, Indonesia, and Morocco), are acceptable in many countries (e.g., Jordan, Kenya, and 

Zimbabwe), and are poor in other countries (e.g., Benin, Ethiopia, Niger, Nigeria, and Pakistan). 

The comparison of published fertility and reconstructed fertility was used as a starting point to explore the 

causes of inconsistencies across surveys. Good quality data should lead to small differences between 

reconstructed and published estimates. Results indicate that published fertility is lower than reconstructed 

fertility in most surveys. Although the differences are small or moderate in most surveys, some surveys 

are affected by large differences. Several proximate causes of the discrepancies between published 

fertility and reconstructed fertility were explored. Displacement of recent births in the birth history table 

of the DHS questionnaire is widespread, but the problem is only marginally related to the discrepancy 

between published and reconstructed fertility. Three other factors were explored in 26 surveys where 

discrepancies were large. Potter effects appear as a possible explanation for part of the discrepancies. 

While the approach we used does not allow measuring the Potter effect in a precise way, the patterns of 

fertility at young ages are compatible with a Potter effect in many of the 26 surveys identified as the most 

problematic. Correcting for the upper value of the Potter effect leads to smaller discrepancies between 

published and reconstructed fertility, but in all the surveys discrepancies remain. Differences in sample 

implementation may account for discrepancies in a few surveys, but do not seem to be a significant issue 

in most surveys. Finally, omissions of recent births may contribute to the discrepancies in most countries. 

While it is difficult to measure omission precisely, ruling out alternative explanations and correcting for 

other data quality problems still leaves differences between reconstructed fertility and published fertility, 

indicating omission of births is a possible explanation. 

These analyses are to a large extent based on a qualitative assessment of reconstructed fertility, and the 

results are indicative of broad patterns of data quality. The use of other methods to evaluate birth histories 

may reinforce or nuance these conclusions. Further in-depth analysis on specific countries would also 

provide more detailed evaluations and a better understanding of the causes of the data quality issues. 

What these results indicate is that data quality is an important matter, and that taking published figures of 

fertility at face value could be risky in some contexts. As shown in this report, not all surveys are equally 

reliable. Users should be aware that fertility may be underestimated by 5 percent to 10 percent in a 

number of surveys, and may be more seriously biased downward in a few cases. The use of these figures 

for evaluating and designing policies, and more generally for describing and projecting fertility changes, 

should thus be made with this in mind. 

47 

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

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49 

Appendix 

Appendix Figure A1. Reconstructed fertility trends by single calendar year, published fertility (last 

three years), and reconstructed fertility with pooled birth histories in 69 countries (181 surveys) 

(Continued...) 
50 

Appendix Figure A1. – Continued

(Continued...) 
51 

Appendix Figure A1. – Continued 

(Continued...) 
52 

Appendix Figure A1. – Continued 

(Continued...) 
53 

Appendix Figure A1. – Continued 

(Continued...) 
54 

Appendix Figure A1. – Continued 

(Continued...) 
55 

Appendix Figure A1. – Continued 

(Continued...) 
56 

Appendix Figure A1. – Continued 

(Continued...) 
57 

Appendix Figure A1. – Continued 

(Continued...) 
58 

Appendix Figure A1. – Continued 

(Continued...) 
59 

Appendix Figure A1. – Continued 

(Continued...) 
60 

Appendix Figure A1. – Continued 

(Continued...) 
61 

Appendix Figure A1. – Continued 

(Continued...) 
62 

Appendix Figure A1. – Continued 

(Continued...) 
63 

Appendix Figure A1. – Continued 

(Continued...) 
64 

Appendix Figure A1. – Continued 
65 

Appendix Figure A2. Reconstructed fertility trends by three-year periods preceding each survey, 

published fertility (last three years), and reconstructed fertility with pooled birth histories (birth 

histories corrected for birth displacement), in 69 countries (181 surveys) 

(Continued...) 
66 

Appendix Figure A2. – Continued 

(Continued...) 
67 

Appendix Figure A2. – Continued 

(Continued...) 
68 

Appendix Figure A2. – Continued 

(Continued...) 
69 

Appendix Figure A2. – Continued 

(Continued...) 
70 

Appendix Figure A2. – Continued 

(Continued...) 
71 

Appendix Figure A2. – Continued 

(Continued...) 
72 

Appendix Figure A2. – Continued 

(Continued...) 
73 

Appendix Figure A2. – Continued 

(Continued...) 
74 

Appendix Figure A2. – Continued 

(Continued...) 
75 

Appendix Figure A2. – Continued 

(Continued...) 
76 

Appendix Figure A2. – Continued 

(Continued...) 
77 

Appendix Figure A2. – Continued 

(Continued...) 
78 

Appendix Figure A2. – Continued 

(Continued...) 
79 

Appendix Figure A2. – Continued 

(Continued...) 
80 

Appendix Figure A2. – Continued 

(Continued...) 
81 

Appendix Figure A2. – Continued 

(Continued...) 
82 

Appendix Figure A2. – Continued 
83 

Appendix Figure A3. Reconstructed fertility trends (women 15-24) by three-year periods over the 

30 years preceding each survey, with and without adjusting for a Potter effect (increase of birth 

intervals by 10 percent), in 18 countries (61 surveys) 

(Continued...) 

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Bangladesh

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Bangladesh (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Benin

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Benin (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Bolivia

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Bolivia (adjusted Potter effect)

Year

TFR (15-24)
84 

Appendix Figure A3. – Continued 

(Continued...) 

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Burkina Faso

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Burkina Faso (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Cameroon

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Cameroon (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Chad

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Chad (adjusted Potter effect)

Year

TFR (15-24)
85 

Appendix Figure A3. – Continued 

(Continued...) 

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Dominican Republic

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Dom. Rep. (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Ethiopia

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Ethiopia (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Guinea

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Guinea (adjusted Potter effect)

Year

TFR (15-24)
86 

Appendix Figure A3. – Continued 

(Continued...) 

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Haiti

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Haiti (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

India

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

India (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Mali

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Mali (adjusted Potter effect)

Year

TFR (15-24)
87 

Appendix Figure A3. – Continued 

(Continued...) 

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Mozambique

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Mozambique (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Niger

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Niger (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Nigeria

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Nigeria (adjusted Potter effect)

Year

TFR (15-24)
88 

Appendix Figure A3. – Continued 

 

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Pakistan

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Pakistan (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Peru

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Peru (adjusted Potter effect)

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Turkey

Year

TFR (15-24)

1960 1970 1980 1990 2000 2010

1.0 1.5 2.0 2.5 3.0

Turkey (adjusted Potter effect)

Year

TFR (15-24)
89 

Appendix Figure A4. Reconstructed fertility trends (TFR 15-49) by three-year periods preceding 

each survey, published fertility (last three years), and reconstructed fertility with pooled birth 

histories with (black thick line) and without (grey thick line) correction for the Potter effect, 18 

countries (61 surveys) 

(Continued...) 
90 

Appendix Figure A4. – Continued 

(Continued...) 
91 

Appendix Figure A4. – Continued 

(Continued...) 
92 

Appendix Figure A4. – Continued 

(Continued...) 
93 

Appendix Figure A4. – Continued 
94 

Appendix Figure A5. Comparison across surveys of the percentage of women (15-34) who have 

completed at least six years of education, reconstructed for the 15 years preceding each survey, 

18 countries (61 surveys) 

(Continued...) 
95 

Appendix Figure A5. – Continued 

(Continued...) 
96 

Appendix Figure A5. – Continued 

(Continued...) 
97 

Appendix Figure A5. – Continued 

(Continued...) 
98 

Appendix Figure A5. – Continued 
Appendix Table A1. Description of the 182 DHS surveys (1990-2012) used in this report 

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Albania DHS-V Europe 5 2008-09 72 1.59 -0.01 Armenia DHS-IV Europe 4 2000 69 1.71 0.07 -0.10 -0.06 

Armenia DHS-V Europe 5 2005 68 1.71 0.00 0.15 0.10 Armenia DHS-VI Europe 6 2010 69 1.70 0.08 Azerbaijan DHS-V Europe 5 2006 67 2.02 0.08 Bangladesh DHS-III Asia 3 1993-94 44 3.44 -0.02 -0.78 -0.18 

Bangladesh DHS-III Asia 3 1996-97 68 3.27 0.06 -0.55 -0.14 Bangladesh DHS-IV Asia 4 1999-2000 68 3.31 0.01 -0.37 -0.11 Bangladesh DHS-IV Asia 4 2004 68 3.03 -0.04 -0.44 -0.12 Bangladesh DHS-V Asia 5 2007 64 2.71 0.02 -0.21 -0.07 Bangladesh DHS-VI Asia 6 2011 68 2.32 0.04 

Benin DHS-III Sub-Saharan Africa 3 1996 42 5.96 0.03 -0.60 -0.09 Benin DHS-IV Sub-Saharan Africa 4 2001 68 5.61 0.10 -0.65 -0.10 Benin DHS-V Sub-Saharan Africa 5 2006 68 5.74 0.18 Bolivia DHS-III Latin America 3 1994 49 4.77 0.08 -0.44 -0.08 Bolivia DHS-III Latin America 3 1998 64 4.23 0.07 -0.58 -0.12 Bolivia DHS-IV Latin America 4 2003 69 3.84 0.05 -0.34 -0.08 

Bolivia DHS-V Latin America 5 2008 62 3.54 0.05 

Brazil DHS-II Latin America 2 1991 69 3.65 0.01 0.51 0.16 Brazil DHS-III Latin America 3 1996 63 2.54 0.02 Burkina Faso DHS-II Sub-Saharan Africa 2 1993 72 6.52 0.17 -1.00 -0.13 

Burkina Faso DHS-III Sub-Saharan Africa 3 1998-99 71 6.43 0.13 -0.63 -0.09 

Burkina Faso DHS-IV Sub-Saharan Africa 4 2003 67 5.88 0.14 -0.99 -0.14 

Burkina Faso DHS-VI Sub-Saharan Africa 6 2010 68 5.99 0.13 Burundi DHS-VI Sub-Saharan Africa 6 2010 69 6.38 0.04 

(Continued...)

99 
Appendix Table A1. – Continued

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Cambodia DHS-IV Asia 4 2000 63 3.77 0.20 -0.26 -0.06 Cambodia DHS-V Asia 5 2005 70 3.40 -0.02 0.01 0.00 Cambodia DHS-VI Asia 6 2010 68 3.04 0.06 

Cameroon DHS-II Sub-Saharan Africa 2 1991 65 5.82 0.08 -0.50 -0.08 

Cameroon DHS-III Sub-Saharan Africa 3 1998 38 4.81 0.10 -0.82 -0.14 Cameroon DHS-IV Sub-Saharan Africa 4 2004 64 4.97 0.11 -0.65 -0.12 Cameroon DHS-VI Sub-Saharan Africa 6 2011 75 5.09 0.12 Central African Republic DHS-III 

Sub-Saharan Africa 3 1994-95 46 5.07 0.07 

Chad DHS-III Sub-Saharan Africa 3 1996-97 74 6.37 0.19 -1.05 -0.14 Chad DHS-IV Sub-Saharan Africa 4 2004 68 6.34 0.18 

Colombia DHS-II Latin America 2 1990 65 2.82 0.00 -0.18 -0.06 

Colombia DHS-III Latin America 3 1995 63 2.97 0.05 -0.07 -0.02 

Colombia DHS-IV Latin America 4 2000 63 2.61 0.01 -0.15 -0.05 

Colombia DHS-V Latin America 5 2005 73 2.39 0.03 -0.04 -0.02 

Colombia DHS-VI Latin America 6 2010 66 2.14 0.01 Comoros DHS-III Sub-Saharan Africa 3 1996 39 4.65 0.12 Congo (Brazzaville) DHS-V Sub-Saharan Africa 5 2005 67 4.80 0.08 

Congo Democratic Republic DHS-V 

Sub-Saharan Africa 5 2007 64 6.28 0.02 

Cote d'Ivoire DHS-III Sub-Saharan Africa 3 1998-99 71 5.18 0.13 -0.45 -0.08 

Cote d'Ivoire DHS-III Sub-Saharan Africa 3 1994 43 5.30 0.15 -0.27 -0.05 

Cote d'Ivoire DHS-VI Sub-Saharan Africa 6 2011-12 72 4.96 0.17 

Dominican Republic DHS-II Latin America 2 1991 68 3.34 0.06 -0.19 -0.05 

Dominican Republic DHS-III Latin America 3 1996 69 3.17 0.03 -0.19 -0.06 

Dominican Republic DHS-IV Latin America 4 1999 44 2.66 -0.07 -0.54 -0.17 

(Continued...)

100 
Appendix Table A1. – Continued

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Dominican Republic DHS-IV Latin America 4 2002 68 2.99 -0.01 0.04 0.01 Dominican Republic DHS-V Latin America 5 2007 64 2.43 0.02 

Egypt DHS-II MENA 2 1992 70 3.93 0.05 -0.27 -0.06 Egypt DHS-III MENA 3 1995 70 3.63 0.04 -0.10 -0.03 

Egypt DHS-IV MENA 4 2000 61 3.53 0.12 -0.10 -0.03 Egypt DHS-V MENA 5 2005 64 3.13 0.08 -0.07 -0.02 

Egypt DHS-V MENA 5 2008 63 3.02 0.15 Ethiopia DHS-IV Sub-Saharan Africa 4 2000 66 5.52 -0.04 -1.23 -0.18 

Ethiopia DHS-V Sub-Saharan Africa 5 2005 69 5.41 0.05 -0.70 -0.11 Ethiopia DHS-VI Sub-Saharan Africa 6 2011 65 4.80 -0.09 Gabon DHS-IV Sub-Saharan Africa 4 2000 68 4.21 0.09 -0.10 -0.02 

Gabon DHS-VI Sub-Saharan Africa 6 2012 61 4.10 0.06 Ghana DHS-III Sub-Saharan Africa 3 1993 45 5.16 0.23 -0.17 -0.03 Ghana DHS-IV Sub-Saharan Africa 4 1998 71 4.44 0.08 -0.38 -0.08 

Ghana DHS-IV Sub-Saharan Africa 4 2003 67 4.45 0.13 -0.14 -0.03 

Ghana DHS-V Sub-Saharan Africa 5 2008 68 4.03 0.18 Guatemala DHS-III Latin America 3 1995 68 5.13 0.07 

Guinea DHS-IV Sub-Saharan Africa 4 1999 65 5.53 0.05 -1.05 -0.16 

Guinea DHS-V Sub-Saharan Africa 5 2005 62 5.71 0.14 

Guyana DHS-V Latin America 5 2009 63 2.78 0.00 Haiti DHS-III Latin America 3 1994-95 68 4.78 0.13 -0.79 -0.14 

Haiti DHS-IV Latin America 4 2000 63 4.69 0.05 -0.16 -0.03 Haiti DHS-V Latin America 5 2005-06 72 3.92 0.08 -0.01 0.00 

Haiti DHS-VI Latin America 6 2012 62 3.53 0.05 

Honduras DHS-V Latin America 5 2005-06 73 3.26 0.03 -0.06 -0.02 

(Continued...)

101 
Appendix Table A1. – Continued

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Honduras DHS-VI Latin America 6 2011-12 72 2.94 0.04 India DHS-II Asia 2 1992-93 59 4.05 0.03 0.12 0.03 

India DHS-IV Asia 4 1998-99 50 2.85 0.02 -0.59 -0.17 India DHS-V Asia 5 2005-06 62 2.68 0.02 Indonesia DHS-II Asia 2 1991 65 3.02 0.10 -0.15 -0.05 

Indonesia DHS-III Asia 3 1994 66 2.85 0.09 -0.15 -0.05 

Indonesia DHS-III Asia 3 1997 69 2.78 0.12 -0.08 -0.03 

Indonesia DHS-IV Asia 4 2002-03 71 2.57 0.18 -0.10 -0.04 

Indonesia DHS-V Asia 5 2007 66 2.59 0.06 0.01 0.00 Indonesia DHS-VI Asia 6 2012 64 2.60 0.04 

Jordan DHS-II MENA 2 1990 70 5.57 0.02 -0.04 -0.01 

Jordan DHS-III MENA 3 1997 66 4.35 0.01 -0.21 -0.05 Jordan DHS-IV MENA 4 2002 66 3.67 0.07 -0.24 -0.06 

Jordan DHS-V MENA 5 2007 66 3.59 0.04 Kazakhstan DHS-III Asia 3 1995 41 2.49 0.04 -0.06 -0.03 Kazakhstan DHS-IV Asia 4 1999 66 2.05 0.00 

Kenya DHS-III Sub-Saharan Africa 3 1993 63 5.40 0.08 -0.24 -0.04 Kenya DHS-III Sub-Saharan Africa 3 1998 39 4.70 0.07 -0.44 -0.09 Kenya DHS-IV Sub-Saharan Africa 4 2003 64 4.88 0.04 -0.32 -0.06 

Kenya DHS-V Sub-Saharan Africa 5 2008-09 71 4.56 0.17 Kyrgyz Republic DHS-III Asia 3 1997 44 3.37 0.06 

Lesotho DHS-IV Sub-Saharan Africa 4 2004 70 3.54 0.04 0.08 0.02 Lesotho DHS-VI Sub-Saharan Africa 6 2009 70 3.30 0.08 Liberia DHS-V Sub-Saharan Africa 5 2007 73 5.20 0.21 

Madagascar DHS-II Sub-Saharan Africa 2 1992 66 6.13 0.09 0.10 0.02 

(Continued...)

102 
Appendix Table A1. – Continued

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Madagascar DHS-III Sub-Saharan Africa 3 1997 45 5.97 0.09 -0.14 -0.02 Madagascar DHS-IV Sub-Saharan Africa 4 2003-04 72 5.17 0.17 -0.43 -0.08 

Madagascar DHS-V Sub-Saharan Africa 5 2008-09 72 4.82 0.18 

Malawi DHS-II Sub-Saharan Africa 2 1992 68 6.73 0.08 -0.19 -0.03 Malawi DHS-IV Sub-Saharan Africa 4 2000 67 6.35 0.22 -0.07 -0.01 

Malawi DHS-IV Sub-Saharan Africa 4 2004 70 6.04 0.17 -0.45 -0.07 

Malawi DHS-VI Sub-Saharan Africa 6 2010 66 5.71 0.06 Maldives DHS-V Asia 5 2009 75 2.54 0.06 Mali DHS-III Sub-Saharan Africa 3 1995-96 48 6.71 0.07 -0.98 -0.13 

Mali DHS-IV Sub-Saharan Africa 4 2001 62 6.78 0.09 -0.45 -0.06 

Mali DHS-V Sub-Saharan Africa 5 2006 66 6.58 0.25 

Moldova DHS-V Europe 5 2005 65 1.69 -0.02 

Morocco DHS-II MENA 2 1992 73 4.04 0.00 0.04 0.01 

Morocco DHS-IV MENA 4 2003-04 70 2.48 0.04 

Mozambique DHS-III Sub-Saharan Africa 3 1997 39 5.17 0.17 -0.94 -0.15 

Mozambique DHS-IV Sub-Saharan Africa 4 2003 69 5.53 0.12 -0.33 -0.06 

Mozambique DHS-VI Sub-Saharan Africa 6 2011 66 5.92 0.08 

Namibia DHS-II Sub-Saharan Africa 2 1992 67 5.37 0.05 0.37 0.07 Namibia DHS-IV Sub-Saharan Africa 4 2000 69 4.18 0.09 0.25 0.06 Namibia DHS-V Sub-Saharan Africa 5 2006-07 72 3.57 0.10 Nepal DHS-III Asia 3 1996 46 4.64 0.01 -0.32 -0.06 

Nepal DHS-IV Asia 4 2001 71 4.11 0.01 -0.08 -0.02 

Nepal DHS-V Asia 5 2006 72 3.13 0.05 -0.11 -0.03 

Nepal DHS-VI Asia 6 2011 71 2.60 0.03 Nicaragua DHS-III Latin America 3 1998 73 3.63 -0.06 -0.30 -0.08 

(Continued...)

103 
Appendix Table A1. – Continued

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Nicaragua DHS-IV Latin America 4 2001 69 3.23 0.10 Niger DHS-II Sub-Saharan Africa 2 1992 63 6.99 0.09 -0.81 -0.10 

Niger DHS-III Sub-Saharan Africa 3 1998 39 7.20 0.10 -1.16 -0.14 Niger DHS-V Sub-Saharan Africa 5 2006 62 7.02 0.25 

Nigeria DHS-II Sub-Saharan Africa 2 1990 65 6.01 0.20 -0.27 -0.04 Nigeria DHS-IV Sub-Saharan Africa 4 1999 39 4.73 0.09 -1.45 -0.23 

Nigeria DHS-IV Sub-Saharan Africa 4 2003 64 5.66 -0.02 -0.71 -0.11 Nigeria DHS-V Sub-Saharan Africa 5 2008 67 5.72 0.13 

Pakistan DHS-II Asia 2 1990-91 60 4.91 0.25 -1.49 -0.23 Pakistan DHS-V Asia 5 2006-07 70 4.08 0.11 Paraguay DHS-II Latin America 2 1990 65 4.71 -0.01 

Peru DHS-II Latin America 2 1991-92 71 3.54 0.06 -0.52 -0.13 

Peru DHS-III Latin America 3 1996 68 3.54 0.09 -0.10 -0.03 Peru DHS-IV Latin America 4 2000 67 2.85 0.06 -0.22 -0.07 

Peru DHS-V Latin America 5 2004-08 69 2.53 0.04 Philippines DHS-III Asia 3 1993 64 4.09 0.01 -0.09 -0.02 

Philippines DHS-III Asia 3 1998 62 3.73 0.03 -0.17 -0.04 

Philippines DHS-IV Asia 4 2003 65 3.53 0.07 -0.11 -0.03 

Philippines DHS-V Asia 5 2008 67 3.26 0.03 

Rwanda DHS-II Sub-Saharan Africa 2 1992 66 6.23 0.01 -0.57 -0.08 Rwanda DHS-IV Sub-Saharan Africa 4 2000 67 5.84 0.15 -0.38 -0.06 

Rwanda DHS-V Sub-Saharan Africa 5 2005 63 6.08 0.01 0.02 0.00 

Rwanda DHS-VI Sub-Saharan Africa 6 2010 71 4.56 0.04 Sao Tome and Principe DHS-V 

Sub-Saharan Africa 5 2008-09 69 4.90 0.15 

Senegal DHS-II Sub-Saharan Africa 2 1992-93 72 6.03 0.09 -0.43 -0.07 

(Continued...)

104 
Appendix Table A1. – Continued

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Senegal DHS-III Sub-Saharan Africa 3 1997 61 5.67 0.03 -0.22 -0.04 Senegal DHS-IV Sub-Saharan Africa 4 2005 62 5.26 0.05 -0.11 -0.02 

Senegal DHS-VI Sub-Saharan Africa 6 2010-11 72 4.98 0.11 

Sierra Leone DHS-V Sub-Saharan Africa 5 2008 64 5.12 0.24 South Africa DHS-III Sub-Saharan Africa 3 1998 62 2.90 0.10 

Swaziland DHS-V Sub-Saharan Africa 5 2006-07 69 3.85 0.16 

Tanzania DHS-II Sub-Saharan Africa 2 1991-92 71 6.24 0.09 -0.14 -0.02 Tanzania DHS-III Sub-Saharan Africa 3 1996 67 5.82 0.14 -0.25 -0.04 

Tanzania DHS-IV Sub-Saharan Africa 4 1999 68 5.55 -0.02 -0.33 -0.06 Tanzania DHS-IV Sub-Saharan Africa 4 2004-05 70 5.66 0.01 -0.11 -0.02 Tanzania DHS-VI Sub-Saharan Africa 6 2010 61 5.43 0.08 

Timor-Leste DHS-VI Asia 6 2009-10 69 5.68 0.14 

Togo DHS-III Sub-Saharan Africa 3 1998 38 5.20 0.07 

Turkey DHS-III MENA 3 1993 67 2.51 0.11 -0.33 -0.11 

Turkey DHS-IV MENA 4 1998 67 2.61 0.08 -0.06 -0.02 Turkey DHS-IV MENA 4 2003 72 2.23 0.06 

Uganda DHS-III Sub-Saharan Africa 3 1995 52 6.86 0.16 -0.62 -0.08 Uganda DHS-IV Sub-Saharan Africa 4 2000-01 71 6.85 0.14 -0.66 -0.09 

Uganda DHS-V Sub-Saharan Africa 5 2006 66 6.67 0.16 -0.28 -0.04 Uganda DHS-VI Sub-Saharan Africa 6 2011 67 6.20 0.03 

Ukraine DHS-V Europe 5 2007 67 1.17 -0.01 

Uzbekistan DHS-III Asia 3 1996 78 3.34 -0.04 

Vietnam DHS-III Asia 3 1997 43 2.33 0.09 -0.21 -0.08 Vietnam DHS-IV Asia 4 2002 45 1.87 0.00 

Yemen DHS-II MENA 2 1991-92 58 8.78 0.14 

(Continued...)

105 
Appendix Table A1. – Continued

Survey Region Phase Year 

Reference 

period 

(months) 

Published 

TFR (last 

3 years) 

Displacement 

index 

Actual difference 

between published 

TFR and 

reconstructed TFR 

Relative difference 

between published 

TFR and 

reconstructed TFR 

Zambia DHS-II Sub-Saharan Africa 2 1992 61 6.46 0.05 -0.15 -0.02 Zambia DHS-III Sub-Saharan Africa 3 1996 68 6.08 0.13 -0.52 -0.08 Zambia DHS-IV Sub-Saharan Africa 4 2001-02 73 5.88 0.09 -0.26 -0.04 Zambia DHS-V Sub-Saharan Africa 5 2007 65 6.17 0.00 Zimbabwe DHS-III Sub-Saharan Africa 3 1994 44 4.29 0.01 -0.33 -0.07 Zimbabwe DHS-IV Sub-Saharan Africa 4 1999 68 3.96 0.00 -0.37 -0.09 Zimbabwe DHS-V Sub-Saharan Africa 5 2005-06 69 3.80 0.04 -0.12 -0.03 

Zimbabwe DHS-VI Sub-Saharan Africa 6 2010-11 71 4.10 0.07 

106